Retailers generally know what they actually sold, but have few ways to know what they had the potential to sell, offline as well as online. But, with way more than a million shoppers having entered measurements and fit preferences in the virtual fitting rooms of Fits.me’s clients, a vast array of anonymous customer data has already been captured, while additional fresh data is collected every month. These data include the measurements and preferences of not just those shoppers that actually bought, but of those that left the store without buying.
Such data reveals what a retailer could have sold if they’d had the right stock, in the right sizes, in the right locations – a practice that Fits.me has termed ‘intelligent stocking’.
Heikki Haldre, co-founder and chief executive for Fits.me, said: “If retailers use emerging big data sources to analyse the measurements not just of their actual but their potential customers, they can buy stock in better proportions than currently. This will enable them to engage shoppers that are currently forced to look elsewhere when shopping.”
Yet there is significantly more sophistication than simply buying-in stock in better proportions. In December 2012, Fits.me revealed that average male waistlines in Birmingham are more than an inch bigger than in Brighton, raising the possibility of using detailed, regional analysis to determine the sizes to stock in bricks and mortar stores in different cities, and in what proportions.
“No-one is seriously predicting the end of clearance sales, but smart use of big customer data could see an end to rows of a particular garment, all in the same size, all at clearance prices, all because the buyer got the proportions wrong or sent them to the wrong part of the country,” Haldre said.
A casual analysis of menswear data reveals that the best growth opportunity for retailers exists with larger sizes: only the smallest 5% of men are part of the 26% that are not catered for by normal ‘S’, ‘M’ and ‘L’ sizes in the UK, France, Germany and the United States. However, 21% of the largest men are not catered for when sizes stocked end at size ‘Large’.
A similar analysis of womenswear shows that 7% of women qualify as a size 6 or smaller, while 13% are only catered for by a size 20 or larger, forming the “forgotten fifth” of women.
“There is quite a long way to go with big data solutions,” cautions Haldre. “The analysis algorithms are complex and still being evolved. One of the reasons for that is that shoppers simply aren’t guaranteed to buy the size that a size chart recommends – if only it were that simple. In fact, only about half do, while the rest buy one or even two sizes up, or one or even two sizes down - and that varies from garment to garment and garment-type to garment-type.
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