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Technology / Data

Returns is All in the Data – But Do We Have Enough?

Earlier this month, in a move which could well be a watershed moment, ASOS modified its returns policy in a bid to take action against ‘serial returners’, writes Oliver Guy, Global Industry Director, Retail, Software AG. 

In an update to customers, ASOS justified the change as a means to “to make sure our returns remain sustainable for us and for the environment” and that “if we notice an unusual pattern, we might investigate and take action”.

Consumers might question why ASOS has made this bold move. The answer is quite simple – ecommerce has revolutionised consumer behaviour, particularly in the fashion world where customers have more choice than ever. Consumers are now able easily order multiple items and send back any that they no longer want. Some may even be ordering items, wearing once and then returning them. Whilst others often order one item of clothing in several sizes to try them on at home – and return the rest. This has facilitated the rise of the ‘serial returner’.

It seems that the number of ‘serial returners’ is increasing. Ultimately, it is behaviours like these however that ASOS is looking to stifle.

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returns dataThe impact of ‘serial returners’

While these changes made headlines given ASOS’ standing as an industry leader, they are by no means the first to take action. Amazon Wardrobe, for example, offers customers larger discounts and credits when they do not return items. Meanwhile, in 2017, Boden took a tongue in cheek approach with their returns policy which read “If you are a pathological chancer and simply can’t help yourself, we recommend trying it on with our competitors instead”.

Such an approach might raise eyebrows, but after delving deeper into the subject, one can hardly blame them. Returns are an ongoing thorn in the side for retailers and cause a huge amount of pressure internally.

If your return rate is 30%, then that is 30% of your inventory that you cannot sell because you do not know where it is. Once an item is sent back, the cost of processing the returns in terms of inspecting and then putting back in inventory erodes margin. Delays in processing mean that you cannot resell the item. Margins in retail are notoriously slim and every return results in shaving more off the key metric of gross margin. In fact, as a result of customers becoming more comfortable returning items, some estimates report that returns are now growing faster than sales at a rate of 10% per year.

Finding a remedy

While these moves mark an important turning point in retailers taking action against ‘serial returners’, what is there to stop them from simply shopping elsewhere? For these policy changes to yield the hoped-for results, a more collaborative approach is necessary, and one which takes full advantage of the data-driven world in which we live.

When shoppers purchase items online, they leave a trail of digital breadcrumbs which begs the question, is this something which could be used to tackle the problem? Retailers are unlikely to voluntarily share their data amongst themselves as a result of all manner of competitive reasons. But, an intermediary body could well be the solution that they are looking for. Working in the same way as a credit rating agency with no data of their own, these bodies could collate data from shopping history across multiple retailers and produce a ‘Returner Rating’ to sell on to retailers.

This third party would need to publish APIs in order to collect purchase and return history data.  Armed with this data, a predictive analytics approach could be leveraged to build a ‘Returner Rating’ predictive model for each consumer.

Retailers could then leverage the model during the consumer’s purchase journey.  Using real-time technologies like streaming analytics would allow the model to be applied in conjunction with other data such as click-stream data.

Through this, retailers could understand how likely a customer is to return an item and act accordingly – for example – offering free express delivery for low returners or asking high returners to pay postage for returning an item. Whatever the response, such an approach is sure to encourage the right behaviour in the eyes of the retailers.

The future is in the data

For this vision to come to fruition, purchase and return history from one retailer alone is not nearly rich enough. Only by combining data across transactions from multiple retailers can this deliver real value – the paradox being that retailers will be reluctant to share data with each other – hence the need for a third party to manage the data and build appropriate predictive models.

Collaboration across the industry, leveraging a third party like this is an example of the establishment of a ‘retail ecosystem’ – a fundamental concept in the evolution of retail.  Emphasising the power of data and ecosystems, McKinsey recently commented that the ‘holy grail’ of the fashion retail industry was the integration of value-add services ‘through effective use of data analytics’.

The move by ASOS may well trigger a domino effect with other retailers following suit. But to make a significant impact, a wider approach is needed. The more data there is to access, the more value that can be derived from it. The reality is that the future is dependent upon data, and that this data needs to be opened up and siloes eliminated. Only then will companies be suitably geared to tackle the ‘serial returning’ phenomenon and the problems associated with it.

See also: Retail Bloodbath Worsens, But Arm Bets Big Data Can Save Bricks-and-Mortar


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CBR Staff Writer

CBR Online legacy content.