Irrational Fashion

Fashion is instant language.

Miuccia Prada

As we mentioned, fashion is not just about clothing; it’s also about identity and expression. Even the most rational among us make decisions about how to clothe and accessorize ourselves based on irrational factors. We don’t “need” a new pair of pants because the old ones don’t function as pants anymore; we “need” them because we don’t like what the old pair is saying about us (that we’re dirty, or careless, or behind the times).

Still, it’s possible—and necessary—to find ways to correlate data with that emotion. Shawn Davis is currently Senior Director of Advanced Retail Analytics at Nike, and previously served as VP of Analytics at ModCloth, an online retailer for indie clothing, accessories, and decor. Shawn told us about his experience at ModCloth: “We’d be regularly sitting in meetings with our merchandising team, and listening to them describe why they think something is hot, or why they think the customer is going to love a particular product, and then trying to translate that into something that we could surface analytically in the data.”

Lorraine Sanders, a San Francisco-based journalist who’s written extensively about the intersection of fashion and tech, and is the host of the “Spirit of 608” podcast, puts it this way: “We’re in the middle of a time when big data is becoming an important factor in just about every industry that deals with human behavior, in order to generate revenue. It’s happening because, frankly, we’re just hitting that time in history where the ability to collect data is becoming widespread and, in many ways, democratized.”

Lorraine goes on to add that, “in a lot of ways, everyone everywhere can collect big data. It’s the question of what to do with it that’s the interesting part. With fashion, the data collected from consumer interactions, engagement, and reactions to products has the potential to add a ton of value in helping brands hone in on what’s going to sell and become more efficient at getting the products they decide to invest in and produce to the exact right people, at the exact right time.”

As Lucie Greene, Worldwide Director of The Innovation Group at J. Walter Thompson put it, “Fashion is about newness and novelty. We see something and feel compelled to buy it.” Studies claim that 90% of all purchasing decisions are made subconsciously, and that those decisions are completed within 2.5 seconds. We buy products, especially fashion goods, based on having our emotions evoked in one way or another. The challenge of data is to find ways to understand, quantify, and use that emotion in a way that both serves customers’ needs and drives sales.

Fashion’s Data Lifecycle

Another unique aspect of the fashion industry is what is colloquially referred to as the “fashion cycle”—the time it takes to get a garment from idea, to runway, to factory, to store. What’s happened recently in fashion is that consumers are an integral part of the full fashion cycle—before the fashion is even made, we see a range of consumer engagement—from designers asking for consumer votes on sleeve lengths, to brands holding contests for user-generated designs, and high-fashion brands taking consumer orders based only on samples shown on the runway. This engagement continues through the sales cycle all the way through post-sales data opportunities, such as the proliferation of online “haul” videos (consumer-recorded videos of recently purchased items) and outfit-based social media posts.

In addition to companies that are finding clever ways to use big data throughout the fashion cycle, a growing number are starting to use data to circumvent the traditional data cycle entirely.

Large companies like IBM and SAP and startups like fashion data analysis company Trendalytics are starting to tap social sentiment analysis (often correlated with historic demand) to more accurately predict trends—and specifically to identify when those trends are likely to begin and—also crucially—when they might end. Companies such as New York-based Moda Operandi, London-based Wowcracy, and Hong Kong-based LuxTNT offer customers the ability to pre-order fashions directly from the runways, instead of waiting up to six months to buy goods when they hit stores.

Regardless of whether they are trying to supplement or circumvent traditional cycles, fashion brands make copious use of different types of data during the design, manufacture, and sale of goods.

Fashion’s Data Startups

When we published the first edition of this report in Fall 2014, we noted nine different fashion-tech startups that focused on big data. In the time since, the space has expanded rapidly, further proof of big data’s importance to the fashion industry. Here are the different types of companies that are populating fashion’s big data world.

Preferences In, Fashion Out

Many fashion brands use the same software and tools as other large companies—especially other large retail companies. However, there are some ways that fashion companies gather and use data that are unique, and they have some unique inputs and outputs as well.

Many fashion brands and companies have mastered the idea of give-and-take conversations with customers. Lorraine Sanders, the fashion tech journalist, told us that, “Fashion does a really good job of engaging its audience in a two-way conversation, and that the two-way conversation that takes place can only make the big data collected from it richer and more meaningful.”

One popular data-collection technique in fashion, for example, is the use of “style quizzes” that give consumers fashion advice or a curated selection of products in exchange for answering questions about their preferences (for example, see Refinery 29). In fact, it’s become almost par for the course that fashion brands offer some kind of way for customers to filter products based on their style of product preferences.

“Styles” are particularly hard to quantify, as we’ll outline in the section on natural language processing. While machines don’t necessarily know the nuanced differences between “Boho-chic” and “Editor-off-duty” styles, the consumer taking the quiz will have very specific ideas about whether or not they want to see a fringed bag, for example, in the search results.

Therefore, a variety of types of data collection are imperative in fashion.

Figure 2-1. A results page from a fashion quiz on the website Refinery 29