Our Partner Case Study
Retail use case with Zomato
Zomato, a global restaurant aggregator and food delivery service, has taken steps to improve its recommendation systems and user experience. The goal was to provide more accurate and personalized food and restaurant suggestions to improve customer satisfaction and engagement on the platform.
Solutions Implemented
To improve personalized meal suggestions, a recommendation system based on advanced data analytics was developed, designed to predict customer preferences and provide real-time, relevant recommendations that increase engagement and conversions.
Results Achieved
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
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Central European time zone
Marta Lewandowska: I speek English
m.lewandowska@martechcompass.com
Bartosz Bielewicz: I speek Swedish
b.bielewicz@martechcompass.com
New York time zone
Sarosh Khan: I speek English
sarosh@martechcompass.com
+1 (510) 367-3881
