When judgement-based sales forecasting may be best for your business and how to do it

Imagine you’re an early-stage startup launching a SaaS product. You don’t have years of historical sales data, and customer demand is still uncertain. Everybody talks about how important data-driven sales forecasting methods are, but intuitively, you know that data-based methods probably won’t really work very well in your situation. 

Fortunately, there is a method that will. It’s called judgment-based sales forecasting, and it can help you create a forecast you can trust – even when you don’t have a lot of historical data to base it on. 

With judgement-based forecasting, you’re consulting your sales team, conducting market research, and studying industry trends to inform your sales forecast. But how exactly do you pull all those different kinds of information together to arrive at a reliable forecast? 

This article will explain that. You’ll learn more about what judgement-based forecasting is and how you can use this powerful sales forecasting method effectively in your business.

What is judgment-based forecasting?

Judgment-based forecasting is a subjective bottom-up method of sales forecasting. Instead of depending on historical trends, this method incorporates human judgment by relying on the experience of sales professionals, managers, and industry experts. Judgement-based forecasting is especially well-suited for unpredictable markets and early-stage companies with little/no historical data to inform their forecasting. 

In a nutshell, sales reps begin by estimating how much revenue they expect to close within a set time frame. They then break this down by region (when applicable) and by individual deals. These forecasts are then combined by managers to create an overall sales forecast. But there’s a bit more to it than that. So, let’s dive in a bit further. 

Components of judgment-based methods in sales forecasting  

Judgment-based forecasting builds on three key things: 

‍Intuitive forecasting

Often, the best insights come from the most experienced people in your company. Senior sales staff, managers, or external consultants share their educated guesses and ‘gut feelings’ about future sales trends. These predictions are based on their experience and current on-ground sales trends, helping to create realistic forecasts.

For example, a sales manager might predict a 30% revenue increase in sales in the next quarter, based on his deep understanding of customer requirements and the company’s product roadmap. 

‍Market research

Qualitative market insights help you understand your customers better. Surveys, focus groups, and competitor analysis can help predict sales trends. 

For instance, a startup offering HRMS might survey HR leaders to measure interest in a new integration. Their feedback can help forecast sales based on the integrations prospective customers want vs. the ones you presently offer. Knowing that a new competitor offering one//more of those integrations has entered the market would further inform your forecast. 

‍Scenario analysis

‍Scenario analysis is always useful, but especially during times of  economic uncertainty or unexpected disruptions. By creating multiple ‘what-if’ scenarios, you can see how factors like an economic downturn or competitive pressure might impact your sales pipeline.

For example, a startup might model three scenarios to estimate revenue. One assumes market growth by 10%, another anticipates slow adoption, and the third one factors in a major competitor launching a similar product.

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When are judgment-based methods most effective?

There are three situations in which judgment-based methods can be particularly effective.

High ticket deals with low velocity

If you sell high-value software with long sales cycles and fewer deals, relying on historical averages can be misleading. Instead, sales teams can create forecasts on a deal-by-deal basis, considering factors like the prospect’s buying process, budget, and readiness to make a decision.

Limited historical data

Historical sales data may not be available for early-stage startups, new markets, or new product features. Combining sales reps’ insights with structured research (experts, market analysis, among others) and CRM-based insights can help with better forecasts.

Unprecedented market disruptions

Judgment-based forecasts are particularly valuable in unpredictable or unique situations where historical data falls short. This is often the case during macroeconomic shifts, industry or technology disruptions, new competitors, or global crises like the pandemic. For instance, when launching a new SaaS product in an emerging market, your team’s insights into the market’s buying behavior may provide a more accurate forecast than relying solely on past trends.

Short-term forecasting

The judgment-based method works well for the shorter term forecast periods, such as for the current or next quarter.

This is because the methods considers qualitative factors that statistical models and other methods may miss, such as customer sentiment, competitor actions, or emerging trends, all of which are important to evaluate for short-term sales forecasts.

Triangulating with forecasts obtained using other methods

Judgment based forecasting is also very useful in helping to triangulate with forecasts created with other methods, such as pipeline-weighted forecasting (aka probability-weighted forecasting), driver-based forecasting, and statistical methods.  

The figure below shows four key categories of sales forecasting methods that can be used effectively for triangulation, including both bottom-up and top-down forecasting methods within each category.

Pros and cons of judgment-based forecasting

Before relying on judgment-based forecasting, consider its trade-offs. While it does a good job of capturing market nuances and emerging trends that data may overlook, it is also subjective and vulnerable to biases. 

Pros:

• Flexibility: Unlike rigid predictive models, judgment-based techniques offer flexibility to incorporate expert insights, market changes, and deal-specific scenarios.
• Contextual accuracy: The approach considers factors like market trends, industry changes, competitor actions, and changing customer sentiment to create more accurate and detailed forecasts.
• Works with limited data: Judgment-based forecasting even works in situations where data is less or unreliable.

Cons: 

• Subjectivity and biases: Forecasts based on a personal judgment can often be influenced by biases, like being overly optimistic, having a preference for certain markets, or underestimating risks. 
• Lack of standardization: Different team members may use different assumptions and methods for forecasting, which can make it difficult to align forecasts across the organization. ‍
• Scalability issues: The judgment-based approach requires a lot of time and effort. It can become difficult to maintain as businesses grow.

Better sales forecasting with Drivetrain

While judgment-based forecasting has its merits, combining it with technology can improve accuracy and consistency. Sales forecasting tools and FP&A software help integrate qualitative insights with quantitative data.

CRM software can help companies track deals, customer interactions, and pipeline progress. FP&A tools like Drivetrain enable scenario planning by combining data with sales team insights to refine sales forecasts.

Drivetrain offers the following features to improve your sales forecasting (regardless of the methods you use):

• Automate updates for qualitative inputs
• Integrate seamlessly with your CRM and sales tools
• Combine qualitative insights with data for better forecasts
• Run multiple scenarios to prepare for various market conditions
• Customizable dashboards to spot trends and discrepancies easily
• Improve collaboration between sales and finance for accurate revenue planning

Explore Drivetrain today to see how easy it can be to create more reliable sales forecasts!