Since the cost of acquiring a new customer is higher than retaining existing ones, it becomes imperative to determine when and why a customer might leave. Without a focused and data-driven retention strategy, the knowledge of why customers churn is delegated to expert judgment alone. This puts an immense challenge on institutionalising that knowledge, de-risking it from the attrition of key employees (experts), and hard to verify and validate.

By pulling together your available customer data and harnessing the power of multiple complementary Machine Learning models, we can help you examine the factors driving customer retention, gain precise insights and create actionable strategies to reduce future churn.

Our approach to retention analytics aims to retain existing customers at optimal cost and to prevent them from exiting to competition. It is designed to extract maximum insight from the data available by minimising the false positive error (predicting a customer as a churner when they didn’t) and the false negative error (predicting customers not churning when they did churn).