Personalized E-commerce Experience

Challenge‍

A medium-sized webshop was struggling with low conversion rates (1.5%) and high cart abandonment. Their generic one-size-fits-all customer experience wasn't resonating with visitors, and they lacked the team size to manually personalize experiences for thousands of visitors.

‍

Solution

An AI personalization engine with behavioral targeting using LLaMA and Make.com that:

• Analyzed customer browsing patterns, purchase history, and engagement metrics
• Created dynamic customer segments based on behavior patterns rather than just demographics
• Personalized product recommendations based on individual user activity
• Customized messaging and promotions based on identified customer preferences
• Continuously learned and improved from interaction data

‍

Implementation

‍The system was implemented in phases:

1. Data collection and analysis to establish baseline patterns
2. Initial segmentation and basic personalization rules
3. Advanced machine learning model implementation for dynamic personalization
4. A/B testing framework to validate effectiveness

‍

Results

• 32% increase in average order value through relevant cross-selling
• 26% improvement in conversion rates site-wide
• 1.2 FTE saved in merchandising work that had previously been manual
• 58% reduction in cart abandonment rate

‍

Maintenance

‍The client's team was trained to manage the system with just 2-3 hours of oversight weekly.

‍