What we do

Case study

Predictive Customer Lifecycle Management

Leveraging Advanced Machine Learning for Hyper Personalized Customer Journeys to Boost Customer Lifetime Value

Our solution

We help our clients to optimize and monitor customer behaviour across four main stages of their retail journey: Acquisition, Onboarding, Engagement, and Retention.

We categorize the customers into distinct lifecycle paths depending on customer attributes and interaction with product. It enables the clients to tailor experiences for each customer.

We leverage machine learning to create personalized interventions, such as targeted promotions, personalized recommendations, and customer service outreach to cross-sell and prevent churn based on customer lifecycle stages.

The result

Early identification of churn and delivering personalized interventions have led to churn reduction of high-value customers by 13%.

By identifying upsell and cross-sell opportunities and delivering personalized recommendations, we were able to increase revenue per customer by 18%.

Personalized interventions

Early churn identification

Machine learning on customer behavior

Customer categorization

Cross-sell and upsell