How Big Data is transforming the retail industry [Case Study included]

According to Mckinsey, Big Data Analytics in a retail chain can improve the operating margin by around 60%.

Have you ever faced a situation in which you went out to buy some groceries but forgot to buy something? How about you order your groceries online but again miss out on a few things?

How about if you don’t have to worry about remembering what all things to order and the recommender system will do all the required stuff for you?

Answering all these queries, Big Data Analytics and Artificial Intelligence are playing a huge role in transforming the retail industry and unlocking hidden business potential.

“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” – By Jim Barksdale, Former Netscape CEO

It is not easy for every company to implement Big Data Analytics and draw insights. But thanks to the innovative Big Data Analytics platforms and tools like Apache Hadoop that are making our task so easy and manageable.

Apache Hadoop is helping big online retails giants to draw effective business insights from the customers’ data and thereby increasing customer satisfaction and loyalty.

Let’s hear some interesting facts about Big Data Analytics in Retail:

In 2018, the Big Data Analytics market in retail was valued at 3496.4 Million USD. With the growth of CAGR 19.2%, it is expected to reach 13299.6 Million USD. The jump will be almost four times in the very short period of 2019-2026.

According to Mckinsey, implementing Big Data Analytics in your retail chain can improve the operating margin of the retail firm by around 60%.

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Power of Big Data in Retail Industry

big data in retail industry

Let us explore some key benefits of Big Data in retail industry:

  • Increase sales in retail stores
  • Boost demand for the products
  • Bring back sleeping/ cold customers
  • Increase the customer’s bill value
  • Enhancing customer satisfaction
  • Data-driven decision making for the store layout, staff management, etc.
  • Upgrading the store merchandising
  • Creating Personalized discounts/offers for the target customers
  • Analyzing customer preferences and purchase behaviors
  • Focusing more on High-value customers
  • Increase customer footfall
  • Smart pricing strategies to generate maximum revenue
  • Listening to Social Media

How Big Data is Applied in the Retail Industry

1. Price Optimization

By 2025, Gartner predicts that the top 10 retail giants will make use of real-time pricing.

Big Data Analytics will help achieve real-time pricing to adjust in-store prices for the customers.

Here retailers try to analyze the impact of change in prices of the various products. “What-if” analysis helps in understanding the impact of price on sales, customer’s purchasing decision, product selection, etc.

Big data analytics help retailers estimate the optimal price, which will increase sales and thereby generate maximum revenue.

2. Making Strategic Decisions

“Where there is data smoke, there is a business fire.” – By Thomas Redman

According to Gartner, Major Business Institutions will leverage the power of Big Data Analytics to improve business decisions by 2022.

Long term decisions like where to open a retail outlet, how various stores are performing, etc. can be taken through Big Data Analytics. Big Data Analytics also helps in taking various short term strategic decisions like offers/discounts, product merchandising, product display, etc.

3. Personalizing Customer Experience

“The goal is to turn data into information, and information into insight.” – By Carly Fiorina, ex CEO of Hewlett-Packard

Analyzing customer’s data helps in customizing the discounts/offers for the focused customers. This data is related to purchase history, search history, average bill value, frequency of visit to the retail stores, etc.

Customized SMSs and emails related to offers/discounts are generated through big data analytics.

Let me share with you a few important buzzwords in the Retail Analytics domain that is Market Basket Analytics, Recommender Systems, Clustering and Segmentation, Predictive Analytics, Trend Analysis, etc.

Concepts of Big Data in Retail

Let’s explore some Big Data Analytics concepts to gain more clarity:

1. Recommender Systems

Amazon generated 29 percent of sales through its recommendations engine, which analyzes more than 150 million accounts. Through this, e-commerce giants earned huge amounts of profits.

When you purchase from an e-commerce platform or online retail, many times you get recommendations of what other things you can buy with a particular product, what things were bought by many people while buying a particular product.

Recommender Systems are most popularly used by e-commerce giants like Amazon, Flipkart, Bigbasket, etc. Amazon uses big data to recommend items for you based on your past searches and purchases.

2. Predictive Analytics

According to Forbes Magazine, almost half of the families buy their monthly groceries online.

While buying groceries or any other commodity, we frequently make use of credit cards for making payments. But there is also a significant increase in credit card frauds these days.

Through predictive analytics, Amazon reduced 50% of the credit card frauds within the first 6 months. Fraud detection tools used by Amazon make extensive use of Predictive analytics.

Increasing profit is the central purpose of every business. But a retailer has to make various decisions related to inventory, how much quantity should be ordered from the manufacturer. How is the market demand for various products? How is the customer responding to various product offerings?

Therefore retailers have to forecast the demand based on various parameters like market conditions, Customer’ willingness to pay, previous sales, product popularity, etc. Predictive analytics comes to the rescue while forecasting future demand and growth.

3. Operational Analytics and Supply Chain Management

Few more questions like where to open a retail store, which stores are performing well, which stores have the highest customer footfalls?

Operational Decisions like these can be solved by analyzing the past sales data, understanding the customer demographics and replicating the best practices across the stores can show amazing results. Apache Hadoop can be effectively used to analyze millions of sales records to generate business insights.

Big Basket Case Study

Utilizing the power of Data Analytics, in just over five years Bigbasket has increased its customer base from 0 to 4 million.

Features like “Smart basket” have helped in analyzing the customer needs more precisely and reduce order time below 3-4 mins from 20-25 mins earlier.

Bigbasket started with its analytics domain in the year 2013. The aim was to increase the customer base through the insights gained from data mining and thereby enhance customer retention. The major issue faced by any online retail firm is customer churn.

Therefore through the data-driven decision making big basket saw huge improvements in customer retention, improvement in average customer bill value and targeted coupons/discounts.

Bigbasket has also managed to increase its customer base from 0 to 4 million in just over 5 years.

Features like “Smart Basket” analyzes your buying patterns, search history, previous purchases, shopping behavior to understand the repetition of items in the grocery. These insights are then used to recommend items during the new purchases.

This helps save time while exploring the products and improves the efficiency of ordering.


Implementing the data-driven insights can significantly improve business efficiency and also will help in differentiating yourself from your competitors. Implementing the concepts like Market Basket Analytics, Predictive Analytics,

Recommender Systems can result in increased customer satisfaction and thereby customer loyalty.

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