Reduce Returns by Focusing on Data-Driven Growth

Reducing returns

If you’re an online fashion and footwear retailer, you’re probably all too aware of the corrosive effect returns have on your bottom line. Across the retail sector, return rates to bricks-and-mortar stores are running at around 8%. Compare that with a hefty 40% returns rate for online purchases.

If these figures seem eye-watering, the return rates for fashion and footwear are even higher, with women’s dresses as the item with the highest return rate, at 53%. In fact, a quarter of shoppers say they regularly buy multiple sizes of the same garment to ensure best fit. These consumers are called size samplers, and often yield high return rates.

Meanwhile, a recent study by returns platform Return Magic confirms that fit (in its widest sense including both size and style) is a major driver of this problem, accounting for 60% of all fashion and footwear returns.

Here, Ashley Maroney, Regional Vice President, at True Fit discusses how using big data analytics and artificial intelligence (AI) will allow retailers to take consumers’ personal preferences and shopper habits into account to generate data-driven growth.

Tackling the cause, not the symptom

Whatever your view on returns, we can all agree something needs to be done. In late 2018, The Wall Street Journal reported that Amazon had begun freezing the accounts of shoppers ‘who made too many returns’. Meanwhile in April 2019, ASOS announced its own crackdown on ‘serial returners’, deactivating transgressors’ accounts, while research by Brightpearl of 200 retail executives found that two-thirds of respondents were willing to follow Amazon and ASOS’s example.

Clearly, there’s a growing consensus within the industry that serial offenders need to be dealt with, but what if we pause for a moment and consider whether retailers are simply treating the symptom rather than tackling the cause of the returns nightmare?

To get to the root of the problem, retailers instead need to ensure that the products offered to individual shoppers online are right for them to start with. This is a complex challenge, but it can now be solved using big data analytics and artificial intelligence (AI) that takes consumers’ personal preferences and shopper habits into account.

As we’ve already seen, fit in its widest sense is a huge driver of returns. One of the biggest issues here is that sizing is inconsistent across the fashion and footwear industry. There are even discrepancies within individual brands, where one style of shoes fits a consumer best in a size 10 in another style a size 12. Seen in this light, freezing the account of a serial returner, because of product sizing discrepancies, is essentially punishing the customer for the shortcomings of the brand/retailer.

Guiding the consumer to purchase items that fit their personalised preferences

However, market-leading solutions can now analyse the discrepancies in multiple sizing points across literally thousands of garments to find the perfect fit that matches the preferred style for any one individual. These solutions can then learn from shopper behaviour, thanks to AI, finely tuning the perfect recommendations.

While fit issues account for 60% of returns, style issues account for a further 8% of returns, according to the Return Magic research.

The answer here is not to freeze the accounts of customers who repeatedly return garments because they don’t like them, the answer is to guide the consumer to purchase items that fit their personalised preferences.

Recommend items consumers will love, and will keep

Rather than offering shoppers recommendations based on previous purchases, granular data-based personalisation analyses the style attributes of any individual’s purchase history (colour, cut, fabric, style, details, etc) and then searches inventory for available garments to recommend the items a consumer will actually love to wear, and will keep.

This data-led approach drastically reduces the probability that items will be returned because the customer doesn’t like the style or they simply change their mind on the purchase (a driver of an additional 12% of returns according to Return Magic). In simple terms, thanks to aggregating and actioning data, customers don’t need to turn their living room into their fitting room because finding the best fit and style have already been achieved.

Not all returns are negative

Used in this way, data can help retailers manage and maintain their returns as well. After all, not all returns are negative. They can be a by-product of active shopping and experimentation. A generous returns policy can also give consumers a sense of comfort, more reasons to buy and stay loyal to a retailer/brand.

Retailers who have used data analytics and artificial intelligence in this way have achieved data driven-growth with a 35% reduction in fit-related returns and a 5-8% increase in net incremental sales. That’s got to be better than freezing accounts and stigmatising returns.

Credit: Ashley Maroney, Regional Vice President, at True Fit