Overview
The project aimed to personalize customer experiences across multiple domains such as retail, healthcare, and education using AI-powered recommendation systems.
The goal was to increase customer retention, cross-sell opportunities, and engagement rates through tailored content and product suggestions.
Problems
Existing static recommendation engines failed to adapt to real-time user behavior changes.
The system relied heavily on broad segmentation instead of individual preferences.
Integration with different platforms (web, mobile, in-store) was fragmented.
Low click-through and conversion rates affected overall revenue.
Marketers lacked actionable insights from user interactions.
Solutions
A modular AI recommendation engine was built using deep learning algorithms, behavioral clustering, and contextual signals.
Recommendations adapt in real-time based on user activity, location, and trends.
Unified APIs enable seamless integration across platforms.
A/B testing and feedback loops continuously improve recommendation accuracy.
This system increased click-through rates by 40% and conversion rates by 25% within three months of deployment.
