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10 Data Science Projects Most E-Commerce Businesses Are Using In India

10 Data Science Projects Most E-Commerce Businesses Are Using In India

Ambika Choudhury

Data science has become a go-term for almost all the industries, including e-commerce. According to a report by a leading newspaper, India is the fastest growing online retail among the top global economies. With a growth rate of more than 50%, e-commerce websites have become more competitive than ever before. As the competition rises, these e-commerce players are resorting towards the use of technology such as analytics and data science to stay ahead of the competition.

With an ever-growing data, it has become crucial for these players to use it in a way to keep the customers happy and satisfies. This is where the importance of data science projects comes into the picture, where they are using it in areas such as fraud detection, inventory management and more. For instance, E-Bay is investing ample funds into data science and personalizing shopbots for enhancing customer’s experience.  Whereas Myntra is using data analytics and AI through its bot Artie, which is a smart bot for gathering consumer insights, and more.

In this article, we list 10 important data science projects that every e-commerce is using to ensure healthy business.

(The list is in alphabetical order)

1| Churn Model

This model can be defined as one of the important projects that every e-commerce should implement because this model helps in picking up which customers have the most probability to switch to other e-commerce websites, thus helping businesses to keep track of the progress.


Why use it here?

As customer retention is crucial for a company to grow and expand, this may prove to be of crucial importance in the e-commerce industry. For instance, an existing customer can recommend other new users to try the e-commerce website and thus help in expanding the market.

How does it work?

The Churn Model calculates the churn rate such as the number of customers that are lost, percentage of lost customers, recurring the value of the loss in business, etc. Data Science tools like unsupervised clustering, predictive modeling, natural language processing, keyword extraction, etc. help in adding advanced analytics.

2| Customer Sentiment Analysis

This tool has been used by almost all the e-commerce websites these days. The easiest way to use this tool is by gathering feedbacks of the customers.

Why use it here?

Asking the customers about the food or ambiance in person is an old-fashioned and time-consuming way. Social media has helped the retailers to get rid of this old fashioned method and drive faster results using analytics, data science, and machine learning.

How does it work?

The brand-customer sentiment analysis is performed based on natural language processing, neutral or negative sentiments, text analysis, etc. The data from online reviews, social media, feedback forms, online surveys, etc. are often extracted for analysis of the sentiments.

3| Customer Predictive Lifetime Value Modelling

In retail, customer lifetime value can be described as the prediction that basically refers to the net profit that the customer is likely to bring to the company.

Why use it here?

The Customer lifetime value prediction helps a company in many ways such as optimization of business strategies, deciding the acquisition and up costs for customer’s purchase, helps in defining growth, net profit, future sales, etc.

How does it work?

The aim is to model the behavior of customers for purchasing anything in order to predict their future activities. The evaluation of this model can be performed by using such as Beta-geometric binomial model for customer alive probability or by using the Gamma-gamma model. These models collect, classify and clean the data around customer’s needs, expenses, recent purchases, etc. The data science algorithm is then used after the processing of data to spot the interdependencies between the choices and behaviors of the customers assuring a better understanding of the customers.

4| Fraud Detection

Security is necessary for all kinds of fields whether it is a social media platform or an online retail website.

Why use it here?

Fraud is one the most challenging areas to deal with in an e-commerce industry, as it can result in huge financial losses. There can be fraud in the areas of merchant identity, advanced fee, and wire transfer scams, chargeback fraud, etc.

How does it work?

Deep neural networks prove to be playing an effective role in detecting frauds. The algorithm use data analysis methods and neural networks based predictor to identify fraudulent patterns that can help the retailer to protect the company from fraudsters.

5| Inventory Management

Lack of required goods can be a deterring factor to retain customers. It is, therefore, necessary to satisfy the need of the customers on-time. The inventory management refers to the process of stocking the goods in a healthy condition and at the proper place for the future purpose.

Why use it here?

With the increase in globalization, the maintenance of the supply chain has become complex in the present day market. Use of inventory data analytics has become a necessary part for online businesses to eliminate such scarcity of products at the main hour.

How it works

Predictive analysis, data analysis, machine learning algorithms help in detecting patterns, supply chains, etc. for inventory management. The analysis helps by detecting the patterns in the most in-demand parameters and goods, and define the inventory strategies using machine learning algorithms.

6| Improve Customer Service

A customer is central to any business and therefore it is important to offer proper customer services in all industries, especially e-commerce.

Why use it here?

The improved customer service can be used to personalize the services for the customers who are facing difficulties and needs a recommendation service. It is about satisfying customers with what they actually need.

How it works

Natural language processing involves communication through speech and text that may be voice-based bots or chatbots. It helps in extracting the online ratings and reviews such as recognizes the text, character and converts them into data and stores it in the database for future purpose.

7| Market basket analysis

This is a modeling technique that defines that if a customer buys something, he or she is likely to buy another thing as well.

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Why use it?

E-commerce websites are always looking forward to encouraging the existing customers by helping them to buy what they are searching for with the help of the data of their previous buys, searches, credit card bills, etc.

How does it work?

The algorithm here works by the Association Rule mining and in such a manner that it trains and identifies the product basket of a customer and the product association rules. To name it, the Apriori Algorithm fits best for identifying the frequent items purchased by the customer.

8| Price Optimisation

Pricing being one of the critical aspects of business, it needs a lot of attention to optimize different avenues including cost analysis, market segmentation, competitor analysis, etc. Big data analysis has started to leverage to optimist the pricing decisions.

Why use it here?

Pricing can impact a business in several ways. It can have a severe impact on market share, profits, revenues, demand, sales, etc.

How does it work?

Price optimization tool using data science includes a number of factors such as the price flexibility, its considerable location, the attitude of the customer, competitor’s pricing, etc. and the data science algorithm predicts the customer’s segmentation to make a response to the change of price.

9| Warranty Analytics

Warranty is a commitment that a manufacturer gives when you buy something from them that if some kind of problem arises in the product within the warranty period, it can be replaced by a spare part without costing a single penny.

Why use it?

The analytics of warranty claims and supplementary data that contain useful information about the quality and reliability of the products are found to be an advantage to the manufacturers for identifying early warnings of abnormalities in the products and save them from the downfall in the business.

How it works

Data mining and text mining are the two techniques that are used as a tool in this model for identifying the claims patterns and problem areas of the product where the data are converted into real-time plans, recommendations, insight, etc. The detection is mainly concentrated on the anomalies in the warranty claims.

10| Recommendation System

This tool is of great help for online retailers by predicting the customer’s behavior. There are some popular recommendation techniques such as collaborative filtering (recommendations will be based on collected data about user’s activities),  content-based filtering (recommendations based on user profiles) and hybrid recommendation filtering (combination of the two filtering methods mentioned above)

Why use it?

Enabling recommendation in an online business can be used to filter choices for a particular user depending on their past searches, purchased data and reviews by giving a personalized view on the same.

How it works

It basically works by filtering data for which it uses either collaborative or content-based filtering. The algorithm learns about the past experience of shopping, searching preferences, needs, etc. to recommend the current product to the customer.

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