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Numbers are becoming as valuable an ally as mentors or critics are to fashion designers.
One example of a successful, crowdsourced set of opinions is fashion startup Go Try it On. The appeal of shopping at retailers still exists because there are tactile nuances that are difficult to imagine or visualize without having tried the item on. Unfortunately, it’s not always possible to go shopping with friends; this is where users of Go Try it On will chime in and give you their opinion of how you look in that article of clothing.
From a business angle, fashion companies are already figuring out clever ways to use data to grow. For example, fashion startup Everlane successfully crowdfunded their expansion into Canada in order to confirm demand and gain capital.
We’re watching fashion designers make the transition from relying purely on taste and intuition to a mixing the two with business intelligence. Companies can now simply track consumer behavior or reactions to their new items, and figure out whether or not they want to bet big on a certain line. This is made possible by the development and accessibility of big data tools.
For the uninitiated, big data is when data is too difficult to track by hand, so software is enlisted to process the information. Often times, data can’t be neatly captured and entered into a spreadsheet. Data has long since been used by forecasters, such as WSGN and Stylesight; some of their clients include United Colors of Benneton, H&M, Puma, Prada, Nordstrom, Bulgari, Uniqlo, and Diesel.
Now, with big data growing more available and with different types of information being measured, designers can reap the new benefits of access to this information.
Previously, the fashion industry was a pain for independent designers, manufacturers, and startups. Imagine this: every season, independent designers have to churn out a few sets of new garments. In a previous life, InteraXon co-founder Ariel Garten was a fashion designer; she once explained in an interview, the moment you factor in the many different variables (such as colors, styles, and sizes), you’ve easily got 20 variants of the same item that all need to be produced in a short time period.
Because smaller independent designers don’t (typically) have much leverage with retailers, they would typically sell on consignment. At the end of the season, these designers would have to take back any goods that were not sold through the retailer — which would be a waste of both labour and resources, as they are out of season.
Data collectors such as Bodymetrics are starting taking into account individuals’ sizes and body measurements, which means both designers and retailers can figure out approximate demands and create variants accordingly. Because they have the data on which sizes are most popular, they can consider this in their production process and minimize the amount of leftover goods.
Similarly, fashion startup EDITD collects data and makes forecasts in order to help designers and retailers make better decisions. Its service has a variety of sources; for example, it is one of many services that keeps its finger on social media in order to figure out consumer sentiments. This is an essential part of the feedback loop that few designers have gained true insight into, as they’ve had no option other than to rely on intuition and experience.
“Because smaller independent designers don’t (typically) have much leverage with retailers, they would typically sell on consignment. At the end of the season, these designers would have to take back any goods that were not sold through the retailer — which would be a waste of both labour and resources, as they are out of season.”
The activity of public relations is difficult enough to gauge; media exposure in gross impressions, attitude change, or which messages were most spread the most and whether they aligned with the company’s intended marketing message. With fashion, it becomes slightly more complicated: What happens after a fashion show or an event? How is success measured?
In addition to the previously mentioned EDITD, a few newer fashion services (such as Fashionbi) allow fashion brands to gauge not only the commentary they see in expert or critic reviews from newspapers, but also the rest of the stuff that’s streaming across the blogosphere and social media.
Fashionbi specifically features a user-friendly dashboard format that can break data down into country-by-country analysis and period-specific analyses. More importantly, it presents harder quantitative data, which can be a valuable resource. The opinion of specific critics, who may not reflect the thoughts of the general public, is weighed less heavily. Tools like Fashionbi also enable designers, and larger companies, to figure out very quickly and conveniently what’s popular in certain locations with as few meetings and bureaucracy as possible. Designers can now better gauge which articles of clothing gain a more favorable reaction by which influencers or users, and style accordingly.
“Designers can now better gauge which articles of clothing gain a more favorable reaction by which influencers or users, and style accordingly. “
Burberry’s famously digital flagship store was the first step in introducing big data into the retail environment. Not only does the technology create a more exciting and visually stimulating environment, it also allows Burberry employees to create a deeper level of service for customers. Specific items are tagged with RFID chips; these chips trigger interactive videos showing complementary items or the craftsmanship of the product in store mirrors. Mobile devices (iPads, to be specific) are used to make sales and avoid line-ups or any congestion.
For example, any employee at any Burberry store equipped with this technology can identify a customer, at almost the moment they walk in. (Scary, right?) They don’t just greet you by name, but also could inquire into whether your girlfriend liked the umbrella you bought her a couple of weeks ago. This is because, according to Forbes, Burberry ties the information it collects from store tills and iPads, and connects it with shoppers’ social media profiles. The information is then cross referenced, and forms an extremely comprehensive customer profile.
At the end of the day, where other designers are looking for big data to improve product development and manufacturing, Burberry is also using big data to enhance its social media — and to step up its customer engagement. They extend the conversation with their customers just a little bit longer; instead of getting left with a peck on the cheek on the front door, they get an invitation for coffee. That’s not to say it doesn’t leverage big data for product enhancement — they recently embarked on a campaign where the company integrates a customer’s mobile device into personalizing a product, through reading the data.
“At the end of the day, where other designers are looking for big data to improve product development and manufacturing, Burberry is also using big data to enhance its social media — and to step up its customer engagement.”