Applications of Data Science in the E-commerce industry
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Applications of Data Science in the E-commerce industry

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The importance of data in today’s world has reached new heights, where companies are relying on data sets to understand performances and arrive at business decisions. 

Data analysis is especially relevant in the e-commerce and retail industry. They can predict the purchases, profits, losses and even manipulate customers into buying things by tracking their behaviour. Retail brands analyse  data to create customer profiles and learn his/her sore points and market their product accordingly to push the customer towards purchasing.

The following examples are instances of how data science is used in the e-commerce industry to drive sales:

1)  Recommendation engines

Recommendation engines are the most important tools in a retailers arsenal. Retailers leverage these engines to drive a customer towards buying the product. Providing recommendations helps retailers increase sales and to dictate trends.

Sounds familiar? Thinking of Amazon and Netflix? That’s exactly how search recommendations work.


How do they do this?

Well that’s simple, the engines are made up of complex machine learning components and deep learning algorithms. They are designed in such a way that they can keep a track record of every customer’s online behaviour and analyse the patterns to suggest shows based on this data.

That’s why everytime Netflix recommends movies or TV series to you, it’s probably something you are going to watch!

The same thing works with Amazon too, based on your past searches and purchase history, amazon provides recommendations and discounts on them as well. Because let’s face it, who can resist buying something that they always wanted, especially when it comes with a discount.

This whole process involves a great deal of data filtering and analysing by machine learning algorithms.

2) Market Basket Analysis

market basket

This is one of the most traditional tools of data analytics which retailers have been profiting off for years.

Market basket Analysis works on the concept- if a customer buys one group of items, they are more or less likely to buy another set of related items. For example, if you went to a restaurant and ordered starters or appetizers without any drinks, then you are more likely to order main course or desserts. The set of items the customer purchases are known as an itemset, the conditional probability that a customer will order main course after starters is known as the confidence.

In retail, customers purchase items based on impulse, and market basket analysis works on this principle by predicting the chances of a customer’s purchasing behaviour. 

This mostly involves a lot of how the marketing of the product is done by the retailers, and in the world of e-commerce, customer data is the best place to look for potential buying impulses. Similar to search recommendations, market basket analysis also works with a machine learning or deep learning algorithm.

3) Warranty Analytics

Warranty data analytics helps retailers and manufacturers keep a check on their products, its lifetime, problems, returns and even to keep a check on any fraudulent activity. Warranty data analysis depends upon the estimation of failure distribution based on data which includes the age and number of returns and the age and number of surviving units in the field.

Retailers and manufacturers keep a check upon how many units have been sold and among them how many have been returned due to issues. They also concentrate on detecting anomalies in warranty claims. This is an excellent way for retailers to turn warranty challenges into actionable insights.

4) Price Optimization

Selling a product at the right price, not just for the customer but also for the retailer or manufacturer is an important task. The price must not only include the costs to make the product but also the ability of a customer to pay for that product keeping in mind competitor prices as well.

All of this is calculated with the help of machine learning algorithms which analyzes a series of parameters like the flexibility of prices, taking into consideration the location, buying attitudes of an individual customer and competitor pricing.

It then comes up with the optimal price that can benefit all the parties.

This is a powerful tool for retailers to market their product in the right way with optimal pricing.

5) Inventory Management

Inventory refers to stocking of goods, for later use in times of crisis. Hence, inventory management is crucial for businesses to optimize resources and increase sales. Retailers need to manage inventories effectively so that even if there’s a sudden spike in sales, supply remains unaffected. In order to achieve that, the stock and supply chains are thoroughly analyzed.

Powerful machine learning algorithms analyze data between the elements and supply in great detail and detect patterns and correlations among purchases. The analyst then analyzes this data and comes with a strategy to increase sales, confirm timely delivery and manage the inventory stock.

6) Location of new stores

Location analysis is an important part of data analytics. Before a business can decide where to open up their business, it is crucial to analyse plausible business locations to settle on the best one .

The algorithm used in this case is simple, yet effective. The analyst analyzes the data giving importance to demographics. The coincidences in zip codes and locations gives a basis for understanding the potential of the market. Competitor markets are also taken into consideration while analysing locations. The algorithm also analyses retailer networks to come up with the most suitable option.

7) Customer sentiment analysis

Customer sentiment analysis has always been around the business world for a long time. But now, machine learning algorithms help simplify, automate and save a lot of time with giving accurate results.

Social media is the most readily and easily available tool for an analyst to perform customer sentiment analysis. He uses language processing to identify words bearing a negative or positive attitude of the customer towards the brand. This feedback helps businesses improve their product.

8) Merchandising

Merchandising is an essential part of any retail business. The idea is to come up with strategies that increase sales and promotions of the product.

Merchandising intends to influence customer decision making via visual channels. While attractive packaging and branding retain customer attention and enhance the visual appearance, rotating merchandise helps to keep assortments fresh and new. 

The merchandising algorithms go through data sets, picking up insights and forming priority sets of customers taking into account seasonality, relevancy and trends.

9) Lifetime value prediction

In retail, customer lifetime value is the total value of the customer’s profit to the company over the entire customer-business relationship. Particular attention is paid to the revenues, as far as they are not predictable by costs. By evaluating direct purchases, businesses can understand two significant customer lifetime methodologies; historical and predictive.


All the forecasts are made on the past data leading up to the most recent transactions. Usually, the algorithms collect, classify and clean the data concerning customer preferences, expenses, recent purchases and behaviour as the input. After the data is processed, a linear presentation of the possible value of the existing and possible customer is received. This algorithm also spots interdependencies between the customers characteristics and their choices.

Conclusion :

Data science has applications across all sectors of technology, it helps businesses make better decisions based on data, also known as data driven decisions. The 9 applications listed above are among the most popular and important ones in the e-commerce field.


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