Customer Churn & Retention Prediction

Challenge

Customer churn poses a significant financial risk for retail companies. In addition to lost revenue, the departure of customers also means the loss of long-term sales and margin potential. Reactivating previously inactive customers presents exciting opportunities for tapping into existing revenue and margin sources. Our client, a national retail company, aimed to reactivate inactive customers and use automated marketing measures to ensure that regular interactions reduce the likelihood of future inactivity.

Approach

In the first step, the existing customer base in both online and offline business, where identifiable through accounts or customer cards, was divided into active and inactive customers. Subsequently, various parameterized activation measures were sent to all inactive customers via email and post.

Based on the responses, a machine learning model was trained to calculate the likelihood of a response/activation based on a combination of action and customer parameters. Additionally, the model can determine the optimal configuration of activation measures for each customer. Finally, the process was implemented into the client's IT infrastructure.

Result

As a result, our client received an automatic, data-driven process that regularly targets inactive customers in an optimal manner to reactivate them. This solution significantly reduces the risk of losing customers while securing regular sales and margin potential.