Business Challenges
Identifying potential churners
Determining whether customers who are predicted to churn can be retained
Assessing feature responsiveness
Which product or service attributes will entice a predicted-to-leave customer to stay?
Estimating time-to-churn – which is critical to plan retention strategy
Predicting sensitive time periods for customers who have sporadic interactions
Capabilities
Data connectors built to seamlessly ingest customer transactions and care data
A Model Building Framework that is a fast “out of core” learning system
Models built during training can be automatically used for scoring
Real-time deployment and maintenance; real-time measurement of model performance on key metrics like accuracy, predictive power, and stability of customer churn analytics over time
Achieved a lift in customer retention YoY for a large telecom client in the Middle East
Helped a media services company create a customer retention campaign by identifying key reasons for churn
Increased LTV by deploying a retail retention model for a major bank in the Middle East
Improved lending rates for an auto lending software company
Business Impact
Real-time visibility into risk and churn behavior using data streaming architecture
More profitable campaigns thanks to customer-specific lift analysis that targets and retains high-value customers
Revenue growth due to customer retention improvements