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Customer Retention and Lifetime Value Optimization

Customer Retention and Lifetime Value Optimization

Have any problems with customer churn and the challenge of identifying high-value customers? We develop customer lifetime value models, allowing proactive retention. Read more…

Ecommerce background

What

Predicting which customers are at risk of leaving and identifying high-value customers for personalized marketing efforts.

How

Machine learning techniques, such as churn prediction models and customer lifetime value (CLV) models.

For What

Improved customer retention rates, enhanced profitability, and personalized customer experiences.

A U.S.-based e-commerce retailer

A U.S.-based e-commerce retailer implemented the solution and saw a 15% reduction in churn rates, resulting in an estimated annual revenue increase of $2.5 million.

Example 1 of 3
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A UK e-commerce company

A UK e-commerce company applied Customer Lifetime Value models and reported a 12% boost in customer lifetime value, contributing to a 20% increase in the annual marketing ROI.

Example 2 of 3
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An Australian e-commerce startup

An Australian e-commerce startup achieved a 10% increase in customer retention within six months of adopting it, leading to a rise in customer lifetime value.

Example 3 of 3
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DataForest, Head of Sales Department
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DataForest company founder
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