How Big Data is Bringing the Elite Fashion Industry to the People
Fashion has always been an industry ruled by the elite. A select group of designers and editors use a mix of creativity, gut feelings and a general knowledge of popular colors and patterns to create the next season’s line, and the rest of us largely accept whatever their suggested fashion trends happen to be.
As we all know, this system is full of flaws. A quick look at the clearance rack will show us all of the trends that failed to have commercial appeal or were simply overstocked. Retailers face a gamble with their profits every season as they sell clothing that essentially hasn’t been tested with the market. The consumer is affected as well. We have all faced that struggle of finding clothes that fit and match our personal style. Sometimes it seems stores are stocked with clothes for humans with strange proportions and an even stranger tastes.
The good news that is big data seems to finally be bringing the fashion industry to the place every other business has always been: creating products based on what the consumer wants.
From Runway to Social
Fashion week is an invitation-only affair, but lately those invitations have been sent to magazine editors less and fashion bloggers more. In fact, bloggers are often the first to give their reports and opinions of what they saw on the runway, but the influence of social media in the fashion world is spreading. Many designers, brands and retailers are tapping into the information they can garner from social media to get feedback on their lines and to determine which trends are popular and which aren’t. Just think what retailers can learn by using big data tools, such as Hadoop as a Service, to see what outfits are appearing most on Instagram, Pinterest boards and Twitter.
Many lines have started revealing new looks to their online audience first specifically to get customer feedback. Oscar de la Renta, for example, revealed an exclusive line on Instagram, and Michael Kors teamed up with other designers to create a fashion week hub on Pinterest.
Trend forecasters have also jumped into the game using complex algorithms to determine which tends will be popular in the future. Forecasting companies use data, such as fashion show analysis, current market offerings, art exhibitions and events to provide ideas for colors and cuts broken down into categories. Tools are also available for retailers who want to see what their competitors are selling, so they can determine just how popular a particular product might be. One startup, EDITD, seeks to provide real-time updates to retailers, so they can adjust inventory based off of how well a particular product is selling. With more data available about which items will actually sell, designers and retailers alike can boost profits and mitigate the risk of having a line completely flop.
Most of us have pretty much accepted the notion that clothes come in either size small, medium or large, but some startups are saying that this doesn’t have to be the case in the future. These small retailers use data analytics to create clothing that is custom-fit en masse. How is this possible? True&Co, a startup focused on lingerie, created an algorithm that, based on a series of questions, tells women which brands and sizes of bras to wear. The company also created its own line of bras based on the data it’s collected about women’s preferences. A kickstarter company called Stantt used body scans of men to create 50 different sizes of shirts for their male customers. Customers then simply send in a measurement of their chest, waist and sleeve length to get the perfect shirt for their size. The project has already long surpassed its funding goal.
With clothes created to fit us perfectly, why not have clothes sent to us that match our style as well? Companies, such as Stich Fix, collect data on their customers, such as their shape, style budget and lifestyle to select styles they think the customer would like and send the clothes directly to the consumer to try on. The customer then only pays for the clothes she decides to keep.
As customers use these services, the use of big data won’t be particularly noticeable, but isn’t that a key indicator of the value of technology: being hidden behind valuable applications? Whether it’s detecting bank fraud or creating the next line of popular clothing, big data is demonstrating its true worth.
Gil Allouche is the Vice President of Marketing at Qubole. Gil began his marketing career as a product strategist at SAP while earning his MBA at Babson College and is a former software engineer.
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