Predicting and managing opportunities in complex B2B sales

In B2B sales, predicting the probability of success is accompanied by numerous cognitive biases. The gut feeling distorts the picture of the sales situation and often leads to sales activities that are not goal-oriented. To proactively manage the sales process, the sales team must have an objective picture of the current sales situation:

  • How is my relationship with the decision-makers, and how does the decision-making process work?
  • How well can we meet the prospect’s requirements and expectations?
  • Who are the competitors, and how can we win against them?

The sales team needs to know the relevant success factors to gain a holistic view of the sales situation. In other words, they must know the questions which determine whether an opportunity will be won or lost.

When developing a solution for predicting and managing opportunities in B2B sales, we focused on two major goals:

  • The solution shall integrates seamlessly with existing CRM systems. That ensures no business-critical information leaves the protected CRM environment.
  • We shall use scientific methods to predict and manage opportunities in B2B sales. That enables transparent and traceable sales management.

The "Sales Forecast 4.0" study

The “Sales Forecast 4.0” study has been the first empirical research to identify these success factors and develop a short psychometric questionnaire for complex B2B sales.

A psychometric questionnaire is a survey method from empirical psychology ensuring a valid and reliable measurement of the success factors by objective quality criteria.

For example, the validity criterion ensures that the questions measure what they are supposed to measure.

Here you will find more details about the study.

The three success factors of the questionnaire

  1. The success factor Relationship & Commitment reflects different aspects of the relationship (7 questions).
  2. Competitiveness considers the relative strength of the strongest competitor (5 questions).
  3. The factor Requirements shows to which extent the solution matches the requirements (4 questions).

The sales team rates the questions on a 5-point Likert scale from “strongly disagree” to “strongly agree.”

The AI-based forecasting model

Due to the reliable measurement of the success factors, the forecasting model has a unique data basis with numerous predictions available.

To achieve the best prediction accuracy, the AI-based forecasting model uses only information from the intersections of the circles. The reason is that only this information has a connection with the purchase decision.

This AI approach makes it possible to “filter out” information like unwanted influences such as the pressure to succeed, situational effects, or bad moods.

Objective measurement of hit rate

ROC (Receiver Operating Characteristics ) curves measure the accuracy of predictions.

Comparing the forecasting model with the gut feeling enables objective proof of the improved prediction quality and thus the hit rate.

The unique combination of the psychometric questionnaire with the AI-based forecast model achieves a hit rate of 82% across all predictions.

With the chance line as the reference value, the increase compared to gut feeling is about 77%.

Efficient management of opportunities with scientific approach

With each response to the questionnaire, the salesperson gets a realistic picture of the current sales situation. Based on the rated success factors, the sales team can manage the sales process in a targeted manner.

In addition, success factors provide an overview of the problem areas of each sales opportunity. The prediction history shows where the probability of success increases, the sales process stagnates, or the deal might start to fail.

If the probability of success of the AI-based forecasting model (AI-PROG) achieves a value of more than 62%Optimal separation value according to ROC-calculation, the closing of the opportunity is almost safewith a  probability of  82%. For more on this topic, check out our blog article.

Opportunity Scoring: Prioritizing opportunities with CRM data and AI

CRM systems such as Microsoft Dynamics 365 Sales or Salesforce support the sales team in managing opportunities efficiently. However, information such as documented sales activities, expected revenues or progress in the sales process plays a less important role in predicting a potential purchase decision.

When this data gets processed with a suitable AI algorithm like Random Forest, a resulting scoring system can classify sales opportunities to rank them. However, in most cases, this prioritization uses random or weak correlations. That means AI could not find meaningful patterns, which means that the prioritization has no predictive power.

You will find more information on this issue in our blog article: Can AI predict behavior in complex purchase decisions?

The added value of our solution at a glance

  • The more accurate prediction of expected revenues gives executives more planning reliability.
  • Sales management can make the most of limited resources and gain more control over expected order income.
  • The sales team controls its sales activities based on objective success factors.
  • By measuring the success factors, the gut feeling becomes transparent and traceable.
  • The solution provides recommended actions to improve the probability of success
  • The solution optimizes the entire sales process and increases the win rate of your pipeline.