Retail Analytics: Customers’ Buying History is Imperative for E-Commerce Firms


If you are a frequent online shopper, you would have noticed that portals usually recommend products related to what you have bought recently. The online retailers enjoy the advantage of having good amount of data to make this prediction and that is made possible with retail analytics. But, they will not be right all the time, and there are chances for random guesses to be suggested to you.

An incident happened with Flipkart as the firm suggested one of loyalty customers with women’s watches, refrigerators and hair strengtheners that he never bought or intends to buy. Eventually, he moved on to shop on other portals such as Amazon and Myntra to get his shopping done. The fragmented nature of the portals has resulted in a limited view of the users’ needs. In order to bridge the gap, both Snapdeal and Flipkart are looking forward to run experiments that will understand the surroundings that they reside in.

Flipkart’s Ravi Vijayaraghavan, the Vice President of Analytics stated that the analytical solutions of Flipkart leverage the data driven understanding of the customer preferences. It also leverages the other demographic and societal trends that drive these preferences.

Flipkart has been attempting to predict the purchasing capacity of a specific city by identifying the same using the pin codes. As the purchasing capacity changes across cities, there will be drastic changes in the purchasing patterns as well.

The firms such as Flipkart are choosing targeted advertising as it is highly important to distinguish them with a good user experience in crowded markets.

As per Arvind Singhal, the Chairman of Technopak, an advisory firm, the users take a look at 2 to 4 websites before making a transaction. They do not have any brand loyalty. Also, Ankit Khanna, the Vice President of Snapdeal’s Product Management commended something on the trend. He stated that they have moved away from depending on the user data for a deeper understanding on the meta data of products.


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