We enhanced a client’s customer churn prediction model by improving model accuracy and actionability and integrating its outputs into production systems for effective customer engagement.
Challenge
The client built an early machine learning model to predict customer churn, but the organization didn’t trust the model’s accuracy and didn’t understand how to use it.
Approach
Our team developed an operating model for implementation and improvement of the ML model. We designed strategies for using and adopting the churn prediction model across multiple teams, customer segments, and worked with data science and analytics teams to build understanding and interest with business stakeholders. After prioritizing use cases based on prospective business value, we structured the collaborative feature engineering process to improve model actionability and delivered high-risk customers to teams where they could engage with retention tactics.
Value Delivered
- Improved customer churn prediction model accuracy and actionability
- Implemented the model outputs into the client’s production system, enabling stakeholder use for things like dynamic targeting of customer success outreach
