Big Data Analytics in E-commerce

A great increase in the availability of computers/smartphones and easy access to the internet has changed the face of the retail industry. Online shopping which is also known as E-commerce has become an important part of our daily life.

What is E-commerce?

I suppose most of us are aware of what e-commerce is but just to clarify, E-commerce refers to selling or buying of products through online platforms. So that's it? All you need to do to run your online store is get an E-commerce platform, upload your products in listing and sell them to customers who happen to visit your site! Isn't it that simple? Well, yes and NO!

The Problem

Let's think of it from the owner's point of view. In a physical retail store, an owner can interact with customers, keep track of inventory or delivery of the products, RECOMMEND them a suitable product from the knowledge he has gathered about the customer. But now think of doing the same when you are the owner of an online retail store. You cannot interact with every customer and gather useful knowledge about that customer, due to which you would fail to RECOMMEND products to that customer and end up making poor decisions. Similarly, you can't keep track of inventory or shipments while running a large online store. Trust me, you will lose all important information about the customers and your online store which is a necessary appetite for the success of your business.

Big Data Analytics to Rescue

If you are wondering what the heck is big data analytics? Please refer to our previous discussion on Big Data Analytics. Big data Analytics can offer insights about the online retail store which are crucial for the growth of our business. It helps us to make our online store more dynamic and efficient towards shopping experience of visitors.

Every big e-commerce store like Amazon, Flipkart, Snapdeal, etc, is usinganalytics to improve their online store user experience. Amazon has noticed a significant increase in its revenue after adopting big data analytics to confront its user experience concerns. But exactly how analytics can help an online retail store?

Today, we will walk through four major use cases of big data analytics in E-commerce.

Use cases

Recommendation System

Personalization is one of the "most important" components of online shopping. Recommendation system has been the strongest contribution of big data to the e-commerce industry. Predictive analytics helps e-commerce stores to analyze customers past activities like product preferences, past clicks behavior, shopping history, etc in real time which allows machine learning to predict the most relevant recommendations for the customer improving shopping experience. Predictive analytics ensures a personalized shopping experience for each customer because all customers are different, they have different preferences. So our recommendation needs to be customer specific as well.

Many corporates have predictive analytics available on cloud services for other e-commerce platforms. Such services help e-commerce to easily integrate a recommendation system on their site using third-partyanalytics.

Supply Chain Management

No doubt, data-driven supply-chain management has revolutionized e-commerce platforms. Retailers can use big data analytics to obtain useful insights on warehousing, inventory, transportation, point of sale. Let's walk through them one by one.


Data gathered from a video camera installed in the warehouse can be used to minimize travel distances for personnel and efficient use of warehouse space which in turns reduces warehousing cost significantly.


Internal and External data sources enable online retailers to identify the demands and supply in real-time. Retailers can use analytics to improve planning processes and demand sensing capabilities which reduces stockout.


GPS tracking, traffic analysis, predicting the shortest route and even fuel consumption, etc, have made shipment more efficient and cost-effective for E-commerce.

Point of Sale

Out-of-stock detection has been a challenge for the e-commerce industry. Real-time analytics can be used to predict the demand and track the availability of the product in stock. Detection of low availability of hot-selling products can reduce the cost of expensive manual stock inspections and increase profits.

To understand the supply chain better, refer to Big data and the supply chain: The big-supply-chain analytics landscape (Part 1).

Fraud Detection

Customers buying products on online sites has been a victim of fraud. For instance, you must have heard of people receiving soap in a box of smartphone from the online store. Well, that was just one case. Earlier, rule-based systems were used to detect obvious fraud scenarios. But the rule-based system requires much more manual work and verifications which eventually harms the user experience. Later, fraud detection used analytics with machine learning making it more efficient, affordable and dynamic solution.

Big data analytics with machine learning helps to prevent frauds by analyzing factors like the likelihood of fraud in the product, seller activities, etc, to both detect and prevent fraud.

Pricing Model

Big data analytics can also help the owner to depict the optimum price for a product through customer sentiment analysis. Analytics can be used to analyze market trends and dynamics in the price of the product resulting in a better pricing model and sales. Many other factors can be involved to predict the final price of the product, for example, demand and availability of the product in the market.

Retailers like Amazon, Flipkart, Snapdeal, etc have established their expensive but effective data engineering team to enable data-driven online experience for a customer. Unfortunately, not every online retailer have that luxury, but companies like webnotch help small online retailers to generate useful insights and improve their customer shopping experience.