Lead Scoring Models That Prioritize USD 20B Sales Pipeline

Business Objective

Our client is the sales and marketing group of a Fortune 100 technology company, with a worldwide footprint.

They needed a lead scoring model with a comprehensive view of the quality of their sales pipeline through a standardized approach of predicting win rates and risks, even as opportunities across different groups within the company differ in their conversion behavior. This would help them to focus sales efforts on opportunities that require attention and put out a reliable forecast of quarterly performance.

  • Different levels of data – at account, opportunity, individual product, and account owner levels
  • Data quality – structural biases in the way the data was recorded by sales representatives
  • Effect of time – the longer an opportunity stays in the pipeline, the lower its probability of conversion

Solution Methodology
  • Identified quality metrics that can be applied to individual opportunities across business units, sales groups, and geographies. Rolled-up and visualized metrics across higher dimensions such as product segment, sales group, etc. for business to validate
  • Developed a model to assign an overall quality score to each opportunity, which can further be simplified to a conversion indicator
  • Addressed differences across product lines/LOBs by introducing variables such as dwell time in each decision stage, opportunity age, decision timeline delays, etc.
  • Scored portfolios of different sales managers using the model. Ensured robustness by predicting conversion rates of previous quarters and comparing to actuals
  • Designed dashboard(s) to provide a holistic view of pipeline quality, which further strengthens the $ win forecast behavior.
Business Impact
  • Trained the client sales managers in using the analytic dashboard(s) to prioritize sales actions.

  • The scoring model based analytics solution is now being used to prioritize USD 20B worth of opportunities in the client sales pipeline every quarter.

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