The recipe for success in fashion retail? The right product, at the right price, at the right time. Simple. It is therefore quite remarkable that predictions in this industry with an annual global turnover of 2.5 trillion euros are still mostly pure speculation.
Perhaps that is why Big Data is being embraced so massively by fashion brands. The analysis of vast amounts of digital info about sales data, clicks, reviews, likes, comments and more can reveal patterns in consumer behavior that entrepreneurs would not discover on their own.
Handy, because the price of wrong predictions is high. Many clothes end up in the Sale at rock-bottom prices, are sold by the kilo to buyers or, in extreme cases, are even destroyed. An enormous loss for entrepreneurs and the environment.
But how do you make smart use of Big Data? There is a joke going around about the phenomenon that everyone is talking about it, no one knows how it works, everyone thinks that others are doing it, and so people are saying en masse that they are doing it themselves.
In any case, big data is not new. One of the first employees that founder Amancio Ortega hired in the early years of Zara was a computer expert. His job was to boost sales by analyzing data from the supply chain and from traffic and sales in the stores.
Today, the role of the computer expert is increasingly being taken over by ultra-smart, self-learning computer programs. With the introduction of Artificial Intelligence (AI) into Big data, the chances of discovering unexpected patterns and trends are only increasing.
Predicting trends with big data
Fashion brands are particularly fascinated by the opportunities that Big data and AI offer to align supply and demand, create more successful designs and provide customers with a more personalized shopping experience.
'Crunching' large amounts of data on sales and consumer sentiment (or having them crunched) helps designers and buyers determine what the best-selling color or pattern will be, the best silhouette, the best fit and the best retail price. But also: the best presentation in the store.
The computer servers of the successful big data startup Editd 'track' more than 50 million individual garments at more than 1,000 retailers. You no longer have to spy on the competition to see what they have on their shelves.
Web store ASOS, one of Editd's major customers, itself reported a 37% increase in sales in the last quarter of 2013, thanks to its use of big data. Fast Company named Editd one of the 10 most innovative companies in the creative industries in 2014.
Boosting sales with big data
Big data is also already being used to track people's shopping behavior. This is a logical application for web shops. With simple tools like Google Analytics you can see exactly which garments consumers click on, how long they stay on a page, how much they hesitate before making a purchase, how many garments are sold and returned, what the peak moments are and what the geographical spread of buyers is. Men’s sweaters, named heren truien in Dutch or merino wollen sokken named merino wool socks in Dutch are one of these consumer products which are very to track for online shopping behavior.
That information is a lot harder to gather in physical stores, although interesting things are happening there too. The company Bluetrace was taken to task by the Dutch Data Protection Authority in November 2015 for using cell phone WiFi signals to track who passes through a store, which shelves consumers stay in front of and for how long.
