Optimizing Customer Retention Programs

Traditional modeling approaches focus on predicting the likelihood of a customer to perform a specific action like purchase or churn. In contrast, uplift modeling focuses on predicting the change in likelihood to conduct the same action. A large majority of direct and database marketers are equally focused on adding new customers and retaining existing ones. But in an economic environment where top-line growth is slow, customer retention programs take on an added significance. Still, firms struggle to manage their retention programs effectively because marketers:

• Fail to agree on a common definition for customer churn.
• Lack the ability to target appropriate offers to churners.
• Try to retain all customers, not just the profitable ones.

Managing customer churn continues to flummox direct marketers. They struggle to define, target, or properly value customers. But increasingly marketers are turning to predictive analytics to power retention programs. Uplift modeling is an emerging technique that can help marketers improve the performance of their retention programs.