If there’s any doubt as to why “big data” has become as ubiquitous in business as pens, chairs and coffee mugs, look no further than the margins. Since becoming the buzzword of the decade, big data has given countless businesses huge competitive advantages by redefining the quality of the information at their fingertips and the speed at which they can react. And profits have soared.
So why have apparel brands lagged in doing the same? In many cases, identifying the “next big thing” — what will sell, and the rate at which it will fly off the shelves — is still steeped in guesswork and unsupported instinct.
For brands, the use case is abundantly clear. The global apparel market is one of the world’s biggest sectors. It’s valued at $3 trillion and accounts for 2 percent of the world’s gross domestic product. This industry is trying to find its footing in a landscape of constant change, driven by new technologies and consumer spending that’s moving away from brick-and-mortar to online.
In the Unites States alone, there were more than 211 million digital shoppers in 2016 who browsed through mountains of information, from new products, pricing shifts, promotions and more. This data, when amalgamated, could be used to provide retailers with a new, in-depth way of exploring the opportunities hidden within the retail landscape.
That sort of on-demand knowledge could show you which dresses priced between $10 to $30 sold best last week — and could be the difference between blindly buying into a declining trend and avoiding it altogether. The possibilities are numerous. But even if you have access to this data, the question is: how can you take advantage of it to make critical decisions, increase customer loyalty and boost sales?
Big data, big opportunity
Big data is exactly what it sounds like: information on a large scale. But more commonly it means a large collection of structured or unstructured data that is pieced together by computers and organized in a way that makes it possible for humans to derive valuable insights rapidly. Used in this way, big data can be the key used to answer previously unanswerable retail questions about what people are buying, the prices they’re paying for them, and when are they buying.
As such, retailers can make better pricing and assortment decisions, reduce markdowns and decrease costs of dead stock by analyzing what’s happening in real time or over a specific period. The focus of the analysis can be as broad as the entire world or narrowed to a single category, sub-category or trend.
Moving to machine learning
Consumer tastes are highly changeable and brands face greater competition in an increasingly saturated market, so big data alone is not enough to make a tangible difference. To compete effectively and grow, retailers need to rely on their own expertise as much as the data they see before them. They need to be able to ask the right questions and know how to follow the answers to see where they lead. Data may be able to show retailers more than they’d ever imagined, but there’s no autopilot.
To obtain that data, machine learning — and recent advancements in artificial intelligence (AI) — can provide retailers with powerful tools to maximize the use of data to make such an impact. These models can use supervised learning to distinguish one product type from another using a curated test set. Models such as neural networks use supervised learning techniques to teach themselves to derive patterns and traits in complicated data by mimicking how our brains process information. Such techniques can be used to recognize specific patterns and shapes when processing apparel images.
In fact, some of the most powerful machine learning algorithms today are applied to areas such as extracting color from product images, analyzing ambiguous children’s wear sizing across a myriad of brands, or determining whether a pair of tights belongs to a sportswear or lifestyle category. The point of each is to give retailers access to the data, without browsing competitors’ sites or sneaking into their stores and hastily trying to count items. With this sort of data, they can jump right in and do what they’re good at: retailing.
This article originally appeared in apparel.edgl.com To read the full article, click here.