Zomato

Improved user engagement and repeat usage through personalization.

Go-To-Market

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Retail & Consumer

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Recommendation Systems

Project Overview

Predicting customer preferences to enhance personalized food recommendations.

Layman's Explanation

Zomato uses machine learning to analyze user preferences and predict what types of food or restaurants users are likely to enjoy, making their app experience more customized and engaging.

Analogy

In the Retail & Consumer industry, this is like a bookstore recommending titles based on a reader's past choices and tastes, aiming to boost sales and satisfaction by personalizing the browsing experience.

Details

Zomato employs machine learning to tailor its recommendations based on various data points, including past orders, time of day, location, and user demographics. By processing and analyzing this data, Zomato can offer more relevant restaurant and food choices to individual users, enhancing their app experience and increasing engagement rates. The algorithm is continuously refined to adapt to users’ changing preferences, improving accuracy over time.

Project Novelty

While recommendation systems are common in e-commerce, Zomato’s approach integrates location and time-specific factors to enhance relevance in the fast-paced food delivery industry.

Project Estimates

Timeline:

6 months

Cost:

$200,000

Headcount:

5

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