What are serial returns costing your business?
A quick google search reveals a few schools of thought about how many types of retail customers exist, ranging from just two kinds of customer to as many as 10. Whatever the true number, we all need to be aware of the “non-customer”. They initially appear to be customers because they purchase merchandise and products. Yes, we are talking about the “serial returners”. No matter how many items they buy, like a boomerang, each and every single one keeps coming back.
Customers have always returned goods, even in the days when all retail was bricks and mortar. It is, after all, our right as consumers to be able to return a product because it may not fit, or because we discovered a fault or problem with it on closer inspection, or because the intended recipient of the purchase didn’t actually need it, or simply because we changed our mind.
Regardless of the reasons for returns, it’s costing retailers. The rate of returns has increased from 10.6 percent in 2020 to 16.6 percent in 2021, according to the NRF, with an average of 20.8 percent. In monetary terms, for every $1 billion in sales, the average retailer incurs $166 million in merchandise returns. It also found that for every $100 in returned merchandise accepted, retailers lose $10.3 to return fraud.
What’s motivating returns behaviour?
As e-commerce has grown, returns trends in certain retail segments have also emerged. Shopping for clothes or footwear online, it is not uncommon for customers to buy two sizes of the same item, returning the one that doesn’t fit.
Data analysis tools and techniques can enable the root cause identification for a return. That’s important because we want to be able to identify these customers, who are valuable because they are, in fact, making purchases, even when items are returned. Still, there is a cost involved in processing returned items. Therefore, customers that mostly buy an item in the right size are more valuable customers, so we want to increase this type of customer.
Reduce return rates with a more focused service
By analysing customer returns data we can then take steps to reduce return rates, by making sizing charts more accurate, or helpfully reminding the customer the last two shirts they bought were in a large size. If several customers have returned similar items for being faulty, then that can potentially flag a supply side issue that needs addressing.
You can reduce the return rates while preserving valued customers, with a better, more focused service.
Having this information helps to identify the “serial, or intentional returners”, those who buy, potentially spending hundreds or even thousands of pounds, but return all or most of those items.
Retailers able to turn returns data into valuable insights, can then take steps to discourage this type of consumer behaviour. Options include limiting numbers of items returned within a given period or waiving free delivery or free returns policies for the consumer in question. Some will do more in-depth product checks to ascertain if items have been worn or goods have been used before they have been sent back.
Whittling out serial returns activity from genuine customer transactions requires:
- A rich set of historical data including returns, including customer interactions.
- Next, you need to consolidate all that data in a single view.
- Then, you need to classify returns, using root cause analysis to create a hierarchy of returns.
- Only with the first three steps implemented can you disincentivise return behaviour, such as by charging for deliveries, charging for returns and doing quality checks.
- Action and monitor. For example, if you remove a free returns policy, you need to know how many people were returning items before the policy was discontinued, and how many people are returning items after you introduced the change to your returns policy.
You also need to know whether your rate of returns among new customers has significantly changed compared with previous new customers, prior to the change in returns policy. Otherwise how will you know whether it has had an impact on sales? Therefore, you might want to try it with a test group before you implement, which requires a change in website infrastructure to ensure only some customers see the free returns policy.
The value in identifying non-valuable customers
As online shopping and volume of such transactions have continued to proliferate, retailers have had to adapt in many ways. The initial focus has been using data analytics to identify valuable customers, but there is also much value in knowing who isn’t valuable, in other words identifying those “non-customers”, or “serial returners”.
Serial returns can distort sales revenue. Not only is there no contribution to net revenue, it actually costs the business money. For every returned item – a cancelled sale – there is an erosion of margin from the cost of returning, rehandling and restocking. While many more retailers and brands with an ecommerce presence are aware they have an issue, they often lack the data analytics, the platform and the skillsets to take action.
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