Addressable Market and Lead Scoring Application

Background

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  • A global healthcare consulting company wanted to bolster its GTM services by creating an ML-based addressable market estimation and lead prioritization platform
  • The client needed to enhance its ability to identify and prioritize high-potential leads and opportunities to improve sales efficiency

Objective

  • To configure, test, and deploy an ML-based addressable market and propensity model that could size and prioritize CRM leads to develop a more focused sales strategy

Solution

WorkStream
  • Understood the current GTM methodology, its limitations, and the unmet needs of healthcare end-clients
  • Collected and consolidated leads data from end-client CRM, web portal, 3P lead partners, and 3P company databases
  • Trained and tested ML models for both addressable market sizing using sales/bookings data, and propensity scoring using historical win/loss data
  • Selected the optimal price based on:
    • –  Selected the best-performing algorithm – logistic, random forest, ensemble, and NN
  • Tested, automated, and deployed the solution in coordination with the application team

Impact

impact
  • Visualized outcomes on a dashboard that was integrated with the application
  • The client monetized the application as a core offering for any GTM service
  • New deployments took 7-8 weeks, with refreshes executed in 1 week