Predicting Workforce Churn for Supply Chain Optimization


Executive Summary

The client is one of the largest shopping and delivery service providers for local groceries and essential household services in the US. The company has a large pool of independent contractors who will shop and fulfill the orders, placed through a mobile app, for their customers. Due to the vagaries of the business cycle, the client found it difficult to predict the precise availability of the fulfillment partners to deliver the orders ontime to their customers. This caused an imbalance in supply and demand for order fulfillment across multiple cities, thereby impacting the business revenue and
customer satisfaction.

To address this challenge, Factspan worked with the client to design and deliver a data analytics model that will help them to better understand the fulfillment partner profile and their likelihood of churn. It will help the client to tailor incentives and retention programs for their active and highperforming segment of fulfillment partners.

Project Highlights

  • Developed machine learning models to predict the likelihood of churn by a fulfillment partner
  • Identified the root causes that lead to churn
  • Segmentation of fulfillment partners and their activity performance
  • Helped in reducing cost of acquisition, overheads, and in designing better retention strategies & incentives
  • 85%+ accurate prediction of the churn likelihood of a fulfillment partner
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