Data Science

Data Science in E-Commerce

Published: 16th May, 2023


AlmaBetter Student at almaBetter

We all are acquainted with e-commerce platforms and have been using them for various reasons, be it any online shopping platform like ..

We all are acquainted with e-commerce platforms and have been using them for various reasons, be it any online shopping platform like Flipkart, Amazon, and Netmeds. Due to the increase in the cases of the Covid-19 in the year 2020 and 2021, people have even started ordering grocery items from online retail stores like Grofers.

With the introduction of Artificial Intelligence, e-commerce platforms are making effective use of data such that the supply of essential items runs in parallel with the demand of items.

Although there are multiple ways in which an MNC may consider using Machine Learning algorithms, we will be focussing on only those algorithms that are considered crucial to any e-commerce platform. These are the following topics that we will focus on:

Time Series Forecasting, Recommender Systems, Advanced NLP

The use of Time Series in E-Commerce


Before discussing the details about the use of Time Series Analysis in E-Commerce platforms, let’s try to answer some questions:

  • We all have heard about Big Billion Days in Flipkart right? According to Flipkart, the Big Billion Days is responsible for almost 40% of the overall annual sales. Out of the highest selling items, mobile phones are one of them. So what may happen, if the Flipkart distribution stores run out of mobile phones on the second day itself?

The above hypothetical scenario may lead to devastating consequences in terms of revenue and user experiences. So, it’s necessary for every E-Commerce platform to predict the number of items that they should have in stock before such a huge annual event.

In such cases, the concept of Time Series Forecasting comes into the picture. Time Series Forecasting consists of three extremely important components, and we will try to explain the use of each component in huge MNC’s such as Amazon and Flipkart.

The main components of a Time Series Analysis are as follows. We will not be discussing the residual component in this blog.

Trend: In statistical terms, it is nothing but the change in mean over time. The trend gives a measure of the dependent feature with respect to time which can be at an yearly, monthly, weekly, daily, or even at an hourly level. With respect to our discussion on the Big Billion Days, suppose we are working on the dataset which gives us information on the number of mobile phones sold on a monthly basis and let’s say we are monitoring the sales of mobile phones. The rise/fall in the line chart depicts whether the sales are increasing/decreasing over time.

Seasonality: In statistical terms, it is the change in standard deviation over time. Sticking to our example, let’s consider a hypothetical scenario. The number of mobile phones sold in Flipkart annually is much higher in the months of October-December, and the least sales are in the month of May. This is because of the ‘Big Billion Days’ and ‘Diwali Sale’ in the month of October and November. The rise in sales (in this case, mobile phones) at a particular time of the year is given by seasonality.

When it comes to E-Commerce platforms, customer satisfaction is always the primary objective. And this is the reason Time Series analysis is one of the most important modeling techniques that is used by every E-Commerce platform. Advanced Modeling architectures like **Facebook Prophet, ARIMA, and SARIMAX, **Machine Learning algorithms as well as advanced Deep Learning algorithms like LSTM are employed, to get the most accurate forecasting results.

Learn "How Flipkart uses Data Science"!

Recommender System in E-commerce

A recommended system works like a well-trained salesman who is good enough in cross-selling and upselling the products. The recommended system uses information like reviews and ratings of customers for different products to suggest good products with a higher price. In an e-commerce context, recommender systems have an influence on financial performance as well as the intensity of consumer conversation.

Specifically, recommender systems can enhance e-commerce in three ways:

Conversion: Turning Browsers into Buyers Increasing the proportion of visitors to a website that makes a purchase. Retailers can use recommender systems to locate goods that best suit customer’s interests and inclinations, which may involve accidental purchases prompted by serendipity from the suggestions.

By increasing Cross-sell: Cross-selling improved by the recommended system by suggesting additional products and services to customers. If the recommendations are good, the typical order size increases. as an example, a site might recommend additional products within the checkout process, supporting those products already within the handcart.

By building loyalty: Building customer loyalty becomes an important aspect of business strategy. Recommender systems can improve loyalty by creating a value-added relationship between the location and therefore the customer. Whenever a customer visits an internet site, the system learns more about that customer’s preferences and interests and is increasingly ready to operationalize this information to personalize what’s offered. By providing each customer with an increasingly relevant experience, a corresponding improvement within the likelihood of that customer returning is achieved.

Content-based recommender systems

A content-based recommender makes recommendations to a user by matching the outline of an item and the profile of the user’s interest. There are three important aspects to a content-based recommender namely, matcher, the item descriptions, and the user profile.

“The user-profile” consists of different types of information:

  • A description of the items-types that interest the user
  • A user’s interaction history with the recommended system

Collaborative filtering systems

Collaborative Filtering systems make recommendations exclusively based on knowledge of users’ relationships to items Within Collaborative Filtering CF we distinguish two important classes, user-based and item-based, supplemented with several optional variations

User-based CF algorithms start by finding a set of neighboring users who purchased or rated items overlapping this target user’s purchased or rated items. These likely-minded users are found using user similarity values. An alternative to user-based CF techniques is item-based CF, a technique that compares each of the users’ purchased or rated items to related items and then combines the most similar items into a recommendation list.

NLP in E-commerce


Computers are generally not designed to understand what we communicate as humans. They communicate in code, which consists of long lines of ones and zeros. If we think that humans and computers can’t truly connect we are wrong, they are connected and can communicate. E.g. Computers respond daily to our search terms, even voice commands with smart products such as Siri, Alexa. These all things are today possible with the help of Data Science.

Retailers are nowadays selling online, and they want to increase customer satisfaction and NLP plays an important role not only in helping customers in the front end, by suggesting some keywords in the search bar, but also retailers at the back end.

It’s human nature if we don’t like the product we don’t buy during shopping offline, but it’s not the case here in online shopping. After getting the product the customer can return it, add comments to that particular product along with the ratings. We can check the ratings manually but let’s say if we have thousands of products. With the help of NLP, a company can understand the behavior of a customer to a particular product, and that will help the company to understand or to rate the seller. Additionally, with the help of search history and comments of a customer, we can predict what a customer actually wants, and show them the relevant ad, or recommend the product directly to save their time.

It can also be used in validating the addresses of customers for example if a customer is putting a wrong or incomplete address, With the help of NLP we can come up with a solution that shows the error if a customer will put this type of address. Also, the retailers have a lot of data of customers and their addresses with the help of which they can suggest the complete address to the customer while typing or adding a new address, which in turn is a good experience.

Efficient Customer Support

Online e-commerce platforms are focusing more on automation, and at the same time trying to increase customer satisfaction. Companies invest a lot of their resources when it comes to Customer Support Executives so that they can attend to any customer whenever a ticket is raised. However, due to a large number of customers, it was often noticed that the ratio of the number of tickets raised and the number of executives are quite high, which made it impossible to attend to every customer, and this led to poor customer satisfaction. To counter this issue, E-Commerce platforms are taking the aid of Machine Learning and Deep Learning algorithms, by building chatbots.

What are Chatbots and how do they work?

Chatbots are automated AI-driven programs that can help simulate a conversation. It is very similar to a customer getting his/her needs attended by an executive through a chatting platform. The major challenge in building a chatbot was the ineffectiveness of a machine to understand data, and at the same time storing the semantic information of a sentence being fed to it. The role of a chatbot is not only to understand what is being asked of it, but at the same time it should give replies to the customer, and the replies must have a proper grammatical structure to it as well. This is why companies use advanced Natural Language Processing tools like Transformers and Berts which have made life much easier for an E-Commerce platform. It’s like having an extra set of executives, working for the MNC.


The need for NLP solutions is expanding as businesses increasingly communicate with consumers in their own language.

According to previous industry research, the NLP market will expand at an annual rate of 18.4% and will be valued at $13.4 billion by 2020.

It’s no surprise that it’s exploding. Retailers must get things right

the first time, in an inventive environment packed with time-pressed customers.

Effective sales have always required two-way communication. That hasn’t changed, even though we’ve all gone digital. Thanks to NLP.

Written by Jatin

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