AI News, Artificial Intelligence at India’s Top eCommerce Firms – Use Cases from Flipkart, Myntra, and Amazon India
- On Sunday, September 30, 2018
- By Read More
Artificial Intelligence at India’s Top eCommerce Firms – Use Cases from Flipkart, Myntra, and Amazon India
Many of the top eCommerce companies are deploying artificial intelligence to create better products, user experience, smarter logistics, target the right demographics, and be the preferred choice for their customers.
During an event organised during Nadella’s visit, Flipkart said in a statement that it plans to leverage AI, machine learning and analytics capabilities in Azure, such as Cortana Intelligence Suite and Power BI, to optimize its data for innovative merchandising, advertising, marketing and customer service (this statement is a claim from the author, who states that he was at said press conference).
Flipkart’s president of product Ram Papatla claims that the company studied its customers and came to the conclusion that a large percentage of their returns could have been prevented with one or two simple questions asked to the customer before their purchase.
“…Now, our users with broad intent (searching for, say, shoes or bedsheets) are guided with relevant questions, conversational filters, shopping ideas, offers and trending collections”, said Papatla.
It is a beginning in helping customers find the exact product they need in online settings that aren’t exactly easy to navigate.” (source: Economic Times) Like in offline shopping where the sales executive asks a customer about her size and other requirements before making a suggestion, Flipkart is trying to get AI to do something similar.
The company has been using machine learning techniques to detect junk addresses, compute address quality scores, correct city-pin code mismatches, and provide suggestions to users to correct wrong addresses.
While we didn’t have the time to confirm specifics on the phone, we can presume that natural language processing is used to assess the written information about a product (in order to fill in information gaps, such as missing brand name).
For example, a catalog size 6 may correspond to a physical size of 15 cm for Reebok while for Nike a catalog size 6 may correspond to a physical size of 16 cm.
At 22:30 into the video below, Rastogi explains some of the processes and challenges behind Amazon’s approach to product recommendations during a speaking event hosted by Kalaari Capital: Deals for Events Machine learning is used to identify relevant products for specific events such as Diwali, Christmas, etc.
By training a machine learning system on past holiday purchase data and current purchase activity, a system may be able to calibrate demand more accurately in order to sell products at the right prices to either (a) move certain items at high volume, or (b) maximize profit margins by matching the highest margin products to users during the holiday season.
“So for instance, the query ‘red Apple iPhone’ belongs to the smartphones category and so we should show smartphones in search results but the query ‘red Apples’ belongs to the grocery category and so we should show grocery items in search results,” says Rastogi.
“Fast fashion” is defined as: A contemporary term used by fashion retailers to express that designs move from catwalk quickly to capture current fashion trends (source).
We pride ourselves in selling unique fashion.” Experience Myntra is using machine learning to improve the payment acceptance rates of online transactions – an issue which is particularly prevalent in India where failure rates are high.
The study cites as statement from Jitendra Gupta, CEO and co-Founder of Citrus: “bank systems in India can be down for anywhere up to 4-5 hours at a time, which makes the experience extremely frustrating for customers.” Using machine learning, the system figures out the best payment gateway the payment needs to be routed through.
It uses “collaborative filtering” (a concept we explain in depth in our recommendation engine article), which recommends products to one person based on what another person has just bought and also helps match which fashion goes well with what.
large online retailers) are in the best position to detect fraud, because they have the most instances of both legitimate and fraudulent activity on record, allowing them to train machine learning systems to predict fraud based on more robust contextual and historical data.
A higher RTO translates to higher losses as many cash on delivery (COD) orders are not delivered for various reasons such as customers not being present or not having cash at that point in time.
What’s cooking at Myntra? AI, fast fashion and… Ratatouille!
In this story: Data-backed design helps Rapid, an in-house team at Myntra, determine bestsellers on the platform.
But don’t be fooled by that cutesy name — a sobriquet for an Artificial Intelligence-based software used by Myntra’s in-house Rapid team, Ratatouille tells these folks just what designs are sure to be top-sellers.
More professionally known as Vorta, it’s also a software that’s helped the team cut down design time from six months to just one.
It’s a team that’s endlessly on the go — iterations upon iterations, and 12-hour workdays, its young members’ infectious energy explains the hunger to better something that, at first glance, is already perfect.
The hypothesis was this: Since Myntra already had large amounts of data, could they somehow leverage this to better understand what consumers want and build products to cater to that demand?
Here’s what the early days looked like: First, someone would enter a query for the top-selling kurtas and T-shirts on Myntra and get the corresponding style ids.
The pictures were arranged according to clusters — 30 red t-shirts, 50 blue, 40 biker t-shirts and so on.
The team would then ascertain, based on data, which of these themes were likely to work and, accordingly, the first batch was designed as per its findings.
And the numbers showed they were onto something — the products sold phenomenally well, giving validation to the endless hours of manual work by the lean team.
From here, the process evolved — from spreadsheets, to complex algorithms, now we actually have a data sciences team looking at all the algorithms,” says Anurag Panganamamula.
Shortly after Yash joined the team, they began to explore how they could automate the tedious process of spotting best-sellers and manually clustering them to determine their success.
“Compare a T-shirt that has had 100 views and sold 10 pieces to one that’s been viewed 10,000 times, selling 20 pieces — despite the latter selling more, the previous one still did statistically better,” explains Yash.
Numbers tell the success stories of these brands: In the two years since Moda Rapido — Spanish for ‘fast fashion’ — was launched, it has grown over 300% year-on-year to reach 1.5% of the platform.
Now, a swift-moving brand with a name that captures the instant gratification mantra of Gen-Y, is a whole other example of how a brand backed by data can scale.
Transitioning from a core analytics background to now managing the business planning, pricing and multiple categories of Rapid brands, Vasu is the man behind the insights.
So the team, with data-backed precision, began work on longlines, completed the required fashion shoots and shortly thereafter, released it on Myntra.
The people wearing it would pair it with baggy jeans, a baseball cap worn backwards, a certain posture and attitude.
“For a designer, the process is usually intuition-led, but when you are backed by data, there are interesting insights to garner, even to a level that shows you what personal preferences might be influencing your designs.
“My take is that we’ll all move ahead and do jobs that are a level higher.” The success of Rapid, to say the least, has been phenomenal — but despite the effort and determination of the team, the magnanimity of it all has still been a bit of a surprise for all involved.
With Rapid, it’s a new era in the making — it’s disrupting the way brands are being built, the way fashion is perceived, and even the way profitability in brands is being looked at.
We are double-indexing on — here’s what you want today and we’ll deliver to you right now,” says Rajesh, adding, “It goes with the founding principle of Myntra, which is democratizing fashion.” Two years since it all began, Vorta is well on its way to becoming a global Software as a Service (SaaS) product.
From Myntra and Flipkart to Tech Providers, How AI Is Driving Fashion E-commerce in India
In an industry that's full of buzzwords, nothing is more talked about these days than artificial intelligence and machine learning aka AI and ML, respectively.
But one area where these technologies are starting to be deployed in a large scale without necessarily being noticed is actually e-commerce - or more specifically, fashion e-commerce.
Krishnendu Chaudhury, Principal Scientist and Head of Image Sciences at Flipkart, has the air of a university professor, and the scientist, who was formerly leading image processing and computer vision projects at Google, holds forth on how the company is making use of AI.
With these changes, technology that so far has seemed like science fiction is getting much closer to reality, and it's based on the gathering of copious amounts of data.
You hear a lot about the sales amount, and the revenue, but the consumer data has so much real value, to build a better experience for our users.'
Using AI, Myntra is able to predict how people will shop on the website, which allows it to build models of traffic and plan its servers accordingly.
It's all very behind the scenes and unexciting on the surface, but it can be the difference between a site that works flawlessly or slows down to a crawl on the day of the big sale.
'So if you search for a red t-shirt, it might have given a wrong result because the description had some word like 'red-hot' in it, but with AI the search isn't just dependent on keywords,' explains Chaudhury.
The company has been working with brands to create intelligent catalogues, and it has also been offering a chatbot based shop-assistant solution for offline retail.
'On the technology side, we have this solution in place wherein a combination of similarity, classification and tagging gets us closer to higher and better accuracy,' explains Dixit.
Talespin Is Bringing Machine Learning and Chatbots to Physical Retail 'Bollywood is a very big fashion influencer in India,' says Rai, 'so one of the things we did was analyse Bollywood songs and movies, by looking at the videos that are available on YouTube, and generate predictions on trends.'
- On Friday, February 28, 2020
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