With the advent of e-commerce in India and rising affluence of consumers, that local shopping experience is being replaced with couchsurfing through apps and websites, as we choose from the deluge of options available. Too impersonal, one may argue, except that personalisation through tech innovations is what powers e-commerce today. On a typical day, e-commerce platforms receive tens of millions of visits and 100s of millions of product pageviews. E-commerce has also become the starting point for most product and price discovery and searches for customers. The scale is indeed mammoth.
And this mammoth data trail provides opportunity for e-commerce companies to deeply understand each and every user. From the moment a user fires up the website, or mobile site or app, thousands of lines of software code run against machine learning (ML) models to constantly generate user insights. And these insights are used to give customers a most personalised experience—akin to what was possible offline. Let’s look at ‘Search’, the most common method for users to find products they want. Typically, two-thirds of user searches are fairly broad, or generic, in nature such as shoes, headphones, mobiles, sarees, etc.
While traditional search engines are great at answering specific questions, they stumble on broad questions, since there are different types and styles of shoes, and many types, patterns and fabrics of sarees. With so many possibilities, users often lose interest unless the right products are surfaced to them as top search results. Which is why the need for innovation to personalise user experiences. The key lies in matching user insights with available products, similar to what we experienced at local shops, except that it has to be done with billions of data points and for millions of users in real-time. With every customer activity, ML algorithms help decode that customer’s shopping psyche—demographics, behaviour, location, product attributes liked, trust level with reviews and ratings , delivery expectations, similarity to other users, etc.
And these insights are then used to select the right products to show as search results when that customer triggers a new search. If, for example, a shopper buys Levi’s jeans for the sake of durability, but picks an unbranded belt to go with it for daily use, what should an e-commerce site show her when she searches for watches? Through her price affinity insight, the systems know she is looking for a watch in the value price range, her brand affinity insight for this category indicates she is interested in branded or unbranded watches, and her recent purchase history suggests she would be interested in a watch design that matches her new attire.
The user insights used to select the right products are generated from the big data platform, an innovative tech platform which helps build, train and launch new ML techniques and Artificial Intelligence models to process behavioural data at scale and distill it to actionable insights. Indian e-commerce has grown significantly over the past few years, but continuing the innovative streak to personalise experiences is a long and continuous journey. We’ve barely scratched the surface of what could be achieved using data and insights—not just for search, but other areas like discovery, product recommendations, merchandising, and advertising. But we’ve made a good start and must keep momentum if we’re to ensure e-commerce fundamentally changes people’s lives for the good.
The writer is vice president, Flipkart Data Services Group at Flipkart
By Anand Lakshminarayanan