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The problem:

Inaccurate sales predictions

In complex B2B sales, the sales team estimates the probability of success for each sales opportunity when preparing the monthly sales forecast at the latest.

Due to the complexity of the sales process, the sales team has to rely on their gut feeling.

The intuitive process of gut feeling has numerous cognitive biases and distorts the picture of the current sales situation at the prospect. However, the perception of the sales situation is essential for managing opportunities.

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The research approach:

Intuitive versus statistical judgment

In clinical psychology, research has been going on for several decades on the intuitive judgment of experts to avoid misdiagnosis with the help of decision algorithms.

In contrast, statistical judgment uses surveys, tests, or other measurements to calculate a diagnosis using an appropriate decision algorithm.

In a meta-analysis (analysis of 136 studies), Grove et al. (2000) demonstrated that statistical judgment is more accurate than intuitive judgment.

The study "Sales Forecast 4.0":

Applying the research approach to complex B2B sales

The empirical study “Sales Forecast 4.0” in psychology demonstrated for the first time that the calculation of the probability of success for sales opportunities based on an AI-based forecasting model enables significantly more accurate predictions than the gut feeling of salespeople.

Method & Implementation

A preliminary study developed a psychometric questionnaire using expert interviews, findings from sales research, and best practice approaches like popular sales methods.

To conduct the survey, salespersons in complex B2B sales applied the questionnaire to two opportunities of their current sales forecast. The sample included 188 opportunities from 82 B2B salespeople of 62 companies.

A personalized follow-up survey recorded the actual purchase decision of the prospect.

Data analyses & results

The data analysis identified three higher-level success factors with 14 questions that showed a statistically significant correlation with the purchase decision.

An AI-based forecasting model that uses gut feeling and success factors to predict the probability of success. Receiver operating characteristic (ROC) curves measured the accuracy of the predictions. The area under the curve (AUC) is a commonly accepted indicator of the accuracy of the predictions.

By comparing the AUCs of the forecasting model with the gut feeling (see figure on the right-hand side), it was possible to demonstrate empirically for the first time that the predictions of the forecasting model are significantly more accurate than the gut feeling of the salespeople.

According to the classification, the ROC showed a fair prediction quality (AUC value: 0.76) for the gut feeling and an good prediction quality (AUC value: 0.84) for the forecasting model.
The hit rate of the AI-based forecasting model reached an impressive value of 82%.

That means that the forecasting model predicted the correct buying decision in 82% of all predictions, regardless of whether salespeople won or lost their opportunities.


The solution:

Implementation as Add-In for CRM-Systems

Since the study findings, the psychometric questionnaire and the AI-based forecasting model have been improved using different methods and subjected to internal validation.

The implementation and integration of the forecasting model into CRM-Systems enables companies to improve the accuracy of their sales forecasts. At the same time, it helps the sales team to manage their opportunities efficiently and increase the win rate of their pipeline.