Aptitive used state of the art machine learning models to score sales opportunities, creating a valuable tool that allowed our client’s sales team to analyze and prioritize sales opportunities in their pipeline on the likelihood they would close.

The Problem

A B2B software company wanted to leverage the data they had collected from their sales pipeline to better manage and prioritize open opportunities.

Although the company had a wealth of data on their sales opportunities, the large number of opportunities, high variability, and number of possible combinations made it nearly impossible for the sales team to see how these factors affected the outcome of the opportunity. The company knew that they could improve their sales process and success rate if their sales team could easily analyze and leverage this data.

Our Solution

Aptitive’s machine learning framework was used to implement a solution to analyze the data and predict if the opportunity would be won or lost. The framework leverages SQL Server’s built-in machine learning services which allows us to leverage open source machine learning algorithms implemented in R by Microsoft to build predictive models.

Examples of Data Collected

Static Data Dynamic Data
Company size Contact with sales
Industry Engagement with emails
Customer type Length of time between stages

The Outcome

We used the historical opportunity data to generate a numerical score that was capable of predicting the outcome of an opportunity with 90% accuracy. This score was augmented with data from the data warehouse and implemented in a PowerBI dashboard that was used by the sales organization to prioritize pursuits and gain enhanced understanding of behaviors that lead to wins. The ease of use allowed in-house analysts to run their own predictions and conduct additional analysis to discover patterns and causal effects. The machine learning generated score and related insights were integrated in the dashboards used by the sales team and inform their decisions on a daily basis.

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