Mitigating Churn for an Automotive Manufacturer’s Subscription Service
Posted by | Fuld & Company
An automotive manufacturer’s aftermarket subscription service was experiencing high churn, and management wanted to review the situation and take corrective measures.
Objective
To identify churn drivers and create a predictive model to flag customers at risk.
Approach
- Understood the client’s business model and analyzed historical data, including service delivery, feedback, and complaint data.
- Created train and test data using currently active and churned customers.
- Leveraged Random Forest and Extra Trees models to identify the drivers of customer churn.
- Created a neural network-based classification model to flag customers at risk; created sales plays that recommended products and discounts that sales agents could propose to customers; redesigned agent incentives to leverage the model’s recommendations and provide feedback to improve the sales plays.
Outcome
- Churn rate reduced by 13.6%.
- The company can identify customers at risk and take timely actions to mitigate churn risk.
- Created roadmap to enable sales plays experiments among customers via sales call centers
Algorithms Used: Random Forest, Extra Tress, ANN
Tools
Tags: Automotive, case study, Customer Analytics, Data analytics
