Case Studies

Lead Scoring Through ML-Based Propensity Modeling

Posted by | Fuld & Company

An enterprise cloud solution provider wanted to increase sales conversions by better prioritizing leads.

Objective

To configure, test, and deploy an ML-based propensity model that would prioritize generated leads based on propensity and help drive conversions for the sales team.

Approach

Before Propensity Modeling - single sales strategy and marketing message to all prospects; After Propensity Modeling - personalized sales and marketing efforts tailored to propensity-based segments

Propensity modeling enables the sales team to personalize and target efforts

  • Understood the current scoring scheme and its limitations.
  • Collected data for all leads from CRM, web portal, 3P lead partners, and 3P company databases.
  • Trained and tested ML classification models on historical customer win/ loss data.
  • Tested and used the best-performing algorithm among logistic classification, Random Forest, ensemble classification, and neural networks.
  • Clustered leads and created targetable sales plays for the sales and marketing teams

Algorithms used include: classification models such as logistic classification, Random Forest, ensemble classification, and neural networks.

Outcome

  • Visualization of classification outcomes and analyses on a Tableau dashboard
  • Integrated classification model with client CRM to identify leads with high probability of conversion
  • Increased conversion rate by 11%

Tools

  

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