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Numbers are changing the way we interact with fashion.

The authors of Big Data: A Revolution That Will Transform How We Live, Work and Think explain it most succinctly:

“As we collect and analyse far more data about people’s interactions, individual preferences will become much better known, more comprehensively and in better detail than ever before.”

Source: Business of Fashion interview

Ecommerce and online social interaction give fashion brands a vast trail of data about how we buy from them, what we like or dislike, and how we interact with our friends. Established data capture techniques – like cookies – help brands market more effectively, placing adverts in front of relevant customers. But bundles of big data scoured from a vast number of consumers could do even more.

What is big data for fashion businesses?

Michael Barnett’s definition of big data is useful in understanding exactly what this sort of information is:

“[It is] higher in volume, comes from more sources and isn’t as easily turned into digestible formats.”

Source: Marketing Week (registration access)

High volume is key in understanding how big data can change fashion business. As of August 2014, ASOS had 8.8m customers who’d bought from them in the last year, totalling just over 25m orders. For each of these transactions, basket size, basket spend, conversion rate and all sorts of other tiny bits of data were recorded. That’s a lot of information.

A diversity of sources – like data from social media, ecommerce and in-store spend – means a vibrant picture of the fashion customer is created. But Barnett mentions a key problem: these vast reams of data aren’t easily wrangled into “digestible formats” and often require niche skill sets to analyse, present and act on them.

Big data and problem solving

Despite these difficulties, big data has the potential to solve two of fashion biggest and connected issues.

First, greater granular customer data means brands have the ability to improve customer satisfaction through improving interactions with marketing, ecommerce, social media and garments. Several bits of research have found the return rate for clothing bought online is around 20-30%, often due to customers buying the same item in different colours or sizes because they don’t know what will or won’t be right. The cost of ‘free returns’ can be high, and is either absorbed by the customer or the business’ profit margin. Brand advocacy is damaged by this experience; customers who can never find the right size or aren’t presented with relevant marketing eventually stop buying from the brand.

The second issue is improved sustainability. If customer satisfaction increases due to better fitting garments and better brand experience, customers are more likely to stick with that brand – and that clothing – for longer. That means:

• Fewer unreturned items which would otherwise go into landfill or flood the second hand market
• Fewer journeys for return delivery trucks which means less CO2 emissions
• More accurate garment sizing which reduces textile wastage at source
• More accurate stock buying, reducing material wastage for the brand
• Better ROI on marketing, helping focus costs on campaigns that will definitely work

 

The barriers to fashion’s big data

Using big data for improved customer satisfaction and sustainability is possible, but there are barriers to be overcome for fashion businesses. I’ve identified four potential barriers and solutions – undoubtedly there are more that will become apparent as the industry develops.

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