Big data plays a key role throughout the fashion supply chain, starting with the way designers create, and how brands market their garments. The fashion industry serves everyone on the planet in one way or another, but it’s big data that is currently altering the way in which clothing is marketed and sold to different types of consumers. The availability of big data and relevant analytics is fast becoming an integral part of the fashion industry. Big data is increasingly playing a role in trend forecasting, analyzing consumer behavior, preferences, and even emotions. Today, big data is how brands build new strategies, by tailoring the consumer experience and enabling the customer to lead the way – and that is big.

Building Big Brands with Big Data

Major players are getting on fashion industry’s big data bandwagon, and fast. One frontrunner is Amazon, which earlier this year made an attempt to determine how people literally measure up. This massive collection of body types is basically a pool of big data, which is collected by Amazon in order to gain a better understanding of how bodies change over time. What this data can potentially do for online retailers is unprecedented, especially since they handle over 40% of product returns when purchased clothes don’t fit. For consumers, to soon have that much-desired ‘perfect fit’ before they purchase a garment online will make way for more satisfied customers.

Big brands, such as Zara and Ralph Lauren, are in the fast lane, with big data in tow. Much of Zara’s success stems from the company’s consistent use of big data, amassing and analyzing the selections and preferences of their customers, to strategically build their brand based on what customers really want, or rather, buy the most. While traditional sales reports were once the norm in every retail business, in the case of Zara and other leading brands, big data forms the backbone of the enterprise. Zara’s in-store data, collected from nearly 100 global markets, is studied by market analysts to determine precisely what their customers are looking for. The results are processed and transferred to Zara’s in-house designers, who in turn, churn out garments, to coin a popular phrase, “for the people, by the people.”

In line with Zara, fashion icon, Ralph Lauren has also embraced big data, using similar consumer data analytics to assess and predict his company’s future trends, including customers’ preferred fabrics, colors, finishes and no less important, price. Ralph Lauren’s world-renowned Polo Shirt, was recently deemed ‘the PoloTech Shirt’ when Lauren teamed up with OMsignal, an advanced biosensing apparel manufacturer. The PoloTech, designed for active sportsmen, was built as one large sensor, gathering real-time data on the wearer’s direction and movement, including biometric data, such as heart and breathing rates, the number of steps taken, and calories burned. Data from the shirt is transmitted to the cloud and analyzed using relevant algorithms. This type of wearable technology can potentially gather all types of customer data to predict new fashion trends far beyond the running track, assessing customer and wearer data from any garment, anytime and anywhere.

Big Data and the Consumer

Just as big data can help leading brands and retailers digitize the supply chain and meet the on-going demand for fast fashion, the proverbial ‘see now, buy now’ consumption model, big data equally serves the consumer, in a big, big way. Big data runs rampant in the fashion industry, used aggressively by retailers to assess consumer habits, not just online, but also, in-store.

Online data represents the public, real unedited opinions regarding consumer likes and dislikes. But first, online data needs to be collected, cleaned, and analyzed, before it can churn out measurable results. An example of harvesting raw data can be found with Le Tote, a rental fashion service that lets customers wear their clothes and accessories, then simply return the items after use. The company collects and processes vast amounts of data on consumer preferences, analyzes their choices via advanced algorithms, and recommends the most relevant garments, tailored to each customer. Stitch Fix, a personal online styling service, collects and processes big data to predict the trends and styles their customers might like. Based on customer input, the company pulls pools of data, and analyzes it, to come up with unique style categories that fit customers’ needs. What big data does in this instance, is to forecast trends before the garment is actually produced. Rocket science? No, it’s data science – and its impact on the fashion industry is huge.

Beyond Big Data

Apart from using big data to gain a better understanding of customer preferences, big data is also used to give online shoppers a real heads-up if a hot product is, for example, about to sell out. In this case, predictive analytics can determine consumer habits and trends, predicting future purchases based on the consumer’s historical data. Retailers too, are immediately in the know as well, enabling them to assess their inventories, production turnaround time, and be primed and ready for their next sale.

Similar to how Google tracks smartphone movement to approximate traffic flows, in-store customer data is derived from tracing the WiFi signals from customers’ mobile devices. To predict movements and patterns, via simple WiFi, customers are tracked to determine their length of stay, which departments they frequent and for how long, and if they’re returning customers. That way big data is used to help retailers appease their customers, for example, by repositioning various collections or displaying a group of items that are frequently purchased together.

When it comes to the runway, big data also plays an important red carpet role. For the average consumer who cannot afford the luxuries donned by catwalk models, many couturier collections must be transformed into affordable off-the-shelf garments. Implementation of big data analytics combined with the right digital software solutionscan enable retailers to alter fabrics, colors, and finish creating wearable designer replicas at far lower prices. Similarly, big data can be used to determine the next new trend. Designers, for example, need to assess whether their new neon jumpsuit or feather-clad fascinator will be accepted by both retailer and consumer. Data derived from social media, in-store and customer behavior, can help retailers decide what will work and what won’t, to either pull a product from the shelves or venture into a new product market.

Big data is undoubtedly a big breakthrough for both fashion suppliers and fashion consumers, who are eager and ready for change. Big data is a dynamic field of innovation, and brands that make data-powered decisions will remain cutting-edge and competitive. But big data needs to be collected and processed by professionals to deem it even feasible, let alone, reliable. Retailers who selectively use big data to create new product lines or to monitor consumer behavior will have a better chance of surviving the surge of e-commerce. While big data cannot replace designer creativity, intuition or innovation, it can allow designers to focus on their creativity and still set new and exciting fashion trends that can become a global phenomenon as they are based on what the consumer likes. Big data can give the fashion industry guidance and direction, to ultimately help designers create the right product, at the right price, at the right time.