DataBeat Recap: Data Science Tackles Fashion
An unexpectedly popular talk on day one of VentureBeat’s DataBeat conference covered an unorthodox subject: fashion. At first glance, it seemed a little out of place on the schedule; after all, this isn’t a topic you’d often think of in conjunction with the methodical and complex undertakings of data science.
Many in the room seemed to have the same wariness as I did, but by the time Stylitics CEO Rohan Deuskar had finished his talk, the whole audience found themselves nodding along excitedly to questions of which shades of pink a teenage girl might wear, or the different combinations of outfits she might be able to put together based on past purchases.
Why on earth would these things matter to a group of data scientists? The answer, it seems, is that Deuskar’s company seems to have hit the sweet spot, creating the perfect mix of a sought-after product, customer trust, and a collection of buyer data that has retailers salivating for access. Stylitics really is a data scientist’s dream project, and a pretty great moneymaker to boot.
Let’s take a closer look at Deuskar’s presentation:
Data collection can make or break a company, that much is true. However, it’s notoriously difficult in retail, and no one’s quite sure how to resolve this issue. Rohan Deuskar identified a problem that many others have also noticed: brands and retailers want to know information about their customers, but the average consumer refuses to tell them outright. To get around this, many stores try to incentivize the process, offering everything from loyalty cards to discounts. Some even go so far as to offer money to customers in exchange for information, but trial after trial has shown that key groups won’t participate.
There’s also the secondary issue of accessible data. Even if you managed to track every single purchase a customer has ever made in your store, as well as their preferences and dislikes, all of the loyalty cards in the world aren’t going to be able to give you insight into your competitors’ proceedings. Because of this, fashion designers and retailers market based on only 5% of information, namely what items customers purchase from their own stores. In doing so, they are unwittingly missing out on the other 95% of information, perhaps an even more important question: “what are my customers buying after they leave my store?”
In creating Stylitics, Deuskar set out to answer this question for retailers. What makes the Stylitics approach so unique, however, is that he chose not to use “Big Data” (with its attendant ethical and privacy concerns), but what he calls “Best Friend Data,” an intimate knowledge of customers provided by customers themselves.
The deal: Stylitics offers a simple exchange, requesting raw data in exchange for organized, accessible data. In other words, the Stylitics app is an honest and open way of getting customers to willingly reveal massive amounts of data about their fashion trends and buying habits in exchange for access to a “virtual closet.”
The virtual closet app offers sortable, searchable shelves on which they can view their entire wardrobe, share with friends, see when they last wore an outfit and how many times they’ve done so, and in general interact with fashion in ways that weren’t possible before.
All of this data, of course, is drawn from information users have provided about their own fashion, whether that’s automatically uploading purchases or even taking pictures of items in their closet and tagging them with required data fields like size or color. (You can read more about the Stylitics app and how it works here.)
In exchange, Stylitics gains access to the most minute details about every facet of their users’ fashion purchases, including shade, size, style, and when and how often they purchase clothing. As with any wardrobe, this covers a number of brands and retailers, making it a gold mine for retailers who want to understand how their own customers interact not only with their brand, but with competitors.
Previously, companies would have had to conduct expensive one-off studies in the hopes of getting even a fraction of the data that customers are willingly handing to Stylitics. Now, they can pay that same $15,000 to Stylitics and gain access to a year’s worth of real-time data from app users, enabling brands to keep track of fashion trends, the success of competitors, and other factors that might better enable them to market to their customers. Stylitics markets this approach as “Unlimited questions. Instant answers. Up to date.” – the perfect formula for retailers looking to get a leg up on their competitors.
This data gamble is paying off: not only does Stylitics have the largest stream of outfit and purchase data, but it’s also now the number one free fashion app in the iTunes App Store, an odd fate for a data collection service. In his presentation, Deuskar stressed that the secret to success for his company is honesty, above all else. They’ve been open and upfront about their collection of data and its intended purpose, but have also made a product that people find valuable enough to hand their information over for anyway.
Put simply, the Stylitics model is all about mutual benefit, and it’s a great example of using data science to attract and retain users, making revenue in the process. And that’s the kind of thing everyone at a data science conference likes to hear.
Want more from DataBeat? Check out our Wednesday recap, or another in-depth post:
- DataBeat Recap: Data Science Today and Tomorrow
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