With the holiday sales behind us, we’re now deep into returns season, and today’s customers are bringing back their items with a vengeance. Last month, The Guardian posted a great piece on the hidden costs of ecommerce businesses, analyzing Barclaycard’s data report and highlighting customer returns as a revenue and operational threat.
Key statistics to call out were that 30% of shoppers are earning the badge of ‘serial returner’; someone who deliberately over-purchases and then returns many items. In an attempt to safeguard themselves against this reality, 33% of online retailers offer free returns but charge for delivery, and 20% are hiking their prices to cover return costs.
Whatever your current or future plan may be to process or negate return costs, what can you do with the information and data collected during inevitably unavoidable returns?
Here's 3 tips to make the most of your returns data...
1. Identify your serial returners
Use sales reporting, such as Brightpearl’s Sales Analysis reports, to identify customers that have a high return rate. Once you’ve identified this lot, you can ensure that they don’t receive certain marketing promotions or discounts. More positively, you can use the same means to identify great customers and send them an additional promotion!
2. Know your product return rates
Looking at the same data with a different lense, you can identify your product return rates, seeing clearly which products are frequently sent back to you. With this information, you could follow the trends identified and increase prices on that item, or you could potentially reduce your efforts on that item - or more aggressively, stop selling it entirely.
3. Discuss your findings with your suppliers
With an understanding on product performance, you’ll be able to see if there’s any particular product lines or vendors whose products are coming back more than they should be. Ensure you know this before placing your next order, so you don’t blindly reorder more and repeat history. If you're not doing so already, you can take this one step further and start capturing return reasons at the point of sale, such as ‘Doesn’t fit’ or ‘Arrived damaged’. Cutting your returns data by these reasons will highlight potential vendor or supply quality issues, or problems within the fulfilment processes, or even just highlight that you need better product descriptions.
The reality is that whilst things can improve, returns aren’t going to just stop overnight, but they can at least be brought into view and under control through data analysis, and mitigated through an improved customer experience, product quality and streamlined processes.