Advanced customer segmentation and behavior analysis platform for an e-commerce retailer, boosting conversion rates by 35% and customer retention by 28%.
A major e-commerce retailer was experiencing declining conversion rates and customer retention despite increasing website traffic. They lacked deep insights into customer behavior patterns and couldn't effectively personalize the shopping experience.
The company needed a comprehensive analytics solution to understand customer journeys, identify high-value segments, and create targeted marketing campaigns to improve engagement and sales performance.
Implemented advanced clustering algorithms to identify distinct customer segments based on behavior, preferences, and purchase history for targeted marketing.
Created comprehensive customer journey maps with touchpoint analysis to identify optimization opportunities and reduce friction in the buying process.
Developed machine learning models to predict customer churn, lifetime value, and purchase propensity for proactive retention strategies.
Built real-time personalization system delivering targeted product recommendations and dynamic content based on individual customer profiles.
Increased overall conversion rate by 35% through personalized product recommendations and targeted marketing campaigns.
Boosted customer retention by 28% using predictive churn models and proactive engagement strategies.
Decreased cart abandonment rate by 45% through behavioral triggers and personalized recovery campaigns.
Generated $4.2M additional annual revenue through improved customer insights and targeted marketing optimization.