Challeng​es

Faced with the challenge of efficiently processing vast amounts of customer data, Zomato needed a solution to deliver personalized recommendations in real-time. The goal was to develop a system capable of accurately predicting customer preferences and providing more relevant dining suggestions, ultimately enhancing user engagement and increasing order conversions.

Results Achieved

Significant Increase in Recommendation Accuracy

The AI-driven system provided more precise recommendations, aligning closely with customer preferences, which increased user trust and reliance on Zomato’s suggestions.

Higher User Engagement and Retention

Personalized and relevant recommendations led to higher user engagement, with more users exploring and interacting with suggested restaurants and dishes.

Improved Conversion Rates

By facilitating downstream recommendations that matched customer preferences, Zomato saw an increase in order conversions, contributing to overall revenue growth.

We are currently using [BaseModel] for generating candidates to facilitate downstream recommendation systems. It generates recommendations using density-based rich customer representation. It allows us to trace customer lookalikes (‘People Like You’) to find similar users with similar cuisine/taste preferences as well as price affinity. We used [Synerise] Cleora for customer-restaurants graph data […] And to our delight, the embedding generation was superfast (i.e <5 minutes). For context, do remember that GraphSAGE took ~20hours for the same data in the NCR region. BaseModel give us a generalised framework for recommendations […] We are exploring ways to use it in other applications such as search ranking, dish recommendations, etc.

Data Science Team

Scroll to Top