India’s Fashion Tech Startup Bigthinx Takes Its Walkabout 3D Avatar To Milan

The touch and feel of offline shopping or the convenience of online shopping? Will ecommerce take over physical stores? These are the most prominent questions India’s retail industry is pondering over today.

Offline retail has a set of advantages that online retail just cannot match – shopping experience, getting a look and feel of products and trials. But the convenience of online shopping has made it a big channel for fashion products as well, despite the fact that the fashion and apparel segment has the highest return rate among products, and more than half of them are due to unsatisfactory size.

The benefits of ease, choice and discovery are now being combined with a high-tech approach to solve this big issue. Clothing size varies enormously between brands, and even within different collections of the same brand, so it’s very easy to get one’s size wrong. Measurement charts and size recommendations aren’t very effective as they are mostly generic and many times people overlook them completely.

Most ecommerce stores see 30-50% returns on clothing and about 60% of the returns are due to incorrect fit and another 30% are due to the look. This is where fashion tech startup Bigthinx comes into the picture.

Bigthinx’s software effectively targets these problems with tech intervention and aims to combine the ease and variety of online shopping with the advantages of offline shopping. The company uses AI to solve the two biggest pain points in online fashion – size and look.

Pivoting From Edtech To Fashion Retail

Bengaluru-based Bigthinx started out as an Edtech company in 2015 building virtual science labs for school and college students where they could perform entire science experiments in virtual reality via VR headsets. Founders Shivang Desai and Chandralika Hazarika even signed up one of the major school chains in India to start using their software but struggled with getting them to pay and eventually decided to move out of the edtech sector.

“We started to look out for other industries where our skills could be leveraged. Fashion retail was one such space that caught our attention. It is a dynamic industry with huge potential for improvement in processes, customer engagement, online retail and several other areas,” Desai told Inc42.

 

 

Just last year, in the US alone, brands lost $284 Bn in potential sales due to online clothing returns. 84% of all returns are incinerated or landfilled, which has a huge environmental impact, Desai added.

The founders realised that retail is also an industry that has seen relatively little innovation, especially in online retail. Virtual mirrors in stores for virtual try-ons became popular somewhere around 2014-2015, but came with a host of inherent challenges – expensive hardware, connectivity, no information on clothing fit, and trials in public spaces. Furthermore, there was no way to use this technology for the rapidly growing ecommerce segment.

“This is when we had the idea to build a method for identifying size, fit, and delivering virtual trials when shopping online to help consumers shop better and enable brands to cut costs on free returns,” Desai said

As Bigthinx cofounder Hazarika recalled, “We reached out at the time to the founder of a fashion unicorn who met with us to share his insights. He told us that his company had previously acquired a US-based startup trying to do the same thing as us but it ultimately didn’t work out. This encouraged us as sizing and returns was a problem in the ecommerce sector, but retailers had just accepted returns as a cost of doing business. We remained convinced that the product we were describing was an absolute necessity for the ecommerce industry,” said Hazarika.

Desai and Hazarika created their first prototype in 2017 when realization dawned that, as a small startup, the only way to compete with some of the existing alternatives in the market was through automated personalisation and clothing creation.

“It was then that we met Mr Pradeep Guha, the former president of the Times Group and current CEO of 9X Media. Pradeep was completely convinced by the vision and joined us as an investor-partner in the venture. This was when we started building the AI which could make us market leaders in the fashion fit and trials segment,” Hazarika told us.

The Creation Of Bigthinx’s Walkabout 3D Avatar

In mid-2019, Bigthinx released a test app of its Lyflike 3D imaging software to understand how users were reacting to and adopting the technology. The Lyflike app lets users to set up a personalised, walkabout 3D avatar and use it for clothing trials. The AI could either auto-generate clothing from any 2D picture or users could design and try on their own clothing. The platform would match their creations to real products available online.

The app got a few thousand downloads and helped the startup shape the core features its B2B products. After this experiment, Lyflike was taken down as a B2C app and reshaped into a B2B product which any online retailer could integrate into their own website or app.

“In September 2019, we released Lyfsize, an application that carries out human body scans from two smartphone pictures. It immediately gained tremendous interest and has been adopted by customers in Sweden, Finland and India with use cases ranging from ecommerce to P2P clothing rentals (Sweden) to modelling talent agencies (India, Sri Lanka, Nepal) to uniforms and workwear (Finland, India),” Desai added.

Bigthinx founders explained the other applications of the Lyfsize software

1. Modelling
   Working with an upcoming talent agency for the fashion industry, Bigthinx is positioning its platform as the future of fashion talent and technology. This collaboration helps models get body measurements using just their smartphones so that event organisers can have clothing customised for them ahead of time. It also prevents the human errors that occur when people measure themselves.
2. Uniforms and Workwear
   Workwear and uniforms form a vital requirement for many companies, especially from a branding and consistency point of view. Large companies have to design uniforms in line with their workforces around the world, adapting to the varying cultural needs of each geography. Not only does the Lyfsize application let them save time and labour costs, but also gives access to data that’s very useful in future decisions.
3. Mass Customisation
   Another advantage of mobile body scanning is it gives information beyond just body measurements. Body shape data can be broadly interpreted to allow clothing brands to deliver mass customisation, intermediate sizes and optimise production according to their audience.

Going Back To The Consumers

Bigthinx’s Lyfsize and Lyflike AI software not only determine precise body dimensions but also matches users with their ideal size for any brand, providing data on fit, flow and fabric movement. It allows them to see how clothing looks on a 3D virtual, walkabout avatar that looks and measures exactly like the user. Naturally, one would expect the consumer-side applications for this tech to flourish, but that’s still nascent in the retail world.

In the offline space too, consumers can use screens or tablets in physical stores to call up their own avatar and instantly virtually try on hundreds of different outfits and combinations without effort. This is particularly useful in sale-season when stores are overcrowded and lines for dressing rooms are endless, and also for stores specialising in ethnic wear where trying on multiple outfits would be a cumbersome and exhausting experience. So clearly, there is a market for this product in the consumer side.

The 3D body scan carried out by Lyfsize using only one’s smartphone gives 44 different body measurements with over 95% accuracy. For reference, this is more accurate than most professional tailors.The Lyfsize (3D body scanning) product operates on a B2B2C model whereby the startup licenses or white-labels the app to retailers and they distribute it to customers.

For the B2B model, Bigthinx’s Lyflike APIs are integrated into etailer websites or apps and allow real-time clothing trials for consumers.

“We have so far partnered with online retailers in ecommerce, bespoke fashion, fashion rentals, uniforms manufacturers, and fashion talent platforms. One such platform is InchStreet, which is an online aggregator of men’s bespoke clothing providers, where one can choose one’s suiting and styling needs and have it created by the provider of one’s choice,” said Desai.

For InchStreet, it was imperative to get accurate body measurements and they chose to use Lyfsize to eliminate inaccuracies in measurements. Customers in India had disparate measurements due to geographical and cultural differences. Bigthinx standardised measurements and helped Inchstreet overcome this limitations.

The startup’s products are priced as SaaS models based on the expected slab usage and billed on a recurring rate. This allows us to serve various customer groups ranging from massive e-commerce companies to independent designers.

“For fast fashion ecommerce platforms, our AI automates the clothing digitisation process and can realistically auto-create up to 100% of menswear and between 60-80% of womenswear (depending on the complexity) in a matter of a hours,” Hazarika told us.

This extends to catalogues ranging up to millions of SKUs. “When compared to the existing methods of digitising clothing – 3D modelers or 3D scanners – our system is vastly faster and cheaper, allowing almost entire catalogues to be newly digitized every time seasons and styles change,” she said.

The Prada Cohort And Future Plans

Bigthinx was invited to Milan by Startup Bootcamp (SBC), one of the world’s largest accelerator networks, to participate in the selection process for its maiden fashion tech accelerator. The accelerator’s main partner is Prada along with several other heavyweights such as Sopra Steria, Accenture, PwC and others. Having already screened over 1200 fashion tech startups, including hundreds of in-person meetings around the world, Bigthinx was among the top 20 contenders invited to Milan.

“They reached out to us only two weeks before the main event and urged us to make a trip to Milan for the four-day selection event as they believed we had the right technology to meet the needs of their partners in the fashion industry. After a lot of discussions with our advisors and also VCs, who encouraged us to attend, we decided to participate,” said Desai.

In Milan, the startup pitched and demoed products to over 150 programme mentors, including the owner and top management of Prada. Bigthinx is the only company from India to be selected and will be working closely with the programme’s partners from January 2020 onwards.

“Over the next year, our plan is to work with the world-leading luxury brands which we now have access to such as Prada, Sopra Steria and others, as well as to deploy our products into ecommerce web shops in Europe with the learnings we derive,” said Hazarika.

As Bigthinx’s products are pure software pieces, the startup has the ability to scale its customer base rapidly. Their plan is to acquire several major European ecommerce players as paying customers within the next few months, after which their sights are set on the US.

The expansion plan is to enter each geography with well-established local partners that have broad and deep ecosystem knowledge, connections and existing networks. In the next two years, Bigthinx expects to serve large consumer bases for fashion products in the US, Europe and a few other Asian markets.

It is also deploying the Lyfsize software in fitness studios to allow users to track body transformations and health risks, and take appropriate course corrections. Over the course of the next three years, Bigthinx plans to diversify into the healthcare and gaming sectors.

“Our competitive edge lies in a combination of several components – focus on automation, constant innovation, defensible IP, speed and agility. Staying ahead of the curve in all of these aspects is what will keep us competitive in the years to come,” Desai said.

Correction Note: Some sections of this article have been edited post-publishing to fix typographical errors and improve clarity of language.