We’ve already looked at some of the most crucial elements of a successful paid returns policy:
Part 1: How to Build a Decent Paid Returns Strategy
Part 2: How to Increase Warehouse Throughput
Part 3: Building a Bulletproof PR Plan for Your Returns Policy
Now, here’s the harsh truth. You can have a top shelf returns strategy, and a sparkling comms plan, but if you aren’t reading between the lines of your returns data, there’s a high probability that you’re p**sing money down the drain.
Many online fashion brands are now experiencing return rates of up to 50%, processing 3-4k returns every day. Managing returns is a monster, especially if brands are using manual processes to get returned products back to a sellable state. But apart from the obvious manpower that’s involved, high returns rates wreak havoc on a brand’s P&L - inventory lives in limbo, margins are skewed, and costs skyrocket.
The final part of this series, we’re focusing on how brands can take the data that return providers gather from customers, and merge it with the data they generate when products return to the warehouse, to build insights that have the power to spark a domino effect across the business:
Serial returners or genuine red flags?
Most fashion and New Luxury brands cite the same reason for high returns rates - customers buy multiple sizes of the same item, knowing that they will return whatever doesn’t fit.
Will introducing paid returns entirely fix the problem of serial returners?
Probably not.
That’s because serial returners aren’t always the issue. For example, you might see from a return report that a specific product is being returned 80% of the time. In this case, serial returners aren’t the most likely culprit - instead, there’s probably something wrong with the build of the product, which should be addressed with the manufacturer.
SKU level reporting
With SKU insights, you can identify red flags for products that are being returned in excessive volumes. You might notice that a blue dress in size 12 has been purchased 1000 times in and returned 650 times in a month - this isn’t cryptic data. It’s tangible information that you can take immediate, actionable steps to rectify. If the most common return reason was that the dress doesn’t fit, the sizing charts are most likely inaccurate for this specific range, style, and size. If the reason is poor quality, design QC can be notified to address the issue immediately.
In the future, some fashion brands will start to use RFIDs on every single product to go one step further than SKU level reporting to determine how many times an exact item has been returned. Think about it - one item could have been sent to customers ten or more times. When you consider the journey of a product - it’s been shipped into the brand’s warehouse from China, picked, packed, and dispatched, then tried on and returned ten times...that’s the stuff of nightmares for brands!
Stock optimisation
A return takes an average of 5-6 days to process (if it’s in the UK) or up to 21 days (if it’s an international order). If you’re processing thousands of returns every day, having the ability to get an estimate of returns stock in transit is the key to stock optimisation.
If items go out of stock, and there’s a preorder system in place, (through a holding app) you can use stock in transit data to populate this. We’ve seen situations where brands have run out of stock and canceled orders thinking that they wouldn’t have any stock for months. Then the next day a dozen units landed back into the warehouse through returns. Those canceled orders could have been avoided if reporting had caught this.
Insights can be built through Loop or Swap using the data that’s available, by looking at how many items have been initiated through a return. You can see what’s been sent by the customer but not received yet, as well as what’s been scanned back at the warehouse but not repackaged yet. With this information, you can determine estimated dates for items to be back in a sellable state. So instead of canceling orders, the team can view a report, type a SKU into the system and if an item has been returned and scanned, they can hold off on canceling the order for 24 hours. In a day if the order is back on the shelf, it can be fulfilled.
Each brand will have their own level where they feel confidence in what they believe their metrics are around predictability (and we can help with this too, just FYI…)
Once you have a decent returns reporting layer, reports can be built for the number of returns and the number of refunds versus:
Number of exchanges
Number of gift cards
Number of store credit
Once you have a platform that provides this data and a business that reports on that data, it makes it very easy to see what works versus what doesn’t for clear, actionable strategic changes that make a real difference to your overall returns rates.
Calculating accurate returns costs and margins
If you want to know how your marketing is really doing, you need to layer your returns stats on top of your sales stats. Often, brands will think that their sales are X and margins are Y, but when returns are factored in, those figures change considerably. Getting a true reading of margins can be as simple as adding a line into your reporting that previously wasn’t there to get a more accurate view of P&L and make better marketing decisions.
Brands can (and should) also use data to get an estimate on the average cost of returns. This involves piecing together key parts of the return journey - for instance, if you’ve paid the courier cost, you can match that back to the courier cost, and then take the average time spent in manpower getting items back into a sellable state. The average cost of returns needs to be known, not necessarily to be the determining factor for how much you should be charging customers for returns, but more so as a way to know how much returns are costing the brand, and looking at how you can offset the costs of returns in a more holistic way. Remember, most brands don’t want the introduction of paid returns to deter customers from ordering with them entirely. Using data is the best way to figure out to strike the balance between offsetting the costs of returns and maintaining or, better yet, improving the overall customer experience.
The future = returns data + sales data
While it will be interesting to see how fashion and New Luxury brands continue to handle returns, be it through tiered charges, penalties for serial offenders, or incentives for store credit, there’s no question that paid returns are here to stay for the foreseeable future.
Introducing paid returns to customers might not initially make a huge difference to your bottom line - but it’s still money that wasn’t coming in before (simple maths - if you have 3,000 returns coming in every day and charge £1.99 a pop, that’s £6,000 to add to your bottom line).
BUT - in order for brands to tackle their growing cost base, decrease their returns rate, and shape their front-end experience, they need to focus just as much on returns data as they do on sales data, because one has the power to feed into the other.
What should be the theme for our next blog series? Share your thoughts in the comments. If you’re interested in building a paid returns strategy and are looking for the best tools to help you do it, send us an email at hello@commercethinking.com