There are two statements from retailers that we hear at Clear Returns, which always raise alarm bells:
1) “We love returns”
2) “Our customers get more loyal the more they return”
Why the red flags? Returns are very complex – and the data almost never backs these statements up. Secondly, they assume that all customers are equally relaxed about returns and that high spend means high value.
Retailers need to understand the shopper’s intent at the point of purchase. If they dislike returning and intended to keep their purchase at the point of sale (and most shoppers do) then a return means the relationship is at risk.
Whereas if a customer intended to return all or most of their order when they bought it, essentially making their selection at home, or wearing and returning, the risk is that the basket profit margin is lost entirely and stock is unavailable to those shoppers who would have bought and kept it.
Assuming a fair returns policy and quick refund equals happy shoppers is not enough – for some shoppers that assumption risks customer satisfaction and future lifetime value. For returns sensitive shoppers, if they have returned an item, then you’ve really messed up in their opinion and a refund alone doesn’t cut it.
A personalised response, following the return, is essential to save the future relationship, which is where Clear Returns Rescue comes in. We focus customer service responses toward those who are most of risk of abandoning.
For example, a previously loyal customer who returns because the retailer has made a mistake, for example due to an error with their order, feels very differently about a return than a shopper who casually bought the same item in two sizes, as this customer explains: https://www.youtube.com/watch?v=csqIx86u7W0&t=78s
Some of the most common customer segmentation methods not only fail to spot the costliest serial returners, based on their spend, they place them amongst the most loyal and valuable customers. As a result, many retailers then actively prioritise their marketing spend towards customers who, once costs and profit margin are factored in, actually cost them more money every time they buy.
A small core of serial, high cost returners typically lock up stock, incur high costs and also draw in discretionary discounts and offers. So, despite their very high spend, they consistently lose the retailer money.
So are all high returners a problem? Not at all – “good” returners should be encouraged, as a return is a step that predicts they are on a path to becoming more profitable as they branch into new brands or categories and over time will begin to keep more of what they buy.
But telling the “good” and “bad” returners apart is simply not possible when analysis is focused solely on spend not profit.
Without the complex proprietary predictions at the heart of Clear Returns big data technology, that factor in customer profitability and sensitivity to returns, plus profit margin and stock availability, retailers can’t be confident that they have a handle on returns or understand the effect they have on an individual shopper’s future buying behaviour.