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.
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|
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.
Bridgette Barry is the Director of Marketing at Aptitive. From data strategies to innovative AI solutions, she loves driving awareness to the awesome data technology solutions that Aptitive and their technology partners can build.