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 Sadhana
Chathurvedula, MINT
Bengaluru,
27 January 2016
When graduate student Anupama Pasumarthy shops online, she says
she is always disappointed by the recommendations.
“It’s tough finding clothes (which are always too large)
or shoes (which are too small) in my size, and I don’t find
stuff that’s similar. I used to shop online but these days
I just go to the store when I want to buy something,” the
22-year-old says.
Tech-savvy millennials like Pasumarthy are the demographic that
most fashion retailers target, but the problems she faces are
all too familiar for anyone who shops online. To help retailers
overcome this, start-ups like Stylumia Intelligence Technology
Pvt Ltd, which offer artificial intelligence-based solutions for
smart visual recommendations, have started to take off.
There are multiple start-ups globally trying to crack visual
search. Visual search start-ups help companies enable their users
to discover products online, based on photos of objects in the
real world. In India, companies like iLenze (which raised $500,000
in funding last year) and SnapShopr (which raised an undisclosed
amount of angel funding) offer visual search platforms.
Chennai-based Mad Street Den, which raised $1.5 million in 2015,
also offers visual search, but its most used offering is a visual-recommendation
engine, which sifts through catalogue data to show relevant recommendations
to users.
With e-commerce booming in India, Singapore-based Visenze, whose
visual search offering is used by companies like Flipkart, is
setting up operations in India to cater to the demand.
Many visual search companies cater to multiple verticals, and
have so far concentrated on consumer applications.
Started by former chief operating officer of Myntra, Ganesh Subramanian,
and machine learning scientist Ram Prakash, who developed Quillpad,
the first machine learning based language input for Indian languages,
Stylumia is different. It focuses only on fashion, and using the
same core technology, it is looking to help both consumers and
businesses make data-driven decisions.
“We are developing a technology which takes natural images,
videos, be it Bollywood videos or TV serials, whatever influences
fashion, and decipher and extract fashion elements from that,”
says Prakash.
The start-up then hopes to use this derived intelligence in two
ways – one, to make smarter recommendations to consumers
browsing for products and two, to give suggestions to fashion
buyers and retailers what to buy and make, based on real world
consumer-purchasing data.
Right now, Prakash says that decisions at fashion companies are
made based on some analytics, but intelligence based on visual
cues is missing.
“They look at the patterns and say this is doing well because
this is a red colour T-shirt with a contrast collar, what they
cannot do right now is look at the same red colour T-shirts with
contrast collars which are not doing well. They do not have a
way to see all the relevant data together. That’s another
problem that we are trying to solve,” says Prakash.
Stylumia is set to launch its product in the first week of April.
It currently has partnerships with retailers (which they it does
not want to disclose before the product launch), says chief executive
officer Subramanian. For now, it is using a team of four engineers
to capture and label data but hope to automate this process very
soon.
“There’s a lot of interest among both online and offline
retailers in this space. Our aim is to provide the most accurate
prediction of demand and our consistency will improve as we work
with more retailers and brands and get more and more data,”
says Subramanian.
Despite the progress in technology, unless there is an overhaul
on the supply-chain side, real impact is difficult to create,
says Devangshu Dutta, chief executive officer, Third Eyesight,
a New Delhi-based consulting firm.
“Large retailers today are planning several months in
advance and are structured in such a way that by and large it
takes them several months to respond to any particular trend.
Till you can address that, data is just data,” he said.
(Published in Mint)
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