Talking to the Future

_{Talking to the Future}

FLIPKART’S RESEARCH CART An IIT-Kharagpur team has proposed a model for estimating the frequency of repeat purchases by customers as well as predicting what product a customer is more likely to buy, allowing to make timely recommendations.

Product classification: IIT-Kanpur has come up with a method to reduce errors in how products are classified to improve search accuracy.

The paper by Carnegie Mellon University aims to discern the genuineness of product ratings given by customers. Mining Twitter conversations: IIT-Kharagpur has developed a technique for mining conversations on Twitter around major events such as Flipkart’s Big Billion Day sale to assess customer sentiment

This technology based on Indian consumers and data that I can start to play around with for different experiments.”

Ravi Garikipati, head of engineering at Flipkart, believes that the “deeply local Indian data” fed into Project Mira can give the company an edge in the domestic market. Through Project Mira, “we let our customers express themselves naturally (so we can) understand exactly what they are looking for or asking about,” he said. “Flipkart has every reason to expect that AI-led innovation through Project Mira will help us create solid competitive differentiation and cement our market leadership.”

For Papatla, every day presents a new challenge in terms of what customers want from the marketplace. “The Kala Chasma song played the previous night, we get Kala Chasma queries the next morning. It is that insane. At that point I do not know what they are looking for--is it Katrina Kaif’s outfit or the glasses. It is hard to understand the intent,” he said.

THE BACKEND CONUNDRUM

If quenching the customer’s needs was one part of the problem, the other was streamlining the backend processes. This includes a variety of tasks—accurate classification of products, accurate product descriptions, avoiding duplications.

Over the past two years, Flipkart has expanded its product selection significantly. It adds more than 10 million products from around 20,000 sellers every month. The first step for any automated catalog is to accurately classify a product, which is hugely difficult given the unstructured data sellers often provide. “We now have a machine learning model that can classify the (product) vertical given an image. For verticals with similar images (say shampoos and body lotions), it uses the product description to classify products with 95% accuracy,” said Papatla.

The machine learning algorithms can also detect incorrect images and morphed images. Posting the right images on the platform is crucial to getting customers interested in a product. “Another pressing problem is duplicate products. Sellers intentionally or unintentionally post duplicate products, which increases user effort in scanning to the desired set of products. Finally, we want to ensure that the descriptors of a product like colour, pattern, etc., are accurate,” said Papatla.

Flipkart will soon guide sellers during their product listing process on what a buyer’s perception is likely to be for a particular image. “We have computed so much training models that if it is an image mistake, we can tell (sellers) to not upload as these kinds of images in the past have seen lower conversions. We are doing it with about 300 sellers now, we are handholding them. Through our sales team, we are providing the content to the sellers, saying this is your quality score, this is the score card for this month, and by the way, here are five opportunities to improve,” said Papatla.

Project Mira is still in its infancy and has a lot of ground to cover, but it certainly is seeing encouraging results

ACADEMIC COLLABORATIONS

While Mira is an internal project, Flipkart is also outsourcing the task of finding solutions to several other problems online marketplaces struggle with. University collaborations play a pivotal role. And for this, Flipkart banks on the expertise of Muthusamy Chelliah, Director, Academic Engagement. Chelliah, who has worked with Yahoo! India in a similar role, ‘marries’ Flipsters with university professors to drive research in core technologies that can improve business for the company.

“We are converging towards match-making between customer problems and published research literature. This process, in turn could help identify the best faculty member to solve the consequent technical challenge” said Chelliah.

Source | Economics Times | 7 April 2017

Regards!

Librarian

Rizvi Institute of Management