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The Complete Guide to Quantitative Market Research

Quantitative research is a chief category in the research sphere, along with qualitative research. An encompassing aspect of market research , it can include both primary and secondary methods of extracting data. 

Although used interchangeably with qualitative research, quantitative research is a distinct process that should not be confused with its counterpart. In fact, it is the opposite of qualitative research.

Let’s navigate through the waters of quantitative research in this complete guide.

What Defines & Makes Up Quantitative Research?

As its name suggests, quantitative research is the process of aggregating quantitative, or numerical data for research purposes. This data is used for a number of applications. These include:

• Quantifying opinions, behaviors, attitudes and problems
• Making generalizations
• Forming predictions
• Discovering patterns
• Determining averages
• Testing relationships

Quantitative research generally relies on a larger sample size in order to quantify any issue or variable. In order to achieve this, this research method involves using mathematical and statistical means. 

This type of research answers the “what” and the “how much” of a subject within a research endeavor. As it forms generalizations, this type of method involves surveying a larger population, using measurable data and processing all the data first and then analyzing it from a statistical standpoint.

The Four Main Types of Quantitative Research

There are four main ways to perform quantitative research. Aside from their methodology, these sub-categories also seek different types of answers and conclusions.

1. Descriptive Research

This is used to determine the state of variables. It describes the situation and environment surrounding a variable or topic. As such, it is used for arranging comparisons, outlining sample characteristics, overlooking emerging trends and confirming existing phenomena.

The data is collected by way of observation. Descriptive Research is used to form a hypothesis, but only after having aggregated all the necessary data.

2. Correlational Research

This research method is used to examine the relationships between different subjects and variables. Analyzing relationships is necessary to either test a hypothesis or a prediction. Because this research focuses on relationships between fixed variables, other outlying variables are not part of the investigation.

Correlational research is in direct opposition to experimental research, as none of the studied variables are manipulated. Correlations can be either positive or negative, with different degrees of the relationship’s strength.

3. Experimental Research

This method is used for finding whether there is a cause and effect relationship among variables. This kind of research relies on the scientific method. Unlike correlational research, experimental research involves manipulating variables.

Researchers would manipulate a variable to uncover its effect on another one. This method is frequently referred to as true experimentation, as no experimental undertaking leaves all variables unchanged; at least one must be influenced in some way. 

This includes manipulating, randomizing or reverting back a variable. The variables are then measured, calculated and compared.

4. Survey Research

The final research method is crucial to understanding behavior. In market research, it is often used to acclimate a brand with its target market’s desires, needs, points of contention and behaviors.

Surveys allow researchers to ask pointed questions to either discover their target audience or get a granular sense of their opinions. As such, they can be conducted within one group or many, for the sake of comparison.

Instead of turning to survey panels , which are likely to have skewed or biased results, researchers should use a random sample of people. A non-panel-based survey will garner more respondents that aren’t motivated by professional compensation.

Surveys can be administered by mail,  in-person, on the phone, or digitally. The latter has even more options: online surveys, third-party surveys, emails and in-app.

Examples of Questions for Quantitative Research

Survey research has a far larger scope of questions than do the other three types, as researchers can ask practically anything to conduct their studies. However, there are some best practices in survey questionnaires, such as focusing on your industry, your product and the desires of customers.

Learn more about asking insightful market research questions . Here are a few examples of quantitative research questions in the three other categories.

• Is working from home the best option to improve productivity for employees with long commutes? Variable: Working from home and in-office Demographic: Employees with long commutes Quantitative Research Type : Experimental
• How has the coronavirus changed employment for white-collar workers? Variable: Employment types and statuses Demographic: White-collar workers Quantitative Research Type : Experimental
• How often do working people travel for a holiday? Variable: Amount of times respondents travel during a holiday Demographic: working people Quantitative Research Type : Descriptive
• How much would you pay for a subscription to an entertainment magazine? Variable: payments for a magazine subscription Demographic: women aged 14-44, those interested in celebrities Quantitative Research Type : Descriptive
• What is the difference in smartphone usage between Millennials and senior citizens? Variable: Time spent on using a smartphone Demographic: Millennials and seniors Quantitative Research Type: Correlational
• Does the leadership style of car shop owners predict the job satisfaction of car salespeople? Variable: Leadership style and job satisfaction Demographic: Car shop employers and salespeople Quantitative Research Type: Correlational 

When to Use Quantitative Research and How to Analyze It

The quantitative research method has specific use cases. You ought to consider which is best for your particular business, which includes your strategy, your marketing and other facets.

The core of quantitative research is to quantify a phenomenon (a problem, an inadequacy, and a slew of other occurrences) and understand its prevalence. Researchers do this by observing large portions of a population.

You should use this form of research whenever you need to be presented with the state of things at a higher level, or from a bird’s eye view. This Is because this type of research can identify links between various factors, look for correlations and discover cause and effect relationships.

Researchers can then use the results of their findings to form predictions. This is useful in market research when launching a new product, brainstorming product ideas or innovations or growing a customer base.

To analyze this research, it should first be made quantifiable and objective. Researchers should pin down the scales and units of measurements in their various studies. Then, they should organize them into easily interpretable formats.

For example, once you gather the numerical data, you can enter it into a spreadsheet. Thereafter, you can organize it by desegregating it into graphs, charts and tables. Finally, you should draw data-based conclusions from your study. You can also do further sleuthing via advanced analytics.

The Benefits and Drawbacks of Quantitative Research

Quantitative research has a bevy of benefits; it also has some hindrances. You should peruse both the positive and negative qualities of this research type before setting out on any major research project. The following may help you choose one form of research over the other, or use aspects of both.

• Larger sample pools: the larger the group of respondents, the more accurate are the results.
• Highly structured: Surveys, questionnaires, and other tools for recording numerical data
• Focused: The design of the study is determined before it begins
• Theory-based: Research tests a theory to provide support/proof
• Designed to Be Analyzed: Numbers/statistics exist as tables, charts, figures and other non-textual forms for easy analysis.
• Objective: Steering clear of bias as the research is separated from the data & only objective responses are sought.
• Direct comparisons of results: The study can be set in different cultural environments, times or different groups of participants with a statistical comparison of results.
• Focuses solely on numbers: This can be limiting as researchers may overlook other data and larger themes.
• Superficial Representations: It cannot adequately describe complex concepts (ex: feelings, opinions) it only shows the numbers behind them. 
• Several factors can invalidate results: A hypothesis and a model for collecting/ analyzing data.is required; any mistake can lead to bias and inaccurate illustrations.
• Erred Structure: If any data is missing or if measurements are not clear, biases easily take precedence.

The Final Word on Quantitative Research

Market research is far too encompassing to fully complete, especially in a limited amount of time. To tackle market research, begin with a research method. Quantitative research is often a good starting point, as it shows you the existence of a problem by way of quantifying it.

Aside from confirming the existence, it can help confirm a hypothesis, find correlations and prove cause and effect relationships. A hard set of data can also help you make educated predictions.

While the three types of quantitative research methods are useful, they do have several disadvantages. The fourth one, ie, survey research helps fill in the gaps and inadequacies of numerical limitations. Interestingly enough, they too can be a source of hard data and numbers. 

Either way, market research is sure to benefit from incorporating surveys as part of the processes.

Frequently asked questions

What is quantitative market research.

Quantitative market research utilizes the techniques of quantitative research in order to better understand the target market. In quantitative research, the information gathered from surveys and questionnaires is converted into numerical values so it can be easily analyzed.

What types of questions do quantitative research answer?

Quantitative research seeks to define “what” and “how much.” It is used for identifying patterns, making predictions, establishing averages, and quantifying opinions, attitudes or behaviors.

What are the four main types of quantitative research?

The four main types of quantitative research are survey research, correlational research, descriptive research, and experimental research.

What type of surveys are used for quantitative research?

Quantitative surveys are best suited for quantitative research. In this type of survey, there are no open-ended questions, and all responses can be assigned a numerical value. In most cases, a quantitative survey is distributed to a large and random sample of individuals.

Why are large sample sizes important when conducting quantitative research?

A small sample size can lead to inaccurate results. The larger the sample size (i.e. the group of individuals who receive the survey), the more likely it is that the results will be statistically significant and accurate.

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What is Quantitative Data? Your Guide to Data-Driven Success

In the world of market research , quantitative data is the lifeblood that fuels strategic decision-making, product innovation and competitive analysis .

This type of numerical data is a vital part of any market research professional’s toolkit because it provides measurable and objective evidence for the effectiveness of market and consumer behavioral insights.

Here, we’ll dive into the different types of quantitative data and provide a step-by-step guide on how to analyze quantitative data for the biggest impact on business strategy, optimization of campaigns, product placement and market entry decisions. All with a little help from Similarweb.

Let’s dive right in!

What is quantitative data?

Simply put, quantitative data is strictly numerical in nature. It’s any metric that can be counted, measured or quantified, like length in inches, distance in miles or time in seconds, minutes, hours or days.

Basically, it’s the type of data that answers questions like ‘how many?’, ‘how much?’ or ‘how big or small?’.

If you’re a market research professional, we’re talking statistics like market share percentage, web traffic visits , product views and ROI – all the crucial data you need to accurately gauge market potential .

Quantitative vs. qualitative data: what’s the difference?

If quantitative data is concerned with numbers, qualitative data deals with more descriptive or categorical information that can’t be as easily measured.

Quantitative answers ‘ how much ’ but qualitative explains ‘why’ or ‘how’ . This can be simple information like gender, eye color, types of cars or a description of the weather, i.e. very cold or rainy.

In business, qualitative data is information collected from things like research, open-ended surveys or questionnaires, interviews, focus groups, panels and case studies . Anything that delves into the underlying reasons, motivations and opinions that lie behind quantitative data.

Together, quantitative and qualitative data paint a reliable and robust picture. Quantitative data offers the assurance of fact and evidence, while qualitative data gives essential context and depth, and is able to capture more complex insight.

This match made in ‘data heaven’ leads to the best possible foundation for informed, data-driven decision making across the entire business.

What are the advantages and disadvantages of quantitative data?

Advantages of quantitative data:

✅ Accuracy and precision

Quantitative data is numerical, which allows for precise measurements and accuracy in the results. This precision is crucial for statistical analysis and making data-driven decisions where exact figures are key

✅ Simplicity

Numerical data can often be easier to handle and interpret compared to more complex qualitative data. Graphs, charts and tables can be used to represent quantitative data simply and effectively, making it accessible to a wider audience

✅ Reliability and credibility

Quantitative data can be collected and analyzed using standardized methods which increase the reliability of the data. This standardization helps in replicating studies, ensuring that results are consistent over time and across different researchers or studies

✅ Ease of comparability

Since quantitative data is numerical, it can be easily compared across different groups, time periods or other variables. This comparability is essential for trend analysis, forecasting, and competitive benchmarking/analysis

✅ Scalability

Quantitative research methods are generally scalable, meaning they can handle large sample sizes. This is particularly advantageous in studies where large data sets are required for generalizability of the findings

Disadvantages of quantitative data:

❌ Lack of context

What quantitative data has in precision, it lacks in broader context – or the “why” behind the data. While it shows the numbers and trends, it may not explain the underlying motives, emotions or experiences which are better captured by qualitative data

❌ Inflexibility

Once a quantitative data collection has begun, altering the process can be difficult or even impossible. This inflexibility can be a disadvantage if initial assumptions change or if unexpected factors arise

❌ Oversimplification

While the simplicity of quantitative data is certainly an advantage, it can also lead to oversimplification of complex issues. Reducing complex human behaviors or social phenomena to mere numbers can sometimes lead to the wrong conclusions or missed nuances

❌ Resource heavy

Quantitative research often requires significant resources in terms of time, money and expertise. Large-scale surveys and experiments necessitate comprehensive planning, robust data collection tools and sometimes sophisticated statistical analysis, making them very resource-intensive

❌ Surface-level insight

Quantitative data can provide broad overviews and identify trends but might not delve deep enough to extract truly useful insight. It tends to offer surface-level insights, which might be insufficient when detailed understanding or deep explorations of issues are required

Quantitative data examples

Quantitative data is an integral part of our day-to-day life, as well as being critical in a business sense. To get a clearer picture of what sort of information qualifies, let’s start with some more everyday examples of quantitative data before moving on to a few quantitative market research examples:

🌡️ Temperature: Most of us check the weather every day to decide what to wear and how to plan our activities; it’s also a critical metric for cooking and heating your home.

⚖️ Height and weight: Regular measurements can monitor growth in children or manage health and fitness in adults.

🕐 Time: We use time data to manage almost every part of our lives, from timing a morning commute or setting alarms for appointments, to making future plans.

⚡️ Speed: This helps in gauging how fast a vehicle travels, influencing travel time estimates and safety considerations.

📚 Test scores: Teachers and students use these to assess academic performance and areas of improvement.

❤️ Heart rate: Monitored during exercise or for health management, indicating physical exertion levels or potential medical conditions.

🥗 Calorie intake : Counting calories is a common method for managing diet and health

🚶 Number of steps: With fitness trackers, counting steps has become a popular way to gauge daily physical activity.

Ready for some market research-specific examples of quantitative data? 

This type of data is absolutely indispensable in market research as it provides a foundation to analyze the market, consumer behavior and business performance. Here’s how market research professionals often leverage quantitative data:

• Sales volume and revenue: These metrics help businesses understand market demand and the financial success of their products and services
• Market share: This is a good example for quantitative data that helps companies gauge their competitive edge and market presence
• Conversion rates: Useful for evaluating the effectiveness of promotional activities and customer service initiatives
• Advertising spend and ROI: Businesses assess the profitability and effectiveness of their marketing campaigns
• Engagement rates: These metrics show how engaging online content is and how effectively it converts viewers into customers
• Web traffic: Analyzed to determine the effectiveness of online presence and digital marketing strategies
• Marketing channel performance : Evaluating direct , organic search , email, social media, paid search and referral traffic are vital for understanding the most lucrative marketing channels to invest in

What are the different types of quantitative data?

1) Discrete data

These are numbers that can’t be broken down into smaller parts and only make sense as a whole when you list them. This could be the number of employees in a business or sales volume, as you can’t have 1.3 of a person or half a unit sold.

2) Continuous data

This is the type of data that can be measured both in full or broken down into smaller parts, making it continuous. Examples of continuous data include height or weight metrics as it is possible to have 0.5 kilograms of flour. In business sense, something like revenue or advertising spend is continuous as it can be any value, including decimals.

3) Interval data

This type of quantitative data measures the difference between points and doesn’t have a real starting point or value of zero. For example, temperature always exists, even at zero degrees – which is merely a point on the temperature scale. But it’s still useful to be able to discuss the difference between 30 and 40 degrees.

4) Ratio data

Unlike interval data, ratio data has a natural zero point, which means that zero means nothing is there. This allows for the calculation of ratios. Examples of ratio data could be time spent doing a task (where 0 hours means no time was spent at all) or conversion or engagement rates (where 0% engagement means no interaction.)

5) Ordinal data

Though this type of data is technically qualitative, ordinal data can often be seen as quantitative, especially when used in statistical models. For example, in categories such as a customer satisfaction scale from 1 to 10, where higher numbers indicate higher satisfaction.

What are the main collection methods of quantitative data?

Most types of research simply would not be possible without quantitative data, and there are many different ways of collecting this type of information, depending on the context. To start, here are some broad ways of collecting quantitative data:

• Experiments
• Observations
• Document and record analysis

In the realm of market research, quantitative data will often be gathered to shed light on market dynamics, trends or consumer behavior. Here are some specific examples of how market research professionals may collect quantitative data:

Market surveys and polls – Surveys and polls are designed to gauge consumer opinions and preferences, and can gather large volumes of data from targeted demographics that can be used to enhance product development and marketing strategies.

Digital analytics – With tools like Google Analytics and Similarweb, market researchers can analyze online behavior and track website interactions, marketing channel engagement and online purchasing patterns.

Customer databases and CRM systems – Transactional data gathered by customer relationship management (CRM) systems can be used to better understand things like purchase behaviors, customer lifecycle and audience loyalty trends.

A/B testing – This is an experimental approach used extensively in digital marketing to compare two versions of something, such as a landing page or email subject line, to determine which performs better in terms of user engagement and conversion rates.

Why is quantitative data so important in market research?

It’s hard to imagine a world without quantitative data. It would likely be very tricky to do your job, depending on what industry you work in.

Indeed, quantitative data is often indispensable to businesses across a wide range of industries as it provides a solid foundation for analyzing trends, measuring the effectiveness of different strategies and predicting future outcomes. But that’s just the tip of the iceberg. Here’s why quantitative data is so critical, particularly within the realm of market research:

Data-driven decision making

Quantitative data takes away a lot of the guesswork and subjectivity when it comes to making important decisions. With numbers and statistics, businesses can move beyond conjecture and personal bias to make more objective, data-backed decisions. In market research, this is particularly important when deciding whether to enter a particular market or expand within an existing one.

This is where Similarweb steps in 👋

Similarweb’s platform offers powerful market research tools that streamline the gathering and analyzing of quantitative research , particularly useful when evaluating a potential new market or expanding within a current one.

Market research professionals need look no further than Similarweb’s Market Analysis feature, which provides detailed insights into how challenging it may be to penetrate a particular market.

It does this by analyzing quantitative data surrounding competitor density, market saturation, and customer loyalty to get a robust picture of the competitive landscape .

As an example, here’s a snapshot of the market difficulty for the Consumer Electronics industry, using Market Analysis:

Here, we can see that based on a variety of analyzed quantitative data, market difficulty is ‘medium’, meaning it would be moderately challenging for new entrants to gain a foothold or existing players to increase market share , and would require time and investment.

You may think this means that an electronics company can simply choose whether on not to launch a new product or grow their market share based on this medium difficulty.

However, the devil is often in the details. When you break down the metrics on display and investigate further, more nuanced insights emerge about how a company can succeed in the market:

Audience loyalty in the Electronics and Technology industry – measured by the percentage of exclusive website visits (meaning the customers did not look at more than one brand) – is fairly low at 22.14%. Here’s a further breakdown, highlighting the top players:

This suggests that customers that are interested in Consumer Electronics sites are not particularly loyal to a single brand and will switch easily, indicating a price-driven market.

Therefore, a new market entrant should focus on developing unique value propositions, loyalty programs, or more competitive pricing models in order to gain traction in this otherwise difficult market.

Consolidation

This engagement metric is concerned with the percentage of players that hold the most market share (measured in website visits). In this industry, the consolidation rate is high, with the top 1% of players getting a whopping 80.03% on website visits.

While this means the competitive landscape is dominated by a few large players (Apple, Samsung etc.,) smaller players may be able to edge their way in:

Indeed, with this information, new entrants can strategically focus on targeting niche segments within the wider industry or creating innovative strategies to set themselves apart from the usual suspects.

Average PPC Spend

The data suggests that, at a glance, there is a high average PPC spend within the Consumer Electronics industry, likely due to strong competition over high-value keywords and ad placements. This can outprice companies with a smaller budget or lead to wasted ad spend with little to no results.

Understanding the investment needed to compete on paid channels can encourage smaller companies to either target more cost-effective options, like more niche or long-tail keywords , or redirect spend to more lucrative marketing channels that will yield better results.

Brand strength

Interestingly, brand strength is measured as ‘medium’ at 59.11% for the Consumer Electronics industry, despite featuring household names like Apple and Samsung. Brand strength is calculated by the percentage of direct and branded traffic to the top websites in the industry:

This means it could be relatively tricky – but certainly not impossible – for new market entrants to build brand awareness .

With the understanding that strong brand recognition and marketing is effective in this industry, potential market entrants can focus significant effort on building a strong, yet unique, brand identity and decide on strategies that will help them cut through the noise, like influencer marketing and PR campaigns.

Understanding consumer behavior

Data analysis for quantitative data is like a compass for understanding what your customers are doing and what they want. Metrics like click-through rate , conversion rate , page visit duration , and bounce rate all tell a story about how engaged your customers are with your website and content. This is instrumental in refining marketing campaigns, improving product or service offerings and elevating the customer experience.

Want another shortcut to understanding consumer behavior and preferences? Similarweb delivers this (and more) with our Demand Analysis feature.

Demand Analysis offers a direct look into what consumers are searching for, the trends shaping their behaviors, and how they respond to various market stimuli.

By leveraging real-time and historical data on consumer search behavior, you can gain a detailed understanding of demand patterns and shifts in consumer interests.

Demand Analysis reveals trends through customized keyword lists. By leveraging these personalized insights, you can forecast demand within your category and track how it evolves over time. This enables you to identify—and potentially forecast—both significant macro trends and nuanced micro trends that are likely to influence your business.

Here’s how demand forecasting works using Similarweb:

Let’s find out how popular the topic ‘dresses’ is based on real-time consumer searches and clicks. Based on a customized keyword list, we can see that demand for this topic has grown by 9.09% over the last three months:

With total searches for dress-related keywords rising by almost 10% in the last 3 months, we can clearly see the demand trend is steadily rising – to be expected as we enter the warmer months. Here, there is also the option to change the time period of comparison, for example to see how demand has changed Year over Year.

Looking at a YoY view of keyword trends, this graph reveals further key consumer insights surrounding demand for dresses, such as:

• The lowest search volumes are seen in more generic keyword s like “dresses for women” and “women’s dresses,” which indicates that consumers are searching more specifically when looking online
• ‘Cocktail dresses’ has the highest search volume among the dress types, peaking at around 116K searches in Sept 2023 and then again in April 2024. However, there is a decrease of 8-30% during these peaks when compared with data from 2022
• The consistently high volume for dresses suggests strong, steady demand throughout the year , however the peak in September for ‘cocktail dresses’ and in November for ‘maxi dresses’ is not quite consistent with the expected seasonal trend, which could point to event-driven consumer demand or targeted marketing campaigns

Benchmarking performance/competitive analysis

Quantitative data analysis is also vital for comparing business performance against competitors, particularly industry leaders . By analyzing competitors’ data alongside their own, like product sales or views, marketing channel performance and engagement metrics , businesses and brands can benchmark their success and better gauge their position in the market. This also helps identify opportunities or areas of improvement.

When it comes to this kind of comparative quantitative data, Similarweb’s platform has it all.

Let’s compare the website performance of two leading click-and-mortar retailers – walmart.com and target.com – using our Website Analysis feature.

Before diving into the nitty gritty, Similarweb offers an overview or snapshot of each company’s key performance metrics, displayed side-by-side for easier comparison:

With this initial overview, market research professionals can quickly gauge where they stand against their competitors in terms of market share, total website visits, desktop/mobile device distribution and how they compare in the global, country and industry arena. 

Diving into the data further, Website Analysis offers a look into high-level traffic and engagement metrics:

Here, there is the option to compare the website traffic trend of each competitor analyzed over a specific period. Then, they can view other engagement trends concerning visit duration, pages per visit , page views , and bounce rate.

Alternatively, this data can be seen even more clearly under our specific Engagement segment:

Next up, the Marketing Channels overview gives a snapshot into the performance of each competitors’ marketing channels, so businesses can compare their most successful traffic sources:

Walmart is the clear winner in this example, taking the lead across every channel. Target may use this information to understand the most lucrative channels to invest in based on their competitors’ success.

And finally, get one last snapshot of quantitative data in the form of some juicy audience demographics for more targeted strategies:

Tracking market trends

Understanding (and anticipating) market trends is one of the most important parts of market research. Trendspotting is possible by tracking certain quantitative data, such as sales numbers, market share, customer demographics, and purchase patterns over time. These data points can help provide clear insight into how a market is evolving, and what might be on the horizon. This is especially useful when forecasting future trends or demand for products and services.

Elevating the customer experience

Last but certainly not least, quantitative data is very useful in getting an idea of how satisfied customers are with a product or service. Gathering feedback via market research surveys can be used to fine-tune product features, elevate customer service and enhance the user experience – sending customer satisfaction, loyalty, and sales through the roof.

That’s a wrap on quantitative data…

In market research, quantitative data is indispensable, fueling data-driven decisions, product innovation and competitive analysis. This type of data provides measurable, objective evidence crucial for assessing strategies, understanding consumer behaviors and predicting future trends.

Similarweb is a goldmine of quantitative data, showcasing the power of these metrics with its advanced analytical tools.

The platform’s Market Analysis feature, in particular, offers deep insights into market dynamics, empowering market research professionals to make data-driven decisions with more precision.

Whether exploring new markets or expanding existing ones, Similarweb provides the essential quantitative data needed to turn data into actionable insights and navigate the complexities of today’s dynamic landscape – with confidence.

Dive into a treasure trove of quantitative data

With the best analytics platform in the world.

Quantitative data refers to any data that can be quantified and expressed numerically. This includes measurements, counts or other data that can be represented by numbers.

Why is quantitative data important in market research?

Quantitative data is crucial in market research as it provides a solid foundation for making objective decisions. It helps in analyzing trends, measuring the effectiveness of different strategies and predicting future outcomes. With quantitative data, businesses can take out the guesswork, allowing for more precise planning and assessment.

What’s the difference between quantitative and qualitative data?

Quantitative data involves numerical measurements and provides insights in terms of numbers and stats, allowing for statistical analysis and more concrete conclusions. Qualitative data is more descriptive and observational, providing deeper insights into thoughts, opinions, and motivations.

Quantitative data is categorized into four main types. Discrete data consists of counts that cannot be meaningfully divided into smaller parts, such as the number of children in a family. Continuous data includes measurements that can be infinitely divided into finer increments, like weight.

Interval data involves measurements where the difference between values is meaningful but lacks a true zero point, such as temperature in Celsius. Lastly, ratio data is similar to interval data but includes a meaningful zero point, allowing for ratio calculations, examples include height, weight, and distance.

How can I find and analyze quantitative data using Similarweb?

Similarweb offers a variety of tools that help in discovering and analyzing quantitative data. Features like Market Analysis provide insights into market dynamics, including competitor density, market saturation and customer loyalty. To track consumer behavior, the Demand Analysis tool offers real-time data on search trends and keyword volumes, making it easier to gauge market demand and interest.

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Quantitative market research questions to ask for actionable insights

Types of quantitative market research questions, 36 quantitative research questions and examples, how to write your own quantitative market research questions, how to collect insightful data from your quantitative surveys, receive quantitative insights in weeks, not months.

There’s a big difference between asking “Why do you like our product?” and “On a scale of 1-10, how much do you like our product?” But both ways of asking are valuable in their own way.

Knowing your audience is not about guesswork or intuition, it is about concrete data. And while it’s valuable to learn the ‘why’ behind the ‘what’ with qualitative research, quantitative research is just as necessary — to spot trends, patterns and more.

Unlike qualitative research, which explores attitudes, opinions, and motivations through open-ended questions, quantitative research zeroes in on the numbers (see what we did there?). It’s the difference between gathering general opinions and collecting measurable, specific data.

But when is this approach the way to go? For starters, whenever you need to track factors over time, such as customer satisfaction. Or when assessing the popularity of a potential product feature, understanding demographic preferences, or analyzing consumer purchasing behavior in different locations.

Quantitative research reveals the impact and scale of sentiments for better decision-making. It’s also valuable when you’re looking to quantify the extent of a trend, measure the impact of a marketing campaign, or pin down the specifics of consumer behavior.

But how do you ask quantitative market research questions that don’t just scratch the surface? We’re here to give you some great examples of quantitative survey questions.

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When thinking of quantitative market research questions, people often think ‘ ah, numbers ‘. But there’s more than meets the eye. Here’s how you can categorize the different types of quantitative research questions:

Descriptive quantitative research questions

These are your what , when , and how many types of questions. They help you sketch out the basic landscape of your market. For example, “How often do you shop online in a month?” or “What is your preferred method of payment while shopping online?” When you give answers people can select, it is quantifiable data. That’s different from asking: ”describe what a day out shopping looks like for you”, which is a qualitative question.

Comparative quantitative survey questions

These questions measure differences or changes over time or between groups. For instance, “How has your spending on online shopping changed since last year?” Comparative questions help you understand the dynamics and shifts in your market. Remember that you’re not just trying to find overlap: it’s just as important to know what differences there are.

Relationship-based quantitative survey questions

These questions aim to uncover correlations or relationships between two or more variables. They can reveal insights like, “Is there a link between age and the likelihood of using mobile payments?” These questions help you understand the deeper connections within your market, as well as test assumptions, as long as you dare to ask questions that challenge what you’re hoping to find.

Now, a quick note on reducing bias in quantitative survey questions . Here are some key points to remember:

• The key is in how you frame your questions.
• Always aim for neutrality.
• Avoid leading questions that suggest a particular answer.
• Be specific and clear to avoid confusion.
• Consider the order of your questions, as earlier questions can influence responses to later ones.

And finally, test your survey with a small group before a full rollout, to catch and correct any unintentional bias. This way, you ensure the data you collect is as accurate and reliable as possible, giving you the best insights to make those crucial business decisions.

If you want to make a quantitative survey that hits the spot, don’t just ask generic questions. We’re here with some examples that you can adapt to make your research a success.

Descriptive market research questions

With a descriptive quantitative research question, you can quickly get the most important info for your respondents on anything ranging from buying frequency to satisfaction levels.

• Insight : this question reveals the frequency of use, indicating customer dependency on your product or service.
• Benefit : understanding usage patterns can guide inventory management and marketing strategies.
• Insight : reveals the communication channels most favored by your audience.
• Benefit : tailor your customer service and marketing outreach to your customers’ preferred channels.
• Insight : provides an average spending figure for budget allocation in that category.
• Benefit : helps in pricing strategies and identifying the most lucrative customer segments.
• Insight : uncovers patterns in online shopping behavior.
• Benefit : optimizes the timing of online marketing campaigns and promotions.
• Insight : identifies the most effective channels for brand discovery.
• Benefit : informs where to allocate advertising spend for maximum impact.
• Insight : measures the likelihood (not effectiveness!) of word-of-mouth referrals.
• Benefit : assesses customer satisfaction and the potential for organic growth.
• Insight : highlights your unique selling points from the customer’s perspective.
• Benefit : guides messaging to emphasize what customers value most about your brand.
• Insight : offers a quantifiable measure of customer service satisfaction.
• Benefit : identifies areas for improvement in customer support.
• Insight : sheds light on the most popular aspects of your product.
• Benefit : informs product development and feature enhancement.
• Insight : uncovers the key motivators behind purchasing decisions.
• Benefit : helps create targeted marketing campaigns to focus on these driving factors. 

Comparative market research questions

If you want to analyze and compare different variables, these questions can help.

• Insight : highlights changes in consumer spending habits over time.
• Benefit : useful for identifying trends and shifts in consumer behavior, aiding in long-term planning. Especially valuable if you add qualitative insights to this quantitative data.
• Insight : compares consumer preferences between different shopping channels.
• Benefit : guides omnichannel marketing strategies and resource allocation.
• Insight : tracks changing consumer values and preferences over time.
• Benefit : useful for aligning product development and marketing with evolving consumer values.
• Insight : compares the weight of price versus brand in purchasing decisions.
• Benefit : informs pricing strategies and brand positioning efforts.
• Insight : evaluates customer perception of marketing efforts in product packaging.
• Benefit : assesses the impact of packaging on brand image and customer approval.

What are the top research platforms in the UK?

Here’s our list of the pros and cons of key market research platforms for UK brands

Relationship-based questions for quantitative research

In quantitative research, especially when exploring relationship-based aspects, the key is not to cram multiple inquiries into one question but to ask them sequentially.

This approach allows for a clearer and more focused response to each individual question. Later, during the analysis phase, you can then correlate the responses to uncover relationships between different variables.

For instance, instead of asking, “How often do you use our product and how satisfied are you with it?”, split this into two separate questions:

• “How often do you use our product (daily, weekly, monthly)?”
• “On a scale of 1-10, how satisfied are you with our product?”

By asking these questions separately, you ensure that respondents clearly focus on each aspect without being overwhelmed or confused by a dual-focused question. This approach yields more accurate and reliable data.

After the survey, you can analyze the results to see if there’s a correlation between usage frequency and satisfaction levels.

Here are some examples of combinations that can work well:

• What is your age group?
• Insight : correlates age with shopping preferences.
• Benefit : you can tailor marketing and sales strategies to different age demographics based on their preferred shopping channels.
• How long have you been using our products/services?
• Insight : links customer tenure with brand loyalty.
• Benefit : assesses the impact of long-term use on loyalty, informing customer retention initiatives.
• What is your approximate annual income?
• Insight : examines the relationship between income levels and purchasing behavior for premium products.
• Benefit : guides product and pricing strategies targeting different income segments.
• How often do you use social media for product discovery?
• Insight : assesses if frequent social media use for product discovery actually influences online shopping behavior.
• Benefit : informs the effectiveness of social media marketing in driving online sales in your target market.
• How would you rate your satisfaction with our post-purchase customer service (scale of 0-10)?
• Insight : links the level of service post-purchase with the likelihood of repeat purchases.
• Benefit : identifies if customer service is negatively or positively affecting repeat custom rates.

Brand tracking questions for quantitative insights

One thing you should definitely gather numerical data on, is your brand’s health. Just like your own health, stats, and numbers matter and can show you where to further investigate to ask qualitative research questions about. Learn if your brand stands strong through market trends and gain insights on whether your brand is growing in terms of awareness — and in which segments.

• Insight : measures brand awareness among the target audience.
• Benefit : helps assess the effectiveness of your marketing and branding efforts.
• Insight : evaluates brand loyalty and the potential for organic growth through word-of-mouth.
• Benefit : indicates customer satisfaction and the potential for brand advocacy.
• Insight: Identifies the most effective channels for brand discovery.
• Benefit: Informs where to focus marketing efforts for increased brand exposure.
• Insight: Measures brand visibility and frequency of encounters with the brand.
• Benefit: Helps evaluate the reach and frequency of marketing campaigns.
• Insight: Determines which brand values resonate most with the audience.
• Benefit: Aids in refining brand messaging and aligning it with customer values.

Quantitative consumer segmentation questions

Quantitative questions about customer segments can go beyond age group and gender. King Charles III is the same age as Ozzy Osbourne – would you say they’re very similar?

It is vital that you look at more variables so you can really tell the difference between your respondents, and make informed decisions based on the whole truth. Putting these consumer profiling questions and answers in specific ranges helps you create segments to tailor your marketing and customer experience for, rather than just aiming at the entire population.

• Insight : helps understand the economic demographics of your customers.
• Benefit : assists in pricing strategies and identifying which income groups are most engaged with your brand.
• Insight : reveals geographical spread and regional preferences.
• Benefit : guides regional marketing efforts and product distribution strategies.
• Insight : helps categorize customers by education level.
• Benefit : useful for tailoring communication and content complexity to different education backgrounds.
• Insight : provides insights into the professional background of your customers.
• Benefit : helps in creating industry-specific marketing campaigns and products.
• Insight : gives an idea of household size and composition.
• Benefit : useful for targeting products and services aimed at families or individuals.
• Insight : identifies customers who are parents of minors (which is different from parents of young adults, or even grown adults).
• Benefit : informs product and marketing strategies aimed at families with children.

Okay, so now you got the gist of it and have seen what quantitative questions can look like — as they come in all shapes and sizes. But they might feel too generic for your research, or you’re looking for something specific.

Here’s how you can whip up your own quantitative questions that deliver the insights you need for data-driven decisions.

Identify the key variables you need to measure

Start by pinpointing exactly what you want to know. Is it customer satisfaction, buying behavior, or brand awareness? Determining the specific variables you need to measure sets the foundation for your entire survey.

Choose the right survey distribution method

Think about how your questions will reach your audience. Will it be online through email or social media, over the phone, or in person? Your method should align with where your target audience is most active and responsive.

Make sure your questions are crystal-clear and unequivocally unbiased

We’ve mentioned it earlier, and we’ll do it again if we have to. The way you phrase your questions can make or break your survey. Aim for clarity and simplicity – questions should be easy to understand and answer. Avoid leading or loaded questions that might sway a respondent’s answer. Remember: it’s a survey, not a sales pitch.

Know where to ask for more detailed information and qualitative data

Quantitative market research questions only tell part of the story. If you see interesting trends in say purchase behavior or price sensitivity, or a particular product gets a bad rating, dig a little deeper. Follow up important questions with qualitative research questions to analyze what’s going on behind the numbers.

If you don’t want to end up with a pile of quantitative data that doesn’t do much for you or breaks the bank unnecessarily, it’s vital you choose a form of distributing the survey that makes sense. You can work with UK market research companies to outsource it all, or do it yourself. Here’s a brief look at the pros and cons of popular methods:

Telephone surveys:

• Pros : good for less tech-savvy demographics.
• Cons : time-consuming, potentially costly, and declining response rates. They might be better for qualitative research.

In-person surveys:

• Pros : also avoids any confusion with tech.
• Cons : logistically demanding and expensive, not suited for quick data collection.

Online survey software:

• Pros : cost-effective, broad reach, real-time data analysis, and versatile formats.
• Cons : it’s extra important to pay close attention to survey design, so people don’t get the urge to give false answers just to get to the end.

The choice is yours, but generally, quantitative research thrives when done with online surveys and it’s the go-to method for most international market research . And here at Attest, we help you get even more out of it by giving you a chock-full toolkit. From various types of questions to robust analytical tools (and a dedicated research expert for when you need a little extra help) — we set you up for measurable success.

Speed and accuracy in market research matter — but we don’t want you to sacrifice quality. With Attest, you get fast, actionable and high-quality insights.

Which market analysis tool is right for you?

Check our rundown of the top platforms for market analysis – and start making better decisions with reliable insights in no time!

VP Customer Success 

Sam joined Attest in 2019 and leads the Customer Research Team. Sam and her team support brands through their market research journey, helping them carry out effective research and uncover insights to unlock new areas for growth.

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How to Do Market Research: The Complete Guide

Learn how to do market research with this step-by-step guide, complete with templates, tools and real-world examples.

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Market research is the systematic process of gathering, analyzing and interpreting information about a specific market or industry.

What are your customers’ needs? How does your product compare to the competition? What are the emerging trends and opportunities in your industry? If these questions keep you up at night, it’s time to conduct market research.

Market research plays a pivotal role in your ability to stay competitive and relevant, helping you anticipate shifts in consumer behavior and industry dynamics. It involves gathering these insights using a wide range of techniques, from surveys and interviews to data analysis and observational studies.

In this guide, we’ll explore why market research is crucial, the various types of market research, the methods used in data collection, and how to effectively conduct market research to drive informed decision-making and success.

What is market research?

The purpose of market research is to offer valuable insight into the preferences and behaviors of your target audience, and anticipate shifts in market trends and the competitive landscape. This information helps you make data-driven decisions, develop effective strategies for your business, and maximize your chances of long-term growth.

Why is market research important? 

By understanding the significance of market research, you can make sure you’re asking the right questions and using the process to your advantage. Some of the benefits of market research include:

• Informed decision-making: Market research provides you with the data and insights you need to make smart decisions for your business. It helps you identify opportunities, assess risks and tailor your strategies to meet the demands of the market. Without market research, decisions are often based on assumptions or guesswork, leading to costly mistakes.
• Customer-centric approach: A cornerstone of market research involves developing a deep understanding of customer needs and preferences. This gives you valuable insights into your target audience, helping you develop products, services and marketing campaigns that resonate with your customers.
• Competitive advantage: By conducting market research, you’ll gain a competitive edge. You’ll be able to identify gaps in the market, analyze competitor strengths and weaknesses, and position your business strategically. This enables you to create unique value propositions, differentiate yourself from competitors, and seize opportunities that others may overlook.
• Risk mitigation: Market research helps you anticipate market shifts and potential challenges. By identifying threats early, you can proactively adjust their strategies to mitigate risks and respond effectively to changing circumstances. This proactive approach is particularly valuable in volatile industries.
• Resource optimization: Conducting market research allows organizations to allocate their time, money and resources more efficiently. It ensures that investments are made in areas with the highest potential return on investment, reducing wasted resources and improving overall business performance.
• Adaptation to market trends: Markets evolve rapidly, driven by technological advancements, cultural shifts and changing consumer attitudes. Market research ensures that you stay ahead of these trends and adapt your offerings accordingly so you can avoid becoming obsolete. 

As you can see, market research empowers businesses to make data-driven decisions, cater to customer needs, outperform competitors, mitigate risks, optimize resources and stay agile in a dynamic marketplace. These benefits make it a huge industry; the global market research services market is expected to grow from $76.37 billion in 2021 to $108.57 billion in 2026 . Now, let’s dig into the different types of market research that can help you achieve these benefits.

Types of market research 

• Qualitative research
• Quantitative research
• Exploratory research
• Descriptive research
• Causal research
• Cross-sectional research
• Longitudinal research

Despite its advantages, 23% of organizations don’t have a clear market research strategy. Part of developing a strategy involves choosing the right type of market research for your business goals. The most commonly used approaches include:

1. Qualitative research

Qualitative research focuses on understanding the underlying motivations, attitudes and perceptions of individuals or groups. It is typically conducted through techniques like in-depth interviews, focus groups and content analysis — methods we’ll discuss further in the sections below. Qualitative research provides rich, nuanced insights that can inform product development, marketing strategies and brand positioning.

2. Quantitative research

Quantitative research, in contrast to qualitative research, involves the collection and analysis of numerical data, often through surveys, experiments and structured questionnaires. This approach allows for statistical analysis and the measurement of trends, making it suitable for large-scale market studies and hypothesis testing. While it’s worthwhile using a mix of qualitative and quantitative research, most businesses prioritize the latter because it is scientific, measurable and easily replicated across different experiments.

3. Exploratory research

Whether you’re conducting qualitative or quantitative research or a mix of both, exploratory research is often the first step. Its primary goal is to help you understand a market or problem so you can gain insights and identify potential issues or opportunities. This type of market research is less structured and is typically conducted through open-ended interviews, focus groups or secondary data analysis. Exploratory research is valuable when entering new markets or exploring new product ideas.

4. Descriptive research

As its name implies, descriptive research seeks to describe a market, population or phenomenon in detail. It involves collecting and summarizing data to answer questions about audience demographics and behaviors, market size, and current trends. Surveys, observational studies and content analysis are common methods used in descriptive research. 

5. Causal research

Causal research aims to establish cause-and-effect relationships between variables. It investigates whether changes in one variable result in changes in another. Experimental designs, A/B testing and regression analysis are common causal research methods. This sheds light on how specific marketing strategies or product changes impact consumer behavior.

6. Cross-sectional research

Cross-sectional market research involves collecting data from a sample of the population at a single point in time. It is used to analyze differences, relationships or trends among various groups within a population. Cross-sectional studies are helpful for market segmentation, identifying target audiences and assessing market trends at a specific moment.

7. Longitudinal research

Longitudinal research, in contrast to cross-sectional research, collects data from the same subjects over an extended period. This allows for the analysis of trends, changes and developments over time. Longitudinal studies are useful for tracking long-term developments in consumer preferences, brand loyalty and market dynamics.

Each type of market research has its strengths and weaknesses, and the method you choose depends on your specific research goals and the depth of understanding you’re aiming to achieve. In the following sections, we’ll delve into primary and secondary research approaches and specific research methods.

Primary vs. secondary market research

Market research of all types can be broadly categorized into two main approaches: primary research and secondary research. By understanding the differences between these approaches, you can better determine the most appropriate research method for your specific goals.

Primary market research 

Primary research involves the collection of original data straight from the source. Typically, this involves communicating directly with your target audience — through surveys, interviews, focus groups and more — to gather information. Here are some key attributes of primary market research:

• Customized data: Primary research provides data that is tailored to your research needs. You design a custom research study and gather information specific to your goals.
• Up-to-date insights: Because primary research involves communicating with customers, the data you collect reflects the most current market conditions and consumer behaviors.
• Time-consuming and resource-intensive: Despite its advantages, primary research can be labor-intensive and costly, especially when dealing with large sample sizes or complex study designs. Whether you hire a market research consultant, agency or use an in-house team, primary research studies consume a large amount of resources and time.

Secondary market research 

Secondary research, on the other hand, involves analyzing data that has already been compiled by third-party sources, such as online research tools, databases, news sites, industry reports and academic studies.

Here are the main characteristics of secondary market research:

• Cost-effective: Secondary research is generally more cost-effective than primary research since it doesn’t require building a research plan from scratch. You and your team can look at databases, websites and publications on an ongoing basis, without needing to design a custom experiment or hire a consultant. 
• Leverages multiple sources: Data tools and software extract data from multiple places across the web, and then consolidate that information within a single platform. This means you’ll get a greater amount of data and a wider scope from secondary research.
• Quick to access: You can access a wide range of information rapidly — often in seconds — if you’re using online research tools and databases. Because of this, you can act on insights sooner, rather than taking the time to develop an experiment. 

So, when should you use primary vs. secondary research? In practice, many market research projects incorporate both primary and secondary research to take advantage of the strengths of each approach.

One rule of thumb is to focus on secondary research to obtain background information, market trends or industry benchmarks. It is especially valuable for conducting preliminary research, competitor analysis, or when time and budget constraints are tight. Then, if you still have knowledge gaps or need to answer specific questions unique to your business model, use primary research to create a custom experiment. 

Market research methods

• Surveys and questionnaires
• Focus groups
• Observational research
• Online research tools
• Experiments
• Content analysis
• Ethnographic research

How do primary and secondary research approaches translate into specific research methods? Let’s take a look at the different ways you can gather data: 

1. Surveys and questionnaires

Surveys and questionnaires are popular methods for collecting structured data from a large number of respondents. They involve a set of predetermined questions that participants answer. Surveys can be conducted through various channels, including online tools, telephone interviews and in-person or online questionnaires. They are useful for gathering quantitative data and assessing customer demographics, opinions, preferences and needs. On average, customer surveys have a 33% response rate , so keep that in mind as you consider your sample size.

2. Interviews

Interviews are in-depth conversations with individuals or groups to gather qualitative insights. They can be structured (with predefined questions) or unstructured (with open-ended discussions). Interviews are valuable for exploring complex topics, uncovering motivations and obtaining detailed feedback. 

3. Focus groups

The most common primary research methods are in-depth webcam interviews and focus groups. Focus groups are a small gathering of participants who discuss a specific topic or product under the guidance of a moderator. These discussions are valuable for primary market research because they reveal insights into consumer attitudes, perceptions and emotions. Focus groups are especially useful for idea generation, concept testing and understanding group dynamics within your target audience.

4. Observational research

Observational research involves observing and recording participant behavior in a natural setting. This method is particularly valuable when studying consumer behavior in physical spaces, such as retail stores or public places. In some types of observational research, participants are aware you’re watching them; in other cases, you discreetly watch consumers without their knowledge, as they use your product. Either way, observational research provides firsthand insights into how people interact with products or environments.

5. Online research tools

You and your team can do your own secondary market research using online tools. These tools include data prospecting platforms and databases, as well as online surveys, social media listening, web analytics and sentiment analysis platforms. They help you gather data from online sources, monitor industry trends, track competitors, understand consumer preferences and keep tabs on online behavior. We’ll talk more about choosing the right market research tools in the sections that follow.

6. Experiments

Market research experiments are controlled tests of variables to determine causal relationships. While experiments are often associated with scientific research, they are also used in market research to assess the impact of specific marketing strategies, product features, or pricing and packaging changes.

7. Content analysis

Content analysis involves the systematic examination of textual, visual or audio content to identify patterns, themes and trends. It’s commonly applied to customer reviews, social media posts and other forms of online content to analyze consumer opinions and sentiments.

8. Ethnographic research

Ethnographic research immerses researchers into the daily lives of consumers to understand their behavior and culture. This method is particularly valuable when studying niche markets or exploring the cultural context of consumer choices.

How to do market research

• Set clear objectives
• Identify your target audience
• Choose your research methods
• Use the right market research tools
• Collect data
• Analyze data 
• Interpret your findings
• Identify opportunities and challenges
• Make informed business decisions
• Monitor and adapt

Now that you have gained insights into the various market research methods at your disposal, let’s delve into the practical aspects of how to conduct market research effectively. Here’s a quick step-by-step overview, from defining objectives to monitoring market shifts.

1. Set clear objectives

When you set clear and specific goals, you’re essentially creating a compass to guide your research questions and methodology. Start by precisely defining what you want to achieve. Are you launching a new product and want to understand its viability in the market? Are you evaluating customer satisfaction with a product redesign? 

Start by creating SMART goals — objectives that are specific, measurable, achievable, relevant and time-bound. Not only will this clarify your research focus from the outset, but it will also help you track progress and benchmark your success throughout the process. 

You should also consult with key stakeholders and team members to ensure alignment on your research objectives before diving into data collecting. This will help you gain diverse perspectives and insights that will shape your research approach.

2. Identify your target audience

Next, you’ll need to pinpoint your target audience to determine who should be included in your research. Begin by creating detailed buyer personas or stakeholder profiles. Consider demographic factors like age, gender, income and location, but also delve into psychographics, such as interests, values and pain points.

The more specific your target audience, the more accurate and actionable your research will be. Additionally, segment your audience if your research objectives involve studying different groups, such as current customers and potential leads.

If you already have existing customers, you can also hold conversations with them to better understand your target market. From there, you can refine your buyer personas and tailor your research methods accordingly.

3. Choose your research methods

Selecting the right research methods is crucial for gathering high-quality data. Start by considering the nature of your research objectives. If you’re exploring consumer preferences, surveys and interviews can provide valuable insights. For in-depth understanding, focus groups or observational research might be suitable. Consider using a mix of quantitative and qualitative methods to gain a well-rounded perspective. 

You’ll also need to consider your budget. Think about what you can realistically achieve using the time and resources available to you. If you have a fairly generous budget, you may want to try a mix of primary and secondary research approaches. If you’re doing market research for a startup , on the other hand, chances are your budget is somewhat limited. If that’s the case, try addressing your goals with secondary research tools before investing time and effort in a primary research study. 

4. Use the right market research tools

Whether you’re conducting primary or secondary research, you’ll need to choose the right tools. These can help you do anything from sending surveys to customers to monitoring trends and analyzing data. Here are some examples of popular market research tools:

• Market research software: Crunchbase is a platform that provides best-in-class company data, making it valuable for market research on growing companies and industries. You can use Crunchbase to access trusted, first-party funding data, revenue data, news and firmographics, enabling you to monitor industry trends and understand customer needs.

• Survey and questionnaire tools: SurveyMonkey is a widely used online survey platform that allows you to create, distribute and analyze surveys. Google Forms is a free tool that lets you create surveys and collect responses through Google Drive.
• Data analysis software: Microsoft Excel and Google Sheets are useful for conducting statistical analyses. SPSS is a powerful statistical analysis software used for data processing, analysis and reporting.
• Social listening tools: Brandwatch is a social listening and analytics platform that helps you monitor social media conversations, track sentiment and analyze trends. Mention is a media monitoring tool that allows you to track mentions of your brand, competitors and keywords across various online sources.
• Data visualization platforms: Tableau is a data visualization tool that helps you create interactive and shareable dashboards and reports. Power BI by Microsoft is a business analytics tool for creating interactive visualizations and reports.

5. Collect data

There’s an infinite amount of data you could be collecting using these tools, so you’ll need to be intentional about going after the data that aligns with your research goals. Implement your chosen research methods, whether it’s distributing surveys, conducting interviews or pulling from secondary research platforms. Pay close attention to data quality and accuracy, and stick to a standardized process to streamline data capture and reduce errors. 

6. Analyze data

Once data is collected, you’ll need to analyze it systematically. Use statistical software or analysis tools to identify patterns, trends and correlations. For qualitative data, employ thematic analysis to extract common themes and insights. Visualize your findings with charts, graphs and tables to make complex data more understandable.

If you’re not proficient in data analysis, consider outsourcing or collaborating with a data analyst who can assist in processing and interpreting your data accurately.

7. Interpret your findings

Interpreting your market research findings involves understanding what the data means in the context of your objectives. Are there significant trends that uncover the answers to your initial research questions? Consider the implications of your findings on your business strategy. It’s essential to move beyond raw data and extract actionable insights that inform decision-making.

Hold a cross-functional meeting or workshop with relevant team members to collectively interpret the findings. Different perspectives can lead to more comprehensive insights and innovative solutions.

8. Identify opportunities and challenges

Use your research findings to identify potential growth opportunities and challenges within your market. What segments of your audience are underserved or overlooked? Are there emerging trends you can capitalize on? Conversely, what obstacles or competitors could hinder your progress?

Lay out this information in a clear and organized way by conducting a SWOT analysis, which stands for strengths, weaknesses, opportunities and threats. Jot down notes for each of these areas to provide a structured overview of gaps and hurdles in the market.

9. Make informed business decisions

Market research is only valuable if it leads to informed decisions for your company. Based on your insights, devise actionable strategies and initiatives that align with your research objectives. Whether it’s refining your product, targeting new customer segments or adjusting pricing, ensure your decisions are rooted in the data.

At this point, it’s also crucial to keep your team aligned and accountable. Create an action plan that outlines specific steps, responsibilities and timelines for implementing the recommendations derived from your research. 

10. Monitor and adapt

Market research isn’t a one-time activity; it’s an ongoing process. Continuously monitor market conditions, customer behaviors and industry trends. Set up mechanisms to collect real-time data and feedback. As you gather new information, be prepared to adapt your strategies and tactics accordingly. Regularly revisiting your research ensures your business remains agile and reflects changing market dynamics and consumer preferences.

Online market research sources

As you go through the steps above, you’ll want to turn to trusted, reputable sources to gather your data. Here’s a list to get you started:

• Crunchbase: As mentioned above, Crunchbase is an online platform with an extensive dataset, allowing you to access in-depth insights on market trends, consumer behavior and competitive analysis. You can also customize your search options to tailor your research to specific industries, geographic regions or customer personas.

• Academic databases: Academic databases, such as ProQuest and JSTOR , are treasure troves of scholarly research papers, studies and academic journals. They offer in-depth analyses of various subjects, including market trends, consumer preferences and industry-specific insights. Researchers can access a wealth of peer-reviewed publications to gain a deeper understanding of their research topics.
• Government and NGO databases: Government agencies, nongovernmental organizations and other institutions frequently maintain databases containing valuable economic, demographic and industry-related data. These sources offer credible statistics and reports on a wide range of topics, making them essential for market researchers. Examples include the U.S. Census Bureau , the Bureau of Labor Statistics and the Pew Research Center .
• Industry reports: Industry reports and market studies are comprehensive documents prepared by research firms, industry associations and consulting companies. They provide in-depth insights into specific markets, including market size, trends, competitive analysis and consumer behavior. You can find this information by looking at relevant industry association databases; examples include the American Marketing Association and the National Retail Federation .
• Social media and online communities: Social media platforms like LinkedIn or Twitter (X) , forums such as Reddit and Quora , and review platforms such as G2 can provide real-time insights into consumer sentiment, opinions and trends. 

Market research examples

At this point, you have market research tools and data sources — but how do you act on the data you gather? Let’s go over some real-world examples that illustrate the practical application of market research across various industries. These examples showcase how market research can lead to smart decision-making and successful business decisions.

Example 1: Apple’s iPhone launch

Apple ’s iconic iPhone launch in 2007 serves as a prime example of market research driving product innovation in tech. Before the iPhone’s release, Apple conducted extensive market research to understand consumer preferences, pain points and unmet needs in the mobile phone industry. This research led to the development of a touchscreen smartphone with a user-friendly interface, addressing consumer demands for a more intuitive and versatile device. The result was a revolutionary product that disrupted the market and redefined the smartphone industry.

Example 2: McDonald’s global expansion

McDonald’s successful global expansion strategy demonstrates the importance of market research when expanding into new territories. Before entering a new market, McDonald’s conducts thorough research to understand local tastes, preferences and cultural nuances. This research informs menu customization, marketing strategies and store design. For instance, in India, McDonald’s offers a menu tailored to local preferences, including vegetarian options. This market-specific approach has enabled McDonald’s to adapt and thrive in diverse global markets.

Example 3: Organic and sustainable farming

The shift toward organic and sustainable farming practices in the food industry is driven by market research that indicates increased consumer demand for healthier and environmentally friendly food options. As a result, food producers and retailers invest in sustainable sourcing and organic product lines — such as with these sustainable seafood startups — to align with this shift in consumer values. 

The bottom line? Market research has multiple use cases and is a critical practice for any industry. Whether it’s launching groundbreaking products, entering new markets or responding to changing consumer preferences, you can use market research to shape successful strategies and outcomes.

Market research templates

You finally have a strong understanding of how to do market research and apply it in the real world. Before we wrap up, here are some market research templates that you can use as a starting point for your projects:

• Smartsheet competitive analysis templates : These spreadsheets can serve as a framework for gathering information about the competitive landscape and obtaining valuable lessons to apply to your business strategy.
• SurveyMonkey product survey template : Customize the questions on this survey based on what you want to learn from your target customers.
• HubSpot templates : HubSpot offers a wide range of free templates you can use for market research, business planning and more.
• SCORE templates : SCORE is a nonprofit organization that provides templates for business plans, market analysis and financial projections.
• SBA.gov : The U.S. Small Business Administration offers templates for every aspect of your business, including market research, and is particularly valuable for new startups. 

Strengthen your business with market research

When conducted effectively, market research is like a guiding star. Equipped with the right tools and techniques, you can uncover valuable insights, stay competitive, foster innovation and navigate the complexities of your industry.

Throughout this guide, we’ve discussed the definition of market research, different research methods, and how to conduct it effectively. We’ve also explored various types of market research and shared practical insights and templates for getting started. 

Now, it’s time to start the research process. Trust in data, listen to the market and make informed decisions that guide your company toward lasting success.

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Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

• Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
• Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
• Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
• Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
• Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
• Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

• Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
• Multiple samples can be analyzed and compared using a cross-sectional survey research method.
• Multiple variables can be evaluated using this type of survey research.
• The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

• In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
• Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
• In situations where the sequence of events is highly essential, longitudinal surveys are used.
• Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

• The relationship between stress and depression.
• The equation between fame and money.
• The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

• Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
• Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
• Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
• Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

• Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
• Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
• Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
• Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
• Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
• Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
• Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
• SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
• Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

• SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
• Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
• Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
• TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

• Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
• Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
• Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
• Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
• Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

• A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
• Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

• Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
• Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
• Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
• Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

• Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
• Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
• Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
• Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
• Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
• Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
• Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

• Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
• Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
• Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
• Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
• Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
• Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
• Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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Your ultimate guide to quantitative research.

12 min read You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

What is quantitative research?

Quantitative is the research method of collecting quantitative data – this is data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analyzed.

Quantitative research deals with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data .

Quantitative data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

To collect numerical data, surveys are often employed as one of the main research methods to source first-hand information in primary research . Quantitative research can also come from third-party research studies .

Quantitative research is widely used in the realms of social sciences, such as biology, chemistry, psychology, economics, sociology, and marketing .

Research teams collect data that is significant to proving or disproving a hypothesis research question – known as the research objective. When they collect quantitative data, researchers will aim to use a sample size that is representative of the total population of the target market they’re interested in.

Then the data collected will be manually or automatically stored and compared for insights.

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Quantitative vs qualitative research

While the quantitative research definition focuses on numerical data, qualitative research is defined as data that supplies non-numerical information.

Quantitative research focuses on the thoughts, feelings, and values of a participant , to understand why people act in the way they do . They result in data types like quotes, symbols, images, and written testimonials.

These data types tell researchers subjective information, which can help us assign people into categories, such as a participant’s religion, gender , social class, political alignment, likely favored products to buy, or their preferred training learning style.

For this reason, qualitative research is often used in social research, as this gives a window into the behavior and actions of people.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative methods. If you want to explore ideas, thoughts, and meanings, use qualitative methods.

However, quantitative and qualitative research methods are both recommended when you’re looking to understand a point in time, while also finding out the reason behind the facts.

Quantitative research data collection methods

Quantitative research methods can use structured research instruments like:

• Surveys : A survey is a simple-to-create and easy-to-distribute research method , which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Quantitative questions tend to be closed questions that ask for a numerical result, based on a range of options, or a yes/no answer that can be tallied quickly.

• Face-to-face or phone interviews: Interviews are a great way to connect with participants , though they require time from the research team to set up and conduct.

Researchers may also have issues connecting with participants in different geographical regions . The researcher uses a set of predefined close-ended questions, which ask for yes/no or numerical values.

• Polls: Polls can be a shorter version of surveys , used to get a ‘flavor’ of what the current situation is with participants. Online polls can be shared easily, though polls are best used with simple questions that request a range or a yes/no answer.

Quantitative data is the opposite of qualitative research, another dominant framework for research in the social sciences, explored further below.

Quantitative data types

Quantitative research methods often deliver the following data types:

• Test Scores
• Percent of training course completed
• Performance score out of 100
• Number of support calls active
• Customer Net Promoter Score (NPS)

When gathering numerical data, the emphasis is on how specific the data is, and whether they can provide an indication of what ‘is’ at the time of collection. Pre-existing statistical data can tell us what ‘was’ for the date and time range that it represented

Quantitative research design methods (with examples)

Quantitative research has a number of quantitative research designs you can choose from:

Descriptive

This design type describes the state of a data type is telling researchers, in its native environment. There won’t normally be a clearly defined research question to start with. Instead, data analysis will suggest a conclusion , which can become the hypothesis to investigate further.

Examples of descriptive quantitative design include:

• A description of child’s Christmas gifts they received that year
• A description of what businesses sell the most of during Black Friday
• A description of a product issue being experienced by a customer

Correlational

This design type looks at two or more data types, the relationship between them, and the extent that they differ or align. This does not look at the causal links deeper – instead statistical analysis looks at the variables in a natural environment.

Examples of correlational quantitative design include:

• The relationship between a child’s Christmas gifts and their perceived happiness level
• The relationship between a business’ sales during Black Friday and the total revenue generated over the year
• The relationship between a customer’s product issue and the reputation of the product

Causal-Comparative/Quasi-Experimental

This design type looks at two or more data types and tries to explain any relationship and differences between them, using a cause-effect analysis. The research is carried out in a near-natural environment, where information is gathered from two groups – a naturally occurring group that matches the original natural environment, and one that is not naturally present.

This allows for causal links to be made, though they might not be correct, as other variables may have an impact on results.

Examples of causal-comparative/quasi-experimental quantitative design include:

• The effect of children’s Christmas gifts on happiness
• The effect of Black Friday sales figures on the productivity of company yearly sales
• The effect of product issues on the public perception of a product

Experimental Research

This design type looks to make a controlled environment in which two or more variables are observed to understand the exact cause and effect they have. This becomes a quantitative research study, where data types are manipulated to assess the effect they have. The participants are not naturally occurring groups, as the setting is no longer natural. A quantitative research study can help pinpoint the exact conditions in which variables impact one another.

Examples of experimental quantitative design include:

• The effect of children’s Christmas gifts on a child’s dopamine (happiness) levels
• The effect of Black Friday sales on the success of the company
• The effect of product issues on the perceived reliability of the product

Quantitative research methods need to be carefully considered, as your data collection of a data type can be used to different effects. For example, statistics can be descriptive or correlational (or inferential). Descriptive statistics help us to summarize our data, while inferential statistics help infer conclusions about significant differences.

Advantages of quantitative research

• Easy to do : Doing quantitative research is more straightforward, as the results come in numerical format, which can be more easily interpreted.
• Less interpretation : Due to the factual nature of the results, you will be able to accept or reject your hypothesis based on the numerical data collected.
• Less bias : There are higher levels of control that can be applied to the research, so bias can be reduced , making your data more reliable and precise.

Disadvantages of quantitative research

• Can’t understand reasons: Quantitative research doesn’t always tell you the full story, meaning you won’t understand the context – or the why, of the data you see, why do you see the results you have uncovered?
• Useful for simpler situations: Quantitative research on its own is not great when dealing with complex issues. In these cases, quantitative research may not be enough.

How to use quantitative research to your business’s advantage

Quantitative research methods may help in areas such as:

• Identifying which advert or landing page performs better
• Identifying how satisfied your customers are
• How many customers are likely to recommend you
• Tracking how your brand ranks in awareness and customer purchase intent
• Learn what consumers are likely to buy from your brand.

6 steps to conducting good quantitative research

Businesses can benefit from quantitative research by using it to evaluate the impact of data types. There are several steps to this:

• Define your problem or interest area : What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis : Ask yourself what could be the causes for the situation with those data types.
• Plan your quantitative research : Use structured research instruments like surveys or polls to ask questions that test your hypothesis.
• Data Collection : Collect quantitative data and understand what your data types are telling you. Using data collected on different types over long time periods can give you information on patterns.
• Data analysis : Does your information support your hypothesis? (You may need to redo the research with other variables to see if the results improve)
• Effectively present data : Communicate the results in a clear and concise way to help other people understand the findings.

How Qualtrics products can enhance & simplify the quantitative research process

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting quantitative research. From survey creation and data collection to statistical analysis and data reporting, it can help all your internal teams gain insights from your numerical data.

Quantitative methods are catered to your business through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of quantitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Stats IQ™ and Driver IQ™ make analyzing numerical data easy and simple. Choose to highlight key findings based on variables or highlight statistically insignificant findings. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualizations quickly:

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Qualitative and Quantitative Marketing Research Methods

Some researchers take a hard approach to data, either falling squarely on the side of the numbers (quantitative data) or on the other side, one of focus groups and individual consumer feedback (qualitative data).

But the richest insights come from a paired approach to research, one that seeks both statistically significant findings to define our product’s place in the market as well as a deep understanding of how our product fits into a single consumer’s life.

Why do you need either of these types of research? Learning about your customers means that you can more efficiently answer their needs. A crystal-clear picture of who your consumer is and why they need your product allows you to not only refine your product but ideate even better solutions to your consumers’ problems.

Quantitative Methods

What Is Quantitative Research?

The most common quantitative research methods are customer surveys and questionnaires. If you have questions that drill down into specifics about your company, your market, or your target customer population, you’re looking for quantitative research. It answers questions like who, when, where, how many, how frequently? These are not general, exploratory questions; they’re questions looking for transactional, numerical data.

Why Do You Need Quantitative Research?

It will tell you:

• Whether customers are looking for or need a product like yours
• Who your target population should be and what convinces them to buy
• What, if anything, your target population knows about your market or your product
• If your target market’s attitude or perception is changing, which can give insights into how you should respond

How Do You Conduct Quantitative Research?

While the big two methods of gathering data are surveys and questionnaires, there are plenty of ways to conduct these methods.

When you only need a few questions answered and can’t justify the cost of rolling out a full-scale survey, an omnibus is a good solution. This style groups questions from multiple businesses into one survey so that the time and effort spent recruiting and compensating participants can be spread across several companies who have only a few questions.

Email or Snail Mail Surveys

Digital or printed surveys are the most common for a reason: They are extremely cost-effective because they are automated. Results roll in without needing a person to make a phone call or interact with every individual respondent. These are most effective when the questions are straightforward, like choosing A vs. B. They’re not so effective in nuanced preference questions, like describing how A vs B makes a participant feel.

Telephone Surveys

The human interaction of phone surveys can make a participant feel more engaged than a digital or printed survey, leading them to give more considered answers – this creates better data. Telephone surveys also allow the researcher to understand a participant’s answer more clearly based on their voice and tone. The statistical reliability of a survey over an interview also remains intact because the questions are consistent across participants. Telephone surveys are best:

• If the survey questions end with a “Why?” or “Can you explain?”
• If the survey questions are longer or slightly complicated and require better participant concentration than normal
• If you are pilot (or metric) testing your online survey questions before they are deployed widely online. “Why?” and “Can you explain?” phone questions can capture the most common answers, which can then be listed as checkbox options A, B, C, and D in the widely distributed online survey. This will discourage write-in answers, making your data easier to analyze.

Face-To-Face Surveys

A criticism of surveys, in general, is that they don’t allow for conversation or elaboration. While this is a benefit in keeping data consistent, it also leaves findings on the table that a researcher could have used to better understand the participant’s feedback. In face-to-face surveys, a researcher still can’t go off script, but they can make note of a scrunched nose when a product is mentioned or a disengaged stance. Face-to-face surveys are best when the survey questions delve into emotions. An example might be survey questions for emotionally charged products or campaigns , like Dove’s Campaign for Real Beauty. “How does marketing around women’s beauty products today make you feel?”

It’s easy for face-to-face surveys to become interviews, but keep in mind the difference is that surveys do not go off script. The survey researcher must stick to the order of the questions and the exact wording to keep statistical reliability, and in an interview, the researcher has control over the order, pacing, and structure of all questions.

Analyzing quantitative results is easier now with survey tools like SurveyMonkey, SurveyGizmo, and Qualtrics. But those tools won’t be able to remove outliers or include questions that will weed out people who didn’t understand the question and people who rushed through without reading. Those tools still require human manipulation to produce clean, statistically reliable data. This data can be analyzed through various different lenses, including segmentation and personas, to better understand the population who answered the survey and predict their behavior.

Quantitative findings can lead to the following findings and deliverables:

• Market segmentation
• Pricing projections
• Net promoter scores
• Customer satisfaction ratings
• Recommendations about a product launch, pricing, messaging
• Changes in sales efforts to increase customer satisfaction and target market segments

Qualitative Methods

What Is Qualitative Research?

Qualitative research is interested in finding the opinions, beliefs, and values of a target population. The most common forms of qualitative research are focus groups and interviews. While not usually statistically significant, qualitative research is valuable in putting a face to a number and answering questions like, “How?” “Why?” and “Would?” This style of research humanizes your consumer base, allowing more accurate predictions of customer behavior. Qualitative research can also identify problems and opportunities that a survey or questionnaire never would have identified. In qualitative research, we pose probing questions that aim to understand consumers as people. A few drawbacks of qualitative research are (A) results are always open to concern about researcher subjectivity, as results are not numerical and are based on conversations and (B) qualitative research can be time-consuming and expensive.

Why Do You Need Qualitative Research?

It will help you:

• Make human-centered decisions across the entire business, not just in the marketing department, leading to a better customer experience overall
• Understand context around quantitative data – like why bounce rates are so high off of one page on your site
• Predict how your product or service would fit in and impact the consumer’s life
• Understand the emotions your target population feels in relation to your product or market
• How do you conduct qualitative research?

Focus Groups

In focus groups, 6-10 people with something in common (like looking to buy a luxury car or having a baby) have a discussion moderated by a researcher. This style is best for exploring “what if”s, researching new concepts, and testing new product ideas. It helps let participants bounce ideas off of each other, spurring memories but also giving individuals time to think while others are talking. There’s less pressure on each participant to answer spontaneously, as in an interview. The drawback is that it’s very easy for a focus group to become derailed by one or two overpowering or disengaged participants.

Customer & Prospect Interviews

Interviews can come in many forms, but the base form is a one-on-one conversation. An interview is meant to find underlying beliefs from a key, representative consumer about some facet of your product or service. A skilled interviewer can recognize when a topic is unproductive versus when a tangent is worthwhile to understand the participant’s perspective.

Contextual Inquiry

In a contextual inquiry, a researcher trails the participant in the participant’s normal activities. Contextual inquiries happen “in the wild.” For example, a lunchbox company might observe a customer all day, from packing lunch in the morning to bringing home the empty box at night, to understand the lunchbox’s deficits, what products the participant uses in conjunction with the lunchbox (like Tupperware, silverware, etc), and ask questions.

One of the best ways to fully understand your audience and what’s important to them is through social listening. A social listening tool allows you to monitor and analyze online posts and comments based on an established keyword criterion. By doing this, a brand can analyze the demographics or their online audience, their purchase intent, what they see as the key product attributes, their feelings on your brand versus the competition, as well as category-level conversations.

An article in Harvard Business Review tells this useful story that came out of social listening “A large pharmaceutical company, for example, learned about an unsuspected customer challenge through a single photo on Flickr. The image showed a man wrapping a part of his leg in foil after applying a pain relief ointment. It turned out that the medication left untreatable stains on certain fabrics, hence the protective foil. Executives had been unaware of the problem despite years of conventional consumer research.”

Customer Experience Research

Interviews are common in customer experience research, from usability studies (analyzing how easy it is to use a product, usually a website) to customer journey research (asking a customer to describe their entire relationship with a product, from learning about it to purchase), and more. The basic idea always comes down to asking a consumer about their relationship with a company’s product or service, though.

Qualitative and Quantitative Research Working Together

Qualitative and quantitative research are not at odds; in fact, they inform and enrich one another. One doesn’t come before the other, either. Personal, individualized insights of qualitative research can help us understand the questions we should be asking of our mass market in quantitative research, but wider quantitative research can also help us understand where we need to drill down and ask more questions from just a few consumers. Actually, the best research comes from a cyclical application of both techniques, allowing us to regularly turn from a wide-lens view of our product and its value to a mass market, to a more narrowed scope, focusing on understanding the unique perspectives of just a few people who we impact.

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• Knowledge Base

Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Quantitative Data: Definition, Examples, Types, Methods, and Analysis

11 min read

35% of startups fail because there is no market need. This is because they haven’t conducted any customer research to determine whether the product they are building is actually what customers want.

To gather the information needed to avoid this, quantitative data is a valuable tool for all startups. This article will examine quantitative data, the difference between quantitative and qualitative data, and how to collect the former.

• Quantitative data, expressed numerically, is crucial for analysis, driving strategic decisions, and understanding consumer behavior and market trends .
• Metrics like DAU, MRR, sales figures, satisfaction scores, and traffic are examples of quantitative data across industries.
• Quantitative data is numeric and measurable, identifying patterns or trends, while qualitative data is descriptive, providing deeper insights and context.
• Nominal data categorizes information without order and labels variables like user roles or subscription types. It is often shown in bar or pie charts .
• Ordinal data categorizes information in a specific order, such as satisfaction ratings or ticket priorities, and is often shown in a bar or stacked bar chart.
• Discrete data is numerical and takes specific values, like daily sign-ups or support tickets , and is often shown in bar or column charts.
• Continuous data can take any numerical value within a range, such as user time on a platform or revenue over time, and is often shown in line graphs or histograms.
• Quantitative data is objective, handles large datasets, and enables easy comparisons, providing clear insights and generalized conclusions in various fields.
• However, quantitative data analysis lacks contextual understanding, requires analytical expertise, and is influenced by data collection quality that may affect result validity.
• Customer feedback surveys , triggered by tools like Userpilot, collect consistent quantitative data, providing reliable numerical insights into customer satisfaction and experiences.
• Product analytics tools track user interactions and feature usage , offering insights into user behavior and improving the user experience.
• Tracking customer support data identifies common issues and areas for improvement , enhances service quality, and helps understand customer needs.
• Implementing A/B tests and other experiments provides quantitative data on feature performance, helping teams make informed decisions to enhance product and user experience.
• Searching platforms like Kaggle or Statista for accurate, reliable datasets enhances product analysis by providing broader context and robust comparison data.
• Statistical analysis uses mathematical techniques to summarize and infer data patterns, helping SaaS companies understand user behavior, evaluate features, and identify engagement trends.
• Trend analysis tracks quantitative data to identify patterns, helping SaaS companies forecast outcomes, understand variations, and plan strategic initiatives effectively.
• Funnel analysis tracks user progression through stages, identifies drop-off points to enhance user experience, and increases conversions for SaaS companies.
• Cohort analysis groups users by attribute and tracks behavior over time to understand retention and engagement.
• Path analysis maps user journeys to identify users’ optimal routes, helping SaaS companies streamline and enhance the user experience.
• Feedback analysis examines responses to close-ended questions to identify user sentiments and areas for improvement.
• If you want to collect quantitative data within your product and analyze it, then learn how Userpilot can help you. Book a demo now !

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What is quantitative data?

Quantitative data is information that can be measured and expressed numerically. It is essential for making data-driven decisions, as it provides a concrete foundation for analysis and evaluation.

In various fields, such as market research , quantitative data helps businesses understand consumer behavior, market trends, and overall performance. Companies can gain insights that drive strategic decisions and improve their products or services by collecting and analyzing numerical data.

Whether conducting a survey, running experiments , or gathering information from other sources, quantitative data analysis is key to uncovering patterns, testing hypotheses, and making informed decisions based on solid evidence.

What are examples of quantitative data?

Quantitative data comes in many forms and is used across various industries to provide measurable and numerical insights. Here are some examples of quantitative data:

• Daily Active Users (DAU) : This metric counts the number of unique users interacting with a product or service daily. It is crucial for understanding user engagement and product usage trends.
• Monthly Recurring Revenue (MRR) : For SaaS businesses, MRR is a vital metric that shows the predictable revenue generated each month from subscriptions. It helps forecast growth and financial planning.
• Sales figures : This includes the total number of products sold or services rendered over a specific period. Sales data helps in evaluating business performance and market demand.
• Customer satisfaction scores : Often collected through surveys , these scores quantify customers’ satisfaction with a product or service.
• Website traffic : Measured in terms of visits, page views, and unique visitors, this quantitative data helps businesses understand their online presence and the effectiveness of their marketing efforts.
• Conversion rates : This metric shows the percentage of users who take a desired action, such as making a purchase or signing up for a newsletter, out of the total number of visitors.
• Churn rate : This represents the percentage of customers who stop using a product or service over time. It’s essential for understanding customer retention .
• Average Revenue Per User (ARPU) : This metric calculates the average revenue generated per user, which helps assess each customer’s value to the business.
• Bounce rate : In web analytics, the bounce rate indicates the percentage of visitors who leave a website after viewing only one page. It’s useful for evaluating the effectiveness of a website’s content and user experience .

Differences between quantitative and qualitative data

Quantitative data and qualitative data are two fundamental types of information used in research and analysis, each serving distinct purposes and represented in different forms.

Quantitative data is numeric and measurable. It allows you to quantify variables and identify patterns or trends that can be generalized. For example, tracking product trends or analyzing charts to understand market movements. Some quantitative data examples include:

• The number of daily active users on a platform.
• Monthly recurring revenue.
• Customer satisfaction scores .
• Website traffic metrics, like page views.

On the other hand, qualitative data is descriptive and subjective, often represented in words and visuals. It aims to explore deeper insights, understand data , and provide context to behaviors and experiences.

Examples of qualitative data include:

• Customer reviews and testimonials.
• Interview responses.
• Social media interactions.
• Observations recorded during user tests .

Different types of quantitative data

Understanding the different types of quantitative data is essential for effective data analysis . These types help categorize and analyze data accurately to derive meaningful insights and make informed decisions.

Nominal data

Nominal data categorizes information without a specific order or ranking. It is used to label variables that do not have a quantitative value.

For instance, in a SaaS platform , user roles can be categorized as ‘admin,’ ‘editor,’ or ‘viewer.’ Subscription types might be classified as ‘free,’ ‘basic,’ ‘premium,’ or ‘enterprise.’

This data type is typically represented using bar charts or pie charts to show the frequency or proportion of each category.

Ordinal data

Ordinal data categorizes information with a specific order or ranking. It is used to label variables that follow a particular sequence.

Examples include:

• Rating customer satisfaction as ‘poor,’ ‘fair,’ ‘good,’ ‘very good,’ or ‘excellent.’
• Ranking support ticket priorities as ‘low,’ ‘medium,’ or ‘high.’
• User feedback ratings on features as ‘1 star’ to ‘5 stars.’

This type of data is typically represented using bar charts or stacked bar charts to illustrate the order and frequency of each category.

Discrete data

Discrete data is numerical values that can only take on specific values and cannot be subdivided meaningfully.

Examples include the number of new sign-ups daily, the count of support tickets received, and the number of active users at a given time.

This type of numerical data is often represented using bar charts or column charts to display the frequency of each value.

Continuous data

Continuous data is numerical information that can take on any numerical value within a range.

In a SaaS context, examples include measuring the amount of time users spend on a platform, the bandwidth usage of an application, and the revenue generated over a specific period. Continuous data, along with interval data, helps identify patterns and trends over time.

Pros of analyzing quantitative data

Analyzing quantitative data offers several advantages, making it a valuable approach in various fields, especially in SaaS. Here are some key benefits:

Provides measurable and verifiable data

Quantitative data is numeric and objective, allowing for precise measurement and verification. This reduces the influence of personal biases and subjectivity in analysis, leading to more reliable and consistent results.

Analyzing customer data using quantitative methods can provide clear insights into user behavior and preferences, helping businesses make data-driven decisions.

Enables analysis of large datasets

Quantitative data analysis can handle large datasets efficiently, enabling the identification of patterns and trends across extensive samples.

This capability makes it possible to draw broad, generalized conclusions that can be applied to larger populations. For example, a company might analyze usage data from thousands of users to understand overall engagement trends and identify areas for improvement .

Allows easy comparison across different groups, time periods, and variables

Quantitative data allows straightforward comparisons across various groups, time periods, and variables. This facilitates the evaluation of changes over time, differences between demographics, and the impact of different factors on outcomes.

For instance, comparing customer satisfaction scores before and after a product update can help assess the effectiveness of the changes and guide future improvements.

Cons of quantitative data analysis

While quantitative data analysis offers many benefits, it also has some drawbacks:

Lacks contextual understanding

Quantitative data can miss the deeper context and nuances of human behavior, focusing solely on numbers without explaining the reasons behind actions. For example, tracking user behavior may show usage patterns but not the motivations or feelings behind them.

Requires analytical expertise

Accurate analysis and interpretation of quantitative data require specialized skills . Without proper expertise, there is a risk of misinterpretation and incorrect conclusions, which can negatively impact decision-making.

Influenced by data collection quality

The reliability of quantitative analysis depends on the data collection methods and the quality of measurement tools. Poor data collection can lead to data discrepancies , affecting the validity of the results. Ensuring consistent, high-quality data collection is essential for accurate analysis.

How to collect data for quantitative research?

Collecting data for quantitative research involves using systematic and structured methods to gather numerical information. Let’s look at a few methods in detail.

Customer feedback surveys

Customer feedback surveys are a key method for collecting quantitative data. Tools like Userpilot can trigger in-app surveys with closed-ended questions to ensure consistent data collection.

Conducting these surveys quarterly or after a specific period helps track changes in customer satisfaction and other important metrics. This approach provides reliable, numerical insights into customer opinions and experiences.

Product usage data

Product analytics tools are essential for tracking user interactions and feature usage. Utilizing these tools allows you to monitor metrics such as user sessions, feature adoption , and user engagement regularly.

This quantitative data provides valuable insights into how users interact with your product, helping you understand their behavior and improve the overall user experience.

Customer support data

Tracking customer support data is crucial for quantitative research. You can record details such as ticket number, issue type, resolution time, and customer feedback by monitoring support tickets.

Organize these tickets into categories, such as feature requests , to identify common problems and areas needing product improvement . This approach helps understand customer needs and enhance overall service quality.

Experiments

Implementing experiments, such as A/B tests , is a powerful method for collecting quantitative data. By comparing the performance of different features or designs, you can gain valuable insights into what works best for your users.

Use the insights gained from these A/B tests and other product experimentation methods to make informed decisions that enhance your product and user experience.

Open-source datasets

Searching for datasets on platforms like Kaggle or Statista can provide valuable information relevant to your research. However, to avoid issues with data discrepancy , ensure these datasets are accurate and reliable before incorporating them into your analysis.

Utilizing accurate open-source datasets can significantly enhance your product analysis by providing a broader context and more robust quantitative data for comparison and insights.

Quantitative data analysis methods for gathering actionable insights

Analyzing quantitative data involves using various methods to extract meaningful and actionable insights. These techniques help understand the data’s patterns, trends, and relationships, enabling informed decision-making and strategic planning .

Statistical analysis

Statistical analysis involves using mathematical techniques to summarize, describe, and infer patterns from data. This method helps validate hypotheses and make data-driven decisions .

For SaaS companies, statistical analysis can be crucial in understanding user behavior , evaluating the effectiveness of new features, and identifying trends in user engagement.

By leveraging statistical techniques, SaaS businesses can derive meaningful insights from their data, allowing them to optimize their products and services based on empirical evidence.

Trend analysis

Trend analysis involves tracking quantitative data points and metrics to identify consistent patterns. Using a tool like Userpilot, SaaS companies can generate detailed trend analysis reports that provide valuable insights into how various metrics evolve.

This method enables SaaS companies to forecast future outcomes, understand seasonal variations, and plan strategic initiatives accordingly. By identifying trends, businesses can anticipate changes, adapt their strategies, and stay ahead of market dynamics.

Funnel analysis

Funnel analysis defines key stages in the user journey and tracks the number of users progressing through each stage.

This method helps SaaS companies identify friction and drop-off points within the funnel. By understanding where users are dropping off, businesses can implement targeted improvements to enhance user experience and increase conversions.

Cohort analysis

Cohort analysis groups users into cohorts based on attributes such as the month of sign-up or acquisition channel and tracks their behavior over time.

This method allows SaaS companies to understand user retention and engagement patterns by comparing how cohorts perform over various periods. By analyzing these patterns, businesses can identify successful strategies and improvement areas.

Path analysis

Path analysis maps user journeys and analyzes the actions taken by users. This method helps SaaS companies identify the “ happy path ” or the optimal route users take to achieve their goals.

By understanding these paths , businesses can streamline the user experience, making it more intuitive and efficient.

Feedback analysis

Feedback analysis involves using questionnaires and examining responses to close-ended questions to identify patterns in customer feedback . This quantitative data helps you to understand common user sentiments, preferences, and areas needing improvement.

Businesses can make informed decisions to enhance their products and services by systematically analyzing feedback.

Collecting quantitative data is important if you want a product that will succeed. Your customers are the only people who can signal your success, so speaking to them and analyzing the quantitative data you collect will help you to produce the best product you can.

If you want help collecting quantitative data and analyzing it, Userpilot can help. Book a demo now to see exactly how it can help.

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Expert’s Guide to Qualitative and Quantitative Marketing Research

Francis Gary Viray

Author & Editor

Founder & CEO

Published on: Sep 14, 2023 Updated on: May 16, 2024

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Get reliable marketing ideas and tactics that drive digital differently from top minds in the digital space.

Qualitative and quantitative marketing research are an essential part of formulating any digital strategy for your brand’s promotional needs.

Qualitative market research allows you to understand an audience’s “why”s, like their likes, dislikes, needs, and pain points with your brand. Quantitative market research, on the other hand, allows you to understand the “what”s of your target audience, like their ages, genders, nationalities, and more.

But what could happen if you put those two important forms of analysis and information collection together in one conclusive study?

By combining these two question types when gathering and analyzing data, you will be able to gain a deeper and more comprehensive understanding of your target audience. You’ll also get the best of both worlds: the objective information about your customers, and the subjective motivations that drive them to action. This will garner more insights that you can use to strengthen your digital content and executions today.

Want to discover ways to leverage both objective and subjective queries to hone your marketing skills and strategies ? Take a look at this comprehensive guide to discover the best tips and techniques for crafting impactful research questions for your future campaigns, right now.

Explaining qualitative research marketing

Before you get into the best tips, practices, and techniques for crafting this multi-pronged marketing strategy, let’s get to know the ins and outs of each analysis form first.

Qualitative research refers to the process of collecting descriptive and subjective data to gain insight into audience needs, pain points, and feelings. Examples of qualitative data collection methods include collecting feedback and reviews among customers, or conducting interviews, surveys, heuristic evaluations , and focus group discussions (FGDs).

For this qualitative research data collection method, queries should be open-ended and clear. Questions should be easy to process without need for further clarification, and should also be open-ended in order to gain descriptive and exploratory responses. Some examples of good subjective inquiries include:

• What have you heard about X brand?
• How would you describe your experience of using X product?
• What changes can be made to make X service better?

Asking open-ended questions is a common data collection method in qualitative research. With this, you can gather answers that are deeper, more exploratory, and more personal than their quantitative counterparts to create marketing content that is rooted in real-life audience experiences.

One example of an excellent qualitative study that revealed customer sentiments is that of the Golden State Warriors . Upon moving into their new stadium in 2019, they leveraged more than 30 survey forms and feedback gathering activities to gain an end-to-end understanding of the fan experience

This comprehensive study resulted in over 20,000 total responses collected and a 19% increase in their net promoter score. This led to an excellent understanding of their target audiences, both online and in person, from the moment fans visit their website to their arena exit after a game.

Understanding quantitative research marketing

Meanwhile, quantitative research refers to the process of collecting numerical and objective information for measurement and analysis. Notable examples of calculable data in digital include conversion rates, ad revenue, and website traffic. With this analysis type, you can track performance and optimize conversions in a more data-driven way today.

For this method, queries should be close-ended to collect objective insights for statistical data analysis. Responses to quantitative questions usually involve numerical or objective information, in order to inform strategic decisions for digital strategy optimization. Some good examples of quantitative questions include queries like:

• How old are you? What country are you from? What’s your gender?
• How much do you usually spend on X product?
• On a scale of one to ten (1-10), how likely are you to recommend X brand?

You can actually use some of the info-gathering methods mentioned in the previous query type for this mode of study. You can easily utilize polls, surveys, and short interviews to analyze audience information like demographics. You can also use more complex methods like analytics, tracking, and heat maps to collect numerical insights on audience behaviors.

There are many great examples of quantitative studies out there. One excellent example is that of Ryanair . By surveying online customers about their overall flight booking experience with the brand, they were able to identify issues with their booking website and improve their online platform’s user experience (UX) accordingly.

Combining qualitative and quantitative market research

Now that you’re familiar with both of these forms of information collection, it’s time to discover the power of the mixed method - that is, when qualitative and quantitative analyses work hand in hand towards driving better digital marketing.

Both of these information collection types tackle different aspects of the comprehensive market analysis process. Quantitative responses visualize precise and accurate information about your audiences, while qualitative responses offer deep, subjective, and contextual customer insights for your brand’s promotional understanding.

By designing a user exploration strategy with both evidence gathering types in mind, you get a more holistic understanding of your audiences and your potential promotional opportunities. This will empower you to make better decisions for your business, thus driving greater digital wins for the brand in the long run.

One example of a successful mixed method study is that of Audi Business Innovation . By utilizing both objective and subjective evidence gathering methods in their study, they were able to collect relevant feedback quickly and practice fast and iterative UX adoptions within their whole company’s internal culture.

Tips to craft marketing research that is qualitative and quantitative

With all these insights in mind, it’s time to equip you with tips and best practices in crafting an analysis that utilizes both qualitative and quantitative tools . Here are some important techniques to craft a comprehensive and holistic marketing analysis strategy:

• Set a clear research objective that aligns with business objectives. By setting a clear data-gathering objective rooted in your brand’s overall goals, you can tailor queries to address all relevant aspects of your business’ needs, thus making the process more effective and efficient in the long run.
• Balance open-ended and closed-ended questions. This ensures a comprehensive approach to collecting insights from audience segments and target markets. With a closed-ended quantitative question, you can collect information that describes the “what”s of your audience. Conversely, open-ended qualitative questions can collect your audience’s more personal “why”s.
• Adapt questions according to the type of data collection. Questions for polls and surveys, for example, may differ slightly from questions for in-depth interviews or FGDs. Polls, surveys, and other short-form gathering practices require questions with short answers. However, in-depth interviews and FGDscan afford to accommodate deeper questions that require longer responses.
• Adapt data collection based on the platforms of distribution. Just as you should adapt queries based on your collection type, you should also adapt info collection formats based on your platforms of distribution. If you’re collecting information via an online survey on social media , for example, then you should keep it short to avoid disrupting a user’s experience on the digital platform.

By utilizing these tips and techniques, you’ll be able to craft evidence gathering methods that yield actionable insights for your digital marketing needs. Remember to set clear objectives, balance question types, and adapt methods according to context, in order to run a successful research campaign for your brand today.

Driving data-driven decision-making

Research and analysis have always played major roles in creating and executing informed promotion strategies. Crafting well-intentioned questions for targeted audiences can help you collect crucial information that drives effective audience understanding - all for the success of your digital marketing today.

Moreover, utilizing data-driven insights for strategic planning inevitably transforms information into actionable steps and decision-making. So always make sure you collect figures in holistic and comprehensive ways to empower your business, respond to audience needs, and drive better brand wins this year.

Key takeaways

Craft masterful research designs for your digital marketing with objective and subjective approaches today. Here are some final tips to take with you as you formulate effective strategies for your brand’s digital this year:

• Root your design in business goals. When in doubt, go back to your brand’s overall goals to guide the objectives of your surveys, polls, and interviews.
• Remain curious about audience contexts. By being open to new information about your audiences, you’ll be able to adapt brand promotion strategies according to their real-life needs and preferences.
• Reach out to experts in marketing analysis design. Not sure where to start with your measurement collection journey? Get in touch with the digital experts at Propelrr to jumpstart your strategies today.

If you have any other comments, send us a message via our Facebook , X , and LinkedIn accounts. Let’s chat!

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Educational resources and simple solutions for your research journey

What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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500+ Quantitative Research Titles and Topics

Table of Contents

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

• “Statistical Analysis of Supply Chain Disruptions on Retail Sales”
• “Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
• “Predicting Stock Market Trends Using Machine Learning Algorithms”
• “Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
• “Impact of Economic Policies on Small Businesses: A Regression Analysis”
• “Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
• “Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
• “Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
• “Price Elasticity of Demand for Luxury Goods: A Case Study”
• “The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
• “Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
• “Examining the Correlation Between Interest Rates and Consumer Spending”
• “Standardized Testing and Academic Performance: A Quantitative Evaluation”
• “Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
• “The Relationship Between Extracurricular Activities and Academic Success”
• “Influence of Parental Involvement on Children’s Educational Achievements”
• “Digital Literacy in Primary Schools: A Quantitative Assessment”
• “Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
• “Correlation Between Teacher Experience and Student Success Rates”
• “Analyzing the Impact of Classroom Technology on Reading Comprehension”
• “Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
• “The Relationship Between Homework Load and Academic Burnout”
• “Assessment of Special Education Programs in Public Schools”
• “Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

• “The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
• “Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
• “Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
• “Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
• “Correlations Between Stress Levels and Immune System Functioning”
• “Smoking and Lung Function: A Quantitative Analysis”
• “Influence of Physical Activity on Mental Health in Older Adults”
• “Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
• “The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
• “Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
• “Impact of Hospital Design on Patient Recovery Rates”
• “Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

• “Examining Social Inequality through Wage Distribution: A Quantitative Study”
• “Impact of Parental Divorce on Child Development: A Longitudinal Study”
• “Social Media and its Effect on Political Polarization: A Quantitative Analysis”
• “The Relationship Between Religion and Social Attitudes: A Statistical Overview”
• “Influence of Socioeconomic Status on Educational Achievement”
• “Quantifying the Effects of Community Programs on Crime Reduction”
• “Public Opinion and Immigration Policies: A Quantitative Exploration”
• “Analyzing the Gender Representation in Political Offices: A Quantitative Study”
• “Impact of Mass Media on Public Opinion: A Regression Analysis”
• “Influence of Urban Design on Social Interactions in Communities”
• “The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
• “Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

• “Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
• “Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
• “Impacts of Data Center Cooling Solutions on Energy Consumption”
• “Analyzing the Reliability of Renewable Energy Sources in Grid Management”
• “Optimization of 5G Network Performance: A Quantitative Assessment”
• “Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
• “The Relationship Between Software Complexity and Bug Frequency”
• “Machine Learning in Predictive Maintenance: A Quantitative Analysis”
• “Wearable Technologies and their Impact on Healthcare Monitoring”
• “Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
• “Analysis of Noise Pollution from Urban Transportation Systems”
• “The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

• The effects of social media on self-esteem among teenagers.
• A comparative study of academic achievement among students of single-sex and co-educational schools.
• The impact of gender on leadership styles in the workplace.
• The correlation between parental involvement and academic performance of students.
• The effect of mindfulness meditation on stress levels in college students.
• The relationship between employee motivation and job satisfaction.
• The effectiveness of online learning compared to traditional classroom learning.
• The correlation between sleep duration and academic performance among college students.
• The impact of exercise on mental health among adults.
• The relationship between social support and psychological well-being among cancer patients.
• The effect of caffeine consumption on sleep quality.
• A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
• The relationship between physical attractiveness and job opportunities.
• The correlation between smartphone addiction and academic performance among high school students.
• The impact of music on memory recall among adults.
• The effectiveness of parental control software in limiting children’s online activity.
• The relationship between social media use and body image dissatisfaction among young adults.
• The correlation between academic achievement and parental involvement among minority students.
• The impact of early childhood education on academic performance in later years.
• The effectiveness of employee training and development programs in improving organizational performance.
• The relationship between socioeconomic status and access to healthcare services.
• The correlation between social support and academic achievement among college students.
• The impact of technology on communication skills among children.
• The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
• The relationship between employee turnover and organizational culture.
• The correlation between job satisfaction and employee engagement.
• The impact of video game violence on aggressive behavior among children.
• The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
• The relationship between bullying and academic performance among middle school students.
• The correlation between teacher expectations and student achievement.
• The impact of gender stereotypes on career choices among high school students.
• The effectiveness of anger management programs in reducing violent behavior.
• The relationship between social support and recovery from substance abuse.
• The correlation between parent-child communication and adolescent drug use.
• The impact of technology on family relationships.
• The effectiveness of smoking cessation programs in promoting long-term abstinence.
• The relationship between personality traits and academic achievement.
• The correlation between stress and job performance among healthcare professionals.
• The impact of online privacy concerns on social media use.
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
• The relationship between teacher feedback and student motivation.
• The correlation between physical activity and academic performance among elementary school students.
• The impact of parental divorce on academic achievement among children.
• The effectiveness of diversity training in improving workplace relationships.
• The relationship between childhood trauma and adult mental health.
• The correlation between parental involvement and substance abuse among adolescents.
• The impact of social media use on romantic relationships among young adults.
• The effectiveness of assertiveness training in improving communication skills.
• The relationship between parental expectations and academic achievement among high school students.
• The correlation between sleep quality and mood among adults.
• The impact of video game addiction on academic performance among college students.
• The effectiveness of group therapy in treating eating disorders.
• The relationship between job stress and job performance among teachers.
• The correlation between mindfulness and emotional regulation.
• The impact of social media use on self-esteem among college students.
• The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
• The impact of renewable energy policies on carbon emissions
• The relationship between employee motivation and job performance
• The effectiveness of psychotherapy in treating eating disorders
• The correlation between physical activity and cognitive function in older adults
• The effect of childhood poverty on adult health outcomes
• The impact of urbanization on biodiversity conservation
• The relationship between work-life balance and employee job satisfaction
• The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
• The correlation between parenting styles and child behavior
• The effect of social media on political polarization
• The impact of foreign aid on economic development
• The relationship between workplace diversity and organizational performance
• The effectiveness of dialectical behavior therapy in treating borderline personality disorder
• The correlation between childhood abuse and adult mental health outcomes
• The effect of sleep deprivation on cognitive function
• The impact of trade policies on international trade and economic growth
• The relationship between employee engagement and organizational commitment
• The effectiveness of cognitive therapy in treating postpartum depression
• The correlation between family meals and child obesity rates
• The effect of parental involvement in sports on child athletic performance
• The impact of social entrepreneurship on sustainable development
• The relationship between emotional labor and job burnout
• The effectiveness of art therapy in treating dementia
• The correlation between social media use and academic procrastination
• The effect of poverty on childhood educational attainment
• The impact of urban green spaces on mental health
• The relationship between job insecurity and employee well-being
• The effectiveness of virtual reality exposure therapy in treating anxiety disorders
• The correlation between childhood trauma and substance abuse
• The effect of screen time on children’s social skills
• The impact of trade unions on employee job satisfaction
• The relationship between cultural intelligence and cross-cultural communication
• The effectiveness of acceptance and commitment therapy in treating chronic pain
• The correlation between childhood obesity and adult health outcomes
• The effect of gender diversity on corporate performance
• The impact of environmental regulations on industry competitiveness.
• The impact of renewable energy policies on greenhouse gas emissions
• The relationship between workplace diversity and team performance
• The effectiveness of group therapy in treating substance abuse
• The correlation between parental involvement and social skills in early childhood
• The effect of technology use on sleep patterns
• The impact of government regulations on small business growth
• The relationship between job satisfaction and employee turnover
• The effectiveness of virtual reality therapy in treating anxiety disorders
• The correlation between parental involvement and academic motivation in adolescents
• The effect of social media on political engagement
• The impact of urbanization on mental health
• The relationship between corporate social responsibility and consumer trust
• The correlation between early childhood education and social-emotional development
• The effect of screen time on cognitive development in young children
• The impact of trade policies on global economic growth
• The relationship between workplace diversity and innovation
• The effectiveness of family therapy in treating eating disorders
• The correlation between parental involvement and college persistence
• The effect of social media on body image and self-esteem
• The impact of environmental regulations on business competitiveness
• The relationship between job autonomy and job satisfaction
• The effectiveness of virtual reality therapy in treating phobias
• The correlation between parental involvement and academic achievement in college
• The effect of social media on sleep quality
• The impact of immigration policies on social integration
• The relationship between workplace diversity and employee well-being
• The effectiveness of psychodynamic therapy in treating personality disorders
• The correlation between early childhood education and executive function skills
• The effect of parental involvement on STEM education outcomes
• The impact of trade policies on domestic employment rates
• The relationship between job insecurity and mental health
• The effectiveness of exposure therapy in treating PTSD
• The correlation between parental involvement and social mobility
• The effect of social media on intergroup relations
• The impact of urbanization on air pollution and respiratory health.
• The relationship between emotional intelligence and leadership effectiveness
• The effectiveness of cognitive-behavioral therapy in treating depression
• The correlation between early childhood education and language development
• The effect of parental involvement on academic achievement in STEM fields
• The impact of trade policies on income inequality
• The relationship between workplace diversity and customer satisfaction
• The effectiveness of mindfulness-based therapy in treating anxiety disorders
• The correlation between parental involvement and civic engagement in adolescents
• The effect of social media on mental health among teenagers
• The impact of public transportation policies on traffic congestion
• The relationship between job stress and job performance
• The effectiveness of group therapy in treating depression
• The correlation between early childhood education and cognitive development
• The effect of parental involvement on academic motivation in college
• The impact of environmental regulations on energy consumption
• The relationship between workplace diversity and employee engagement
• The effectiveness of art therapy in treating PTSD
• The correlation between parental involvement and academic success in vocational education
• The effect of social media on academic achievement in college
• The impact of tax policies on economic growth
• The relationship between job flexibility and work-life balance
• The effectiveness of acceptance and commitment therapy in treating anxiety disorders
• The correlation between early childhood education and social competence
• The effect of parental involvement on career readiness in high school
• The impact of immigration policies on crime rates
• The relationship between workplace diversity and employee retention
• The effectiveness of play therapy in treating trauma
• The correlation between parental involvement and academic success in online learning
• The effect of social media on body dissatisfaction among women
• The impact of urbanization on public health infrastructure
• The relationship between job satisfaction and job performance
• The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
• The correlation between early childhood education and social skills in adolescence
• The effect of parental involvement on academic achievement in the arts
• The impact of trade policies on foreign investment
• The relationship between workplace diversity and decision-making
• The effectiveness of exposure and response prevention therapy in treating OCD
• The correlation between parental involvement and academic success in special education
• The impact of zoning laws on affordable housing
• The relationship between job design and employee motivation
• The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
• The correlation between early childhood education and social-emotional learning
• The effect of parental involvement on academic achievement in foreign language learning
• The impact of trade policies on the environment
• The relationship between workplace diversity and creativity
• The effectiveness of emotion-focused therapy in treating relationship problems
• The correlation between parental involvement and academic success in music education
• The effect of social media on interpersonal communication skills
• The impact of public health campaigns on health behaviors
• The relationship between job resources and job stress
• The effectiveness of equine therapy in treating substance abuse
• The correlation between early childhood education and self-regulation
• The effect of parental involvement on academic achievement in physical education
• The impact of immigration policies on cultural assimilation
• The relationship between workplace diversity and conflict resolution
• The effectiveness of schema therapy in treating personality disorders
• The correlation between parental involvement and academic success in career and technical education
• The effect of social media on trust in government institutions
• The impact of urbanization on public transportation systems
• The relationship between job demands and job stress
• The correlation between early childhood education and executive functioning
• The effect of parental involvement on academic achievement in computer science
• The effectiveness of cognitive processing therapy in treating PTSD
• The correlation between parental involvement and academic success in homeschooling
• The effect of social media on cyberbullying behavior
• The impact of urbanization on air quality
• The effectiveness of dance therapy in treating anxiety disorders
• The correlation between early childhood education and math achievement
• The effect of parental involvement on academic achievement in health education
• The impact of global warming on agriculture
• The effectiveness of narrative therapy in treating depression
• The correlation between parental involvement and academic success in character education
• The effect of social media on political participation
• The impact of technology on job displacement
• The relationship between job resources and job satisfaction
• The effectiveness of art therapy in treating addiction
• The correlation between early childhood education and reading comprehension
• The effect of parental involvement on academic achievement in environmental education
• The impact of income inequality on social mobility
• The relationship between workplace diversity and organizational culture
• The effectiveness of solution-focused brief therapy in treating anxiety disorders
• The correlation between parental involvement and academic success in physical therapy education
• The effect of social media on misinformation
• The impact of green energy policies on economic growth
• The relationship between job demands and employee well-being
• The correlation between early childhood education and science achievement
• The effect of parental involvement on academic achievement in religious education
• The impact of gender diversity on corporate governance
• The relationship between workplace diversity and ethical decision-making
• The correlation between parental involvement and academic success in dental hygiene education
• The effect of social media on self-esteem among adolescents
• The impact of renewable energy policies on energy security
• The effect of parental involvement on academic achievement in social studies
• The impact of trade policies on job growth
• The relationship between workplace diversity and leadership styles
• The correlation between parental involvement and academic success in online vocational training
• The effect of social media on self-esteem among men
• The impact of urbanization on air pollution levels
• The effectiveness of music therapy in treating depression
• The correlation between early childhood education and math skills
• The effect of parental involvement on academic achievement in language arts
• The impact of immigration policies on labor market outcomes
• The effectiveness of hypnotherapy in treating phobias
• The effect of social media on political engagement among young adults
• The impact of urbanization on access to green spaces
• The relationship between job crafting and job satisfaction
• The effectiveness of exposure therapy in treating specific phobias
• The correlation between early childhood education and spatial reasoning
• The effect of parental involvement on academic achievement in business education
• The impact of trade policies on economic inequality
• The effectiveness of narrative therapy in treating PTSD
• The correlation between parental involvement and academic success in nursing education
• The effect of social media on sleep quality among adolescents
• The impact of urbanization on crime rates
• The relationship between job insecurity and turnover intentions
• The effectiveness of pet therapy in treating anxiety disorders
• The correlation between early childhood education and STEM skills
• The effect of parental involvement on academic achievement in culinary education
• The impact of immigration policies on housing affordability
• The relationship between workplace diversity and employee satisfaction
• The effectiveness of mindfulness-based stress reduction in treating chronic pain
• The correlation between parental involvement and academic success in art education
• The effect of social media on academic procrastination among college students
• The impact of urbanization on public safety services.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Japanese(日本語)

Diverse Methods of Marketing Research and

Their applications: a guide to business success.

In the previous article , we discussed the purpose and importance of marketing research in detail. By capturing market voices accurately and understanding consumer needs, you can build a foundation that supports your business growth. Marketing research is a powerful tool that underpins this process.

What marketing research methods are currently available? And how are they useful in different business scenarios? Marketing research can be broadly categorized into two main types: “qualitative research” and “quantitative research.” Each of these methods has its own strengths and applicable scenarios. Additionally, desk research is effective as a supplementary information-gathering method.

In this article, we will introduce the characteristics and benefits of each type of marketing research and specific applicable scenarios. This will help you choose the most suitable research method for your business challenges and develop more effective strategies.

Types of Marketing Research

Marketing research can be categorized primarily into two main types: "qualitative research" and "quantitative research." Each research method has unique characteristics and advantages, and it is crucial to use them appropriately, depending on the situation. The primary data obtained from "qualitative research" and "quantitative research" requires manual data collection, which can be time-consuming and costly. However, this effort can yield new unique information that only some know, allowing you to acquire valuable yet widely unavailable data.

In marketing research, it is efficient to review secondary data through desk research first and then collect any missing information as primary data.

Quantitative Research

Quantitative research is a method designed to gather data in the form of numbers or objective indicators by having respondents choose from predefined options. This method enables extensive data collection and works well when collecting objective facts. Below are some commonly known methods of quantitative research:

Internet Research : This method involves collecting data by having eligible respondents complete surveys online. It is cost-effective, requires less effort, and can be conducted in short-term research, making it the mainstream method for quantitative research today.

Mystery Shopping : This involves sending researchers disguised as customers to stores or other service locations to evaluate service quality, staff behavior, and other aspects. It offers the advantage of capturing actual service conditions.

Face-to-Face Interviews : This method involves researchers visiting respondents at their homes or workplaces to conduct interviews. The advantage of this approach is that it allows for the presentation and discussion of actual products or advertisements during the interview.

Mail Surveys : This involves sending questionnaires to respondents by mail, which they complete and return. It is beneficial for reaching older generations who may not use the internet.

Automated Telephone Surveys : This method uses an automated voice response system to conduct surveys. Respondents follow voice prompts and press keys to provide answers. It allows for rapid data collection without human intervention.

• Omnibus Surveys : This method involves multiple companies adding their questions to a shared survey, thus distributing the data collection costs between them. Each company receives responses to its specific questions only. It is cost-effective and allows for quick data collection.

Qualitative Research

Qualitative research is a method where respondents are allowed to express themselves freely, and their words are the data themselves. This approach works well when you want to understand consumers' emotions, opinions, and reasons for their behavior or when you want to explore complex issues that are difficult to quantify or the motivations behind consumer actions. Below are some representative methods of qualitative research.

Focus Groups : This method involves conducting a discussion session where participants talk about a specific theme, and opinions and ideas are collected. It typically involves 6 to 8 participants in a group with a moderator facilitating the discussion.

Depth Interviews : This method involves a one-on-one interview between the respondent and the interviewer. It allows them to dig into topics that may be difficult to discuss in front of a larger group.

Observational Research : This anthropological method involves observing the subjects' natural behavior and activities in their environment. Cameras and recording devices are set up in sales areas to conduct interviews while watching the footage or documenting and analyzing observed behaviors.

Specific Application Scenarios and Examples

Marketing research plays a crucial role in a wide range of business scenarios. Below are some specific application scenarios and examples.

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Automotive Industry Marketing Research Case Study

• Research Objective: Measure the PR effectiveness of a taxi dispatch app
• Research Type: Quantitative research (20 screening questions + 5 main survey questions) with monthly fixed-point surveys
• Research Target: Men and women aged 20 and over. 2,000 responses for the main survey
• Research Period: 3 days

Screening questions are included in the questionnaire to group "taxi users" by area.

The survey examines first recall acquisition and usage of the dispatch app to investigate the effectiveness of PR activities by age and area. You can assess the promotional effects by comparing them with past results.

While some information cannot be conveyed fully through text alone in the questionnaire, including logo images, videos, and links to service sites ensures accurate data collection. ーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーー

IT Industry Marketing Research Case Study

• Research Objective: Brand image survey of a cloud service
• Research Type: Quantitative research (10 screening questions + 15 main survey questions)
• Research Target: Men and women aged 20 to 69, 1,000 responses for the main survey
• Research Period: 2 days

The survey aims to clarify the image of the product, brand, and company using a questionnaire, thereby understanding the accurate status of the company in the market. ーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーー

Toward Future Success

Net research is a highly effective method to deeply understand consumers and aid in product development and customer acquisition. GMO Research & AI supports strategic decision-making for your business challenges. For detailed information or consultations, please feel free to contact us .

Beginner's Guide to Successful Online Survey

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• Business Strategy

How to Do Marketing Research in Less than 30 Minutes? Nike Case Study

Table of contents.

Marketing research is a complex and time-consuming process, but what if I told you that you can do it in less than 30 minutes?! With AI capabilities and the right tools, market research is no longer a tall order. You need only two things: a media monitoring project for collecting data and an AI Brand Assistant to analyze data for you.

Let’s say that in your market research, you want to learn about Nike’s online performance in the past 30 days.

It couldn’t be easier!

Marketing research  is the process of gathering, analyzing, and interpreting information about a market, including its products, services, customers, focus groups, competitors, and industry trends, to support decision-making and strategy development. Usually, the market research process is resource- and time-consuming, but with the right tool, you can conduct it in less than 30 minutes.

So, if market research is one of your responsibilities and you have been struggling with it so far, you are in the right place!

Read further to compare 3 helpful tools and discover the secret to market research in 3 steps and less than 30 minutes.

Let’s go!

What is marketing research? Definition

Marketing research is your secret weapon for business success! 

Imagine having a crystal ball that shows you what your customers really want, what your competitors are up to, and how you can win your target market. 

That’s marketing research for you! 

It’s all about gathering data, crunching numbers, and getting insights to help you make smart decisions. 

Whether you’re launching a new product, improving your services, or just wanting to know what people think about your brand, marketing research gives you the answers. 

It’s like having a superpower that keeps you one step ahead, ensuring you’re always in the know and ready to grow!

Conduct marketing research in less than 30 minutes with Brand24!

Purposes & benefits of marketing research

• Consumer research:  Understanding customer needs, preferences, and customer satisfaction level
• Market analysis:  Evaluating market potential and market share
• Competitor research:  Analyzing competitor strategies and performance, evaluating market saturation
• Marketing efforts assessment:  Assessing the effectiveness of marketing campaigns based on comparison with historical data
• Exploratory research:  Identifying new market opportunities and trends allowing to reach new potential customers
• Business idea evaluation:  Conducting research for product development and innovation

Marketing research made easy with Brand24.

How to do marketing research?

Marketing research can take a lot of work. 

But what if I told you you could conduct it in less than 30 minutes?

Yes, that’s possible.

Incorporating smart marketing research techniques is the answer.

One last thing you need to know is that there are two types of market research regarding the source of data:

• Primary research – based on primary data collection methods, meaning that you create new, original data, e.g., through surveys and interviews
• Secondary research – relying on existing data produced and collected by somebody else to support your market research

And two types of market research depending on the amount and detail of data:

• Qualitative research – closely analyzing a small sample for very precise insights into consumer preferences.
• Quantitative research – collecting and analyzing massive datasets for a general overview of consumer markets. Especially useful for creating focus groups that will realistically reflect your customers.

Market research in 3 steps

Step 1: ask ai tools.

The fastest and most efficient way to do market research is to use AI marketing tools. 

You can ask this software about anything; it will serve the needed insights in seconds.

Whether you need insights into your brand, influencers, focus groups, product, or service, AI holds the answer to all your questions.

You can choose between many solutions depending on the depth of insight you need for your market research.

I will discuss Chat GPT, Perplexity, and Brand24.

What is the difference between them?

• Chat GPT  is a general-purpose conversational AI that provides general responses based on its training set data and online insights. It can be used for secondary market research.
• Perplexity  focuses on data accuracy and up-to-dateness. It bases its answers on online sources with a good reputation. This allegedly limits unfounded and outdated answers. With Perplexity, you can do secondary research.
• Brand24  combines Chat GPT knowledge with its own data analysis. It works based on a media monitoring project that collects all online mentions of a given keyword. Thanks to this combination, it can answer very specific questions about your brand or support you in competitor research. Brand24 offers primary market research with some secondary research insights. It’s also a handy tool both for qualitative research (individual mentions analysis) and quantitative research ( brand metrics analysis). In short, it’s a versatile tool that can meet all your needs regarding marketing research.

Primary market research made easy with Brand24.

Let’s choose a sample market research question to ask all of them. I’ll go for something general like: Present customer insights for Nike over the past 90 days.

While the answer by Chat GPT is generally correct, it doesn’t tell you much for your marketing research. Unfortunately, it’s too general and quite common knowledge. 

Additionally, you cannot ask Chat GPT about a specific period like “the past 90 days”.

Chat GPT cites Brand24 as a secondary data source in the 3rd and 4th point to support its answer. 

However, it is a one-year-old article—clearly not information about Nike’s performance in the past 90 days. 

Although it’s not entirely wrong, it’s rather useless for thorough market research.

Let’s try Perplexity, then.

That’s another correct yet very general answer.

It is informative for someone outside the company, but I bet everybody at Nike already knows the information shared by Chat GPT and Perplexity.

This means that you need a tool that conducts primary research and provides completely new market insights.

That’s where Brand24 and its AI Brand Assistant come in.

This tool combines Chat GPT’s knowledge and language processing skills with its unique data collected through media monitoring. 

By leveraging both primary and secondary research, Brand24 can provide in-depth insights into your product or service.

Additionally, by combining both qualitative and quantitative data it can conduct a thorough analysis for all target markets.

Thanks to its method, it knows:

• What people are saying about your brand online
• Which of your products is the most loved and which is the most hated
• What is your brand reach online and social media reach
• Everything about your last hashtag campaign
• How you perform compared to your competitors
• What is the AVE of your mentions
• and so much more!

Let’s ask the same question we tested on Chat GPT and Perplexity.

That’s what we needed!

Detailed insights into your online performance deeply informing your market research.

This critical tool combines primary research with external resources to give you ultimate insights into your market research.

Do market research in less than 30 minutes with Brand24!

There’s also a concise conclusion and a visual, so don’t worry if you don’t like digging through numbers.

Additionally, this saves you time and improves your business decisions.

Let’s try one more question: What is the sentiment around Nike?

Chat GPT again tries to use Brand24 data for its secondary research, but again, it’s wrong. 

That “past 30 days” is data for February 2023. Chat GPT took this information from one of our blog posts without checking the date. 

This makes Chat GPT useless as:

• It doesn’t have its own data
• It tries to conduct secondary market research based on others’ data but does it wrong

What about Perplexity?

Again, the answer is not wrong, but it’s also not what you would expect. The information is too general to support market research in any way.

Let’s see what the AI Brand Assistant by Brand24 has to offer.

The AI Brand Assistant supports its answers with its own quantitative research results. Thanks to this, its answers are precise, up-to-date, and reflect the actual state of your brand online.

You can also ask follow-up questions. 

Let’s check the reasons behind the negative sentiment around Nike.

As you can see, the answer is very detailed, referencing particular mentions.

With these in-depth insights, you can adjust your marketing strategy, improve business decisions, and prevent PR crises by addressing customer needs and the most common concerns.

Conduct market research faster than ever with Brand24!

And what else can you ask our AI Brand Assistant regarding marketing research?

1. Which source generates the most mentions?

2. How many mentions did Nike receive in the last 30 days?

3. What is the target market for Nike?

4. What other brands appear in mentions?

5. What is the most trending topic?

6. What is the most mentioned product?

7. What time the target audience is most active on Instagram?

8. How do customers perceive Nike compared to Reebok?

9. How do customers feel about Nike?

10. What is the share of voice compared to Reebok?

11. Conduct a competitor analysis of Reebok.

12. Conduct sentiment analysis of Nike.

13. Present customer insights

And there’s so much more waiting for you to discover!

A hit for market researchers! Streamline your work with Brand24.

What is best is that you don’t even need to ask all those questions. You can completely delegate the market research to our AI Brand Assistant. 

For instance, use this prompt: Conduct market research for Nike. Include sentiment analysis, competitors analysis, reach, and SWOT analysis .

Streamline your market research with AI Brand Assistant!

Step 2: Ask the target audience and focus groups

This is so-called primary research, meaning you collect new, original data directly from your audience or a particular focus group.

So, how to collect customer feedback?

You can ask your target customers about their preferences in survey research. These can be distributed through email, social media, or other channels.

Another option is conducting marketing research through real-time interviews.

But let’s be fair; the former is usually quite ineffective, and the latter is very time-consuming.

There are three ways for you to go.

• Use a dedicated tool to improve your customer experience with your survey. There are many solutions to choose from for your primary research. The most prominent are Google Forms, Survey Monkey, Survey Lab, and Zoho Survey.
• Delegate your whole team to do the interviews for faster results.
• Leverage social listening tools .

If I were to choose, I’d go for social listening . 

Why would you generate more data when there’s already so much existing data to be analyzed online? 

Another thing is that customer behavior changes depending on the method of conducting market research. 

Indeed, for honest consumer insights, social media monitoring works better than any other primary research method.

Thanks to monitoring online mentions, you can also conduct exploratory research. 

Take a moment to browse your customers’ words, and you may identify groundbreaking market research insights. 

There may be aspects of your company that you never thought of as issues, opportunities, etc.

Primary research with Brand24 can inform your strategy and lead you to completely new discoveries and conclusions. 

Based on them, you can create an informed strategy to better appeal to your focus groups and potential customers.

Discover customer preferences with Brand24!

Step 3: Benchmark with competitors

Competitor analysis is the last crucial step of every marketing research. 

By understanding what your competitors are doing, you can identify gaps in the market, spot market trends, and find opportunities to differentiate your brand. 

It helps you benchmark your performance, see where you stand, and understand why certain competitors are more successful. 

Competitive analysis also prepares you to anticipate and respond to threats, ensuring you stay competitive and innovative. 

It helps you to conduct a SWOT analysis for your business as well.

In Brand24, we understand the importance of competitor research .

That’s why we offer a complex comparison feature. With its insights, you can gain a competitive edge over other companies in your niche.

Besides the raw data analysis, you can also ask our Brand Assistant about: 

• Two brands comparison 

What’s great is that the tool prepares a concise conclusion with a visual.

• Strategies to outperform your competitors

• Your competitors’ biggest mentions source
• and anything else that comes to your mind and is based on your project data.

Benchmark competitors with the Brand24 market research tool!

Challenges in the marketing research process

Marketing research is a critical component of business strategy, but several challenges can affect the accuracy and usefulness of the insights gathered. 

Here are some of the main challenges faced when conducting marketing research:

01  Sampling issues

• Challenge:  Selecting a representative market segment is crucial, but it can be difficult to access and ensure that the sample accurately reflects the target consumers.
• Solution:  Use robust sampling techniques and consider multiple methods to improve representativeness. You can also set up focus groups to ensure inclusive sampling.

02  Data collection methods

• Challenge:  Choosing the right data collection method can be difficult. Each method has its pros and cons, and the wrong choice can impact data quality and final results.
• Solution:  Evaluate the research objectives and the characteristics of the target audience to select the best data collection method(s). Remember, media monitoring is a great starting point for any marketing research.

03  Data quality and reliability

• Challenge:  Ensuring the accuracy and reliability of the data collected can be difficult, especially with self-reported data or secondary sources.
• Solution:  Implement data validation techniques and cross-check data from multiple sources when possible. Leverage media monitoring tools for first-hand insights into your customer satisfaction.

04  Data analysis complexity

• Challenge:  Analyzing data, especially large and complex datasets, requires specialized skills and tools. Misinterpretation of data can lead to incorrect conclusions.
• Solution:  Use advanced analytical tools like Brand24 or involve skilled data analysts who can correctly interpret the data.

05  Time constraints

• Challenge:  Conducting comprehensive research takes time, but businesses often need quick insights to make timely decisions.
• Solution:  Plan research timelines carefully and use agile research methodologies. Start your media monitoring project before conducting market research to ensure representative and relevant data.

06  Keeping up with technological changes

• Challenge:  The rapid evolution of technology means new data analysis methods and tools are constantly emerging, making it hard to stay up-to-date.
• Solution:  Choose a tool that keeps up with technological developments and embraces solutions like machine learning and artificial intelligence. Brand24’s AI models have been trained for the past 12 years, ensuring a modern approach with significant reliability.

07  Respondent bias

• Challenge:  Respondents may provide socially desirable answers or may not fully engage with the survey or interview, leading to biased results.
• Solution:  Design surveys and interview questions carefully to minimize bias. Use techniques like anonymous surveys to encourage honest responses. Leverage social listening for brutally honest customer review examples.

08  Interpreting and communicating results

• Challenge:  Even with good data, interpreting and communicating findings effectively to stakeholders can be difficult.
• Solution:  Use clear, visual representations of data and tailor the communication of results to the audience’s level of understanding and interest. At Brand24, we understand this issue, so we offer a wide array of visual data representations, such as charts, graphs, and infographics.

Addressing these challenges requires careful planning, skilled execution, and ongoing adaptation to new trends and technologies in marketing research.

Address marketing research challenges with Brand24!

Marketing research is a challenging, time- and resource-consuming process that is prone to mistakes.

At the same time, marketing research is the key to your success. It informs your marketing strategy, provides key demographics of your clients, and helps you get ahead of your competitors.

Fortunately, with the right tool, effective market research can be done in less than 30 minutes and in 3 simple steps.

How to conduct market research?

Long story short:

• Ask AI Brand Assistant to conduct market research for you.
• Leverage primary research data from social listening to learn about customer satisfaction and focus groups.
• Benchmark with competitors to ensure your leadership in the target market.

Effective market research? Brand24!

Final thoughts:

• Marketing research helps companies better understand their business environment, consumer behavior, market size, and product or service performance. With the right tools, you can get all of those insights in less than 30 minutes.
• While tools like Chat GPT and Perplexity base their answers on secondary research, Brand24 has unique primary data about your brand that nobody else can provide.
• Brand24 combines primary and secondary research by listening to and analyzing your consumer attitudes and transforming them into unique quantitative research data.

Market research conducted in-house in less than 30 minutes.

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Two changes to a RBC quantitative analyst’s Top 40 list of Canadian stocks

Daily roundup of research and analysis from The Globe and Mail’s market strategist Scott Barlow

RBC Capital Markets analyst Bish Koziol made two changes to the firm’s Top 40 stock list derived from value, momentum, growth and predictability criteria. National Bank of Canada was added, North West Company was removed,

“Our Canada Overall Top 40 Portfolio gained 4.8 % last month, while the S&P / TSX Composite rose 5.9 %. Year -to -date the Portfolio advanced 14.3 % versus the benchmark ‘s 12.3 %. All sectors contributed to the return last month but were led by Communication Services. With the addition of National Bank this month, the weight of Financials in the portfolio rose to 30 %”

The list in now Imperial Oil Ltd., Pason Systems Inc., Cenovus Energy Inc, Canadian Natural Resources Ltd, Trican Well Service Ltd, Suncor Energy Inc., Ovintiv Inc, Keyera Corp, Stella-Jones Inc, Alamos Gold Inc, Labrador Iron Ore Royalty Corp, Teck Resources Ltd, CCL Industries Inc., Exchange Income Corporation, Finning International Inc., Richelieu Hardware Ltd., TFI International Inc, Toromont Industries Ltd, Linamar Corp, Metro Inc., Loblaw Companies Ltd, Fairfax Financial Holdings Ltd, AGF Management Limited, Intact Financial Corporation, National Bank Of Canada, CIBC, Bank Of Montreal, Bank of Nova Scotia, Great-West Lifeco Inc, IA Financial Corporation Inc, TMX Group Ltd, Toronto-Dominion Bank, Open Text Corp, Celestica Inc, Enghouse Systems Limited, Cogeco Communications Inc, Quebecor Inc., Rogers Communications Inc. and TransAlta Corporation.

Citi global strategist Chris Montagu does not sound confident about U.S. large caps or the U.S. economy,

“Volatility jumped in July through early August as investor sentiment shifted dramatically from euphoric to cautious then outright defensive late last week. Small-cap and Value outperformed most styles in July but reversed last week. The heightened recession risk reflected in our economists’ expectation of 50 bps Fed rate cuts in September and November is the biggest headwind for the nascent small-cap rally. However, continued negative surprises coupled with crowded positioning among mega-cap names do not bode well for the Technology sector and Price Momentum factor. We continue to advise investors to actively mitigate unintended macro risks and diversify across sectors and stocks”.

BMO chief investment strategist Brian Belski retains his belief in a ‘broad and significant catch-up trade’ for domestic equities,

“The equity “rotation trade” was on full display in July, with the S&P/TSX gaining a solid 5.6% in July, sharply outperforming the S&P 500 which was up just 1.1% as mega-cap momentum waned. From our perspective, this is the type of performance trends we expect to unfold in the second half of the year and into 2025 ... Interestingly, the market rotated heavily into the Real Estate sector in July, which we flagged last month as the most oversold and deepest value sector in the TSX. Indeed, the Real Estate sector was the top-performing sector in July, gaining a solid 10.6% on a price return basis. To be clear, we expect this rotation trade to favour many of these oversold areas of the TSX, including areas like Communication Services, Utilities and Financials. Overall, we remain steadfast in our view that Canada remains the contrarian call in terms of developed markets in 2024 and is well positioned for a broad and significant catch-up trade. As the reality of a more resilient economy (for both Canada and the US), coupled with increasingly stable and lower interest rates become clear in the second half of 2024, we believe fundamentals will begin to rebound faster than currently expected”

Diversion: “Path to precision: Targeted cancer drugs go from table to trials to bedside” – Ars Technica

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Assistant Professor of Marketing

• M.J. Neeley School of Business
• Opening at: Aug 7 2024 at 10:00 CDT

Application Deadline:

Open Until filled.

Position Details:

This full-time faculty position is on-campus and in-person. The Department of Marketing at Texas Christian University (TCU) invites applications and nominations for the position of Assistant Professor (tenure-track) within the Neeley School of Business. The successful candidate will begin employment in August 2025. Candidates for the position should possess a PhD in marketing or a related area. The ideal candidate will have strong quantitative skills and will conduct research that has implications for marketing strategy. The position entails conducting state-of-the-art research, teaching analytics or other quantitative courses at both the undergraduate and MBA level, publishing in top-level marketing academic journals, interacting with business leaders, and performing other faculty duties. The successful candidate is expected to contribute to enhancing the research environment in the Neeley School. Candidates should also possess excellent interpersonal communication skills including verbal and written, active listening, and critical thinking.

Department Details:

The Department of Marketing provides an exceptional work environment, with highly supportive colleagues who are dedicated to the vision of being world-class thought leaders who also make a difference in the lives of students. We realize this vision through excellence in research, teaching, and professional service. We are seeking candidates who share in our vision and can provide leadership, contributing effectively to our highly productive and collaborative environment. Faculty in the Department of Marketing regularly publish in the top-tier journals and have held editorial roles for the leading marketing journals (e.g., Journal of Marketing and Journal of the Academy of Marketing Science). Both tenure-track and teaching-track faculty have also been recognized as outstanding teachers by the Neeley School, TCU, and Poets and Quants. Several on the faculty have held/hold leadership roles in major professional associations (e.g., AMAF Board, SCP, AMA). Our faculty also provide invaluable service to the Neeley School, such as leading the Neeley Behavioral Research Lab, directing the Neeley Leadership Program, and leading the TCU Sales Center.

School/College Description:

The nationally ranked TCU Neeley School of Business has approximately 3,000 students enrolled across undergraduate and graduate programs, with approximately one third of TCU students in its programs as majors, minors or advanced degree seekers. Departments include Accounting, Business Information Systems, Entrepreneurship and Innovation, Finance, Management and Leadership, Marketing and Supply and Value Chain Management, as well as seven graduate programs including full-time, part-time and online MBA and MS degrees. The Neeley School was ranked #9 in the nation and #1 in Texas for highest starting salaries for undergraduates in 2022 (Poets&Quants). The school’s BBA is ranked #21 Best Undergraduate program in 2023, and was named one of the top 10 Undergraduate Business Schools to Watch in 2022 (Poets&Quants). The Neeley School has the #4 ranked full-time MBA in Texas in 2022 (Bloomberg Businessweek) and the #32 MBA in the World in 2022 (Wall Street Journal/Time Higher Education). The Neeley School is committed to fostering an inclusive, scholarly community composed of individuals who, through their diverse and sometimes competing perspectives, contribute to a free and intellectually challenging culture where students, faculty, staff and alumni have equitable opportunities and can forge paths toward personal and professional growth. We build upon the momentum generated by living the Neeley Promise: The Neeley School of Business unleashes human potential with leadership at the core and innovation in our spirit.

University & Fort Worth Description:

ABOUT TCU Founded in 1873, Texas Christian University sits on 302 acres nestled in a primarily residential part of Fort Worth, just minutes away from downtown. The University includes seven schools and colleges, in addition to the John V. Roach Honors College and the Burnett School of Medicine. Currently, TCU enrolls more than 10,200 undergraduates and 1,700 graduate students. Twenty-eight percent of students self-identify as a member of a minority group, five percent are international students, and forty-five percent are from out-of-state. Our students are supported by more than 2,200 faculty and staff. The University has more than 700 full-time faculty members and is a top 100 National University as classified by US News and World Report and has a Carnegie Classification of R2: Doctoral Universities – High Research Activity. At TCU, diversity, equity, and inclusion (DEI) are an educational imperative directly tied to the University mission, vision, and strategic plan. Fulfilling TCU’s mission to develop ethical leaders and critical thinkers in a global community depends on the University’s ability to attract and retain students, faculty, and staff from diverse backgrounds. A diverse and inclusive campus leads to innovation, broadened perspective, and understanding—values that are foundational aspects of higher education. For the fourth consecutive year, TCU has earned the Higher Education Excellence in Diversity Award, which highlights ongoing commitment to build a comprehensive DEI strategy that aligns with core values and creates a campus culture where everyone is respected and included. ABOUT FORT WORTH Like TCU, Fort Worth has the approachable, friendly charm of a smaller town, but offers the amenities, cultural activities, diversity and unique personality of a much larger city. Fort Worth, Texas, is the 13th largest city in the United States with an ever-growing population nearing 1 million. Fort Worth and TCU have grown together in a nearly 150-year relationship. You’ll find that many Horned Frogs remain here after graduation, thanks to the region’s thriving job market. Fort Worth is part of the Dallas-Fort Worth metropolitan area, the fourth largest metropolitan area in the United States, and the number 1 tourist destination in Texas. In 2018, bizjournals.com ranked Fort Worth the 7th most affordable city to live and work in the United States and U.S. News and World Report named Fort Worth one of the Best Places to Live. Fort Worth, Dallas and Arlington all rank among the top 25 most diverse cities in the country. Fort Worth is known for its vast array of cultural, educational and entertainment opportunities. The city boasts three world class art museums—the Kimbell Art Museum, the Modern Art Museum of Fort Worth and the Amon Carter Museum of American Art. The Bass Performance Hall is one of the premier performance venues in the country. Concerts, film festivals and other events are held regularly at Sundance Square, Panther Island and Near Southside venues.

Required Application Materials & Application Instructions:

Applications and other documentation must be submitted electronically through the TCU HR system at jobs.tcu.edu. From there, click on “Find Openings” and select “Business Positions.” Applicants should attach the following to the online application: (1) a cover letter indicating interest in the position, (2) curriculum vita demonstrating teaching and research accomplishments in the area of marketing, (3) and the names three references with addresses, email addresses, and phone numbers. References will not be contacted without approval of the candidate. Review of applications will begin immediately and will continue until the position is filled.

AA/EEO Statement:

As an AA/EEO employer, TCU recruits, hires, and promotes qualified persons in all job classifications without regard to age, race, color, religion, sex, sexual orientation, gender, gender identity, gender expression, national origin, ethnic origin, disability, genetic information, covered veteran status, or any other basis protected by law.

TCU Annual Security Report & Fire Safety Report Notice of Availability

Texas Christian University is committed to assisting all members of the campus community in providing for their own safety and security. TCU’s Annual Security Report and Fire Safety Report is published in compliance with the Jeanne Clery Disclosure of Campus Security Policy & Campus Crime Statistics Act (Clery Act) and the Higher Education Opportunity Act. This report includes statistics for the previous three calendar years concerning reported crimes that occurred on campus, in certain off-campus buildings owned or controlled by the University, and on public property within, or immediately adjacent to and accessible from the campus. The statements of policy contained within this report address institutional policies, procedures, and programs concerning campus security, alcohol and drug use, crime prevention, the reporting of crimes, emergency notifications and timely warning of crimes, sexual and interpersonal violence, and personal safety at TCU. Additionally, this report outlines fire safety systems, policies and procedures for on-campus housing facilities, as well as residence hall fire statistics. 

The Annual Security Report and Fire Safety Report can be found on the TCU Police Department website at https://police.tcu.edu/annual-security-report , or a paper copy of the report may be obtained by contacting the TCU Police Department at 817-257-7930, or via email at [email protected] .

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Quantitative Research Analyst – 2025 University Graduate (Asia)

Hong Kong, Singapore

Attach A Resume

Choose A File

(.docx, .doc, .pdf, .txt - max 10MB file size)

Job Description

• Conceptualize valuation strategies, develop, and continuously improve upon mathematical models and help translate algorithms into code
• Back test and implement trading models and signals in a live trading environment
• Use unconventional data sources to drive innovation
• Conduct research and statistical analysis to build and refine monetization systems for trading signals
• Bachelor’s or master’s degree in mathematics, statistics, physics, computer science, or another highly quantitative field
• Strong knowledge of probability and statistics (e.g. machine learning, time-series analysis, pattern recognition, NLP)
• Background working in a data driven research environment
• Independent research experience
• Excellent analytical skills, with strong attention to detail
• Strong written and verbal communications skills

About Citadel Securities

Citadel Securities is the next-generation capital markets firm and a leading global market maker. We provide institutional and retail investors with the liquidity they need to trade a broad array of equity and fixed income products in any market condition. The brightest minds in finance, science and technology use powerful, advanced analytics to solve the market’s most critical challenges, turning big ideas into real-world outcomes.

Quantitative Research Internship, Bachelor’s or Master’s Degree (Summer 2025 -Shanghai)

Ready to accelerate your growth in one of the most fascinating and dynamic industries? Our Research Summer Internship program will give you real insights into how data and research is used to improve global financial markets. Expand your knowledge of the financial markets and solve challenging problems that could impact the way we trade. Plus, if you’ve excelled over the summer and shown us your potential, you could receive an offer to join us as a graduate quantitative researcher.

With Optiver’s internship program, your work improving the market starts today.

Who we are:

Optiver is a global market maker founded in Amsterdam, with offices in London, Chicago, Austin, New York, Sydney, Shanghai, Hong Kong, Singapore, Taipei and Mumbai. Established in 1986, today we are a leading liquidity provider, with close to 2,000 employees in offices around the world, united in our commitment to improve the market through competitive pricing, execution and risk management. By providing liquidity on multiple exchanges across the world in various financial instruments we participate in the safeguarding of healthy and efficient markets. We provide liquidity to financial markets using our own capital, at our own risk, trading a wide range of products: listed derivatives, cash equities, ETFs, bonds and foreign currencies.

Since its establishment in 2012, our Shanghai office is a rapidly growing participant in the Chinese markets, trading exchange-listed futures, options and equities in China mainland. Our vision is to become the trusted partner in the development of Chinese financial markets. With the culture of a start-up but the backing of a 35+ year-old trading firm, the Optiver Shanghai office is truly unique. Everyone who joins us will help shape the future of our company and its global impact. Get ready: we are only just beginning. 

What you’ll do:

As a Quantitative Research Intern, you’ll work with our researchers and traders on real-life research projects, that directly impact the way we trade. Our quantitative researchers are responsible for the accuracy of our core pricing models. They work closely with our traders to analyse and improve all facets of our trading strategies.

As part of the internship, you’ll get to:

• Perform extensive analysis in order to implement new algorithms that support and improve our existing models.
• Develop risk management and portfolio optimisation tools to improve our execution algorithm.
• Work with petabytes of low latency, high-frequency market data sets.
• Collaborate with our developers to test and drive changes to our trading system, that will improve our ability to make successful trades.
• Keep up to date on the latest development of new models and technologies
• No previous experience in trading or financial markets? You bring the passion and we'll have the training to support you along the way  

Who you are:

• Bachelor/Masters student, who will graduate during or after 2026.
• Major in a highly quantitative field.
• Strong knowledge of probability and statistics, experience in machine learning and time-series analysis is a big plus.
• Programming experience in any language (C, C++, Python, JAVA, etc.), ideally with a preference towards Python.
• Ability to carry a project on your own in a structured way within a short timeframe.
• Experience in working with large datasets.
• Both a self-motivated contributor and a team player, with an entrepreneurial attitude and hunger for success.
• Interest in the trading/quantitative finance industry.

What you’ll get:

• The chance to work alongside diverse and intelligent peers in a rewarding environment.
• Competitive remuneration, including an attractive bonus structure and additional leave entitlements.
• Training, mentorship and personal development opportunities.
• Daily breakfast, lunch and snacks.
• Gym membership, sports and leisure activities, plus weekly in-house chair massages.
• Regular social events, clubs and Friday afternoon drinks.

Diversity, Equity and Inclusion statement

As an intentionally flat organisation, we believe that great ideas and impact can come from everyone. We are passionate about empowering individuals and creating diverse teams that thrive. Every person at Optiver should feel included, valued and respected, because we believe our best work is done together.

Our commitment to diversity and inclusion is hardwired through every stage of our hiring process. We encourage applications from candidates from any and all backgrounds, and we welcome requests for reasonable adjustments during the process to ensure that you can best demonstrate your abilities.

HOW TO APPLY

If you’re interested in taking your career to the next level and work on one of the most exciting trading floors in China mainland, apply now via the form below. Applications are open until  8 October 2024 .

While we love how bilingual our teams are, be sure to submit the below application materials in English:

• Academic transcripts, including Bachelors and Masters if any

For any other inquiries, please email [email protected] .

PRIVACY DISCLAIMER

Optiver 重视个人信息的保护。请您在提供个人信息给我们之前，认真阅读Optiver China Privacy Notice, 了解我们如何收集及处理您的个人信息。

Personal information protection is of utmost importance to Optiver. Before you provide any personal information to us, we strongly urge you to read Optiver China Privacy Notice for acknowledging how we collect and process your personal information.

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As one of the world’s leading Market Making firms, we at Optiver pride ourselves on our ability to improve the market by providing liquidity in all market conditions. Our Institutional Sales teams are central to that success, trading directly with counterparties across the globe.  As we look to continue our growth, we are seeking an […]

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How to Write a Salary Increase Letter (Example Included!)

Negotiating your salary can be a key step in advancing your career and boosting your financial stability—but it can also be pretty intimidating. The good news is that with the right approach, it doesn’t have to be so scary. That's where a salary increase letter comes in.

Whether you're asking for a raise due to your great performance, increased responsibilities, or changes in the market, a well-crafted letter asking for salary increment can be a powerful (and smooth) way to make your case.

In this article, we'll walk you through everything you need to know about writing a salary increase letter, from understanding its purpose to tips on crafting an effective one. We'll also include sample letters and templates to help you get started. Plus, we’ve interviewed Muse career coach Jenn Smith , who shares her top advice on navigating this critical career move.

Need a higher salary? Check out open jobs on The Muse for your next big move »

What is a salary increase letter?

A salary increase letter is a formal document that employees use to request a raise from their employer. Unlike a salary review letter—which is typically initiated by the employer to communicate pay adjustments—a salary increase letter is written by the employee seeking a boost in compensation.

Writing a salary increase letter can be necessary for several reasons:

• Performance improvements : You've consistently exceeded your performance goals or achieved a significant milestone and believe your contributions are worth a higher salary.
• Increased responsibilities : Your role has expanded significantly, and your current salary no longer reflects the scope of your responsibilities.
• Market adjustments : Industry standards and market rates for your position have increased, and your current salary needs to catch up to these benchmarks.

When writing a letter to request a salary increase, it's generally more effective to address it to your direct manager or your department’s director rather than HR. Your manager is more familiar with your work, contributions, and the value you bring to the team. They are also likely involved in budget decisions and have the authority to advocate for your raise.

Is it OK to ask for a raise through a salary increase letter?

Yes, writing a salary increase letter can be a formal and respectful way to request a raise. It allows you to clearly articulate your reasons, provide evidence of your achievements , and give your employer time to consider your request. Plus, a letter is a documented record of your request and can be reviewed by decision-makers at different levels of the organization.

On the other hand, having an in-person conversation can be generally more effective. “This allows you to present your case dynamically, outlining your accomplishments, contributions, and the value you bring, and respond to questions or concerns in real-time,” Smith says, adding that a direct conversation also allows for immediate feedback. “Your manager can provide insights into decision-making, share any constraints or considerations, and offer guidance.”

She also believes it’s a good idea to supplement your conversation with a follow-up email to ensure clarity and provide a reference for future discussions.

How to write a salary increase letter

These tips will prepare you for writing an effective pay raise letter:

1. Research salary benchmarks

Conducting extensive research will strengthen your case and help you present a compelling argument.

“Research industry salary benchmarks for your role, experience level, and geographic location,” Smiths says. “Use reliable sources like industry salary surveys, compensation reports, and online salary databases.”

Additionally, be sure to understand your company's salary ranges, performance evaluation criteria, and typical raise percentages.

2. Choose the right time

Timing is crucial when it comes to writing a letter requesting pay increase. Making your request at the wrong time can significantly reduce your chances of success.

“Typically, organizations have annual or semiannual performance review cycles,” Smiths says. “Discuss this with your manager before the performance review process starts so they can consider it as they begin budget conversations.”

One common mistake she sees is “asking for a raise at an inappropriate time, such as during a company's financial downturn or immediately after a major organizational change or layoffs.” Avoid doing that at all costs.

3. Keep it clear and straightforward

Begin your letter by setting the context for your request and remind your employer of your role within the company. Clearly state your position, tenure with the company, and the purpose of the letter.

4. Detail your contributions and impact

In the main section of your letter, outline your accomplishments and contributions to the company. Highlight specific achievements, projects, or responsibilities that demonstrate your value.

Provide evidence of your impact, such as performance metrics, positive feedback from clients or colleagues, and examples of how your work has benefited the company, explaining how your contributions justify the proposed raise.

5. Conclude with gratitude and reaffirmation

Summarize your key points and reiterate your appreciation for the opportunity to discuss your compensation. Express gratitude for the support and experiences you have gained and reiterate your commitment to the company. This positive tone reinforces your professionalism and leaves a lasting impression.

Salary increase request letter example

Here’s a sample letter for salary increase request to show you how these tips can be put into practice:

Alex Johnson 123 Elm Street Springfield, IL 62704 [email protected] July 25, 2024

Emma Thompson Director of Sales Innovative Tech Solutions 456 Maple Avenue Springfield, IL 62704

Dear Ms. Thompson,

I hope you are well. I am writing to formally request a review of my current salary. I have thoroughly enjoyed working at Innovative Tech Solutions over the past three years and appreciate the opportunities for growth and development that have been provided to me.

During my time here, I have consistently exceeded expectations and made significant contributions to the Sales team. For example, I spearheaded a new email marketing campaign that increased sales by 15% and successfully launched our new TechY product line, resulting in a 20% revenue boost.

In addition to my core responsibilities, I have taken on new challenges, such as leading the training program for new sales representatives and managing key client accounts, which have significantly contributed to our team's success.

I have also undertaken several professional development activities, including completing a certification in Advanced Sales Strategies and attending workshops on market trends, which have further enhanced my skills and ability to contribute to our team.

Based on my research of industry standards and salary benchmarks for my role and experience level, I believe that an adjustment in my compensation is warranted. Therefore, I respectfully request a salary increase to $85,000. This adjustment would better reflect the value I bring to the team and align my compensation with industry standards.

I am confident this increase will further motivate me to continue delivering high-quality work and contributing to the success of Innovative Tech Solutions. I am more than willing to discuss this request in person and provide any additional information that may be required.

Thank you for considering my request and for your ongoing support.

Sincerely, Alex Johnson

Raise request letter template

Now, here's a template for a raise request letter to help guide you in drafting your own:

[Your Name] [Your Address] [Email Address] [Date]

[Recipient’s Name] [Recipient’s Title] [Company’s Name] [Company’s Address]

Dear [recipient’s name],

I hope you are well. I am writing to formally request a review of my current salary. I have thoroughly enjoyed working at [Company’s Name] over the past [number] years and appreciate the opportunities for growth and development that have been provided to me.

During my time here, I have consistently exceeded expectations and made significant contributions to the [Department] team. For example, I [List your accomplishments, using quantifiable results whenever possible, such as increased sales by 15% through a new email marketing campaign; successfully launched a new product line, resulting in a 20% revenue increase; etc.].

In addition to my core responsibilities, I have taken on new challenges, such as [List additional responsibilities].

In addition to these accomplishments, I have undertaken several professional development activities, including [certifications, courses, and training programs], which have further enhanced my skills and ability to contribute to our team.

Based on my research of industry standards and salary benchmarks for my role and experience level, I believe that an adjustment in my compensation is warranted. Therefore, I respectfully request a salary increase to [desired salary or salary range]. This adjustment would better reflect the value I bring to the team and align my compensation with industry standards.

I am confident this increase will further motivate me to continue delivering high-quality work and contributing to the success of [Company Name]. I am more than willing to discuss this request in person and provide any additional information that may be required.

Sincerely, [Your name]

How often should I make a salary raise proposal ?

Typically, you should ask for a raise once a year, ideally around your annual performance review. If you have taken on significant additional responsibilities or have had exceptional achievements, it might be appropriate to request a salary review sooner. However, be mindful of your company's financial health and the timing of your request.

Should I wait for a performance review?

Waiting for a performance review is often a good strategy, as this is a natural time for salary discussions. However, if you feel that your contributions have significantly outpaced your current compensation, you might consider requesting a meeting outside of the review cycle. Just ensure your request is well-timed and substantiated.

What if the salary increase request is denied?

If a salary review is denied, consider asking for specific feedback. “Work with your manager to set clear goals—create a development plan that outlines the steps you need to receive a raise,” Smith says. “Consider discussing alternative forms of compensation, which could include bonuses, additional vacation days, flexible working arrangements, and professional development opportunities.”

Key takeaways

Whether you opt for a formal letter via email , a direct conversation, or a combination of both, the key is to present a well-reasoned case for your increased-salary request. When crafting your letter, keep these takeaways in mind:

• Avoid approaching the conversation with an aggressive or entitled attitude. Politeness and professionalism will help you make a positive impression.
• Document any professional development activities you've undertaken , such as certifications, courses, training programs, or conferences. This shows your commitment to growing within your role and adds weight to your request.
• Be confident in your request to demonstrate your self-assurance and understanding of your worth. Clearly state your desired salary or salary range and show you are informed about salary ranges for your position.
• Explain how a salary increase will help you contribute even more to the company's success. Position your request as a mutually beneficial arrangement that will enhance your productivity and the value you bring to the organization.
• Use clear and concise language, avoiding jargon or overly complex sentences. Ensure your message is easily understood and directly addresses your key points. After writing it, don’t forget to proofread it.