qualitative forecasting methods market research

A Straightforward Guide to Qualitative Forecasting

Published: June 14, 2023

In sales, numbers are key, but they don't always give you a comprehensive picture of your org's performance and potential — particularly in forecasting. So while you can't ignore quantitative forecasting, you still need to consider factors beyond those hard figures for a thorough understanding. That’s where qualitative forecasting comes in.

Qualitative forecasting accounts for the more subjective elements of sales. By accounting for both sides of the forecasting process, you can put yourself in the best position to set accurate targets, plan for the future, and predict the success of your upcoming campaigns.

Here, we'll take a closer look at qualitative forecasting as a concept, review some methods and techniques you can use to get the most out of the process, see some examples of what it looks like in practice, and weigh its pros and cons. Let's jump in!

Table of Contents

• What is qualitative forecasting?

Benefits of Qualitative Forecasting

Qualitative forecasting methods and techniques, qualitative forecasting examples, advantages of qualitative forecasting, drawbacks of qualitative forecasting, what is qualitative forecasting.

Qualitative forecasting is a type of forecasting that involves more subjective, intuitive, or experiential approaches. It could revolve around elements like knowledge of a business's customer journey, market research, or company leadership's personal experience in a field.

There's no denying that numbers are a crucial part of any sales forecast — you should never try to put one together without them. But as touched on earlier, hard figures can't always give you a complete enough picture to inform an accurate forecast.

Qualitative forecasting fleshes out a more thorough understanding of customer and market behavior — helping businesses account for more angles and potential curveballs when conducting their sales efforts over a fixed period.

Qualitative forecasting helps when companies explore new sales methods or expect sales to deviate from the typical results. As companies grow, they might find themselves in uncharted territory — setting unprecedented goals and making plans they're not well-acquainted with. Here's why qualitative forecasting is so important in those situations.

Alt: Benefits of Qualitative Forecasting. Uses leading indicators instead of lagging indicators. Accounts for more variables. Uncovers expert insights.IMG name: qualitative-forecasting-benefits

Qualitative forecasting uses leading indicators instead of lagging indicators.

A study by Gong highlighted that while 63% of sales professionals considered sales forecasting extremely critical to the success of their business, only 27% said that it produces accurate results.

Forecasting based purely on historical data doesn't account for economic fluctuation, upcoming technologies, or unexpected market trends. In times of unprecedented change, qualitative forecasting accounts for external market conditions and helps you anticipate the impact of a given variable on your sales cycle — rather than trying to identify its consequences in hindsight.

Qualitative forecasting accounts for more variables.

Quantitative forecasting is traditionally limited to measurable objectives like revenue, customers, and product units sold. But qualitative forecasting is more expansive — it considers subjective elements like customer satisfaction, brand perception, and employee engagement.

Including those less tangible variables helps you anticipate the demand for your products or services in a given market — providing better insight into how much effort you need to put into a campaign and where your focus should lie.

Qualitative forecasting uncovers expert insights.

Armed with the right qualitative data, you can draw on the experience and knowledge of industry experts to inform your decisions. Use their firsthand insights to anticipate customer behaviors and better understand what needs to be done to move forward.

Qualitative forecasting helps you identify where there might be potential gaps between expectations and reality — helping you make more meaningful and informed decisions.

So how do you approach qualitative forecasting? There are several ways to go down this path.

Alt: Qualitative Forecasting Methods. Experience (Executive Opinion). Qualitative Forecasting Methods. Consultancy. Delphi Method. Surveys. Market Research. Sales Force Composite. IMG name: qualitative-forecasting-methods

1. Experience (Executive Opinion)

In many cases, some of the necessary insight and information to inform effective qualitative forecasting can come from within the company — typically from leadership.

Managers (or occasionally regular employees) might already have extensive knowledge of or experience with a certain market, product, or customer base. In those instances, they can be an excellent resource for assisting with qualitative forecasting.

2. Consultancy

Not every business has leadership seasoned enough to put together reliable qualitative forecasts based on personal experience — especially if a company is younger and scaling.

That's why companies often outsource their qualitative forecasting responsibilities to third parties. Consultants with a more developed pulse on an industry, market, or customer persona can be an excellent resource for a company struggling with qualitative forecasting.

3. Delphi Method

The Delphi Method is similar to the ones listed above in that it relies on experts, but the process is a bit more elaborate and sophisticated than most others. Instead of just asking experienced managers or consultants for their opinions off-hand or collaboratively, the method involves questioning multiple parties about a sales forecast separately to prevent groupthink.

The risk you run when leveraging the Delphi Method is a lack of consensus. If too many experts offer varying perspectives, it can be hard to piece together a cohesive, accurate qualitative forecast.

Surveys are another way to inform thoughtful, effective qualitative forecasting. This method is one of the more tried-and-true, relatively accessible options listed here. Hearing directly from your target audience helps you tailor a forecast backed by firsthand qualitative insight.

A well-constructed survey gives you insight into new markets, helps you understand shifting tides within your industry, and allows you to identify your customers' collective tendencies better. With several applications to create and distribute surveys at your disposal, this method is worth considering when putting together qualitative forecasts.

5. Market Research

When a business plans to enter a new market, it can use market research to boost its qualitative forecasting. This practice helps a company determine if breaching a new market is worth the effort and resources.

It also offers perspective on what potential new customers are looking for from the business. Resources like focus groups, product testing surveys, and polls can all be used when leveraging this method.

6. Sales Force Composite

Your sales team interacts with your customers more closely than anyone else and possesses a wealth of firsthand knowledge about customers’ buying habits.

The sales force composite forecasting method draws the insights of salespeople, sales management, and other channel members to produce sales forecasts. Train salespeople on how to forecast accurately, explicitly emphasize the importance of this market intelligence, and regularly review the data they provide to control the quality of your forecasting.

Virtually any significant decision any business makes can benefit from qualitative forecasting techniques.

When a company is either just starting or getting off the ground, its leadership will likely need to account for market research to determine if its idea, offering, business model, messaging, pricing, and marketing are viable.

In those cases, the organizations in question don't have existing numerical data to analyze and rely on — making accurate quantitative forecasting nearly impossible. Instead, those companies have to take different, more creative roads to produce a solid picture of what they can expect from their sales efforts and target prospects.

Qualitative forecasting is also an asset for more mature companies looking to release a new product or service. Quantitative methods can only get you far if you've never sold a specific offering. That's why businesses in this position generally look beyond those strategies to accurately understand what's to come.

Scenario 1: Launching a New Product

A tech giant like Samsung wants to introduce a new smartphone. Apple is the current market leader, and Samsung hopes this new product, which revolutionizes the OS, will give them an edge.

The problem is the global economy is heading into a recession, and this smartphone is 1.5x the price of its competitors. Samsung wants to gauge whether this new product is a wise financial decision and whether customers have the purchasing power to make it worthwhile.

The company can't rely on quantitative forecasting alone since inflation has risen in the past two months, and it might not be the best time to launch. Samsung turns to market research to understand how much customers are planning to spend on tech in the next quarter and how they perceive the value of their new, revolutionary product.

Scenario 2: Expanding Into a New Market

A mass fashion retailer like Zara wants to expand into the East Asian market and produce clothes representing local culture. It doesn't want to risk committing a faux pas by wrongly representing local trends, so it turns to qualitative forecasting.

The company looks for local influencers, surveys customers in the new market, and runs focus groups to get an accurate representation of what people want. It learns that launching a new brand instead of marketing existing products is the way to go and that locals respond better to combining traditional and modern elements.

A majority East Asian team is also a better way to approach this expansion since locals are more likely to trust the brand if people from their own culture represent it.

For some sales leaders, using anything besides numerical analysis in sales forecasting can seem intimidating or pointless — but qualitative forecasting offers several advantages that extend beyond those of its quantitative counterpart.

Qualitative forecasting provides relevance and flexibility.

Qualitative forecasting doesn’t care about last year’s sales numbers. Instead, it does care about more timely, relevant information, such as new technology your business has adopted or global trends that may affect the economy.

Qualitative forecasting takes non-numerical events and assigns weight to how they might impact a company's performance and operations — offering that business higher flexibility in its decision-making when those variables take hold.

Qualitative forecasting gives you a broader perspective.

When paired with quantitative forecasting, qualitative forecasting can give a company a holistic look at virtually every factor — both objective and subjective — when considering a significant decision.

This point is particularly relevant to larger companies with historical numerical data and the resources to supplement it with internal or external expertise and market research. With the ability to deliver on both sides of the forecasting token, these businesses can reliably make comprehensive, accurate sales predictions.

Qualitative forecasting works particularly well for new and growing companies.

While larger enterprises likely have reliable quantitative data to pair with qualitative insight, startups, and small businesses might not be so lucky. In most cases, those companies haven't been around long enough to accrue a significant bank of hard sales figures — making qualitative data central to their forecasts.

Though qualitative forecasting has tremendous upsides, it still comes with its fair share of drawbacks.

Qualitative forecasting can be compromised by bias.

Whether a company turns to skilled employees, consultants, or customer insights, it risks compromising insight with bias. Qualitative data is inherently subjective, and subjective information is naturally prone to bias.

Qualitative forecasting is prone to inaccuracy.

Without definite numbers to rely on, qualitative data can produce incorrect results due to manual errors. This point ties into the one above — biased data is generally naturally inaccurate.

For instance, a customer might respond to a survey or poll a business is running to vent about a single negative experience. Or, a manager relying on past experiences to inform forecasts might bring too personal a spin to the process or see past events and trends through a warped lens.

Qualitative forecasting might be invalid.

Hired consultants or expert panels outside the business can provide a different perspective, but their separation from the company could render their forecasts invalid. Companies turning to subjective insights risk receiving illegitimate or irrelevant forecasts.

Alt: Qualitative forecasting pros and cons. Advantages. Provides relevance and flexibility. Gives you a broader perspective. Works well for new and growing companies. Disadvantages. Can be compromised by bias. Is prone to inaccuracy. Might be invalid.IMG name: qualitative-forecasting-pros-cons

Use Qualitative Forecasting for Improved Decision-Making

Any time a business needs to make a decision or step forward, it needs a comprehensive forecast to help set goals, milestones, and expectations. Data analysis can always help guide a business, but quantitative data doesn’t always provide the whole picture.

That’s why qualitative forecasting is so important. It can provide deeper insight that considers varying viewpoints, experiences, and real-world events, letting a company be as prepared as possible to move forward effectively.

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Qualitative Forecasting Methods: Definition, Techniques, Examples

Qualitative forecasting is a method of predicting future outcomes based on expert opinions, market research, and subjective data, rather than solely relying on historical numbers and statistics. It provides insights into market trends, customer behavior, and external factors that may impact sales and revenue.

Key Benefits:

• Provides insights into market trends and customer needs
• Useful when historical data is limited or unreliable
• Leverages expertise from industry professionals
• Complements quantitative forecasting for a comprehensive view

Common Qualitative Forecasting Methods:

When to Use Qualitative Forecasting:

Challenges and Potential Solutions:

To achieve optimal results, qualitative forecasting should be combined with quantitative methods, leveraging the strengths of both approaches for a more accurate and reliable forecasting process.

Related video from YouTube

Qualitative Forecasting Methods

Delphi method.

The Delphi method gathers expert opinions to reach a consensus forecast. It involves a panel of experts who anonymously share their views on a topic. Their responses are summarized and shared with the panel, allowing them to revise their opinions based on the group's feedback. This process repeats until a consensus is reached.

• Reduces bias and dominant personalities' influence
• Encourages diverse perspectives
• Allows anonymous feedback, reducing groupthink risk
• Suitable for long-term forecasting and strategic planning
• Time-consuming and resource-intensive
• Requires a diverse panel of experts
• Challenging to achieve consensus, especially with a large panel

When to Use:

• Long-term forecasting and strategic planning
• Complex or uncertain market conditions
• Need for diverse perspectives
• Identify a diverse panel of experts
• Define the issue or question
• Distribute a questionnaire to the panel
• Summarize responses and provide feedback
• Repeat steps 3-4 until a consensus is reached

Executive Opinion

Executive opinion relies on the judgment and expertise of top-level executives or managers. It involves gathering opinions and insights from executives with a deep understanding of the market, industry, and company.

• Quick and cost-effective
• Leverages executives' expertise and knowledge
• Can provide valuable insights and perspectives
• Subjective and biased opinions
• Limited to executives' knowledge and experience
• Can be influenced by personal agendas and biases
• Quick and rough estimates of future sales or revenue
• Need for high-level insights and perspectives
• Limited time and resources for forecasting
• Identify knowledgeable top-level executives
• Gather their opinions and insights through interviews or surveys
• Analyze and summarize the responses
• Use the insights to inform forecasting decisions

Market Research

Market research involves gathering data and insights from customers, competitors, and market trends. It involves analyzing customer surveys, focus groups, and competitor analysis to understand market dynamics and trends.

• Provides insights into customer needs and preferences
• Helps identify market trends and opportunities
• Can inform product development and marketing strategies
• Can be expensive
• May not provide accurate or reliable data
• New product development or launch
• Market entry or expansion
• Need for customer insights and market trends
• Identify research objectives and scope
• Gather data through customer surveys, focus groups, and competitor analysis
• Analyze and summarize the data

Consumer Surveys

Consumer surveys gather opinions and insights from customers. This method involves asking customers about their needs, preferences, and behaviors to understand market trends and dynamics.

• Need for customer feedback on products or services
• Understanding customer preferences and behaviors
• Identifying market trends and opportunities
• Define the survey objectives and target audience
• Design the survey questions and format
• Distribute the survey to the target audience
• Analyze and summarize the survey responses

Real-World Applications

Qualitative forecasting methods are widely used across various industries to make informed decisions and drive growth. By combining qualitative insights with quantitative data, businesses can gain a more comprehensive understanding of market trends and customer needs.

Retail and E-commerce

In retail and e-commerce, qualitative forecasting helps predict consumer behavior and identify trends. For example, a fashion retailer might gather expert opinions and conduct market research to forecast demand for a new clothing line based on current fashion trends and customer preferences. This information guides inventory management, pricing, and marketing strategies.

Finance and Banking

Financial institutions use qualitative forecasting to predict market trends, identify investment opportunities, and manage risk. For instance, they might gather expert opinions through the Delphi method to assess the potential impact of economic changes on the stock market.

Qualitative forecasting is crucial in healthcare for predicting disease outbreaks, anticipating patient demand, and allocating resources effectively. A hospital might use market research and expert opinions to forecast demand for flu vaccines during a pandemic.

Manufacturing and Supply Chain

Manufacturers use qualitative forecasting to anticipate demand, manage inventory, and optimize production. For example, a manufacturer might conduct consumer surveys and market research to predict demand for a new product and adjust production accordingly.

Technology and Software

In the technology and software industry, qualitative forecasting helps predict market trends, identify opportunities, and inform product development decisions. A software company might gather expert opinions and conduct market research to forecast demand for a new product feature based on current trends and customer needs.

To effectively integrate qualitative forecasting, organizations should:

• Identify the appropriate qualitative method for the specific problem or opportunity
• Gather diverse perspectives from experts and stakeholders
• Analyze data to identify patterns and trends
• Use insights to inform decision-making and drive growth
• Continuously monitor and evaluate the effectiveness of the forecasting method

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Challenges and limitations.

While qualitative forecasting methods offer valuable insights, they also come with some challenges and limitations that need to be considered.

Personal Bias

One major challenge is the influence of personal opinions and biases. When experts or individuals provide their insights, they may unintentionally introduce their own biases, leading to inaccurate or skewed forecasts. To minimize this, it's crucial to gather diverse perspectives from multiple experts and stakeholders. Additionally, structured techniques like the Delphi method can help reduce the impact of personal biases.

Accuracy Concerns

The reliability and accuracy of qualitative forecasts can be a limitation. Since these methods rely on expert opinions and subjective data, there is always a risk of inaccuracy. To improve accuracy, it's essential to use multiple qualitative methods, validate the results with quantitative data, and continuously monitor and evaluate the forecasting process.

Obtaining Expert Input

Sourcing expert insights can be challenging, especially in industries where expertise is scarce or difficult to access. To overcome this, consider using alternative sources of expertise, such as industry reports, academic research, or online forums. Additionally, online platforms or tools can facilitate the collection of expert opinions more efficiently.

Time and Resource Needs

Qualitative forecasting methods can be time-consuming and resource-intensive, especially when gathering expert opinions or conducting market research. To manage these resources effectively, prioritize the most critical areas of forecasting, allocate sufficient time and resources, and use tools and platforms that streamline the process.

In today's fast-moving business world, qualitative forecasting methods play a key role in helping companies make smart decisions. By gathering expert opinions, market research, and customer feedback, qualitative forecasting provides insights into market trends, customer behavior, and external factors that can impact sales and revenue.

Throughout this guide, we explored various qualitative forecasting techniques, including:

• The Delphi Method : Gathering anonymous expert opinions to reach a consensus forecast.
• Executive Opinion : Relying on insights from top-level managers and executives.
• Market Research : Analyzing customer surveys, focus groups, and competitor data.
• Consumer Surveys : Gathering opinions and preferences directly from customers.

While these methods offer valuable insights, they also come with challenges:

To achieve optimal results, it's crucial to combine qualitative forecasting with quantitative methods. By leveraging the strengths of both approaches, businesses can create a more accurate and reliable forecasting process.

As the business landscape evolves, the importance of qualitative forecasting will continue to grow. We encourage readers to explore and incorporate these techniques into their forecasting processes, while acknowledging the significance of combining them with quantitative methods for best results. By doing so, businesses can gain a competitive edge, make informed decisions, and drive success in today's fast-paced market.

What is an example of a qualitative forecast?

A qualitative forecast is a prediction based on opinions, research, and feedback rather than just numbers. Here are some examples:

1. New Product Launch

A company plans to launch a new product. They conduct consumer surveys to gather opinions on features, pricing, and marketing. This feedback helps them make decisions about the product's development and launch.

2. Market Trend Predictions

A company uses the Delphi method to gather anonymous expert opinions on future market trends. This information helps them make strategic decisions about investments and resource allocation.

In both cases, qualitative forecasting provides insights into market trends, customer behavior, and factors that can impact sales and revenue. By using these insights, businesses can make informed decisions and drive success.

Advantages of Qualitative Forecasting

Challenges of Qualitative Forecasting

While qualitative forecasting offers valuable insights, it's essential to address these challenges and combine it with quantitative methods for optimal results.

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• A Comprehensive Guide to Qualitative Forecasting

• August 4, 2021

Rayomand Engineer

Planning organizational objectives for the future? You need a forecasting process. Setting targets and predicting the success of upcoming campaigns is not easy. While you mostly rely on numbers, they alone cannot give you a holistic view of your company’s current or potential performance. You have to go way beyond just numbers or quantitative forecasting if you want to make effective decisions for your company. 

Qualitative forecasting can help executives make predictions about company finances, based on expert judgments. It is performed by analyzing past and future operations. This allows executives to predict how the company may perform in the future. The information they look at is collected from sources like staff polls or market research. Accounting for both sides of the forecasting process can help you set accurate targets and put plans in motion. Through qualitative forecasting, you can understand customer and market behavior in a deeper way, which is particularly helpful if your company is exploring new sales methods. As your company grows, qualitative forecasting will help you make sound decisions and reliably predict what your sales might be. 

Why Do You Need Qualitative Forecasting?

Need to decide how many new hires to make, or how much inventory to keep? Or how to adjust sales operations to be more efficient? Or maybe you’re curious about which feature of your company’s product or service works best in advertisements. Qualitative forecasting can help you find out that, and more. You can rely on sources apart from numerical data, and predict trends accurately. Here’s why you need it. 

It’s the Other Side of the ‘Forecasting Coin’

In conjunction with quantitative forecasting, qualitative forecasting can give you a holistic perspective of both subjective and objective factors, before you make a business decision. This is very useful for bigger companies with reams of numerical data that can supplement it with market research and expertise. This helps the business put out comprehensive, accurate sales predictions. 

Uses History to Help With the Future

There’s a saying that those who don’t learn from history are doomed to repeat it. Qualitative forecasting prevents just that by forcing business owners to take into account their firm’s past performance, and not dismissing any anomalies as a one-time thing. Qualitative forecasting allows you to use objective, quantifiable historical data to create sales projections, expense predictions, and revenue forecasts based on your firm’s history. This is a great way to prepare for worst-case scenarios if one ever occurs. 

Good for Small Businesses and Startups

As mentioned earlier, bigger companies with huge bundles of data could benefit from qualitative forecasting; even smaller businesses that don’t have many numerical data could make use of qualitative forecasting to help them achieve their business goals and objectives. In the face of a lack of volume of data, other factors might help these firms make decisions. 

Great for Attracting Stakeholders

Let’s face it. Hard numbers based on data will always make your pitches more attractive, whether you’re trying to find a loan, attract investors, secure a new line of credit, expand with a partner, or even sell your firm. Investors usually feel more secure when they see solid numbers that point toward a logical forecast. They may not be lured by vague ambiguous statements like “our past experience sells us.” They want you to back that statement up with data. 

Recognize Patterns and Make Better Decisions

Computer programs have made it possible for us to whittle down to the most useful data to make accurate projections. For example, even a seemingly simple Excel sheet can help you find useful patterns like changes in sales over a year or more. You can segregate data by date, customer, vendor, or whatever parameters you want. You could predict production costs based on a pattern found over the last 5 years, or more. This can help you make sound business decisions.

 How to perform Qualitative Forecasting

There are several methods of doing this. They include – 

The Delphi Method

In this method, a panel of experts is individually questioned, soliciting their opinion one at a time. This is done to avoid any bias so business predictions aren’t affected by personal opinions. Other employees then study these responses, replying back with their own analyses and queries. The teams then settle on a prediction that is practical for the firm and move forward from there. This method is great because it involves questioning multiple people about sales forecasts separately, cleverly avoiding group thinking or off-hand and collaborative opinions. However, this method leads to a lack of consensus because many experts can offer widely different perspectives, making it tough to put together a sensible qualitative forecast. 

The survey is the old tried and tested, and the highly accessible option of performing effective qualitative forecasting. You get your data from your consumers, and that firsthand insight can help you penetrate new markets or study your target audience’s behavior. There are so many ways of creating and distributing surveys, and most of these collect data about ‘experience.’ Emails, cold calls, or inviting clients to the office for personal interviews can help firms collect information, which they can use to make useful predictions about a company’s future, based on data from their existing customers.

Market Research

Using market research, you can evaluate the success of your company’s products or services by introducing them to consumers and recording the reactions. Companies can either use their own employees or outside agencies and conduct market research in focus groups or blind product testing. Companies can study consumer reactions to decide what products or services to push forward and which ones to revise to better meet consumers’ needs. Market research is a great insight into what potential new consumers want from your organization. 

Outsource it to a Consultancy

Sometimes, the best thing to do is to leave qualitative forecasting to the experts, especially if you’re a start-up or a small business, with limited experience and without the means to put together reliable qualitative forecasts. Many companies outsource their qualitative forecasting responsibilities to consultancies that have a better insight into the industry, or markets. 

Like all forecasting techniques, qualitative forecasting too has a few drawbacks. 

Some of them are – 

• Bias clouds subjective information, and qualitative data is inherently subjective. 
• Humans are error-prone, and without concrete numbers, qualitative forecasting could produce inaccurate results. 
• A consultancy might not really know the company well enough to perform a detailed or accurate enough analysis. 

In conclusion, qualitative forecasting is indispensable for an organization. If you want your firm to move toward its goals, you need a proper forecast to set those goals, and break them down into milestones, etc. While analyzing numbers can help your firm to a certain extent, qualitative forecasting provides that three-dimensional insight that brings to the table varied experiences, and a tinge of the real world into strategy and decision-making so companies can prepare for any scenario. 

Final Thoughts

Qualitative forecasting is used to help companies make sales and marketing decisions. Health care employees use it to find trends in public health, colleges rely on it to predict student trends. It finds use in construction, agriculture, and virtually every other industry with managers relying on it to reach business goals. Read our blog on how you can leverage social media to amplify your business, use the internet to your advantage, to make more sales.  

I am a writer based out of Kolkata, West Bengal, and I like to write on tech, politics, travel, music, environment, and wildlife amongst others. I’ve also written scripts for branded content, and also scripts for short films. I’ve been writing for more than a decade and I love it.

2.2.1 Qualitative Forecasting Methods

The three primary approaches used in qualitative forecasting are the expert opinion approach, the Delphi method, and the market survey approach.

The expert opinion approach is simple and easy to implement. For example, for many of the stand-alone, one-time activities that take place in a project, an opinion based forecast is all that is either necessary or desirable. The opinion of the person who is most knowledgeable in that field is sought. Furthermore, if a project is brand new, the likes of which have never been seen before and for which no historical data is available, then the only recourse for a project manager is to seek the opinion of an expert to get a forecast or an estimate regarding the concerned event or activity.

The disadvantage of relying on the opinion of a single expert is the inherent element of bias. Further, larger issues in the project may arise where an opinion based forecast of a single expert may be not be adequate. This can occur with forecasts involving such things as the timing of the introduction of a new technology into the market place or a change in public behavior as these could have a significant bearing on the decision to start a project or the timing of market entry. When a new product is introduced it can become a guessing game as to how the market will respond and how and when competitors might respond. Answers to questions such as these may require the opinions of several experts, perhaps across a range of subjects, not simply an opinion from those closest to the job. In such cases, the Delphi method may an appropriate forecasting method.

Devised by the Rand Corporation in the U.S., the Delphi technique is a popular method of qualitative forecasting that generates a view of the future by using the knowledge of experts in particular fields. The name derives from the ancient Greek Oracle of Delphi that was supposed to foretell the future. The steps of the Delphi method are as follows:

• Questionnaires are circulated to the team members, who may not be aware of each others' identities, and each is invited to make his own prediction of future progress in a particular field. As far as possible, projections must be quantified and the questions must be framed accordingly: e.g., what proportion of all households do you expect to have a personal computer by the year 2010?
• After the first round of replies, the results are analyzed statistically (giving the distribution of responses) and the results re-circulated; panel members are asked to reconsider their views in the light of the new statistics. If their view lies outside the inter-quartile range, they must either revise their opinion or give their reasons for their extreme view; this will be seen by the other panel members. This process can be repeated for a third or fourth round until a consensus of opinion is obtained.

Results of Delphi studies are given in the form of timescales and probability levels for the feature being forecast. Some large corporations have used the method for assessing long term trends and the development strategies that may be open. Research by the Rand Corporation indicates that with current technologies and trends, the Delphi panel does tend to move towards a consensus view which is generally correct, but there tends to be less accuracy when forecasting new developments. On occasions, no consensus view is obtained after several rounds.

The market survey approach is the third qualitative approach that can be used to generate forecasts of project events. This approach involves surveying past customers or potential customers about any plans they may be considering the future. The project organization's marketing staff is perhaps the ideal source to obtain such information because of their direct contact with customers. In addition, the marketing staff, along with the procurement staff, which is in direct contact with suppliers, can also provide market intelligence reports regarding competitors who are contemplating new projects or new technologies.

Mastering Predictions: Understanding Qualitative Forecasting Techniques

Qualitative forecasting techniques are methods used by businesses to predict future outcomes based on expert judgment, opinions, and subjective assessments rather than historical data and statistical models. These techniques are valuable when historical data is limited or when factors such as market trends, consumer preferences, and technological advancements play a significant role in shaping future events.

Key Points about Qualitative Forecasting Techniques

• Definition: Qualitative forecasting techniques rely on subjective assessments, expert opinions, and judgment to forecast future outcomes. These methods are particularly useful in situations where historical data is unavailable, unreliable, or insufficient for making accurate predictions.
• Expert Opinion: Expert opinion involves gathering insights and predictions from individuals with expertise and experience in a particular industry or field. Experts use their knowledge, intuition, and judgment to forecast future trends, market conditions, and events.
• Delphi Method: The Delphi method is a structured approach to gathering and synthesizing expert opinions on a particular topic. It involves multiple rounds of anonymous surveys or questionnaires, with feedback from each round used to refine and converge on a consensus forecast.
• Market Research: Market research techniques, such as surveys, focus groups, and interviews, gather insights from consumers, stakeholders, and industry experts regarding future market trends, consumer preferences, and competitive dynamics.
• Scenario Planning: Scenario planning involves developing multiple plausible scenarios or narratives about future events and their potential impact on business operations. By considering various scenarios, businesses can prepare for a range of possible outcomes and develop flexible strategies.
• Flexibility: Qualitative techniques are flexible and adaptable to diverse situations, allowing businesses to incorporate new information and insights into their forecasts.
• Subjective Insights: Qualitative methods capture subjective insights, expert opinions, and contextual factors that quantitative models may overlook, providing a more holistic view of future possibilities.
• Useful in Uncertain Environments: Qualitative techniques are particularly useful in environments characterized by uncertainty, volatility, and rapid change, where historical data may not accurately reflect future conditions.
• Early Warning Signals: Qualitative techniques can identify emerging trends, risks, and opportunities before they manifest in historical data, enabling businesses to proactively respond to changing market dynamics.
• New Product Launch: A technology company planning to launch a new smartphone may use qualitative forecasting techniques to anticipate consumer preferences, competitor actions, and market trends. The company could conduct expert interviews with industry analysts, gather consumer feedback through focus groups, and explore various scenarios to assess potential demand and market reception for the new product.

Conclusion: Qualitative forecasting techniques provide businesses with valuable insights and predictions in situations where historical data is insufficient or unreliable. By leveraging expert judgment, market research, and scenario planning, businesses can make informed decisions and better prepare for future uncertainties.

Reference: Armstrong, J. S. (Ed.). (2001). Principles of forecasting: A handbook for researchers and practitioners. Springer Science & Business Media.

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Qualitative Forecasting

In the business world, the ability to predict future trends and outcomes is an invaluable skill. Enter qualitative forecasting: a method that leverages subjective data and expert opinions to forecast future events. By analyzing qualitative factors such as consumer behaviors, market trends, and industry insights, businesses can gain a deeper understanding of the factors that influence their success. This article explores the concept of qualitative forecasting, its advantages, and how it can help organizations make informed decisions in an ever-changing landscape.

Table of Contents

Defining Qualitative Forecasting

What is qualitative forecasting.

Qualitative forecasting is a method used to predict future events or outcomes based on subjective factors, such as expert opinions, market trends, and customer preferences. It involves gathering information and insights from individuals or groups well-versed in the subject matter, rather than relying solely on historical data or statistical models. Qualitative forecasting is often employed in situations where quantitative methods may not be applicable or sufficient.

How is qualitative forecasting different from quantitative forecasting?

Qualitative forecasting differs from quantitative forecasting in several key ways. While quantitative forecasting relies on historical data, mathematical models, and statistical techniques, qualitative forecasting takes into account subjective information and experiential knowledge. Quantitative forecasting aims to provide precise numerical predictions, whereas qualitative forecasting provides insights and perspectives that may not be easily quantifiable. Qualitative forecasting is especially useful in uncertain or rapidly changing environments where historical data may not accurately reflect current conditions.

Advantages of Qualitative Forecasting

Incorporating expert opinions.

One of the advantages of qualitative forecasting is the ability to incorporate expert opinions in the forecasting process. By consulting individuals with deep knowledge and experience in a particular field, qualitative forecasting can leverage their insights and expertise to make informed predictions. Expert opinions add value to the forecasting process by providing unique perspectives, identifying potential risks and opportunities, and offering guidance on decision-making based on their knowledge and understanding.

Flexibility in handling unique situations

Another advantage of qualitative forecasting is its flexibility in handling unique and complex situations. In scenarios where historical data is scarce or unreliable, qualitative forecasting allows for adaptation and customization. It enables forecasters to consider various factors that may affect the outcome, such as market conditions, industry trends, and political or social factors. This flexibility allows for adjustments and refinements to the forecast based on new information or changing circumstances.

Understanding market trends and customer preferences

Qualitative forecasting provides valuable insights into market trends and customer preferences. By analyzing qualitative data from market research or expert opinions, organizations can gain a deeper understanding of their target audience, their needs, and their preferences. This understanding can help businesses design effective marketing strategies, develop appealing products and services, and make informed decisions regarding pricing, packaging, and distribution. Qualitative forecasting allows organizations to stay ahead of their competition by anticipating customer demands and adapting to changing trends.

Methods Used in Qualitative Forecasting

Panel consensus.

Panel consensus is a method of qualitative forecasting that involves gathering a group of experts or stakeholders to discuss and reach a collective forecast. The panel members share their knowledge and insights, and through a structured process, the group works towards a consensus forecast. This method ensures that multiple perspectives and expertise are considered, helping to mitigate individual biases and enhance the accuracy of the forecast. Panel consensus is commonly used in scenarios where a range of opinions and viewpoints is valuable, such as economic forecasts or strategic planning.

Market Research

Market research is a vital method used in qualitative forecasting to collect and analyze data related to customer preferences, market trends, and competitor analysis. It involves gathering information through surveys, interviews, focus groups, and observation. Market research helps organizations understand their target market, identify emerging trends, and make informed decisions based on customer feedback and preferences. By analyzing qualitative data from market research, organizations can gain valuable insights and develop effective forecasting strategies.

Delphi Method

The Delphi method is a structured approach to qualitative forecasting that involves multiple rounds of anonymous surveys and feedback to reach a consensus forecast. In the Delphi method, experts or stakeholders provide their forecasts and reasoning in each round. The responses are then summarized and shared anonymously in subsequent rounds until a consensus is reached. This method allows for unbiased opinions and encourages experts to reconsider their forecasts based on the input of others. The Delphi method is commonly used in industries where individual expertise is crucial, such as technology forecasting, healthcare, and engineering.

Scenario Analysis

Scenario analysis is a qualitative forecasting technique that involves creating plausible scenarios to explore different potential outcomes. By considering a range of possible events and their potential impacts, organizations can develop a more comprehensive understanding of the future and its uncertainties. Scenario analysis helps decision-makers quantify the potential impacts of specific events and identify strategies to mitigate risks or seize opportunities. This method allows planners and forecasters to think creatively and develop strategies based on multiple future possibilities.

Historical Analogy

Historical analogy is a qualitative forecasting method that involves understanding past events and patterns and applying those lessons to future situations. By comparing similar situations in the past and their outcomes, forecasters can draw insights and make predictions about the future. Historical analogy allows forecasters to consider historical precedents, identify patterns, and assess the likelihood of certain outcomes. However, it is important to note that historical analogy has limitations, as past events may not always repeat themselves in the same way, and new factors or dynamics may come into play.

Definition and process

Panel consensus is a method of qualitative forecasting that involves assembling a group of experts or stakeholders to collectively reach a forecast. The panel members bring their expertise and insights to the discussion, sharing their knowledge and perspectives on the topic at hand. The process typically involves a structured discussion or a series of meetings, where the panel members present their forecasts, discuss them, and work towards a consensus. The goal of panel consensus is to pool together diverse viewpoints and expertise to arrive at a more accurate and informed forecast.

Advantages and limitations

Panel consensus offers several advantages over other forecasting methods. By involving multiple experts, panel consensus can help mitigate individual biases and minimize the influence of outliers. The diverse range of perspectives can enhance the accuracy of the forecast by considering a broad array of factors and insights. Panel consensus also fosters collaboration and dialogue, allowing experts to challenge each other’s assumptions and build a more robust forecast.

However, panel consensus also has some limitations. It can be time-consuming and resource-intensive, as it requires coordinating the schedules of multiple experts and organizing structured discussions. The process of reaching consensus can sometimes be challenging, as individuals may have conflicting opinions or hidden agendas. Additionally, the accuracy of the forecast heavily relies on the expertise and knowledge of the panel members. If the forecasters lack relevant experience or information, the forecast may be less reliable.

Real-life applications

Panel consensus is commonly used in various fields, including economic forecasting, strategic planning, and policy development. For example, government agencies often convene panels of economists, industry experts, and policymakers to forecast economic indicators, such as GDP growth or unemployment rates. These panels contribute their expertise and insights to inform important policy decisions. Panel consensus is also utilized in the technology industry when forecasting emerging trends or estimating the market potential of new innovations. The collective wisdom of experts can help identify promising opportunities and shape strategic decisions.

Collecting and analyzing data

Market research is a fundamental method used in qualitative forecasting to gather information and insights about the market, customers, and competitors. It involves collecting data through various techniques, such as surveys, interviews, focus groups, and observation. Market research enables organizations to understand customer behavior, preferences, and buying patterns, which are essential factors in forecasting future demand or market trends.

Determining customer preferences

One of the key objectives of market research in qualitative forecasting is determining customer preferences. By analyzing qualitative data obtained from surveys or interviews, organizations can gain insights into what customers value and desire in a product or service. Understanding customer preferences allows organizations to tailor their offerings to meet customer needs, which in turn improves the accuracy of product demand forecasts.

Identifying emerging trends

Market research plays a crucial role in qualitative forecasting by identifying emerging trends in the market. By analyzing qualitative data and observing market dynamics, organizations can detect shifts in customer behavior, preferences, or industry practices. This insight allows forecasters to anticipate future trends, such as the adoption of new technologies, changing consumer habits, or emerging lifestyle trends. Identifying emerging trends through market research helps organizations stay ahead of the competition and make timely strategic decisions.

Challenges and considerations

While market research provides valuable insights, there are challenges and considerations to keep in mind. Collecting accurate data can be time-consuming and resource-intensive, as it requires careful planning and coordination. Additionally, interpreting qualitative data may be subjective and open to various interpretations, making it essential to ensure data validity and reliability. Market research also requires staying abreast of changing trends and evolving customer preferences, as forecasts based on outdated information may be less accurate. Finally, privacy and ethical considerations need to be taken into account to ensure that customer data is collected and used responsibly.

Sequential rounds of anonymous surveys

The Delphi method is a qualitative forecasting technique that involves a series of anonymous surveys conducted with a group of experts or stakeholders. In each round, participants are asked to provide their forecasts and reasoning on a specific topic. The responses are then summarized and shared anonymously in subsequent rounds, allowing participants to revise their forecasts based on the input from others. The goal of the Delphi method is to reach a consensus forecast by iteratively refining individual opinions.

Expert opinions and consensus-building

The Delphi method leverages the expertise and insights of individuals who possess relevant knowledge in a particular field. By ensuring anonymity and equal participation, the method encourages participants to provide honest and unbiased forecasts. The iterative nature of the Delphi method allows for the development of consensus through the exchange of opinions and feedback. The Delphi method aims to reduce biases and foster the collective wisdom of experts, resulting in more accurate and informed forecasts.

Applications in various industries

The Delphi method finds applications across various industries. In healthcare, it is used to forecast the demand for medical services or to reach a consensus on treatment guidelines. In technology forecasting, the Delphi method helps identify emerging trends and estimate future market potential. In strategic planning, the method is utilized to assess risks and uncertainties and develop contingency plans. The Delphi method’s ability to leverage expert knowledge and foster consensus makes it a valuable tool in situations where individual opinions need to be reconciled.

Benefits and drawbacks

The Delphi method offers several benefits in qualitative forecasting. It allows for anonymity, encouraging participatory and candid responses from experts. By incorporating multiple rounds of feedback, the Delphi method enables experts to revise and refine their forecasts based on the input of others. The method mitigates the influence of dominant personalities or biases, resulting in more well-rounded and reliable forecasts.

However, the Delphi method also has some drawbacks. The iterative process can be time-consuming, as each round requires coordination and analysis of responses. Delays in obtaining consensus may reduce the usefulness of the forecast, particularly in rapidly changing environments. The quality of the forecast heavily depends on the expertise and knowledge of the participants; if the participants lack relevant experience or information, the forecast may be less accurate. Additionally, the method may be susceptible to groupthink or convergence towards the average, potentially limiting creativity and challenging unconventional ideas.

Creating plausible scenarios

Scenario analysis is a qualitative forecasting method that involves creating plausible narratives or stories about the future. Scenarios are developed to capture different potential outcomes, taking into account various factors and uncertainties. Through scenario analysis, forecasters explore a range of possible futures and develop a deeper understanding of the uncertainties and dynamics at play.

Exploring different outcomes

The primary goal of scenario analysis is to explore different potential outcomes and their implications. By considering a variety of scenarios, forecasters can break free from conventional thinking and challenge assumptions about the future. Scenarios provide a framework for thinking about alternative futures and help forecasters identify critical drivers of change and potential turning points.

Quantifying impacts of potential events

Scenario analysis helps forecasters quantify the impacts of potential events or trends on the forecasted outcomes. By specifying the assumptions, drivers, and uncertainties associated with each scenario, forecasters can assess the potential risks and opportunities that may arise. Scenario analysis allows for a more nuanced understanding of the range of possible outcomes and helps forecasters prioritize actions based on the likelihood and impact of each scenario.

Applying scenario analysis to forecasting

Scenario analysis can be a powerful tool in qualitative forecasting. It helps forecasters anticipate and prepare for a wide range of future possibilities, allowing for more proactive decision-making. By considering multiple scenarios, organizations can identify vulnerabilities, devise contingency plans, and develop strategies to seize opportunities. Scenario analysis provides a holistic perspective and enhances the resilience of forecasting by considering not only the most likely future but also various alternative outcomes.

Understanding past events and patterns

Historical analogy is a qualitative forecasting method that relies on understanding past events and patterns to make predictions about the future. By examining similar situations from the past and their outcomes, forecasters draw insights and identify patterns that can inform current forecasting efforts. Historical analogy recognizes the value of historical experience and knowledge in guiding future decision-making.

Applying lessons learned to future situations

Historical analogy allows forecasters to apply lessons learned from the past to future situations. By understanding how similar events unfolded and their consequences, forecasters can adapt strategies, mitigate risks, and capitalize on opportunities. Historical analogy provides a contextual framework for decision-making and enables forecasters to avoid repeating past mistakes or to leverage successful strategies.

Comparing similar situations

To apply historical analogy effectively, forecasters compare similar situations and identify relevant parallels. This can involve analyzing historical data, studying case studies, or engaging in rigorous historical research. By identifying factors that were critical in shaping past outcomes and comparing them to the current situation, forecasters gain valuable insights into how the future may unfold.

Limitations and alternative approaches

Despite its benefits, historical analogy has limitations. Past events may not always repeat themselves in the same way, and new factors, technologies, or dynamics may come into play. Historical analogy requires careful consideration of the context and a nuanced understanding of the similarities and differences between the past and the present. To complement historical analogy, other qualitative forecasting methods, such as scenario analysis or expert opinions, can be used to provide a broader perspective and balance the limitations of relying solely on historical analogies.

Factors Influencing Qualitative Forecasting

Expertise and experience of forecasters.

The expertise and experience of forecasters play a crucial role in qualitative forecasting. Forecasters with deep knowledge and experience in a particular field are likely to provide more accurate and insightful predictions. Expertise allows forecasters to analyze complex information, recognize patterns, and make informed judgments about future outcomes. Organizations should ensure that the forecasters involved possess the necessary domain knowledge and experience to achieve reliable qualitative forecasts.

Availability and quality of data

The availability and quality of data influence the accuracy and reliability of qualitative forecasts. Market research, surveys, and other data collection methods need to be carefully planned and executed to capture relevant and reliable information. Adequate sample sizes, appropriate survey designs, and robust data analysis techniques are essential for obtaining accurate insights. Additionally, the timeliness of data is crucial in rapidly changing environments where outdated information may lead to less accurate forecasts.

Industry dynamics and market conditions

Industry dynamics and market conditions significantly affect the accuracy of qualitative forecasts. Change in market dynamics, such as emerging trends, disruptive technologies, or regulatory shifts, can have a substantial impact on the forecasted outcomes. Forecasters should continuously monitor and assess industry trends and market conditions to ensure the accuracy and relevance of qualitative forecasts. Understanding the competitive landscape, customer behaviors, and supply chain dynamics is crucial for informing qualitative forecasts accurately.

Economic, social, and political factors

Economic, social, and political factors are essential considerations in qualitative forecasting. Changes in economic conditions, such as recessions, inflation, or political instability, can significantly impact future outcomes. To produce accurate qualitative forecasts, forecasters need to closely monitor these factors and consider their potential effects on the forecasted variables. Economic forecasts, consumer sentiment analysis, and political risk assessments are vital inputs for qualitative forecasting.

Technological advancements

Technological advancements are a significant factor influencing qualitative forecasting. New technologies can disrupt industries, change consumer behaviors, and create significant market shifts. Forecasters should closely monitor technological advancements and their potential implications for their respective industries. By understanding the impact of technology on market dynamics, forecasters can make more accurate predictions and develop strategies that embrace or respond to technological changes.

Summary of qualitative forecasting

Qualitative forecasting is a method of predicting future events or outcomes based on subjective factors, such as expert opinions, market trends, and customer preferences. It differs from quantitative forecasting by incorporating subjective information and experiential knowledge rather than relying solely on historical data or statistical models. Qualitative forecasting provides valuable insights into future scenarios and helps organizations make informed decisions in uncertain or rapidly changing environments.

Application areas for qualitative forecasting

Qualitative forecasting finds applications in various fields, including economic forecasting, strategic planning, innovation management, and policy development. It is particularly useful when historical data is scarce, unreliable, or insufficient to predict future outcomes. Market research, panel consensus, scenario analysis, historical analogy, and the Delphi method are some of the methods commonly used in qualitative forecasting. Each method has its advantages, limitations, and best use cases, offering organizations flexibility in selecting the most appropriate method for their specific forecasting needs.

Considerations for using qualitative forecasting

When employing qualitative forecasting methods, organizations should consider several factors to enhance the accuracy and reliability of their forecasts. The expertise and experience of forecasters are vital, as they significantly influence the quality of the forecasts. The availability and quality of data play a crucial role in obtaining accurate insights, while industry dynamics, market conditions, economic factors, and technological advancements should be carefully analyzed and monitored. By considering these factors, organizations can leverage qualitative forecasting techniques to make informed decisions, anticipate market trends, and gain a competitive advantage.

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Home Market Research

Qualitative Market Research : The Complete Guide

Content Index

What is Qualitative Market Research?

Qualitative market research methods and techniques, 4 types of qualitative market research testing methods, examples of qualitative market research.

• Ethical Considerations for Qualitative Market Research?

What are the Applications of Qualitative Market Research?

Advantages of qualitative market research, disadvantages of qualitative market research, online qualitative market research software- questionpro communities.

Qualitative market research is an open ended questions (conversational) based research method that heavily relies on the following market research methods : focus groups, in-depth interviews, and other innovative research methods. It is based on a small but highly validated sample size, usually consisting of 6 to 10 respondents .

The small size enables cost saving, while the “importance” of the samples and the lack of a defined questionnaire allows free and in-depth discussion and analysis of topics. Usually, the discussion is directed by the discretion of the interviewer or market researcher. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

It is always better to have more heads than one. By canvassing a group of respondents for ideas and competence the quality of the data that is obtained is far more superior. This concept is known as crowdsourcing, derived from the two words “crowd” and “outsourcing”.

LEARN ABOUT: Perceived Value

Qualitative market research is most frequently used in political campaigning to understand voter perception of political candidates and their policies, interviewing business leaders and diving deeper into topics of interest, psychological profile studies and so on.

Qualitative market research is a relatively less expensive method to understand 2 critical factors in details – “what” the respondents think and feel about a certain topic and “why” they think and feel that way.

LEARN ABOUT:  Market research industry

Why do we ask for an opinion? Any opinion for that matter? We ask because the person’s opinion matters to our decision making. None of the successful organizational decisions are made through mere guesses or speculations, but through real information gathered from real and valuable people.

Market research , in general, has played a critical role in inducing a thought process in present day’s organizational leaders where information and data dictate policies and decisions.

However, in market research design , not all information is just numbers and quantitative research . Some are just – conversational and qualitative!

LEARN ABOUT: Research Process Steps

Remember the super hit series Desperate Housewives? And do you remember the lovely housewives calling their friends over for a cup of tea or a couple of drinks to discuss the flashy new products they have bought?

It is not just a vague practice to flaunt these products but a thoughtful one because it matters what the friends think. Whether, they agree or disagree with the quality, brand and other features of those products. It matters what people think. Voila! Welcome to the world of qualitative market research.

Qualitative market research is all about understanding people’s beliefs and point of views and what they feel about the situation and what are the deciding factors that influence their behavior.

LEARN ABOUT: Marketing Insight

To conduct qualitative market research usually, one of these market research methods are used:

• Focus groups: As the name suggests, a group of people comprising usually of  6-10 members are brought together to discuss a particular product and its market strategies. Usually, experts in that particular field will comprise of the group. This group will have a moderator who will stimulate the discussion amongst the members to derive opinions. Since the focus groups are becoming a rare occurrence, platforms like Communities is on the rise.
• In-depth Interviews: It is usually a one-on-one interview method conducted with a group of people, either face to face or over the telephone. This method is more conversational and asking open-ended questions helps gather better data.
• Innovative research methods: In this method, the researcher can click photographs of the person who is answering the questions or can even record their videos. Observing these photographs or videos later would tell the researcher about their responses/reactions to various situations.
• Observations or “Shop-alongs”: Qualitative Observations or shop-alongs are now becoming an increasingly used research method in qualitative market research. This method allows the researcher to observe from afar and actually see how a consumer reacts to an actual product and purchase experience. This mitigates the scope to be dishonest with feedback or even forget about the shopping experience at a later stage.

LEARN ABOUT: Qualitative Interview

• Lifestyle Immersion: A newer method of conducting qualitative market research is attending a social or family event that user/s are at and collecting feedback. This helps with the getting feedback from users when they are in a comfortable environment. This is a great way to collect candid feedback in a comfortable environment.
• Online Focus Groups: With the ease of access to social media, online focus groups are becoming easier to manage. It is easy to recruit people to a focus group based study and even manage data collection and analytics.
• Ethnography: Ethnographic research is the process of being in an end user environment and seeing the user indulge with a product in a real-life example. This qualitative research method is best positioned to help create immediate and impactful product tweaks.  
• Projective Techniques: Projective techniques are conducted by trained moderators who uncover hidden thoughts of the respondents. The questions or questioning methods are of an indirect nature and the moderator then deduces and uncovers underlying feelings that aren’t explicitly mentioned.  
• Online Forums: Online forums is now becoming an increasingly preferred way of conducting qualitative market research. Members in a panel are brought onto a common platform to discuss a certain topic and the moderator ensures the discussion is driven in the direction of the outcome required. The moderator probes, asks the right questions and coerces to ensure a thorough discussion is conducted.
• Online Sentence Completion and Word Association: One of the easier but exhaustive nature of completing qualitative market research is to get respondents to match words that may be related to a product or even complete sentences online and this provides a deeper insight into the thoughts of the user.

Learn more about qualitative research methods 

Here are the steps involved in conducting qualitative market research:

• Planning & Determining research objectives: Each research study needs to have a desired outcome at the outset so that the resources behind planning and executing are not wasted and it helps towards business agility.
• Deciding the method to conduct the research: Qualitative market research can be conducted in many ways. Depending on the nature of the study, target audience demographics , geographical location, a product that is being surveyed etc., would the survey method be utilized.
• Getting the right personnel for the job: Conducting a qualitative market research study requires moderators that know how to elicit and track responses from potential respondents.
• Purposive Sampling : In this method, the sample is created with a purpose in mind. The contours of the demographics are planned well in advance and users that fit this criterion are onboard for the market research survey.
• Quota Sampling: Quota sampling is the process of selecting samples from a given quota and the selected users are said to be a representative of the larger population. This can be a random sampling or put some qualifying criteria in.
• Snowball Sampling:   Snowball  sampling model is based on a reference model. Users that match criteria are asked to refer users that they are personally aware of that match the criteria.
• Survey design: The survey has to be designed in a way to elicit maximum value so that the responses received build towards robust and actionable feedback.
• Data collection: The data collection can be done via online or offline methods. It is imperative to collect the data in such a way that sense could be made of it and it could be used to analyze and report.
• Data Analysis: Data means nothing if it is not analyzed. Data that has been analyzed can give actionable insights for a product or brand to build on and this is imperative for a qualitative marketing research survey.
• Reporting: Once data has been collected and analyzed, it has to be reported in an easy to consume format to the relevant stakeholders as a milestone in the market research process.

LEARN ABOUT: Steps in Qualitative Research

There are 4 distinct types of qualitative market research testing methods that can be conducted. They are:

• Direct Exploration: This qualitative market research method is a no holds barred feedback method for a potential idea or product. This method is conducted where the users are told about the idea where no physical product is provided and all possible feedback is collected. This feedback is then collected and explored to form the basis of the new product.
• Monadic Testing: This method evaluates feedback by providing users with one single idea, concept, feature or product and asks for feedback. In this method, despite there being multiple concepts available, other designs are not shown. This method is important to elicit individual piece of feedback about a desired feature or concept.
• Sequential Monadic Testing: This testing method is similar to monadic testing because each concept, product or feature is shown one time. The only difference is that an alternate design to each concept is shown at the same time and feedback is collected on both from a user. This testing method is also called paired testing or paired nomadic testing.
• Discrete Choice Testing: Discrete choice testing is like paired nomadic testing but the only difference is that all choices are provided at once, not sequentially and the users are asked to pick one feature over another and then explain their choice.

LEARN ABOUT: User Experience Research

Successful businesses tend to use qualitative market research to keep pace with the ongoing market trend analysis , to make better-informed decisions and to achieve business excellence.

Whether your business is a start-up or a well-established entity, qualitative market research is a powerful method to identify your target audience and understand how they will respond to your product.

Before we dig deeper here are some of the real-time examples of qualitative market research case studies:

• AP, Norc and QuestionPro partner on geolocation exit polling app
• Washington State Ferry

Some examples of business expansion where qualitative market research plays a critical role by crowdsourcing concrete ideas for optimized decision-making :

• Branding : Many companies fail to understand how consumers perceive their brand or what is the brand positioning in comparison to their competitors.  The research is typically done by conducting interviews with customers or organizing focus groups to collect feedback on marketing content and collaterals. In this way, the surveyor can explore different topics in-depth and get feedback from the respondents. Using this market research method, brands can gather information that can help them upscale and reposition their brand better in the market. LEARN ABOUT: Brand health
• Understanding the Consumer Behaviour: Sometimes, organizations/ companies/ entrepreneurs need more information about their consumer in order to place their product in a better manner.  To do so they might need information about their gender, age, marital status etc. Qualitative market research helps them gather such information. For understanding the consumer behavior conducting in-depth interviews is the best option, as these interviews are conducted on one to one basis a decent amount of information can be collected.
• Measuring the reach of marketing activities: Many businesses go an extra mile to do a better job in promoting their brands. Here is where their marketing activities come into play.  Market research can provide organizations with information about their marketing effectiveness by gathering first-hand information on how consumers look at their marketing message. This helps organizations maximize their marketing budget.
• Identifying new business opportunities: Market research helps organizations explore new opportunities leading to business expansion.  By gathering data through market research through focus groups, organizations can pin a location, understand business dynamics, know their key competitors etc., to grow their business in the right direction.
• Getting insights on products: If a company comes up with a new product or looking to improve a current one, it is always better to take a market research in order to understand how acceptable is the product amongst the consumers.  When a product comes to the market people have an opinion about its shape, size, utility, color, features etc. Qualitative market research through in-depth interviews will help gather systematic data that can be later used to modify or make the existing product better.

LEARN ABOUT:  Market Evaluation

Employee Experience: Definition

Research ethics are as important as important as the ethics in any other research field. It is important to safeguard the participants’ interest. Like there is training and formal processes for researchers in other fields like in healthcare and medical research, market research is also governed by similar policies.

Due to the nature of qualitative market research, it is very important to have informed consent from a participant to be a part of the research study. This means that they are aware  of basic information like:

• Nature of the research
• Expected time of completion
• If there are any sociological or physical risks or benefits
• Will a monetary or remuneration in other form be present
• Confidentiality protection
• How will the name and other personal details be used
• Any legal repercussions

Since this is a relatively less expensive and a more flexible method of market research there are a few applications of this market research methodology:

• It helps to understand the needs of the customers and their behavioral research pattern.
• What consumers think and perceive your product as.
• To understand the efficiency of your business planning and also to know if the strategies and planning that you put in place are working or not.
• What sort of marketing messages has a strong impact on the consumers and what just fall on deaf ears?
• Whether or not there is a demand for your product or services in the market?

LEARN ABOUT:  Test Market Demand

Ultimately, qualitative market research is all about asking people to elaborate on their opinion to get a better insight into their behavioral pattern. It’s about understanding  “Why” even before “What”.

LEARN ABOUT: Behavioral Targeting

• It helps you gather detailed information: One of the major advantages of this market research method is that it helps you collect details information instead of just focusing on the metrics of data. It helps you understand the subtleties of the information obtained thus enabling in-depth analysis .
• It’s adaptive in nature: This market research can adapt to the quality of information that is collected. If the available data seems not to be providing any results, the researcher can immediately seek to collect data in a new direction. This offers more flexibility to collect data.
• It operates within structures that are fluid: The data collected through this research method is based on observation and experiences, therefore, an experienced researcher can follow up with additional open ended questions if needed to extract more information from the respondents.  
• Helps communicate brand proposition accurately: Through this market research method, the consumers can communicate with the brand effectively and vice versa. Any product terminology, product jargons etc are effectively communicated as this research method gives a chance to the brand and the consumer to express their needs and values freely, thus minimizing any miscommunication.
• It helps reduce customer churn : Consumer behaviors can change overnight, leaving a brand to wonder what went wrong. By conducting qualitative market research, brands have a chance to understand what consumers want and if they are fulfilling their needs or not, thereby reducing customer churn . Thus the brand-consumer relationship is maintained.

LEARN ABOUT: Market research vs marketing research

• It is time-consuming: Qualitative market research can take days, weeks, months and in some cases even years to complete. This isn’t good to get quick actionable insights. In some cases, the premise with which the survey began may be non-existent due to market evolution.
• It is expensive: Due to the time taken to complete, qualitative market research is extremely expensive. They are also expensive to conduct and create actionable insights because the data is humungous and people with certain research skill sets are required to manage the research process.
• It is subjective: What one user may think could be very different from another. Due to this, there is no standardization of responses. This also means that the lines between true and false blur out to the point that each response is to be considered at face value.
• No result verification: Data collected cannot be verified because in most cases in a qualitative market research, the data is based on personal perceptions. Hence for analysis, each opinion is considered as it is valid.
• Halo effect: Due to the highly subjective nature of the research, the preconceived notion of the moderator or the person conducting the analysis skews the reporting of the research. It is human tendency to gravitate towards what’s known and it is very tough to get rid of this research bias .

LEARN ABOUT: Self-Selection Bias

With the increasing competition in the business world, the extensive need for business research has also increased. QuestionPro Communities is a qualitative research platform that is interactive, where existing customers can submit their feedback and also stay well informed about the market research activities, helps researchers undertake studies to maximize sales and profits. Through the communities platform, researchers can carry out research to effectively target and understand their customers, understand what is the market trend, prevent future research problems and thereby reduce customer churn .

This qualitative research platform helps in developing businesses to know their competitors and help identify the latest trends in the market. To carry out a well-directed research, businesses need a software platform that can help researchers understand the mindset of the consumers, interpret their thoughts and collect meaningful qualitative data .

QuestionPro Communities is the World’s leading platform for conducting analytics powered qualitative method . This online qualitative market research software helps researchers save their time, using niche technology like text analysis , where computers are used to extract worthwhile information from human language in an efficient manner, increase flexibility and improve the validity of qualitative research questions . This online platform help researchers reduce manual and clerical work.

QuestionPro Communities Qualitative Market Research Tools Includes:

Discussions

The online qualitative research software and tool, Discussions, allows a researcher to invite respondents to a community discussion session and moderate the focus group online. This can also be done live at a specific time that is convenient to the researcher and offer the users the flexibility to post responses when they login to their community. Invitations can be sent out well in advance to a specific target group the researcher would like to gather feedback from.

In case you are looking for respondents to share their ideas and allow others to analyze and offer a feedback and vote on the existing submissions, then this is a great tool to manage and present your results to the key stakeholders.

In this online community, you can submit topics, cast your vote in the existing posts and add comments or feedback instantly.

QuestionPro Communities is the only panel management and discussion platform that offers a seamless mobile communities experience. When it comes to engagement, how you reach respondents matter! Go mobile and take Discussions, Topics, and Idea Board anywhere your respondents go.

Feel free to explore our latest blog discussing practical examples of qualitative data in education – a valuable resource to deepen your insights into student experiences and learning dynamics. Why not give it a read and discover fresh perspectives for enhancing educational practices?

Learn about the other market research method: Quantitative Market Research

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Qualitative Methods :Measuring Forecast Accuracy : A Tutorial

Qualitative methods.

Common Qualitative Forecasting Methods EXAMPLE: Life Cycle analogy Analyzing the Life Cycle Data for the Previous Version Questions to Consider When Using the Life Cycle Analogy to Forecast for a New Product

Common Qualitative Forecasting Methods

• Sales force composite
• This involves having product managers or sales reps developing individual forecasts, and then adding them up
• Both methods have experts work together to develop forecasts
• The Delphi method has experts develop forecasts individually, then share their findings. The process is repeated until a consensus emerges.
• Life cycle analogy
• Used when the product is new. The technique is based on the fact that many products have well-defined life cycle stages (Growth, Maturity, and Decline)

h2. EXAMPLE: Life Cycle Analogy

• A consumer products company is coming out with a new version of smoking cessation gum
• Sales history for the previous version is shown below

Analyzing the Life Cycle Data for the Previous Version

Due to competitive pressures and innovations, the product has a definite life cycle.

h2. Questions to Consider When Using the Life Cycle Analogy to Forecast for a New Product

• How long will each life cycle stage last? What are we basing this on? (opinion, survey, etc.)
• In general, will demand levels be higher or lower? What are we basing this on? (opinion, survey, etc.)
• Key point: Using life cycle data from a similar product provides a starting point and helps us focus on the right questions
• SCM Tutorials

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11 Types of Forecasting Models

Thinking about creating a new product?

Or maybe you’re ready to enter into new markets or open up branches in new areas? However, you don't know the size of your market, or how the market will be evolving in the following years.

To identify how your business would work in different future situations, you should use a forecasting model.

Forecasting models take into account the following:

• The events that you’re trying to forecast,
• The type of information that is available to you, and
• The level of accuracy of your results.

However, it’s important to understand how to implement the different types of forecasting models, but also to figure out which model is the most appropriate for a certain situation or problem.

Apart from forecasting models, in this article, we'll cover the top two financial forecasting methods — quantitative and qualitative. We’ll also mention some forecasting tools and how they can facilitate your future projections.

What is forecasting?

According to the PMBOK guide , “a forecast is an estimate or prediction of conditions and events in the project’s future, based on information and knowledge available at the time of the forecast.”

We may use forecasts in various situations. For example, in finance, companies use financial forecasting to project employee’s wages or set the annual budget. On the other hand, in stock trading and investing, forecasting is used to predict the future market price and performance.

Moreover, in a business setting, forecasting can help business analysts study the impact of certain changes in the working environment (such as adjusting business hours). Another type of forecasting is weather forecasting, which predicts future atmospheric changes for a certain area and time, or changes on the Earth surface, based on meteorological observations.

In this blog post, we’ll stick with the financial forecasting.

What is financial forecasting?

Financial forecasting refers to projections made about the future performance of a company in order to estimate how current trends and business metrics will affect the financial position of the company. This can be done by exploring historical information regarding business performance (sales, revenue, or expense figures) as well as current business trends, and other important business variables.

Now, why do we need financial forecasting in the first place?

Financial forecasting focuses on overcoming business challenges regarding strategy planning processes, including:

• Budgeting, and

Companies use forecasting to determine if their expectations align with the possible outcomes. In other words — forecasting helps them make predictions about how current business and market trends (revenue, costs, consumers, demographics, etc.) will affect the company’s performance or operations.

Why does your company need forecasting?

Forecasting is an important part of business planning and operations because it helps businesses estimate their financial situation . With forecasting, companies can analyze current and past data in order to make predictions about future trends and changes.

Since business decisions are based on current market conditions and forecasts about future events, stakeholders will be more comfortable in making informed decisions and developing better business strategies. For example, forecasts may help you decide whether to fund a specific project, increase the staffing, or estimate the annual budget .

Top benefits of forecasting

Let’s see how forecasting can help your business succeed:

• Forecasting helps in setting goals and plans ahead of time — Analyzing data and statistics helps businesses better evaluate their progress and adapt business operations accordingly.
• Forecasting helps in allocating a business budget — A forecast will give you estimates about the amount of revenue or income that is expected in a future period. This, in turn, helps companies get insight into where to allocate their budget.
• Forecasting helps in predicting market changes — Data and projections help companies make better adjustments to their strategies and improve operations in order to meet current market trends. This, in turn, helps them stand out from the competition.

 To sum up, forecasting is an absolute necessity for any business because it helps you:

• Plan for both short and long term future,
• Invest your money wisely,
• Expand into new markets,
• Use real-time data,
• Improve collaboration between team leaders, and most importantly,
• Plan the next steps for your business.

There are various tools that help businesses get better insight into how operations and processes currently work, and find out what needs to be changed or improved. We will mention a few forecasting tools below. 

Forecasting method vs. forecasting model

Sometimes, the terms forecasting method and forecasting model are used interchangeably, however we want to point out that these terms are completely different .

In fact, a forecasting method uses mathematical calculations (created for a specific purpose) that does not elaborate on what actually happens in the data, but rather it is solely used to produce forecasts , with or without a forecasting model.

On the other hand, a forecasting model breaks up the data into a structure and allows you to examine the process further.

Given the rising competition and volatile customer loyalty, it has become challenging for businesses to work out any reasoning behind certain events. For that reason, forecasting models can be used to change and control different business variables and give a clearer picture of the company’s future. Many organizations employ forecasting models to predict various business metrics including sales, profits, consumer behavior, supply and demand, and then set yearly goals.

For example, forecasting models can help you understand whether your marketing strategies are effective or whether your sales are struggling. In addition, they help businesses allocate their resources properly and plan the upcoming period of time regarding the aforementioned business metrics.

What is forecasting in project management?

What are the two main forecasting methods?

Businesses can choose between different types of forecasting methods, including:

• Quantitative forecasting methods, and
• Qualitative forecasting methods.

Quantitative methods of forecasting 

In order to make realistic and accurate forecasts, quantitative methods include mathematical processes such as:

• Permutations and combinations,
• Set theory,
• Matrix algebra, and
• Integration.

In addition, mathematical techniques such as linear programming, dynamic programming, and inventory control can also help decision-makers guide their business strategies.

Quantitative methods also include statistical processes such as:

• Standard deviation,
• One factor analysis of variance,
• Multi-factor analysis of variance,
• Two sample t-test for equal means,
• Autocorrelation, and
• Hypothesis test.

So, rather than basing the results on opinion and intuition — quantitative methods implement readily available data to interpret results. These methods are usually used to make short-term predictions by analyzing older, raw data.

Finally, quantitative methods can be further divided into:

• Time-series forecasting models — Examine past patterns in the data in order to predict future patterns. Examples of time-series models include: straight-line method, moving average, exponential smoothing, and trend projection.  
• Associative models (causal models) — The variable that is being forecasted is associated with other variables, thus, the projections are built on that relationship. Examples of causal models include: simple linear and multiple linear regression.

Qualitative methods of forecasting

Unlike quantitative methods, qualitative methods are subjective in nature and rely largely on:

• Expert opinions,
• Emotions, or
• Personal experiences.

These types of forecasting methods do not implement any mathematical calculations, and are mostly used when the historical data is too narrow or not expected to be followed in the future.

Qualitative methods are also used when the available data cannot be projected into numerical analysis, or when the trends and habits are constantly changing.

Qualitative forecasting methods can be further classified into 5 forecasting models :

• Market survey,
• Sales force opinion,
• Delphi method,
• Visionary forecasting, and
• Panel consensus.

We will explain these types of forecasting models in further details below.

Types of quantitative forecasting models

The entire range of forecasting models is enormous and is growing rapidly every day.

As we mentioned earlier, quantitative forecasting methods are based on mathematical or numerical values, and they are objective in nature. Thus, the types of forecasting models under this category rely on mathematical computations and calculations, using data from past company operations.

Let’s take a closer look at the different types of quantitative forecasting models . Bear in mind that these models are basic (there are also some more advanced models), however they can all help you forecast how future trends will change in the forthcoming years.

Time-series forecasting models

Time-series is a popular forecasting model which explores past company behavior to forecast future company behavior (consumer behavior, sales behavior, etc.). This type of forecasting model uses historical data in terms of hours, weeks, months, and years to come at a point in the future based on these past values.

Time-series uses information gathered over several years to analyze sales velocity based on the business needs. Based on those figures, you can create future forecasts using mathematical formulas. There are several models of completing time-series forecasting which will help you formulate future estimations.

The subtypes below are all examples of time-series forecasting models :

• Straight-line method,
• Moving average model,
• Exponential smoothing model, and
• Trend projection model.

Let’s find out more about each one of them.

#1 Straight-line method

The straight-line method is a time-series forecasting model that provides estimates about future revenues by taking into consideration past data and trends.

For this type of model, it’s important to find the growth rate of sales, which will be implemented in the calculations.

For example, the annual growth rate of a company can be fixed (6%) over the past 5 years. Consequently, the company anticipates that the growth will continue at 6% over the next couple of years. Using this information, the company can make accurate forecasts about its future decisions for the following years. This will help businesses predict how growth might affect the available data. 

So, if your company has a continuous growth rate, the straight line forecast can help you get an idea of the ongoing growth at the same rate. Aside from revenue predictions, this model can also be used to predict additional business needs in order to make quick financial decisions.

#2  Moving average model

The moving average model is similar to the straight-line forecasting, except that it’s often used to predict short-term trends (such as daily, monthly, quarterly, or half-yearly intervals). Companies use the moving average model when they need to forecast sales, revenue, profit, or other important business metrics.

With regards to calculating future revenue, for example, the model focuses on observing the past and current revenues (i.e., the average number of the revenues in a given time period) to predict future outcomes. This type of forecasting model is useful when calculating the performance of a specific metric within a certain time limit.

For example, if you want to forecast the sales for the upcoming month, you may take the averages of the previous quarter. This will help you identify the demand during peak selling periods.

Here’s how you can do the calculations to get the moving average:

A1 + A2 + A3 … / N

A = Average for a specific period

N = Total number of periods

Let’s say you want to calculate the moving average of sales figures for a period of 4 years (2019-2022) taking 2 years at a time (a two-year moving average). You would need to find averages for the following data subsets: 2019-2020, 2020-2021, and 2021-2022.

So, how would you calculate the moving average of sales with the above data set over a period of 4 years?

In order to find the average sales value for all 4 years, you will need to include all the 4 total numbers of sales, and then divide them by 4.

(4M + 7M + 6M + 5M) / 4 = 5.5M

Then, we get a moving average of $5.5 M.

#3 Exponential smoothing model

Similar to the moving average, exponential smoothing is another time-series forecasting model which can be used to predict new values by using a set of weighted averages based on past observations.

Exponential smoothing helps to predict the future by using past company data. The weights start declining exponentially with past observations in order to predict the upcoming period. To put it simply, if the observation is a more recent one — the associated weight is higher. This means that more weight is given to recent values instead of past values.

New forecasts are predicted by including the past forecast and the percentage of value (the difference between the current and the past forecast). The idea behind this model is to attribute importance to more recent values in the series — when observations become older, past values get exponentially smaller.

How to create a project timeline

#4 Trend projection model   

The trend projection model works best in situations where you could work out the future influence of certain variables (dependent or independent) based on its past behavior. The model examines past events in order to identify patterns and trends that could recur frequently.

Trend projection can be used to forecast future activity since it considers that all factors involved in past trends will continue in the future as well. The model requires long and reliable time-series data which is arranged in chronological order for evaluation.

By identifying the patterns of trends, the company will be able to get a vision of the future. Consequently, once the trend has been identified, it will be able to predict future demand.

Associative (causal) forecasting models 

According to associative or causal forecasting models, the forecasted variable is interconnected to other variables in the business system. Therefore, the forecast projections rely on these associations. 

Associative models are an advanced way to forecast your sales because they implement specific mathematical calculations to identify the connection between different variables that can affect your business activity.

The subtypes below are all examples of causal models :

• Simple-linear regression model,
• Multiple linear regression model.

#1 Simple linear regression model

The simple linear regression is a type of associative forecasting model that provides a more detailed context to your forecast by examining how the independent variable is correlated to the dependent variable .

The dependent variable is the predicted value (e.g. sales), and the independent variable (e.g. profit) guides the expected value of the dependent variable.

Simple linear regression can be visualized on a graph by portraying one metric on the X axis, and the other one on the Y axis.

Here’s the formula for calculating simple linear regression:

Y = BX + A Y = dependent variable (predicted value) B = the slope of the regression line (measure of its steepness i.e the ratio of the rise to the run, or rise divided by the run) X = independent variable A = Y-intercept (the point on the Y-axis by which the slope of the line sweeps)

Apart from identifying the relationship between sales and profits, it can also demonstrate the rate of increase and how that rate varies in order to help you find ways to maximize profit.

Calculating the simple linear regression is a tedious process, so you might want to use statistical programs to help you analyze the data.

#2 Multiple linear regression model

As its name suggests, the multiple linear regression model follows the same approach — i.e. makes the same assumptions as the simple linear regression — except that it applies it to a number of different business variables.

So, when business performance is influenced by more than one variable, this model allows you to explore the relationship between two or more independent variables and one dependent variable. This will help you get a clear picture of the situation and a more accurate forecast.

For example, multiple linear regression can be used to determine daily cigarette consumption, which can be predicted by independent variables such as smoking duration, starting age of smoking, type of smoker, etc. Or when the margin for a certain product is affected by variables such as labor cost, materials, machine efficiency, etc.

However, it can be difficult to perform a multiple regression by hand as these models are complex, especially when there are too many variables involved, so you’ll likely need statistical software.

Types of qualitative forecasting models

Another method of forecasting is qualitative forecasting .

Qualitative forecasting methods are different from quantitative methods because they’re subjective and intuitive in nature. They are based on the opinion and judgment of consumers and experts.

In addition, qualitative methods use factors such as demand trends and seasonality to create more accurate forecasts.

They can only be used if past data isn’t readily available.

All the forecasting models below belong to the category of qualitative forecasting methods .

#1 Delphi method

The Delphi method is a type of forecasting model that involves a small group of relevant experts who express their judgment and opinion on a given problem or situation. The expert opinions are then combined with market orientation to come up with results and develop an accurate forecast.

The Delphi method is performed in such a way that each expert is questioned individually to gather their insights. This helps to prevent bias and ensures that the company’s forecast is based solely on their own expert opinion.

Furthermore, other employees or outsourced parties collect, summarize, and analyze experts’ responses. They may pose additional questions to the participants who can then reconsider their original responses in order to come up to a meeting point or final consensus that would be beneficial to the company.

Highlights of Delphi method

Some of the highlights of this model include:

• Freedom of expression — Since each expert is questioned individually, they have the freedom to express their own opinion without feeling peer pressure, 
• Ability to make up their mind and give a second thought — The experts can change their opinion and provide additional information in case they have reassessed the problem,
• Consistent feedback — After each round, the participants are informed about the opinions of other group members and then make discussions, and
• Quantitative results — This type of model is qualitative in nature, however there is possibility to analyze the results quantitatively.

When to use Delphi?

The Delphi method can be used to:

• Predict trends in sales,
• Forecast outcomes in economic development,
• Identify risks and opportunities,
• Create work breakdown structures, and
• Compile a report from opinions.

Other situations where the Delphi method makes sense is when you:

• Want to gather subjective statements from a larger group,
• Find it difficult to perform a face-to-face discussion due to the group size,
• Need to retain the anonymity of the participants, and
• Feel there is a dominant person in the group who could interfere with the discussion.

#2 Market research model

Market research is a qualitative forecasting model that evaluates the performance of a business’s products and services by interviewing potential customers about them. Their reactions and responses are recorded, and then they’re analyzed in order to come up with a sales forecast.

This model can be performed by staff members or third-party agencies (specialized in market research) by:

• Opinion poll,
• Personal interviews, or
• Questionnaires.

Some examples of market research strategies include:

• Focus groups,
• Consumer surveys, or
• Product testing.

However, the strategies might be adapted based on the current market conditions and challenges.

These techniques are used to gather valuable insights from consumers so that the company understands which products or services to continue launching and which ones need to be revised.

Highlights of market research 

The market research model can help companies:

• Create consumer-oriented marketing policies,
• Study marketing problems in order to come up with a solution,
• Minimize the gap between consumers and manufacturers,
• Introduce new products,
• Identify potential markets, and
• Choose marketing methodologies.

When to use market research?

The market research model can be used:

• Before the launch of a new product or service,
• Once a product or service has been launched in order to find out if it’s been accepted, or
• As a continuous plan that will help businesses get information about its market standing.

Conducting market research is also beneficial when you need to:

• Estimate the market size,
• Define potential customers,
• Find out why sales are declining, or
• Support business growth.

#3 Panel consensus model

Panel consensus (also called expert opinion) is a qualitative forecasting approach where experts or employees from all levels of an organization (from low-level to top-level) discuss a product or service. The members act like a focus group, expressing their thoughts and recommendations in order to develop a forecast.

Anyone can speak up during the discussion, however, sometimes lower-level employees may feel intimidated to express their opinion due to their lack of market knowledge. This is one of the drawbacks of this model.

On the other hand, the forecasting process involves a high number of participants, therefore, the outcome would be more balanced and reliable compared to an individual person’s opinion. The meeting will end once a consensus has been reached.

Highlights of panel consensus

Some of the highlights of the panel consensus model include the following:

• Members from all levels within an organization can establish the forecast,
• A large number of ideas can be presented based on each individual’s knowledge, and
• A further panel of five top managers is required to come up with a final decision.

When to use panel consensus?

The panel consensus model can be used:

• In the absence of appropriate data for forecasting,
• For short-term projections, or
• For department-specific forecasts.

Other examples where panel consensus may be appropriate is when companies need:

• To identify which products are being returned most often and why,
• To get insights what customers are looking for,
• To detect new trends at an early stage, or
• To forecast new product sales in the market.

#4 Visionary forecast model

The visionary forecasting model is based on personal opinions, judgements, and insights of a relevant and experienced individual. The projections are backed up by data, information, and facts in order to predict future scenarios. When available, historical analogies can also be used to hypothesize potential future forecasts.

In other words, the ‘visionary’ prophesies a set of future events by examining past events and developments. Therefore, this model is subjective and non-scientific in nature, and is solely based on an individual's guesswork and imagination.

The only downside of visionary forecasting is that there might be a confirmation bias because visionaries may only look for evidence that supports their own beliefs and disregard any contradicting evidence.

Highlights of visionary forecast

The visionary forecasting model is:

• Characterized by the vision of the expert,
• Based on intuitive judgment and opinion, and
• Backed up by subjective probability estimations.

When to use visionary forecast?

The visionary forecast model can be used to:

• Study the market environment,
• Estimate the sales of a new product,
• Anticipate and track the progress of a new product, or
• Predict recent trends in new market conditions.

Additionally, this model can be used in the absence of historical data . Some steps in the business planning process do not require the use of historical data (analyzing the current financial situation, studying the company’s competition, making future scenarios, or using existing industry trends), so this model could come in handy.

#5 Sales force composite model

Another reliable qualitative forecasting model is sales force composite where the input of sales staff is used to estimate future sales. When estimating future demand, the company may decide to collect information from the salesperson that would help in determining customer’s needs and predicting the sales in a certain region and given time period.

According to the sales force composite model, the sales agent better understands the needs of the customers since they interact with them on a regular basis. This information will help in adjusting business operations in order to meet the client’s needs and maximize sales. 

The sales person is questioned about the experience and satisfaction of the customers with the company. This model is easy to conduct, since it only requires an appointment with salesforce experts. Each person gives their own opinion about what they expect to sell in their specific region. 

Highlights of sales force composite 

Some of the highlights of sales polling are the following:

• The personal knowledge of the sales force can be used in favor of the company,
• The model is reliable since it involves a large population sample, and
• The sales staff has direct interaction with the customers. 

When to use sales force composite?

The sales force composite model can be useful when you need to:

• Make shorter-range forecasts for more accurate sales estimates,
• Get an opinion on sales trends,
• Get more accurate insights based on the experience of sales staff,
• Estimate the sales of new products, promotions or strategies, or
• Forecast the entire market, but also individual areas and territories.

How to choose the right forecasting techniques?

Many forecasting techniques (or methods) have been developed over the years so it becomes challenging for managers to select a proper technique for a particular situation. That’s why it’s essential to understand the possibilities of each method and how they can help when forecasting a specific problem or situation.

There are some key considerations that need to be taken into account when choosing a forecasting method and model.

The following factors influence which forecasting method and model will be used:

• The context of the forecast,
• The relevance and availability of data,
• The degree of accuracy required,
• The time period available for analysis, and
• The costs or benefits of the forecast to the company.

However, before selecting the method and the subsequent model, you will also need to consider the purpose of the forecast and what variables should be included. These considerations will help the forecaster choose the right method and model that will generate accurate results for the business.

Project Management: 31 best techniques, practices, and tools

What are the best forecasting tools?

As we previously mentioned, accurate forecasts are a crucial part of every organization, because they help them estimate and prepare for the future. However, you'll need certain tools that will help you make projections and plan ahead of time.

Furthermore, forecasting tools can automate such processes, which in turn, improves efficiency. Your team can stay focused on completing projects and addressing your customers’ needs while you get reliable and real-time data for forecasting.

When it comes to forecasting tools, you have plenty of options to choose from. But you need to find the right one for your business. Luckily, we’ve already done the research for you.

So, here are the most common types of forecasting tools you may use:

• Cash flow statements — Help in identifying the amount of money your company is spending and receiving from clients, and predict when transactions will happen.
• Expert reports — Essays or videos from respected and experienced industry professionals will help you create a better forecast for your company.
• Organization charts — Allow you to identify areas where you may need to hire new staff, or combine departments on specific projects.
• Production charts — Display what products and inventory the company needs in order to keep operations running smoothly.
• Performance indicators — Help you make effective forecasts by focusing on those areas where employee performance is at its highest.

There are a variety of tools available, so it’s important to find the one that aligns with the scale of your organization and available budget.

How to calculate and rate employee performance

How to create forecasts in Clockify

Clockify can help you get a better understanding of how your project is performing based on the time you track in the app.

This time-tracking software can help you analyze your project performance each month and make more accurate forecasts by keeping track of all project stages. In other words, this means that tracking project performance can help you identify unforeseen changes and find ways to approach them.

Additionally, Clockify allows you to get insight into how much time each task requires and avoid time overruns and delays. This can help you plan your upcoming projects.

In Clockify, you can track time for each task to avoid overtime and lengthy hold-ups.

Clockify also allows your team members to clearly see their tasks and get a better idea of what they are expected to accomplish in a given time period. Tracking project performance will also help you estimate and allocate the annual budget.

Final words: The best forecasting model is the one that aligns with your business goals 

There are dozens of forecasting models, therefore, it’s important to know how to choose the right one for your business.

There isn’t a single model that will work for every business, industry, or situation, since each model has its own strengths and weaknesses. Moreover, not all models would necessarily serve the business’ purpose, so you will need to choose the one that aligns with your specific needs and goals.

We gave our best to carefully examine 11 forecasting models for you because there is no one approach that fits all of your business problems. You can choose the one that nearly solves your problem, and then experiment, or adjust the others over time.

Additionally, you may combine the forecasting methodologies that you think are more accurate for your unique situation, and in that way, eliminate the shortcomings of one model by substituting it with another.

After all, it’s up to you to decide what type of forecasting model is best suited for your business. 

References:

• https://redstagfulfillment.com/what-is-demand-forecasting/
• https://corporatefinanceinstitute.com/resources/financial-modeling/forecasting-methods/
• https://online.hbs.edu/blog/post/financial-forecasting-methods
• https://www.analyticsinsight.net/how-to-choose-the-right-forecasting-method/
• https://pangeatech.net/qualitative-forecasting-techniques-an-overview/
• https://www.eazystock.com/uk/blog-uk/inventory-forecasting-models-quantitative-qualitative-methods/
• https://www.indeed.com/career-advice/career-development/forecasting-models
• https://www.indeed.com/career-advice/career-development/qualitative-forecasting
• https://www.xenonstack.com/insights/what-is-forecasting
• https://www.wallstreetmojo.com/forecasting-methods/  
• https://www.leadfuze.com/sales-forecasting-models/
• https://www.linkedin.com/pulse/what-types-forecasting-methods-hari-k/
• https://efinancemanagement.com/financial-management/forecasting-models
• https://studiousguy.com/forecasting-methods-with-examples/#2_Casual_or_Associative_Forecasting_Models
• https://www.managementstudyhq.com/different-forecasting-techniques.html
• https://www.causal.app/blog/choosing-the-right-forecasting-method-for-your-goals
• https://www.runn.io/blog/financial-forecasting-methods
• https://efinancemanagement.com/financial-management/forecasting-models#Qualitative_Means_of_Forecasting_Models
• https://hmhub.in/quantitative-methods-of-forecasting/
• https://www.indeed.com/career-advice/career-development/quantitative-vs-qualitative-forecasting-pros-and-cons
• https://books.google.mk/books?id=Z4LuBwAAQBAJ&pg=PA142&lpg=PA142&dq=visionary+forecasting&source=bl&ots=s35whdyMsb&sig=ACfU3U3bzRtEmMupS4Pibfs8DbTVjfOZEg&hl=en&sa=X&ved=2ahUKEwjHos2rro_8AhVoXvEDHYciD9AQ6AF6BAhGEAM#v=onepage&q=visionary%20forecasting&f=false
• https://brixx.com/the-importance-of-forecasting-in-business-what-you-need-to-know/
• https://www.projectmanager.com/blog/business-forecasting
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What is Qualitative Forecasting? Definition and Methods You Can Use

Forecasting is crucial for business owners to increase the success of their enterprises. They are able to forecast future sales and labor demand and make adequate budgetary plans thanks to effective demand forecasting. Whether businesses choose to use qualitative or quantitative methods, management can make more informed decisions that will meet their specific goals by being aware of the advantages and drawbacks of each strategy. Demand forecasting can generally be divided into two categories: qualitative and quantitative.

This approach to forecasting focuses on the perceptions, assessments, and practical knowledge of sector experts. Businesses can assemble a group of subject-matter experts to get feedback on suggested budgets, demand for particular goods or services, labor requirements, and other topics. As an alternative, they can conduct questionnaires with members of their target market as well as market or consumer surveys to find out how people feel about particular goods or services.

Forecasting – Qualitative methods

Why is qualitative forecasting important?

For executives to make decisions for the company, qualitative forecasting is crucial. Making decisions about how much inventory to keep on hand, whether to hire new employees, and how to modify a company’s sales operations can all be informed by qualitative forecasting. Additionally, qualitative forecasting is essential for creating projects like marketing campaigns because it can highlight the aspects of a company’s service that should be highlighted in advertisements.

Using sources other than numerical data, being able to foresee future business trends and phenomena, and using information from industry experts are some advantages of qualitative forecasting.

What is qualitative forecasting?

With the help of expert judgment, qualitative forecasting makes predictions about a company’s financial health. By identifying and analyzing the relationship between current knowledge of past operations and potential future operations, skilled employees carry out qualitative forecasting. This enables the experts to predict how a business will perform in the future based on their insights and the data they gather from other sources, such as staff surveys or market research.

Industries that use qualitative forecasting

Almost any industry can benefit from the use of qualitative forecasting by businesses to make predictions about their upcoming operations. Heres how a few industries might use qualitative forecasting:

Qualitative vs quantitative forecasting

Another method of forecasting is quantitative forecasting. Because it uses numerical values and calculations to make predictions and guide decision-making, quantitative forecasting differs from qualitative forecasting. In contrast to qualitative reasoning, which uses analysis of judgments and opinions to inform decisions, qualitative reasoning is based on objective data from previous operations. Historical data forecasts and associative data forecasts are the other two categories of quantitative data. The mathematical calculations used in these forecasts can assist a business in recognizing trends in areas like sales or investments.

Here are five methods of quantitative forecasting:

Examples of qualitative forecasting methods

Here are a few examples of qualitative forecasting methods:

Delphi method

The Delphi method entails asking each member of a group of experts for their individual opinions. Avoiding bias and making sure that any consensus regarding business predictions comes from the experts’ individual opinions can be achieved by interviewing or gathering information from the experts one at a time as opposed to in a group. Then, other staff members evaluate the experts’ responses and send them back with more inquiries before arriving at a prediction that makes sense for the company.

Jury of executive opinion

This strategy is dependent on the opinions of professionals from the sales, finance, purchasing, administration, or production teams. By using executive opinion, a team can complete a forecast quickly and take into account a variety of viewpoints from various departments to produce the most accurate forecast possible. Some businesses might combine a quantitative approach with executive opinion forecasting.

Market research

By exposing potential customers to a company’s services or products and tracking their reactions, market research analyzes how successful they are. Companies can either hire outside firms that specialize in market research activities or use their own employees to help with the research. Focus groups, consumer surveys, and blind product testing, in which a customer tries a product they have never heard of before, are a few methods of conducting market research. Companies can decide which goods or services to continue producing based on participant feedback, and which ones might require revision during the production stage.

Consumer surveys

Customers of a business are questioned in surveys about their experiences as consumers. Customers may receive consumer surveys from businesses via email or mail-in questionnaires. Cold-calling customers on the phone and inviting customers into the office for in-person interviews are additional methods for conducting consumer surveys. Employees can use the details they learn to help inform their predictions about the future of a company based on the experience of their current customers after collecting information from consumer surveys.

Sales force polling

Speaking with salespeople, who interact frequently with customers and may have detailed knowledge of their satisfaction and experiences with the business, is known as sales force polling. The fact that sales force polling uses data from staff who are frequently involved in actual business operations can help to ensure that the specifics are accurate and pertinent. Since it only requires meeting with salespeople and concentrating on the data they provide, sales force polling is also simple to carry out.

What is qualitative forecasting example?

Qualitative forecasting techniques are based on the perception and assessment of experts and consumers, and they are only appropriate in the absence of historical data. Examples of qualitative forecasting techniques include market research, informed opinion and judgment, and the Delphi method.

What is quantitative forecasting?

Qualitative forecasting is based on information that can’t be measured. Since there isn’t much historical data available when a company is just getting started, it’s particularly crucial. Quantitative forecasting is based on measurable and manipulatable historical data.

What is the advantage of qualitative forecasting technique?

Sales teams use quantitative forecasting, a data-based mathematical process, to analyze performance and forecast future sales using data and patterns from the past. Businesses can make educated decisions about strategies and procedures to ensure ongoing success by forecasting results.

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An introduction to quantitative and qualitative demand forecasting models

What is demand forecasting?

Demand forecasting is the process of predicting future customer demand for your products to help with inventory management. This will feed into your demand planning strategy to ensure resources are allocated to efficiently and effectively meet customer demand.

Accurately forecasting customer demand allows businesses to make data-driven decisions to ensure they purchase stock that will sell. Any stock that doesn’t sell risks becoming dead stock and tying up money that could be used elsewhere. On the other hand, not buying enough stock to meet demand will result in lost sales, hitting the balance sheet and losing the company money. 

Demand forecasting models

Whether you’re a manufacturer, wholesaler, or retailer, forecasting future demand or customer orders is the logical starting point of all business planning activity, including inventory purchasing. Therefore, choosing the right demand forecasting models to aid management decision-making is essential.

Accurate demand forecasting gives you control over your inventory, enabling you to optimize your inventory levels, improve fulfillment rates, and reduce carrying costs, which all directly impact your bottom line. Choosing the most appropriate inventory forecasting model can significantly improve forecasting accuracy , leading to more informed, data-driven business decisions.

Types of inventory forecasting models

There are two top-level inventory demand forecasting models to consider when calculating demand: the quantitative forecasting model and the qualitative forecasting model . Qualitative forecasting is generally based on subjective opinions, market research, and insights, whereas quantitative forecasting uses previous demand data or historical sales data in statistical calculations to predict the future.

Quantitative forecasting

Quantitative forecasting takes historical demand data and combines it with mathematical formulas to determine future performance. For this reason, it is also often called statistical demand forecasting . Data sets can go back decades, be run for the last calendar year, or be based on the previous few weeks’ consumption.

Quantitative forecasting models consider factors such as demand trends and seasonality to help make the predictions more accurate. They rely on having sufficient, good-quality data about the past to assess the future reasonably.

Time series analysis (Time series forecasting): Time series analysis is perhaps the most common statistical demand forecasting model. It examines patterns in past behavior over time to forecast future behavior. There are two main types used in quantitative forecasting:

Moving average forecasting: This takes a previous period’s demand data (e.g., four weeks of sales data) and calculates the average demand over that period (average sales per week), then uses this average as the forecast amount for the coming period.

Moving average forecasting has two drawbacks: it gives equal weight to each period and only considers data during the chosen period.

Exponential smoothing: This more advanced approach overcomes the problems above. Exponential smoothing looks at the actual demand of the current period and the forecast previously made for the current period. These observations are exponentially weighted to decrease over time to forecast the upcoming period.

Statistical forecasting using only historical consumption data works well if you sell or produce the same amount of each item in every period. If sales/usage fluctuates over a couple of periods, you will suffer from inaccurate forecasts that lead to either stock-outs or excess inventory.

While effective demand forecasting should consider demand trends, seasonality , and the product lifecycle stage of your stock items, doing so manually can be arduous and time-consuming. If you’re ready to elevate your statistical forecasting, it might be time to consider demand forecasting software . These tools can streamline your forecasting process, allowing you to focus on other critical aspects of your business.

Qualitative forecasting

Qualitative forecasting models are based on opinions, market research, experience, and – sometimes – best guesses.

With qualitative demand forecasting, predictions are based on expert knowledge of how the market works. These insights could come from one person or multiple people internally or externally to the business.

There are several qualitative forecasting methods:

Panel approach: this can be a panel of experts or employees from across a business, such as sales and marketing executives, who get together and act like a focus group, reviewing data and making recommendations. Although the outcome is likely to be more balanced than one person’s opinion, even experts can get it wrong!

Delphi approach (Delphi method): this involves crafting a questionnaire and sending it out to relevant experts (like customers and suppliers) who complete it. The results are analyzed and returned anonymously to the participants, who get to reconsider their original responses in light of other views and opinions until a final consensus is found. This more formal approach helps reduce influences from face-to-face meetings but could still include inherent bias from the experts chosen.

Scenario planning: this can be used to deal with situations with greater uncertainty or longer-range forecasts. A panel of experts is asked to devise a range of future scenarios, likely outcomes, and plans to achieve the most desirable outcome. For example, predicting the impact of a new sales promotion, estimating the effect a new technology may have on the marketplace, or considering the influence of social trends on future buying habits.

Which demand forecasting model is best?

You can gain a more comprehensive and confident perspective by incorporating both qualitative and quantitative demand forecasting techniques. For example, you might use a statistical moving average calculation that looks at historical sales data to establish your base demand forecast and see how demand for a product has changed (a quantitative forecasting approach).

If you see a trend forming, you could then use qualitative methods, such as a panel, interviews, or market research groups, to gain further understanding and discuss future market trends into the upcoming year.

If you find inventory forecasting challenging, contact the EazyStock team today . Our artificial-intelligence-powered demand forecasting software uses machine learning to give you advanced demand forecasting capabilities to enhance your day-to-day business operations quickly.

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Glossary:Qualitative forecasting method

Qualitative forecasting methods are subjective, based on the opinion and the judgment of consumers and experts; they are only appropriate when past data is not available.

Examples of qualitative forecasting methods are, for instance, Informed opinion and judgment, Delphi method and Market research.

Further information

• Handbook on Data Quality - Assessment Methods and Tools

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Multi-factor fuzzy sets decision system forecasting consumer insolvency risk

• Research Article
• Open access
• Published: 02 September 2024

Cite this article

You have full access to this open access article

• Tomasz Korol   ORCID: orcid.org/0000-0002-7623-3404 1  

The objective of this study is to develop a multi-factor decision system predicting insolvency risk for natural persons with the use of fuzzy sets. Considering that the financial situation of households is affected by various endogenous and exogenous factors, the main assumption of this study is that the system for predicting financial difficulties should not be limited to the use of only a few financial variables concerning consumers, but also include variables describing the environment. The author proposes a system consisting of three different forecasting models that connect the macroeconomic and microeconomic environments. It monitors the economic situation of households by also identifying those environmental variables, which may directly, or indirectly, endanger the consumer, such as unemployment rate (job market situation), inflation and interest rates, exchange rates, or economic situation in the country (GDP growth rate, the dynamics of retail sales, etc.). Moreover, the created cause-and-effect tool is in the form of a flexible application that can be easily adapted to changing economic conditions. Another unique feature of the study is the proposed use of newly developed ratios in household finance, similar to that in financial ratio analysis, which is commonly used in corporate finance. The proposed ratios demonstrated high predictive abilities. The paper also identifies the predictive capabilities of selected macroeconomic variables from the perspective of their impact on the risk of consumer insolvency. The research relies on four samples consisting of a total of 2400 consumers from Taiwan and Poland. The author created three forecasting models separately for the South-East Asian and Central European regions, and two multi-factor systems, each consisting of 1260 decision rules. The findings clearly showed that fuzzy logic is a significantly more effective method compared to traditional models based on classical logic.

Avoid common mistakes on your manuscript.

Introduction

Consumer bankruptcy is an important phenomenon for financial and economic stakeholders and their decisions (banks, financial analysts, households themselves, financial institutions and even the government). This type of risk is common regardless of the region of the world or the level of wealth of a society. Despite this, personal bankruptcy is not a central element of any known economic theory and household finance is the science devoted mainly to wealth, consumption level and patterns, financial literacy, portfolio allocation, and debt decisions (Gomez et al. 2021 ; Guiso and Sodini 2013 ). Understanding the process of going distressed and thus an early identification of consumers’ insolvency risk may reduce the number of bankruptcies in the economy. Moreover, the importance of the consumer bankruptcy phenomenon has significantly increased worldwide, owing to the global character of the COVID-19 pandemic, affecting not only the labor markets, but also disrupting the logistics channels of many industry sectors. This has a direct negative impact on the economic situations of both households and enterprises. Another example of a crisis increasing the risk of default is the current war in Ukraine, which has exacerbated the financial problems of many households across Europe. The system of interconnected economic interdependencies between the world's regions, companies, and consumers is dominant. The rising prices of raw materials, energy, and food contribute to an increase in inflation rates, which, in turn, translates into increased interest rates. In 2022, the risk of bankruptcy of enterprises and insolvency of households increased dramatically, depending on the country, on average, from 30 to 100%. In 2023 and 2024, we can expect a further increase in the risk of insolvency owing to the deterioration of most macroeconomic data that affect companies' investment decisions. This, in turn, influences consumers’ economic situation. The above two examples of crises only confirm that the risk of consumer bankruptcy is sensitive to any turbulence in the economy.

Both, the scale, and negative economic and social consequences of consumer bankruptcies require a scientific search for new and innovative ways of forecasting this type of risk. Worse still, most research on predicting default risk focuses on predicting the risk of failure for businesses, not for households. A review of the sparse literature devoted to predicting consumer insolvency reveals three common drawbacks. First, the available models are based only on demographic and financial information about natural persons. These models lack any constructed ratios, unlike in forecasting the default risk of enterprises, whereby financial ratios (e.g., liquidity or profitability) are implemented to evaluate insolvency risk (Ari et al. 2021 ). Second, they lack the connection between macroeconomics and microeconomics (meaning personal economic factors of households) to consider and use information about changes occurring in the macro-environment of households, to forecast their insolvency risk. In the first research strand (macroeconomic approach), the scholars mainly quantify the role of adverse, unforeseen shocks such as recession, increase in interest rates, and inflation rates that may lead to consumer bankruptcies (Aller and Grant 2018 ; French and Vigne 2019 ; Gross and Poblacion 2017 ; Gross and Notowidigdo 2011 ; Luzzetti and Neumuller 2016 ). Another factor of a macroeconomic study is an increase in unemployment in the labor market, preventing borrowers from paying off their loans (e.g., Anastasiou et al. 2016 ; Paskevicius and Jurgaityte 2015 ; Barba and Pivetti 2009 ). In turn, in the personal economic circumstance research strand, most scholars have used only the following variables to evaluate consumers’ risk of insolvency: age, education level, gender, income level, mortgage expenditures, mortgage length, marital status, number of dependents, employment status, credit card expenditures, number of loans, and value of assets (e.g., Aristei and Gallo 2016 ; Diaz-Serrano 2005 ; Ghent and Kudlyak 2011 ; Guiso et al. 2013 ; Haughwout et al. 2009 ; Hira 2012 ; Jackson and Kaserman 1980 ; Patel et al. 2012 ; Worthington 2006 ). The usage of such single information loads about evaluated consumers (e.g., the age of consumers) seems quite outdated. The third inadequacy is expressed by the use of classical logic to forecast and assess uncertain and undefined phenomena. Households’ wealth, and thus their economic situation and degree of solvency, is affected by many internal (e.g., behavioral and educational) and external (e.g., GDP growth, inflation, unemployment, and interest rates) factors, which cannot be defined precisely and unambiguously. Furthermore, it is imprecise to assess a household as "good" or "bad" financially, as analysts rarely deal with 100% solvent or 100% insolvent households in the current economic realities. The majority of the models are based on classical set theory (zero/one, good/bad), which makes it difficult to determine the precise degree of risk.

To fill this gap in the literature, this study develops a multi-factor decision system to forecast bankruptcy risk separately for Taiwanese and Polish consumers. Poland is an example of a country from the European Union that has grown rapidly over the past twenty years. Taiwan, on the other hand, is an Asian country that, like Poland, has gone through a series of political and economic changes and has become an example of a fast-growing economy. The author deliberately took two countries from two culturally different regions of the world to compare the specifics and factors that can have an impact when predicting consumer bankruptcy risk.

Both systems will monitor not only the insolvency risk of households, but also identify macroeconomic variables that may directly or indirectly influence (positively or negatively) the economic situation of consumers. Considering the major drawbacks of current forecasting models, the author developed a proposal for an early warning system against the risk of bankruptcy, which consists of three different forecasting models (one microeconomic representing personal economic circumstance and two macroeconomic) for each economic region (Poland and Taiwan). The models were connected to one system via programmed rule blocks. The distinguishing feature of this study is its multi-criteria approach using fuzzy logic methodology. An additional unique feature of this proposal is an attempt to implement new innovative ratios in forecasting models that contain a much larger load of information than the commonly used variables in the literature. These ratios are constructed by combining the evaluated consumers’ financial and demographic characteristics into a set of 11 unique financial ratios.

This study contributes to the literature on forecasting the bankruptcy risk of natural persons in a fourfold manner. First, it develops early warning systems against bankruptcy for two specific economic regions of households: Central Europe (for example, Poland) and South-East Asia (for example, Taiwan). Such a research approach will allow not only the development of two specialized forecasting systems for two different economic regions but will also help distinguish the forecasting processes between these regions. Second, it proposes new innovative ratios that contain a much larger load of information than commonly used variables in the literature. Such indicators can set a completely new direction for research on the use of ratio analysis in personal finance, similar to the financial situation of enterprises. Third, it identifies the best predictors of households’ financial distress from macroeconomic and personal economic perspectives. Fourth, the developed systems will be in the form of an 'open' application that can be easily modified by converting a set of decision rules. The number of adaptations in these systems is practically unlimited.

The early identification of consumers’ insolvency risk may reduce the number of bankruptcies in the economy. Both, consumers and banks can use such forecasting systems. With the popularization of effective prediction methods, consumer awareness can increase. In banks, such systems may be used as supporting tools in the decision-making process.

This study is comprised of five sections. In this section, the introduction, the author justifies the topic, study objectives, and contributions and innovations to the literature. Section two presents a literature review covering different sources and determinants of the insolvency of natural persons. Section three explains the study’s research methodology and hypotheses. In the fourth section, the developed early warning systems and their results are presented. The last section concludes the paper and formulates implications for future research.

Literature review

As previously mentioned, most studies on insolvency forecasting focus on predicting the risk of financial distress for enterprises rather than for households. The available studies devoted to predicting consumer insolvency are based on traditional credit scoring methods that help banks decide whether to grant credit to consumers who apply for them (Lyn 2000 ). This study focuses on the development of a comprehensive multi-factor early warning system against insolvency risk. Before starting to program such a system, it is necessary to understand the most important and common sources of this type of risk in the economy. Based on a review of scholarly papers from 1980 to 2022, the author proposes the classification of the sources and determinants of consumer insolvency according to the type of risk—systematic and specific. Figure  1 presents this concept, devoted to the assessment of selected factors on the insolvency risk of natural persons.

The classification of the sources of insolvency risk of natural persons due to the type of risk

The first area affecting consumers’ financial situation is the systematic risk factor. These factors relate to the general public and the economy; therefore, they cannot be controlled or influenced by households. The level of this risk can be influenced only by governmental institutions, the government, or the central bank.

The first aspect of systematic risk is credit risk. This risk is determined by the type and structure of the various sources of household wealth financing, and can be partly controlled by a natural person. The increased amount of debt in the form of bank credit or the burden of credit cards increases consumers’ financial costs. Excessive debt has been found to contribute directly to the likelihood of consumer insolvency (Bauchet and Evans 2019 ). Some excessive loans may be due to poor personal financial management, but there are also cases where excessive debt is incurred due to high medical expenses (Linna 2015 ). The risk of medical treatment depends on the type of healthcare provided in a given country.

Excessive consumer indebtedness creates additional risks related to interest-rate fluctuations. The interest-rate increases financial costs. Petersen and Rajan ( 1997 ) showed that entities threatened by insolvency are willing to accept higher interest rates on loans. This increases the bankruptcy risk. At the same time, the greater the consumer's risk of bankruptcy, the greater his preference for borrowing at higher interest rates, which further increases the debt spiral and the cost of servicing it (Burton 2021 ). This is consistent with the study conducted by Xiao and Bialowolski ( 2023 ), who proved that increasing interest rates are correlated with wider gaps in premiums between high- and low-risk borrowers, which particularly affects consumers in lower-income groups and those with lower creditworthiness.

The inflation rate directly affects the interest rate. Therefore, it has a threefold impact on consumers’ financial situation. It directly affects the cost of living and the cost of debt service but also affects the situation of enterprises, which may limit investments and employment. As a result, inflation indirectly affects the risk of consumer insolvency through the increased risk of unemployment (French and Vigne 2019 ; Gross and Poblacion 2017 ).

The fifth major exogenous factor is the risk of unemployment. Numerous studies have proven the existence of a significant relationship between the solvency level of households and the unemployment rate, as rising unemployment makes borrowers unable to pay their debts (Anastasiou et al. 2016 ; Barba and Pivetti 2009 ; Madeira 2018 ). This risk is also associated with industrial risk. The increase in the number of bankruptcies of enterprises in a given sector of the economy directly translates into the financial situation of the people employed in this industry. A given industry may be exposed to an increased risk of bankruptcy for various reasons, such as technological changes, aging of the entire industry, regulatory changes, and availability of production materials (O’Connor et al. 2019 ; Paskevicius and Jurgaityte 2015 ).

Another important exogenous variable that influences insolvency risk is exchange rate. The exchange rate clearly has a direct impact on the economic situation of enterprises (both exporters and importers), but it should also be noted that households are also under their influence (Messai 2013 ), either directly (in case of, for example, incurring debts in foreign currencies) or indirectly (through increases in the cost of living in the case of countries dependent on imported goods, such as petrol or gas).

The business cycle, and therefore, the economic situation of the country, is another source of risk affecting households’ insolvency. Recession disrupts the sources of household income, which decreases households’ ability to repay their loans and increases the financial distress of natural persons (Beck et al. 2015 ; Quagliariello 2003 ).

The final exogenous risk is the probability of a disaster. Here, we can point out different types of risk (e.g., flood, earthquake, fire) that can have a sudden and very negative influence on the wealth of households.

The second area influencing households’ financial standing is a specific risk factor. These factors may be fully (endogenous) or partially (exogenous) controlled by consumers. Thus, they are associated with the households’ future decisions. Therefore, most of these factors are demographic and social in nature.

The first factor of specific risk concerns the income of natural people. Household financial status is typically expressed using this measure. Consumers are evaluated positively if their income level is above the median and poor if it is below (Collins and Urban 2020 ). In general, households with less income, and liquid, investment, and real assets, are expected to be more likely to be involved in debt (Adzis et al. 2017 ). This, in turn, affects the risk of insolvency. However, the income measure does not capture how well households manage their financial resources. There are consumers who are financially secure even with relatively small incomes, and there are people with relatively high incomes who are financially distressed. This can be connected to education, financial literacy, and the age of consumers (Oliveira et al. 2023 ). Studies show that these three factors are strongly correlated with income and wealth, and hence affect insolvency risk. Financial capability reflects people’s knowledge of financial matters and their ability to manage capital and control their finances (Taylor 2011 ). Especially during economic crises, when additional pressure is placed on household finances, financial management skills can become even more important. Moreover, financial capabilities are also expected to increase with age. Specifically, older consumers are expected to demonstrate higher levels of both objective and subjective financial literacy, more desirable financial behaviors, and higher levels of perceived financial capability (Xiao et al. 2015 ). Financial literacy refers to the understanding of concepts, such as interest rates and inflation, and is positively associated with retirement preparedness and savings (Braucher 2006 ; Li et al. 2022 ). This relates to how a natural person understands and processes financial information and selects financial products and services. Households’ financial behavior is strongly correlated with their financial knowledge (Rostamkalaei and Riding 2020 ).

Some scientists have also found that marital status and gender influence the insolvency risk of natural people. Caputo ( 2008 ) and Domowitz and Sartain ( 1999 ) proved that those who are separated or divorced are a few times more likely to experience solvency problems than married or single natural persons. Studies have also shown the influence of gender on the risk of financial difficulties (Białowieski et al. 2020 ). Caputo ( 2008 ) proved that women were more likely than men to declare personal bankruptcy between 1986 and 2004 in the USA.

Another important factor is the consumption behavior. Several studies have examined the relationship between materialism and debt.

Highly materialistic consumers have more favorable attitudes toward spending and compulsive shopping as well as a higher likelihood of incurring relatively higher debts than those with low levels of materialism (Adzis et al. 2017 ; Xiao et al. 2013 ). Some studies have also identified a relationship between religion and risk-taking attitudes. One of the latest studies conducted in Germany showed that religiously affiliated consumers were generally more risk-averse than non-religious people. This can also be caused by the influence of religion on less materialistic behaviors. Furthermore, it has been proven that Muslims in Germany generally exhibit fewer risk-taking behaviors than Catholics and Protestants (Leon and Pfeifer 2017 ).

Research objectives and model building

The aim of this research is to develop two early warning systems against the bankruptcy of Taiwanese and Polish consumers. The concept of the system is based on utilizing personal economic factors and macroeconomic information, using three forecasting models. The assumption of this concept is the use of fuzzy sets to connect all three models into a single forecasting system. The distinguishing feature of this research is its multi-criteria and multi-factor nature, taking into account macroeconomic factors affecting the future economic situation of consumers. Such identification of leading factors influencing the risk of insolvency of consumers will allow us to predict not only the risk itself but also the variables that have an impact on it (e.g., exchange rates). The schematic of this system is shown in Fig.  2 .

Proposal of a scheme of an early warning system forecasting consumer risk bankruptcy. The source: based on own studies

Additionally, based on the literature review and conclusions of the author’s previous research on the problem of bankruptcy risk forecasting for natural persons, the following two research hypotheses are formulated:

H1 : A multi-factor early warning system that utilizes macroeconomic and personal economic elements is superior to a single forecasting model in predicting the risk of consumer bankruptcy in terms of higher effectiveness and the structure of Type I and II errors.

H2 : The implementation of the newly developed types of ratios in predicting personal risk bankruptcy positively ensures the high effectiveness of the forecasting model.

Taking into account the theories of overindebtedness developed by Braucher ( 2006 ), who formulated the structural dimensions of consumer credit in conjunction with the cultural aspects of households, and the findings of Bauchet and Evans ( 2019 ), who identified personal bankruptcy determinants, we formulate the first hypothesis (H1). According to this research hypothesis, the introduction of macroeconomic factors (non-financial indicators) increases the effectiveness of the forecasting. By developing a multivariate system combining the macro-structural dimensions of consumer credit with personal economic factors, we will verify this research aspect.

The second research hypothesis we formulated (H2) is dictated by the desire to test whether creating financial ratios for personal finance is a good direction for research development. The conducted literature review showed also that there are several sources of consumer insolvency risk. Hence, to achieve the study objective, the author formulated new types of ratios for use in personal finance. Their idea lies in the financial ratio analysis commonly used in corporate finance. By combining several economic aspects into one financial ratio, a set of indicators was developed in the last century, which is still widely used in forecasting the bankruptcy risk of companies. As mentioned earlier, in the case of personal finance and predicting the distress risk of households, most studies have used only a set of single variables, such as age, education level, gender, income level, mortgage expenditures, mortgage length, marital status, number of dependents, employment status, credit card expenditures, number of loans, and value of assets, among others (e.g., Aristei and Gallo 2016 ; Diaz-Serrano 2005 ; Ghent and Kudlyak 2011 ; Guiso et al. 2013 ; Haughwout et al. 2009 ; Hira 2012 ; Jackson and Kaserman 1980 ; Patel et al. 2012 ; Worthington 2006 ). Hence, the author develops a set of 11 ratios (X1–X11) presented in Table  1 by combining 12 following financial and demographic single indicators (V1–V12) of the evaluated households to be used in the forecasting system:

V1: annual income of consumer,

V2: the value of total assets owned by the analyzed consumer,

V3: total value of loans taken by a private person,

V4: monthly interest rates paid by consumer,

V5: monthly income of a natural person,

V6: the value of credit card debt taken by the entity,

V7: level of education,

V8: age of consumer,

V9: number of dependents in the household,

V10: marital status of analyzed natural person,

V11: the length of employment,

V12: annual interest rates paid by consumer.

This study is one of the first attempts to implement ratio analysis in the usage of household finance in worldwide literature.

Five demographic indicators (V7, V8, V9, V10, and V11) were quantified to allow them to be used in the proposed ratio analysis. The author determined the quantification results as listed in Table  2 .

In addition to the above personal economic variables and ratios calculated to be used in the core model (Fig.  2 ), the author collected the macroeconomic indicators for use in the two supporting models (Fig.  2 ) of each system. Table 3 presents these indicators, with the identification of the countries concerned.

To conduct the objective of the study, the author developed two learning samples (one for each country), each consisting of 200 consumers, with a balanced structure of 50% bank clients who experienced no financial disturbances by paying back loans, and 50% of natural persons who became insolvent. For testing purposes, the author created two samples (one for each country). Each test sample was composed of 500 solvent and 500 financially distressed consumers. The ratio analysis of the presented 11 ratios (Table  1 ) was performed for all analyzed 2400 consumers from both samples and countries using the information from the years 2012 to 2019. The sampling period before the COVID-19 pandemic ensured that a sudden unpredictable event did not affect the quality of the forecasts.

Table 4 presents the detailed distribution of the demographic variables of bankrupt Taiwanese consumers in the testing sample. The first observation is a significantly higher share of bankrupt men (72.4%) compared to women, who accounted for only 27.6% of the sample. Such a high percentage of males can be caused by cultural factors such as riskier financial behavior of men than women in South-East Asia. Among the 138 bankrupt women analyzed, the group of females aged 27–50 (71.74%) with a bachelor's degree (43.43%) or higher (35.35%) were dominant. In the case of bankrupt men, two age groups predominate in numbers, i.e., 27–50 (58.29%) and 51–60 (19.89%). Looking at the level of education in these two age groups, the first group was dominated by men with bachelor’s (44.55%) and master/doctorate’s education (28.90%); the second group was dominated by people with high-skilled (26.39%) and master/doctorate education (56.94%). On the other hand, both, females and males younger than 26 years are characterized by a very high share of people with the lowest education (elementary).

For non-bankrupt Taiwanese consumers (Table  5 ), there is an equal distribution of females (45.20%) and males (54.80%). Furthermore, the age distribution in both sexes is similar, that is, the group of people aged 27–50 has the largest share (61.68% among men, and 53.98% for women), followed by those aged 51–60 (males: 16.42%, females: 20.80%), and the last two groups, younger than 26 years (14.16% of females and 13.14% of males) and older than 60 years (11.06% females and 8.76% males). Concerning education level, both, women and men who are not at risk of insolvency in all age groups are characterized by an even share of three types of education levels: high-skilled, bachelor, and master/doctorate.

As for the distribution of gender and age of Polish consumers in the testing sample (Fig.  3 ), there was an equal distribution of non-bankrupt males (240 cases, 48% share) and females (260 cases, 52% share) and a significantly higher share of bankrupt males (340 cases) than bankrupt females (160 cases).

The gender (top) and age distribution of nonbankrupt (left) and bankrupt (right) consumers in the testing sample of Polish households. Source : Own calculations

Among the 340 bankrupt men, 202 were aged 27–50 years (59.41%), 69 were aged 51–60 years (20.29%), 48 were younger than 26 years (14.11%), and 21 were older than 60 (6.18%) years. On the other hand, among the 160 bankrupt women (Fig.  3 , right side), 108 were aged 27–50 (67.5%), 32 were aged 51–60 (20%), 3 were older than 60 (1.88%), and 17 were younger than 26 (10.62%) years.

As for the distribution of age of non-bankrupt consumers (Fig.  3 , left side), the highest share of both, females and males was in the group aged 27–50 years (62.31% of females, 162 cases, and 64.17% of males, 154 cases). Furthermore, among non-bankrupt women, two other age groups, i.e., 51–60 and less than 26 years old, are characterized by similar numbers (36 and 37 cases, respectively). Regarding non-bankrupt males, there are 38 cases for men aged 51–60 years and 29 for consumers younger than 26 years (Fig.  3 , left side).

The distribution of education levels in particular age groups is also interesting. From Fig.  4 (left side), we can see that among bankrupt females, the highest share in all three age groups (27–50, 51–60, and > 60 years) is for those holding a bachelor’s degree. High-skilled and elementary education levels dominate only in the youngest group of bankrupt females. In the case of bankrupt males (Fig.  4 , right side) younger than 26 years, the riskiest group has only elementary-level education (23 cases, 47.92% share). In the 27–50 age group, 76.74% of bankrupt males (155 males) have the highest education level (Master’s/doctorate). Most male consumers in the oldest group (≥ 60 years) had a Bachelor’s or Master’s/doctorate level of education (6 and 3 cases, 42.86% share).

The education level distribution in different age groups of bankrupt females (left) and males (right). Source : Own calculations

In the case of non-bankrupt consumers in the Polish testing sample (Fig.  5 ), both males and females younger than 26 years are characterized by an even distribution between the four types of education levels (from 21.62 to 32.43% for women and 17.24–34.48% for men). Additionally, most non-bankrupt males in all three age groups (27–50, 51–60, and older than 60 years) have a university education (Bachelor’s, Master’s, or Doctorate). For non-bankrupt females across the three age groups, there is an additional share of high-skilled employees.

The education level distribution in different age groups of nonbankrupt females (left) and males (right). Source : Own calculations

The effectiveness of the forecasting models and both multi-factor systems is assessed using three formulas that are widely implemented in the literature on bankruptcy forecasting (Korol 2020 ):

overall effectiveness of model/system: S  = {1 − D1 + D2)/(BR + NBR)]} × 100%

type I error: E1 = D1/BR·100%,

type II error: E2 = D2/NBR·100%,

where D1 is the number of insolvent natural persons forecasted by the model/system as consumers with no risk of financial distress; D2 is the number of non-bankrupt households classified as persons at risk of insolvency; BR is the number of bankrupt natural persons in the sample; and NBR is the number of non-bankrupt entities in the sample.

Results and discussion

Owing to the high degree of complexity of this research, it was divided into several stages. In the first stage, all 11 proposed financial indicators (Table  1 ) were calculated for 2400 analyzed consumers to develop the core model for each of the two systems in the second stage. The objective of this forecasting model is to evaluate bankruptcy risk using personal economic variables such as demographic and financial information on households. The five best entry variables for both models were selected based on the correlation matrix, choosing only those weakly correlated ratios that were strongly correlated with the grouping variable, representing information on the risk of insolvency or lack thereof for the given consumer. In the next stage, macroeconomic data for the three countries were collected and used to develop two supporting models for each system separately. The aim of the first supporting model is to forecast the macroeconomic risk of consumer bankruptcies by predicting the change in the number of non-performing loans in the country. The objective of the second supporting model was to forecast the exchange rate of PLN/USD for Polish consumers and NTD/USD for Taiwanese consumers. Both supporting models were developed in the fourth stage of the study. In the fifth stage, the author implemented all three separate forecasting models into one early warning system for each country. In the final stage, the effectiveness of both these systems was assessed. The architecture of the system is illustrated in Fig.  6 .

The architecture of decision system predicting consumer bankruptcy risk. Source : own study

The financial situation of households is affected by endogenous and exogenous factors. Moreover, these factors often overlap with each other. Therefore, the bankruptcy prediction system should not be restricted to using only a few financial variables for consumers but must include variables describing the environment. Therefore, the developed system monitors the economic situation of households by identifying environmental variables that may directly or indirectly endanger the consumer, such as the unemployment rate (job market situation), inflation and interest rates, exchange rates, and economic situation in the country (GDP growth rate, dynamics of retail sales, etc.).

The multifactor system consists of four rule blocks. Rule blocks I, II, and III are separate forecasting models (one personal economic circumstance and two macroeconomically oriented). The fourth rule block connects all the three forecasting models to one system. The entry variables for Rule Block IV are the output results of each model. All four rule blocks are based on fuzzy sets and are defined by the author sets of rules in the form of IF–THEN, where expert knowledge is stored.

All the entry variables of the first three rule blocks are defined by three fuzzy sets and their corresponding membership functions. The fuzzy sets and descriptions of all membership functions are presented in Table  6 . The forecast of the rule blocks, that is, output, is as follows:

rule block I: the output represents the forecast of financial distress risk for consumers based only on the use of financial ratios, and it takes values from 0 (100% bankruptcy risk) to 1 (0% bankruptcy risk). It is defined by three membership functions—“LOW” for none or low risk, “AVG” for average risk and “HIGH” for high risk;

rule block II: the output represents the prediction of macroeconomic risk of natural persons’ bankruptcies, and it takes values from − 25 to + 25% (the percentage states the predicted change of volume of nonperforming loans in the country). It is identified by three functions: “DEC” for decreasing trend, “STB” for the stable situation and “INC” for the increasing volume of bad loans;

rule block III: the output represents the fluctuation of the exchange rate (NTD/USD; PLN/USD) from − 25 to + 25% (negative values mean depreciation of USD against NTD and PLN, and positive values indicate USD appreciation against these currencies). The output is defined by as many as five membership functions “HDEP” for high depreciation, “DEP” for depreciation, “STB” for stable currency, “APP” for appreciation and “HAPP” for intensive appreciation of the USD;

rule block IV: the output represents the final forecast of bankruptcy risk for the consumer, considering the data of all three forecasting models. It is defined by three membership functions: “LOW” for none or low risk, “AVG” for average risk, and “HIGH” for high risk. The output takes values from 0, representing a 100% probability of insolvency, to 1, representing a 0% probability of financial distress for the consumer.

A graphic example of the membership functions of the output for rule block IV is shown in Fig.  7 .

The membership functions of the OUTPUT of the early warning system. Source : Own study

The use of fuzzy sets and three membership functions allows for a smooth transition from full bankruptcy risk to no such risk. Figure  7 shows that the "HIGH" function representing a high risk of insolvency is for values from 0 to 0.5, whereas full membership in the set representing 100% risk is for the output value from 0 to 0.2. On the other hand, the output value, for example, at the level of 0.3, means 50% belonging to the "AVG" function, meaning the average risk of bankruptcy, and 50% belonging to the "HIGH" function, that is, representing this risk at a high level. In case of no risk of insolvency, it can be seen that the "LOW" function also starts with a value of 0.5, but only values from 0.8 to 1.0 mean full belonging to the set of low or zero risk. A smooth transition is possible only through the use of fuzzy logic. With classical logic, there is no gradual progression from low-to high-risk.

The table below shows the precise range and type of membership functions for all the entry variables of the system. The symbols for each function ("DEC," "STB," "INC," "LOW," "AVG," and "HIGH") are also explained. The same symbols were used in the development of the decision rules for the individual rule blocks.

For a better understanding of the membership functions, Figs.  8 and 9 show the courses of the two exemplary input variables of the system. Thus, variable X5 (Fig.  8 ) represents the relationship between the share of the net value of total assets in the value of all liabilities in the household. The higher the value of this ratio, the better the level of solvency of consumers. It can be seen that there are three membership functions: “LOW,” “AVG” and “HIGH.” The threshold for the values of this ratio that are considered to positively or negatively influence the risk of insolvency was fuzzified. Some values are partly "LOW" and "AVG" and other values are partly "AVG" and "HIGH.” Only values below 0.5 mean full membership in the fuzzy subset "LOW," implying low values of the ratio (negatively affecting the solvency of consumer), and values above 10 mean full membership in the fuzzy subset representing a high level of the ratio (positively affecting the financial standing of household).

The membership functions of the variable X5 of the Rule Block I. Source : Own study

The membership functions of the variable M10 of the Rule Block II. Source : Own study

In turn, the variable, M10 (Fig.  9 ) indicates the actual market fluctuation of the USD against PLN. The developed decision rules of rule block III assume that the appreciation of the USD will result in a higher risk of consumer insolvency because it will influence the increase in the cost of living. Additionally, it is often positively correlated with the CHF, and in Poland, there is a large group of citizens with bank loans denominated in Swiss Francs. Thus, the appreciation of this currency will have a direct negative impact on creditworthiness. Figure  9 shows that the appreciation and depreciation trends begin with a variable value equal to 0. However, this value represents the full membership (100%) of the "STB" set, which indicates a stable situation in the currency market. On the other hand, decreasing values in the range of − 25 to 0% indicate greater belonging to the depreciation set, and increasing values in the range of 0–25% indicate stronger belonging to the appreciation set.

The entire early warning system consists of 1260 decision rules. There are five entry variables with three possible states (“LOW,” “AVG,” “HIGH”) in rule block I, offering 243 possible decision rules. Rule block II consists of 243 decision rules too, as there are also five entry variables with three possible conditions (“DEC,” “STB,” “INC”). In turn, there are six entry variables with three possible variants (“DEC,” “STB,” “INC”) in rule block III, offering as many as 729 possible decision rules. The last rule block, connecting all three prediction models into one system, consists of 45 decision rules with three inputs. The first two inputs have three possible states (those are the outputs of the models of rule block I: “LOW,” “AVG,” “HIGH,” and of rule block II: “DEC,” “STB” and “INC”) and one input of five states of the output of rule block III (“HDEP,” “DEP,” “STB,” “APP,” “HAPP”). Owing to the space constraint of the paper, the author presents all the rules in rule block IV of the final stage of the system (Table  8 ) and 20 exemplary decision rules for rule blocks I, II, and III (Table  7 ).

Based on a set of 1260 different decision rules, the system can forecast the risk of financial distress for natural persons. The rules were constructed considering three risk areas. The first is the prediction of the financial behavior of the analyzed consumer. This is represented by R1 in Table  8 . The second area is the forecast of macroeconomic risk for households in the form of the change in the volume of nonperforming loans in the region, which is represented as R2. The third aspect taken into account is the prediction of the exchange rates (R3). For example, rule number 30 (Table  8 ) assumes that the overall risk of insolvency will be high for a consumer when his/her risk of bad financial decisions is “average” (“AVG), the macro situation in the country is stable (“STB”) but there will strong appreciation of USD (“HAPP”). The second example is rule number 4, which predicts that the overall risk of insolvency is low. It is due to the fact that there is a low risk of bad financial decisions (R1 is “LOW”), and the macroeconomic risk is decreasing (R2 is “DEC”). Under these favorable conditions, the appreciation of USD (“APP”) cannot impact the increase in distress risk for consumers.

Table 9 presents the results of both systems, and both single fuzzy set models were run on the test samples. Analyzing the results, we answer the following three questions:

Are the developed multi-factor systems effective tools in forecasting consumer insolvency risk?

Does the introduction of macroeconomic factors positively influence the quality of the forecast?

Are the developed, new types of ratios effective in forecasting personal risk bankruptcy?

The obtained results clearly prove that the early warning systems created, outperform single forecasting models. The fuzzy logic model based solely on personal economic data is characterized by 94.2% effectiveness in the case of forecasts in Poland and 90.60% in the case of analyses in Taiwan. The system generated a 1.4 p.p. higher effectiveness (95.6%) in Poland and 1.2 p.p. (91.8%) in Asia. Both systems possess better predictive abilities for forecasting such risks in a dynamic and uncertain environment. It can be observed that apart from better efficiency, both types of errors (I and II) decreased evenly. For Polish consumers, both errors decreased by seven cases (Type I error from 22 cases to 15 and Type II error from 36 cases to 29). For Asian households, there were 39–34 Type I errors and 55–48 Type II errors (Table  9 ). It is evident that implementing macroeconomic factors in the process of forecasting insolvency risk positively affects prediction quality. Thus, the first hypothesis (H1) has been confirmed. Moreover, the results of this study support Brauchet's (2006) over-indebtedness theories, which point to structures that drive demand for loans (e.g., job loss, divorce, increased spending, stagnant wages) and structures that drive debt supply (interest rates, recession, etc.). Our multivariate systems consist of two forecasting models with macro variables. The first macro model implements information on the state of the economy (M1 representing the GDP growth rate), the labor market situation (M5 representing the percentage change in unemployment), the increase or decrease in the cost of borrowing (M8 representing the change in interest rates), the change in the cost of living (M9 representing the change in the inflation rate) and exchange rate fluctuations (M10/11), since both countries are heavily dependent on the import and export of goods and raw materials. The second macroeconomic model implements variables that directly affect the exchange rate (PLN/USD and NTD/USD), and thus indirectly affect consumer default risk in the economy. This model consists of variables that inform about the GDP growth rate (M1), relative interest rates between Poland, Taiwan and the US (M2), relative inflation rates between the two countries under study and the US (M3), percentage change in the trade balance (M4), percentage change in income (M6) and percentage change in retail sales (M7).

Thus, the answer to the first two research questions is positive. First, the combination of several forecasting models into a multidimensional system provides an effective tool for assessing the risk of consumer bankruptcy. Second, the results confirm that the approach used of combining macroeconomic variables with personal economic data in the forecasting process results in high efficiency.

This research also proves that the new types of ratios created are considerably effective and can be used in personal finance. Both, the fuzzy logic models and the systems, achieved high forecasting effectiveness. The results higher than 80% are considered in the literature as a very effective forecasting model (Brygala and Korol 2024 ). At this point, it is worth taking a closer look at the information used to develop selected financial indicators. In the case of predicting default risk for Polish consumers, we can distinguish the top five predictors—X1 (annual interest paid/value of total assets), X2 (annual income/total loans), X3 (monthly interest paid/monthly income), X5 ((value of total assets—total loans)/total loans) and X10 (education level/(total loans/annual income)). While the model for Taiwanese consumers consists of indicators X2 (annual income/total loans), X3 (monthly interest rates paid/monthly income), X5 ((total asset value—total loans)/ total loans), X7 (education level/age) and X10 (education level/(total loans/annual income)). Thus, both models commonly use as many as four indicators (X2, X3, X5, X10).

This is an important finding, proving that forecasting processes do not differ in terms of the type of personal economic information used in the models in two completely different cultural regions of the world. This conclusion is consistent with recent studies in the literature. The first example of a recent study for reference is the research of Nor et al. ( 2019 ), who, based on a research sample from Asia, showed that personal variables such as age, number of loans, monthly income, type of employment contract, number of children and monthly expenses are important factors in forecasting the insolvency risk for Malaysian consumers. Another reference could be Bauchet and Evans' ( 2019 ) study of a sample of consumers in the USA. They showed that personal economic factors such as—age, income, race, number of children, marital status, gender, level and type of debt—are involved in assessing the risk of default. A final reference is a study also conducted on the US consumer population by Brygala ( 2022 ). This study identified gender, age, marital status, level of income and debt, and period of loan default as important factors in the risk assessment process.

The innovation and novelty of our study is the development of financial ratios with the use of the above commonly accepted personal economic factors. It proves that such a research approach is highly effective. The proposed set of ratios is effective predictors of insolvency of consumers both in Europe and Asia. Thus, the second research hypothesis stating that the implementation of the newly developed types of ratios in predicting personal risk bankruptcy positively ensures the high effectiveness of the forecasting model is also confirmed.

Conclusions

Due to increasing uncertainty and instability in the global economy, increasing costs of living, and the rising costs of servicing bank loans, forecasting the insolvency of natural persons is becoming increasingly important. Today, we are faced with the question of whether to predict financial distress risk, but what new methods and variables should be implemented to increase the effectiveness and reliability of the forecast in the third decade of the twenty-first century?

Although the phenomenon of consumer bankruptcies is an important issue in economic life from the perspective of stakeholders, the related literature lacks both theoretical and methodological discussions. This study fills a fourfold manner to the gaps in the literature and offers practical solutions for forecasting this type of risk.

First, this study proposes a completely new approach for predicting the financial distress of natural persons. The developed tool is not just another forecasting model in the literature, but constitutes a whole system connecting macroeconomic and personal economic variables, enabling better forecasting properties for the identification of risk for households. The idea of using such a tool to forecast the financial situation of consumers is new in worldwide literature. The distinguishing features of this research are its multi-criterion and multi-factor characteristics. The basic assumption lies in the consideration of, and attempt to, forecast macroeconomic factors that affect the future financial standing of consumers. The developed system enables the prediction of not only the effect, namely the insolvency of consumers, but also the reasons affecting this risk (e.g., exchange rates). Second, the proposed systems are in the form of an 'open' application that can be easily modified by converting a set of decision rules. The number of adaptations in these systems is practically unlimited.

Third, this study proposes the use of newly developed ratios in household finance, similar to the financial ratio analysis used in corporate finance. They combined the evaluated consumers’ financial and demographic characteristics into ratios. The proposed ratios demonstrated high predictive ability. None of the 11 created ratios is denominated in monetary value or strictly in demographic units (e.g., age), which would limit their usage to only one country. This versatility means that they can be widely used in other countries.

Fourth, it identifies the best predictors of households’ financial distress from macroeconomic and personal economic perspectives. In addition, the author hopes that the presented multi-factor system will initiate a new discussion in the literature on various ways to improve the process of forecasting the risk of consumer insolvency.

Considering that this tool is an application that can be easily adapted to different economic regions, and employs versatile ratios in terms of universal measurement units, it can be a useful tool not only in supporting the assessment of the solvency of natural persons, but also in the formulation of macroeconomic policies by various government institutions.

The author is aware of the limitations of the conducted research. The main obstacle was the hard-to-collect reliable data. Moreover, it was a very time-consuming process as information about each consumer was collected individually. The author plans to continue research towards the development of such a multifactor system for Latin American countries.

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This research was prepared within Grant Project No. 2017/25/B/HS4/00592, “Forecasting the risk of consumer bankruptcy in Poland.” This research was funded by the National Science Center in Poland (Narodowe Centrum Nauki).

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Korol, T. Multi-factor fuzzy sets decision system forecasting consumer insolvency risk. Decision (2024). https://doi.org/10.1007/s40622-024-00399-8

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Accepted : 17 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1007/s40622-024-00399-8

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How ethical behavior is considered in different contexts: a bibliometric analysis of global research trends.

1. Introduction

2. literature review, 2.1. ethical behavior, 2.2. bibliometric, 3. methodology, 4.1. countries and their concerns about ethical behavior, 4.2. key themes in research terms, 4.3. bibliographic coupling analysis, 4.3.1. journals, 4.3.2. authors, 4.4. co-citation analysis, 4.4.1. publications, 4.4.2. journals, 4.4.3. authors, 5. discussion, 5.1. ethical behavior in consumption, 5.2. ethical behavior in leadership, 5.2.1. social learning theory (slt), 5.2.2. social exchange theory (set), transformational leadership, authentic leadership, spiritual leadership, 5.3. ethical behavior in business.

• Focus on social responsibility;
• Emphasis on honesty and fairness;
• Focus on “Golden Rules”;
• Values that are consistent with a person’s behavior or religious beliefs;
• Obligations, responsibilities, and rights towards dedicated or enlightened work;
• Philosophy of good or bad;
• Ability to clarify issues in decision making;
• Focus on personal conscience;
• Systems or theories of justice that question the quality of one’s relationships;
• The relationship of the means to ends;
• Concern with integrity, what should be, habits, logic, and principles of Aristotle;
• Emphasis on virtue, leadership, confidentiality, judgment of others, putting God first, topicality, and publicity.

Values, Business Ethics, and Corporate Social Responsibility (CSR)

5.4. ethical behavior in the medical context, 5.4.1. autonomy, 5.4.2. beneficence, 5.4.3. non-maleficence, 5.4.4. fairness, 5.5. ethical behaviour in education, 5.5.1. violation of school/university regulation, 5.5.2. selfishness, 5.5.3. cheating, 5.5.4. computer ethics, 5.6. ethical context in organization, 5.6.1. context of organizational ethical climate, 5.6.2. context of organizational ethical culture, 6. conclusions, 7. limitations and future research, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Vu Lan Oanh, L.; Tettamanzi, P.; Tien Minh, D.; Comoli, M.; Mouloudj, K.; Murgolo, M.; Dang Thu Hien, M. How Ethical Behavior Is Considered in Different Contexts: A Bibliometric Analysis of Global Research Trends. Adm. Sci. 2024 , 14 , 200. https://doi.org/10.3390/admsci14090200

Vu Lan Oanh L, Tettamanzi P, Tien Minh D, Comoli M, Mouloudj K, Murgolo M, Dang Thu Hien M. How Ethical Behavior Is Considered in Different Contexts: A Bibliometric Analysis of Global Research Trends. Administrative Sciences . 2024; 14(9):200. https://doi.org/10.3390/admsci14090200

Vu Lan Oanh, Le, Patrizia Tettamanzi, Dinh Tien Minh, Maurizio Comoli, Kamel Mouloudj, Michael Murgolo, and Mai Dang Thu Hien. 2024. "How Ethical Behavior Is Considered in Different Contexts: A Bibliometric Analysis of Global Research Trends" Administrative Sciences 14, no. 9: 200. https://doi.org/10.3390/admsci14090200

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Yekaterinburg & Sverdlovsk Oblast

History, Politics, and Economics

Yekaterinburg lies at the crossroads between Europe and Asia, east of the slopes of the Ural Mountains in central Russia. The continental divide is 30 kilometers west of the city. Yekaterinburg is Russia’s third or fourth largest city with a population of 1.5 million. It was founded in 1723 and is named for Peter the Great’s wife, Catherine I. Peter recognized the importance of Yekaterinburg and the surrounding region for the rapid industrial development necessary to bolster Russia’s military power.Today, Yekaterinburg is primarily known both as a center of heavy industry and steel-making, the Russian equivalent of Pittsburgh, and as a major freight transportation hub. Its major industries include ferrous and non-ferrous metallurgy, chemicals, timber, and pulp and paper. Yekaterinburg has long been an important trading center for goods coming from Siberia, Central Asia and Europe. The city also has a reputation as a center of higher education and research. The Urals Branch of the Russian Academy of Sciences is located there with its 18 institutes and numerous research facilities linked to industry. Yekaterinburg is also well known as a center for the performing arts. Its Opera and Ballet Theater dates back to 1912. The Urals Philharmonic Orchestra is the largest symphony orchestra in central Russia.

Yekaterinburg is the capital of Sverdlovsk Oblast (an oblast is the equivalent of a American state). Economically, Sverdlovsk is among 10 of the 89 administrative subdivisions of the Russian Federation that are net contributors to the federal budget. Sverdlovsk has produced many prominent political figures, including Russia’s first President, Boris Yeltsin, and Russia’s first elected Governor, Eduard Rossel. Since the establishment of the Russian Federation, Sverdlovsk Oblast has been one of the nation’s leaders in political and economic reform. In 1996, Sverdlovsk became the first oblast to conclude agreements with the Federal Government granting it greater political autonomy and the right to conduct its own foreign economic relations.

Economic reform has gathered momentum in Sverdlovsk Oblast. The majority of Sverdlovsk’s industries have been privatized. 75% of enterprises are at least partially owned by private interests. About three-quarters of retail sales and industrial output is generated by private enterprise. Services have grown to 40 percent of oblast GDP, up from only 16 percent in 1992. About 25,000 small businesses are registered in the oblast. Small businesses make up about one-third of the construction, trade and food service.

Industry and Natural Resources

Sverdlovvsk Oblast, like most of the Urals region, possesses abundant natural resources. It is one of Russia’s leaders in mineral extraction. Sverdlovsk produces 70% of Russia’s bauxite, 60% of asbestos, 23% of iron, 97% of vanadium, 6% of copper and 2% of nickel. Forests cover 65% of the oblast. It also produces 6% of Russia’s timber and 7% of its plywood. Sverdlovsk has the largest GDP of any oblast in the Urals. The oblast’s major exports include steel (20% of its foreign trade turnover), chemicals (11%), copper (11%), aluminum (8%) and titanium (3%). In terms of industrial output, Sverdlovsk ranks second only to Moscow Oblast and produces 5% of Russia’s total. Ferrous metallurgy and machine-building still constitute a major part of the oblast’s economy. Yekaterinburg is well known for its concentration of industrial manufacturing plants. The city’s largest factories produce oil extraction equipment, tubes and pipes, steel rollers, steam turbines and manufacturing equipment for other factories.

Non-ferrous metallurgy remains a growth sector. The Verkhnaya Salda Titanium Plant (VSMPO) is the largest titanium works in Russia and the second largest in the world. A second growth sector is food production and processing, with many firms purchasing foreign equipment to upgrade production. The financial crisis has increased demand for domestically produced foodstuffs, as consumers can no longer afford more expensive imported products. Many of Yekaterinburg’s leading food processors — including the Konfi Chocolate Factory, Myasomoltorg Ice-Cream Plant, Myasokombinat Meat Packing Plant and Patra Brewery — have remained financially stable and look forward to growth.

Foreign Trade and Investment

Sverdlovsk Oblast offers investors opportunities mainly in raw materials (metals and minerals) and heavy industries (oil extraction and pipeline equipment). There is also interest in importing Western products in the fields of telecommunications, food processing, safety and security systems, and medicine and construction materials. Both Sverdlovsk Oblast and Yekaterinburg city officials have encouraged foreign investment and created a receptive business climate. The oblast has a Foreign Investment Support Department and a website which profiles over 200 local companies. The city government opened its own investment support center in 1998 to assist foreign companies. Despite local efforts, foreign investors face the same problems in Yekaterinburg as they do elsewhere in Russia. Customs and tax issues top the list of problem areas.

Sverdlovsk Oblast leads the Urals in attracting foreign investment The top five foreign investors are the U.S., UK, Germany, China and Cyprus. About 70 foreign firms have opened representative offices in Yekaterinburg, including DHL, Ford, IBM, Proctor and Gamble, and Siemens. Lufthansa airlines has opened a station in Yekaterinburg and offers three flights per week to Frankfurt.

America is Sverdlovsk’s number one investor with $114 million in investment and 79 joint ventures. The three largest U.S. investors are Coca-Cola, Pepsi and USWest. Coca-Cola and Pepsi both opened bottling plants in Yekaterinburg in 1998. USWest has a joint venture, Uralwestcom, which is one of Yekaterinburg’s leading companies in cellular phone sales and service. America is Sverdlovsk Oblast’s number one trading partner. In 1998, Boeing signed a ten-year titanium supply contract valued at approximately $200 million with the VSMPO titanium plant. Besides the U.S., Sverdlovsk’s top trading partners include Holland, Kazakhstan, Germany and the UK.

Yekaterinburg, like most of Russia, has a continental climate. The city is located at the source of the Iset River and is surrounded by lakes and hills. Temperatures tend to be mild in summer and severe in winter. The average temperature in January is -15.5C (4F), but occasionally reaches -40C (-40F). The average temperature in July is 17.5C (64F), but occasionally reaches 40C (104F). Current weather in Yekaterinburg from  http://www.gismeteo.ru/ .

• Sverdlovsk Oblast Map
• Yekaterinburg Map

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