e-commerce Retail technology

ASOS introduces new fit tool to cater to wider range of shapes and reduce returns

ASOS's new fit tool
ASOS’s new fit tool

British retailer ASOS is rolling out a sizing tool that combines machine learning with a visual questionnaire in a bid to cater to a wider range of customer shapes, as well as reduce returns. At present, almost half of the items bought online are sent back, mainly due to incorrect fit.

The fit assistant, which works both on the retailer’s app and e-commerce site, goes beyond the shopper’s weight and height to take into account different body shapes. It asks for the tummy and hip shape (with options that range from straighter to wider), age and bra size, plus the fit the customer is looking for: tighter, average, or looser.

The brand is using machine learning to blend questionnaire data with recommendations based on the customer’s previous purchases and returns, as well as what sizes other customers with similar body shape were happy with. The same technology is being used in features like ‘Your Edit’, ‘Style Match’ and the ‘You Might Also Like’ carousel on product pages.

ASOS's new fit tool
ASOS’s new fit tool

Excessive returns and the pressure this puts on fulfilment and ultimately profit is one of e-commerce’s biggest problems, therefore this feature is likely going to have a major impact on the bottom line. In the UK, 47% of items bought online are returned, according to a study from Barclaycard released in earlier this year. UK shoppers have even earned the title of “serial returners,” as £7bn worth of purchases are sent back every year, leading to a ‘phantom economy’ of lost revenue for retailers. Returns and refunds have risen at four in ten online stores selling fashion, footwear, and accessories since 2016.

Additionally features like ‘Try Before You Buy’ can also trigger a flood of returns. Globally, 25% of retailers will roll out this feature by 2019, but a report from Brightpearl looking at the US market shows that most aren’t ready for the proliferation of returns that could quadruple return costs for retailers. For example in 2017 alone, total merchandise returns accounted for more than $351 billion in lost sales for US retailers.

Fit tools for online shopping may not be new, but so far they have done little to prove their effectiveness in preventing returns. However from a customer retention standpoint, tools that enable e-commerce consumers to shop more confidently will only help instil a further sense of loyalty and trust, which in turn will hopefully translate to a happy purchase.

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