Scaling your fashion metric

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Convenient, timely, and stylish - StyleNook by Kuntal Malia and Arti Gupta covers the essential urban women's fashion needs using AI and data-driven algorithms.

By Nisthula Nagarajan

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Published: Thu 24 Dec 2020, 6:35 PM

As long as humans have worn clothes, there has been a desire to express individuality through fashion. Women pay a great deal of attention to how they style themselves. Most people spend a vast majority of their life at their workplace. Thus, selecting appropriate yet stylish attire for a professional setting without succumbing to monotony is a hassle. What if garments, personalised to individual preference, could be delivered to the doorstep without the customer moving a muscle?

The world today is rapidly digitising. With more people shopping online in the last year than ever before, having a box of clothes, hand-picked for every individual customer, is a dream come true. Even with online shopping portals, customers spend numerous fruitless hours searching for the right outfits and when it arrives at their doorstep, clothes may not fit, and if they do fit, they do not flatter. As there is no human on the other side of this transaction, it is impossible to decipher what clothes are the right ones for a customer.


The fashion industry is always looking for novel ways to present their products to customers and generate demand and awareness in the market. This is where Augmented Intelligence (AI) in fashion makes its entrance in the story. More and more brands are using machine learning and predictive analytics to optimise consumers' shopping journeys and smoothen sales systems through intelligent automation.

StyleNook, a Mumbai-based fashion tech start-up, is using this concept to revitalise fashion. A personal shopping and styling service targeted at urban working women, StyleNook marries the convenience of requesting an affordable box at a click with personal stylists handpicking clothes for individual customers. SHE caught up with co-founder, Kuntal Malia, about how she ventured to use her knowledge of AI to solve the problem with fashion, and applied it to her entrepreneurial adventure.


Kuntal Malia and Arti Gupta

The journey

StyleNook began as an inspired solution to co-founders, Kuntal Malia and Arti Gupta's problems with their own shopping experiences. Both women have a love for clothes and experimenting with them, but as life got busier, time was slim. Neither could spend countless hours browsing in a mall or on their phones to find one perfect piece of clothing. Then the question arose: why couldn't they build an experience where everything is curated to the customer's size, price, and style using AI? 

"A personal stylist or shopper has always been available but at a heavy cost and time. What data allowed us to do is take the aspect of a personal stylist - someone who knows you really well makes selections on your behalf and has you try things on and then listens to you - accessible to more people," Kuntal said.

How it works

Customers fill out a 10-minute questionnaire, on the StyleNook website, detailing their size, tastes, style, and preferences. The more detailed their answers, the better. An assigned personal stylist will study the answers and curate a box of apparel that fits the customer's budget, body type, job and individual style. Then, the customer goes through the list of shortlisted products online and picks what they like, giving feedback on what they don't. Once they checkout, the package is assembled and shipped.

Behind the scenes

Each customer's information, as well as feedback on products, is broken down into data points, which are then used by various algorithms to understand what garments should be selected for their client. They deploy AI where, using customer-provided information, machines comb through thousands of options to produce a curated list from which human stylists pick products. This method combines the ability of rote task performance best achieved by machines and the creativity and empathy only found in humans.

Kuntal adds: "At StyleNook we are getting first-party, relevant data right and actionable user data, compared to derived or third-party data. This kind of data, which I term as high-intensity data, is far more valuable than just plain old Big Data. It has allowed us to successfully style thousands of clients and also work with brands to introduce data-driven collections."

Algorithms are also deployed in other aspects such as understanding what stylist to pair with a user, product planning as well as determining what aspects of a product work well together to influence design decisions.

The UAE market

The decision to enter the UAE market was purely user-based. StyleNook has always received and serviced orders for Indian clothes from the UAE but recently customers began to demand Western clothes as well and the need for StyleNook in the UAE became apparent.

"Currently, we are identifying and onboarding brand partners that are relevant for this market. Since the party season is upon us, we've also tied up with brands such as The Arab Crab. We love local emerging brands and will be keeping that as our focus," Kuntal stated.

The future of fashion

Kuntal believes that personalisation and curating is the future of fashion. This will not only influence shopping behaviour, but also buying, design and production. AI enables companies to scale these aspects. "Styling is the holy grail of personalisation. A stylist works through the plethora of possibilities to pick what is relevant to a specific customer. Those picks will work only for that customer and no one else," Kuntal adds.  StyleNook and companies like it focus on what is relevant versus being faster or cheaper, allowing them to thrive in spite of the pandemic where traditional fashion brands are struggling. Fashion fades but style is forever, and StyleNook and companies like it are here to ensure that.

Kuntal's style mantra "Dress so you are comfortable in your own skin - that's the secret of good style!"For more information, visit www.stylenook.in

Instagram: @stylenook.in


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