What is a qualitative forecasting model and when is it appropriate?

As sales professionals traverse a growing digital landscape, the means by which they predict sales also grow. While sellers have used a wide variety of tactics to generate insights and build sales forecasts, those tactics have traditionally fallen into two overarching categories:

• Qualitative forecasting, which is based on opinion
• Quantitative forecasting, which is based on historical data

Sales forecasting methods from each of these groups have their own unique place within forecasting processes. Quantitative forecasting methods employ data and analytics to produce statistical projections of future sales and provide sales teams with an objective starting point for predicting revenue. In contrast, qualitative forecasting methods invoke the human element of sales, relying on experts’ opinions, gut feelings, and analytical judgment. Qualitative, opinion-driven methods are thought of as the oldest form of forecasting but still provide valuable sales predictions that quantitative forecasting methods can’t.

Sales professionals looking toward their next forecasts may be considering the various techniques that fall under each forecasting model and how they can be used for their business. In this article, readers can explore the various uses of qualitative forecasting methods, find qualitative forecasting examples, and gain insight into how qualitative forecasting is ultimately only a portion of a robust, accurate sales forecast.

What is qualitative forecasting?

Qualitative forecasting methods are based on immeasurable data such as opinions and intuition. Designed to analyze the human element of sales, market demand, and market trends, qualitative forecasting methods include documenting expert opinion, surveying in-house sales teams, and even performing market research to understand buyer opinions and behavior and how they may shift in the months and years to come.

When a sales rep ends a promising sales call and just knows, without any concrete reason, that the client is ready to move forward, that salesperson is using qualitative forecasting techniques. When a competitor unveils a new product and a business leader calls it a “game changer,” that leader is using qualitative forecasting. Expert opinions carry a lot of weight in how invested parties may shift or maneuver within the industry. If a new product set to release next quarter is given that “game changer” label, competing businesses may jump-start product development for their competing product.

While quantitative forecasting leverages data to produce objective predictions based on numbers, it fails to consider the worldly influences that affect all sales, such as current market trends, consumer demand, and major world events. Because qualitative forecasting seeks to analyze those factors, it acts as the yin to quantitative forecasting’s yang. Of course, the lack of objective analysis of statistical data is a shortcoming for businesses looking to capture the most accurate forecasts possible.

What is a qualitative forecast example?

For an example of what qualitative forecasting looks like in practice, imagine a weekly sales meeting:

Each week, a sales team gathers to share movement on their deals. When it’s Bob’s turn to report in, he shares a conversation he had with a prospect, who indicated that he was ready to move a contract on for signature by the end of the week. Previously, the team had projected that deal to close next month, in Q3, but Bob’s personal insight has moved that forecast up, and now the sales manager is confident that the deal will close in Q2.

Gaining insight into expert knowledge or tapping into the gut-feeling of sales pros gives forecasters a leg up when determining where to place their bets for the next cycle’s sales outcomes or product and service demand.

What are the benefits of qualitative forecasting?

There are many benefits to using qualitative forecasts techniques to make impactful predictions for the future. Specifically, what are qualitative forecasting techniques good for?

Sourcing expert knowledge and experience

Probably the biggest benefit to qualitative methods of demand forecasting is the ability to tap into the experience of industry experts as they make predictions about customer behavior and future sales outcomes. For example, one expert may have knowledge about an upcoming technology or product that will affect demand within a future sales cycle. Another may simply know enough about a given market to anticipate its movements in the near future. That experience can help a company make better forecasts and adjust in preparation.

Comparing against quantitative forecast results

Forecasters may use qualitative forecasting techniques to better attune their quantitative forecasting results to reflect the experience, opinion, and judgment of experts. Because historical data neglects to understand the human experience and current trends, it can’t put together the most accurate view of the future. Acting like a gut check, qualitative forecasting enables businesses to compare against their quantitative forecasting results to account for any upcoming industry trends, new products and services, and so forth.

Flexibility with nonnumerical data

Collecting, organizing, attributing, and analyzing — driving insights using quantitative forecasting consumes a hefty amount of financial resources and manpower. Qualitative forecasting techniques rarely require constant upkeep and management, which makes them more accessible to smaller, newer teams that may lack resources or historical data.

Young brands will inevitably find it harder to forecast because they have zero to little historical data to leverage. Without several years’ worth of clean, consistent data, businesses have to turn to experienced experts to make judgments about future sales outcomes. Qualitative forecasting offers companies still in their starting years to compile those forecasts.

What are the qualitative methods of forecasting?

“Qualitative forecasting” is an umbrella term for many methods and techniques used to make predictions based on expert judgment and human intuition. Every business is unique, so while some frequently used qualitative forecasting methods may work for many businesses, they won’t work for all. The best qualitative forecasting techniques include purpose-driven processes that align with the needs of the business.

What are the qualitative techniques? Here’s a list of some of the most popularly used techniques:

• Jury of executive opinion
• Sales opinions
• Expert opinions
• Delphi method
• Market research

Jury of executive opinion

Product and service leaders are tremendous resources for forecasting potential demand or sales outcomes for the future. They have key knowledge about upcoming offerings, competitor products, and future releases and about how a changing market may impact the measurable forecast. In the jury of executive opinion, forecasters consult key internal experts within different departments of a business, such as products and services teams, sales groups, and operations teams. This can take the form of a survey emailed to company leadership, a quarterly meeting to gather opinions, or one-on-one conversations. The collected opinions of the experts are then averaged to plot a subjective view of the future.

Pros: By taking the time to understand the perspectives of company leadership, sellers can better understand the overall direction of their organization and predict revenue.

Cons: Polling only internal opinions may not be a balanced approach to understanding a business’s true direction because the strong opinions of the leaders may influence the opinions of others in the group.

Sales opinions

Interviewing sellers may be the best way to tap into how customers think. Each sales representative is an expert on a unique type of customer, whether that’s based on regional territories, relative industries, or even client spend. By nature of their interaction with customers, sales professionals build an intimate understanding of those customers’ desires or frustrations as well as of upcoming market shifts, industry trends, and demand fluctuations for that group. Individual sellers may rely on their own opinions when producing forecasts for themselves, or sales teams may meet to gain consensus on deals while building projections. This information is critical for sales teams to collect and analyze when making predictions about the future.

Pros: Sellers know their customers better than anyone else, and they can pick up on indicators of individual buyer behavior in a way that no statistical model can.

Cons: Putting the responsibility of the forecast on the shoulders of the sales teams who directly impact its success can be intimidating for them and therefore may affect their responses. Some sellers may be inclined to overestimate their own performance, while others may intentionally underestimate to protect themselves come review time.

Expert opinion

External experts can also be a treasure trove of qualitative insight useful in preparing sales forecasts. Industry analysts, academics, and tastemakers with their fingers on the pulse of a company’s target market can offer perspectives that internal teams lack on what the competition is up to, how macroeconomic movements may impact buyer behavior, and other short-term trends. Sellers can access these opinions in the public sphere by staying up-to-date with industry publications, or they could invite experts to conduct market research or present their findings to sellers and other leadership within the company.

Pros: As with all forms of forecasting, more data helps produce more informed predictions — especially when that data is the informed opinion of an expert.

Cons: Experts don’t get it right 100% of the time. No prediction is truly accurate, and it can be easy to put too much weight on an expert opinion, only for it to completely miss the mark.

The Delphi method

Perhaps the most well-known technique, the Delphi method involves consulting a panel of industry experts through a series of questionnaires regarding specific topics about the business or industry. The Delphi method is almost like the Supreme Court of qualitative forecasting; experts work together to make informed predictions about future outcomes. The Delphi method typically involves multiple rounds: Each round ends with a peer review of the questionnaire results and then an open discussion so the experts can entertain unique considerations and little-known information. The goal for the Delphi Method is to draw consensus among the experts and build a forecast based on that.

Pros: The pool of experts is not limited to internal resources, accounting for external perspectives. Generally, the Delphi Method is carried out remotely in an effort to limit strong personalities and group-think problems that can sometimes plague the jury of executive opinion.

Cons: Sometimes, experts simply cannot agree, making it an unreliable method of forecasting.

Market research

When forecasting for customer demand, the opinions of past, present, and prospective clients can tell a business everything it needs to know about what’s coming down the pipeline. Customers’ needs, interest in future products, shared pain points — these are just a few insights that can be claimed from conducting customer and market research. This can include collecting insights from customer reviews, sending recent buyers surveys about their experience and intent to purchase in the future, and reviewing the research of other industry organizations. Consider all the ways that customers interact with a business. Collecting data and feedback from those customer touchpoints, such as social media and customer service departments, could produce important insights into the customer experience and their wants and needs. When the research can pull enough data, analysis can pinpoint commonalities and shared trends.

Pros: Market research focuses on what matters most — the customer — and provides clear indications of trending interests, struggles, and needs.

Cons: Conducting market and customer research requires detailed processes and a lot of resources. Smaller groups may not have the bandwidth or the budget to complete large-scale market research. Additionally, the opinions of the participants may not reflect the feelings of the entire customer base.

How is qualitative forecasting used to build the best forecasts?

The best way to build sales forecasts utilizing qualitative insights is with modern, artificial intelligence–enabled tools that incorporate the qualitative alongside the quantitative to provide prescriptive recommendations for sellers. With Collective[i], modern sales teams are able to capture a broader range of data, including up-to-date buyer interactions, to cut back on the busy work and enable individual sellers to follow their instincts and punch above their weight.

At Collective[i], we believe that artificial intelligence is the future of sales and demand forecasting. Our forecasting platform harnesses AI to develop real-time forecasts based on statistical customer data and trending data. By automating data collection and attribution, our software eliminates the burden of spending time and resources on data management and stockpiles data points from industry competitors to gather a full scope of sales expectations. That enables businesses using Collective[i] to pivot their sales tactics, increase engagement with qualified leads, and spend more time selling.

Ready to see it in action? Explore Collective[i] today.

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