Key Result
28% revenue uplift
A growing e-commerce brand was struggling with poor product discovery across their platform. Customers were finding it difficult to navigate a catalogue of 50,000+ SKUs, resulting in low engagement and missed cross-sell opportunities.
The existing static recommendation system was failing to capture user intent, serving the same generic suggestions regardless of browsing behaviour or purchase history. Repeat purchase rates were declining quarter over quarter.
The brand needed a personalised recommendation engine that could dynamically adapt to individual user preferences in real time, driving both average order value and customer retention.
AINinza built a personalised recommendation engine combining collaborative filtering, content-based signals, and LLM-powered reranking. The system processes real-time user behaviour to deliver contextually aware product suggestions across every touchpoint.
Real-time user behaviour tracking captures browsing patterns, cart activity, and purchase history to build dynamic user profiles. The LLM reranking layer ensures recommendations feel natural and contextually relevant, not just statistically optimal.
28%
Increase in AOV
35%
Higher Click-Through
15%
Uplift in Repeat Purchases
3 weeks
5 weeks
3 weeks
3 weeks