EVERNILE

How to Identify & Arrest Customer Churn

Losing customers is expensive

Only about 2% return back, and acquiring new ones costs 5-10 times more. Predictive models identify red flags, enabling proactive measures before it's too late

The Approach

Making it Happen

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Fortunately, advanced analytics and machine learning now empower the development of potent Churn Prediction Models. Simply tap into your customer data pool, curating a feature set from diverse touchpoints like purchases, interactions, and feedback.

The result? A probability score predicting potential attrition or non-purchase in a defined timeframe (which may be based on your average order gap). Most of these algorithms reveal the relative weights of the features, offering insights into why a customer might churn.

Implementing the Churn Prediction Model means categorizing customers into High, Medium, and Low buckets based on their scores.

For the High-risk group, unleash compelling incentives like exclusive coupons or enticing cashbacks. Simultaneously, be proactive in addressing potential churn reasons. For instance, promise an expedited delivery to a customer who has faced delays before. The key is precision—matching the right strategy to each level of churn risk for maximum impact and retention.

We developed the Churn Prediction Model for a hyper-growth D2C player, seamlessly integrating it with their CRM systems. Targeted automated campaigns, tailored for customers with a churn propensity exceeding 0.5, resulted in a significant ~10% reduction in voluntary customer attrition.