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Worried that the dress you just bought online might be a size too small? Wondering if the size 10 shoes you just ordered are the same size 10 as all your other shoes? Are you one of those seasoned shoppers who have learned the hard way – through trial, error, and spending significant postage on returns – that one manufacturer’s “medium” is another’s “extra large”?

Singaporean startup Pixibo wants to change all that with its size recommendation engine Pi, which can be built into online stores. The fact that several high-profile angel investors from the ecommerce and retail industries have backed its recent “pre-series A” funding round suggests that it may be onto something.

The amount raised is “in the sub-million dollar range,” founder and CEO Rohit Kumar tells Tech in Asia. Investors who participated include Julia Atwood, chief business officer at payments firm Liquid and a former business strategist at UK retail giant Tesco; Anja Graw, CFO at online personal shopping service Outfittery; Shailesh Rao, a former Google and Twitter exec; and Tim Rath, co-founder and chief people officer at Lazada.

PIXIBO Logo - Courtesy Pixibo

PIXIBO Logo – Courtesy Pixibo

Kumar is an ex-Googler himself, having held analyst roles for the company in Europe and India before coming to Singapore in 2013 to head up Asia-Pacific operations for ecommerce advertising company Sociomantic. It was here that Kumar first noticed something wasn’t quite right with online fashion shopping – lots of people were browsing clothes on ecommerce sites, but very few of those visits were converting into sales.

The average conversion is 1.5 percent,” he says. “That means 98 out of 100 folks leave the website without buying anything.

 

Smart sizing

Kumar wanted to find out why people who aren’t simply window shopping – those who have the money and the intent to make a purchase when they visit a shopping site – aren’t buying.

His team’s research found that the top reasons for this low conversion rate are uncertainty on the part of the shopper around size and fit. Many websites provide fitting guides and sizing charts, but customers often find these to be inadequate.

PIXIBO App - Courtesy Pixibo

PIXIBO App – Courtesy Pixibo

I never know if it will fit me, I don’t have access to a fitting room,” are the thoughts going through online shoppers’ heads before they decide not to purchase, Kumar explains. “I call it online fashion’s oldest problem.

At the root of the problem is the lack of detail that individuals have about the kind of precise body measurements that tailors use to make formal dresses, suits, and other fitted clothes.

We realize men and women don’t know their bodies that well,” says Kumar. “Unless you’re getting married and getting a wedding dress made, you probably don’t know all those different measurements. But what people do know are the more basic things like their height, weight, and their bra size.

PIXIBO Webview on Browser - Courtesy Pixibo

PIXIBO Webview on Browser – Courtesy Pixibo

Data is at the core of Pi, which appears as a chatbot feature on ecommerce sites and uses the information at its disposal to provide smart sizing recommendations to buyers.

This data includes both publicly available brand size charts and banks of non-public information on sizing standards, customer trends, and behaviors of different types of fabric from clothes manufacturers and retailers around the world. By using this data, Pixibo aims to to fill in the gaps in people’s – and retailers’ – knowledge about their body measurements.

As Kumar explains, sizes can vary widely from region to region, and even from brand to brand. In the Nordic countries, a “small” size is likely going to be significantly bigger than a “small” in Asia, simply because average heights are larger in northern Europe. Throw in the arguably unhealthy tendency among some brands towards “vanity sizing” – where the same nominal size gets physically larger or smaller over time, depending on perceived consumer trends and sentiment – and there are even more hurdles to buying the size you want.

The trouble with this is that the consumer buys online on intuition, receives the garment, tries it on, and sends it back when it doesn’t fit,” says Kumar. “That’s why online fashion has this return rate problem – it can be as high as 40 percent in Europe.

Pi gets garment technical specifications from its retailer clients. These give it knowhow above and beyond the simple sizing charts that appear on many websites. “It’s just that different brands have their eccentricities,” says Kumar. “We typically get 18 or 19 different data points for each different product, and translate that into actionable advice.

In other words, Pi uses information not easily available to the public – such as the stretch factor of a particular cotton-mix fabric, for example – in an attempt to give a more accurate size recommendation.

This means that someone visiting an online clothes store can select a garment, input their own size measurements, and get personalized suggestions from Pi on which size to go for based on their body size and shape, whether they want a loose or a snug fit, and the type of fabric the garment is made from.

 

Out of the box

Pixibo is far from being the first company that has tried to solve “online fashion’s biggest problem.” US-based True Fit, Germany’s Fit Analytics, and Estonia’s Fits.Me – which was acquired by Rakuten in 2015 – are some of the significant players in the space.

But Kumar argues that Pixibo can offer something different for ecommerce companies. “The question I asked myself before quitting Sociomantic was this: The problem is real, every retail and ecommerce CEO admits return rate is a problem, so why haven’t these guys widely adopted any of the available solutions?

His explanation is that ecommerce players were unable or unwilling to integrate the sizing technology from third-party providers, which typically need to go through weeks and even months of in-house development work to be ready for implementation on the company’s sites. Kumar claims that Pi can be set up in a much shorter timeframe – even 20 minutes – since his startup has done the lion’s share of the development work itself, and can license out the tech and APIs for etailers to customize the widget at their end.

It is this promise of an out-of-the-box, plug-and-play solution that has enabled Pixibo to sign up six clients to date. Among them are Hong Kong’s Grana, Thailand’s Pomelo, and India’s Abof. Kumar reports that the startup is in advanced discussions with another dozen or so prospects.

Under this SaaS model, Pixibo signs clients up to a 12-month contract and charges a monthly license fee of “a few thousand dollars,” rather than pricing according to the number of sales each client makes. It charges an additional set-up fee for clients that opt for the full user interface, rather than those that just license the development kit.

 





A size bigger

Kumar says that the pre-series A funding will help to tide the startup over and put its growth plans into action prior to raising its first institutional round, which is already in the works and is tentatively slated to close in January next year.

As for the business, the Pixibo team is looking at expanding into adjacent services so that it can enhance the package it offers to online retailers. Kumar describes the size-and-fit tool as “just the opening act.

The startup is now working on a discovery product that will suggest purchases to customers based on their personal preferences. “Some of these sites have thousands of garments,” says Kumar. “My wife is only ever going to interested in a few of those – because she doesn’t like pink, she doesn’t like lycra, and so on. Our solution will create a personalized version of the site for the user.” He says the aim is to launch this white-label “personal shopper” in January.[1]

 

This Article Curated by Benang Merah Komunikasi’s Editorial team.

We consider to take journalism ethics and contents reposting etiquette seriously, that you can find here about media ethics. We do curation article for our audiences, not for search engine bots. By addressing this growing area of concern we hope reader can be smart to filter and understand between content plagiarism and content curation method.

References Sources:

[1] Taken from Ex-Googler’s startup gets funding to solve ‘online fashion’s oldest problem’” written by Malavika Velayanikal and Michael Tegos for TechInAsia.

Editorial Benang Merah Komunikasi

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