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


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

Do you think it could help? Happy to tell you how.