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According to a report, India's online food delivery sector is anticipated to reach $4 billion. Before 2010, ordering takeout for delivery at home required calling the establishment.

This growth is expected due to rising smartphone usage, an expanding e-commerce market, a growing number of young people entering the workforce, and rising internet penetration.

But how does zomato manage all its operations to guarantee customer satisfaction? 

Data science has become the key! 

In 2015, Zomato began using data science techniques to improve its services. They created algorithms that could recommend places based on user preferences and past actions taken by users on the site. This allowed them to create more accurate search results than before.

The ability to interpret, analyze and interpret the insights gained from data is one of the most important concerning business growth in the current era. The ability to understand where change is happening in your industry and how it varies between different areas will help you reach new customers and improve your conversion rates. 

In this blog post, I would like to share some insights into how Zomato uses Data Science for its business.

Data Science

Data science is collecting and analyzing data to develop a computer model or algorithm for making decisions about the future. It can also be used to predict outcomes based on past data. Data scientists use a variety of techniques, including statistics and machine learning.

About Zomato 

Zomato is a restaurant search service that allows users to find restaurants and other types of businesses. Zomato was founded in 2010 by Deepinder Goyal and Pankaj Chaddah. It began as a website that allowed people to search for restaurants and other types of businesses using keywords or tags. The company's mission is to help people find the best food, drink, and entertainment near them.

Zomato now uses machine learning algorithms that are trained on billions of data points about what customers want when searching for restaurants. These algorithms aim not only to let people find restaurants but also to provide them with information about what kinds of foods they might like at those places, so they can make better decisions when going into different eateries.

The Zomato ML team emphasizes image processing and NLP-based review extraction to improve product optimization, create recommendation engines, and implement features that enhance the operational elements of the sector. Predictions are generated using this interface once the machine learning model has been deployed, which produces a server to do so using APIs. For more information on NLP and other ML techniques, visit the IBM data science course in Chennai, and learn the concepts in detail. 

How data science assists zomato? 

Initially, image processing and NLP evaluations were used to moderate user-generated content. This gradually expanded to cover things like product optimization, recommendation engines, and feature upgrades and is now extended to business and operational parts of the organization.

Today, to provide customers with personalized experiences, we need to be aware of their preferences in real-time. Zomato is investigating machine learning methods to handle issues like food delivery, allocating the best partners for delivery, food preparation time, etc. This raises revenue, boosts the company, and promotes consumer satisfaction.

To improve customer service through predictive analytics, zomato uses machine learning models to predict when customers will be unsatisfied with their meals and provide customer support at that time. 

The specific models used depend on the type of restaurant being reviewed, but all models are trained using historical data from other customers' experiences with each restaurant type (i.e., they are predicting how likely someone is to have an unsatisfactory experience based on previous data).

Use of Big data in Zomato

Enhancing the menu using big data 

With Big Data solutions at their disposal, their meal delivery software may collect client input regarding the items on the menu of many registered eateries.

The restaurants can then receive recommendations from the system regarding menu items that will help them generate more business. Restaurants can increase their operational efficiency by changing menu items. By doing this, they can increase the promotions they run on popular goods and increase sales.

2. Faster Deliveries

Fast delivery times are the most prominent of the many elements contributing to food technology firms' success. Compared to its rivals, a meal delivery service will do better if it provides users with faster delivery times. Though it might seem straightforward, numerous challenges must be overcome to transport freshly prepared food from restaurants and deliver it to the customers. With Big Data Analytics systems, they can keep an eye on various factors, including traffic, weather, and roadblocks along the route, and provide the shortest route to ensure that the food is delivered as quickly as possible.

3. Customer Sentiment analysis

You cannot afford to ignore customer feedback on social media in the modern world, where social media is a key factor in determining an app's success or failure. Companies that have Big Data technologies can determine how customers feel about their brand.

Questions such as: 

1. Do your delivery boys bring the food on time? 

2. Are your customers satisfied with the performance as a whole? 

3 Are your efforts having an impact? 

After looking at every business mentioned on every social media platform, including Facebook, Twitter, Instagram, and Linked In, you can find the answers to all of these questions. The food-tech sector is currently using this data to inform commercial decisions.

Conclusion

A large amount of data is available in Zomato, and this big data is not just a source of value but also could prove harmful if its vulnerabilities are not addressed on time. The Data Science team at zomato ensures that the security and privacy of user data are controlled and hacking attempts are prevented. Through various operational checks, they optimize the data flow while deriving insights and derive performance reports that help the research and product teams take business decisions quickly. Furthermore, this group also monitors anomalies in traffic which helps prevent hackers from entering via compromised accounts.