The most dangerous information gap currently facing retailers is a robust and realistic view of why a customer really returned their purchase. Not why they said they did. Or why you suspect they did – but robust, quantifiable and most importantly actionable insight.
Ecommerce return rates of 30% to 40% are common for some retail categories, with tens of millions of pounds of stock locked up every day for large retailers. It is not a sale until the customer decides to keep it. But whereas pre-purchase literally every click the customer makes is scrutinised, the causes of returns are typically assessed from a few codes on a returns form, along with an organisational assumption of the product or delivery likely being at fault.
The purchase to return insight black hole is intimately connected to damaged profitability. Returns have a disproportionate impact on the bottom line – so reducing a return rate by just 1 percentage point can boost gross profits by 1.6% and operating profits by a massive 15%.
Returns are not inevitable or unavoidable – if measured and understood they can be managed and reduced, resulting in increased profits and increased customer satisfaction and loyalty. And once fully understood, they can be reduced without impacting top-line growth.
But trying to solve the problem without deep insight around cause and customer motivation is inefficient, speculative and risks that the customer’s profitability is jeopardised.
Specialist returns insight is essential, because this is a highly complex interplay of customer, product and marketing causes requiring cutting edge big data analytics. Clear Returns have done nothing but returns data modelling and analysis for almost 5 years and we’ve learned a few important things I’d like to share for those thinking about tackling this in house.
With the DIY approach to returns analysis, 3 things typically happen:
- The retailer usually looks to the product and its depiction first – because looking to the customer is far harder and complex. Low hanging fruit can be found, but change can be slow and insights can’t usually be generated and actioned fast enough for a big commercial impac
- A lot of effort then goes into attacking the symptoms not cause – eg fault testing, new returns reason codes, delivery or policy changes, sizing and fitting room technologies. This can be costly, time consuming and yet the ROI remains elusive. You’re very busy dealing with returns but still not seeing those efforts translate to the bottom line
- Most marketing efforts continue to focus solely on the sales conversion without realising the real point of purchase – the new final stage of the sale – is the keep/return decision that is made in the customer’s home. Therefore marketing efforts can drive returns even higher and profitability downwards. And at the same time, there is often internal resistance to targeting for keeps or enforcement of returns policy in case you’ll damage the top line and send customers elsewhere.
Not true……you can improve both the top line and operating profits if you truly understand the causes and impacts of returns.
We’ve learned – after trillions of data points and hundreds of thousands of iterations of our predictive models – that the secret to solving returns and boosting profits is shedding illumination into the knowledge black hole that represents the customer and product interplay that occurs between purchase decision and return decision.
Clear Returns uniquely and specifically focuses here because prediction and very early warning means that once fully understood, returns can be strategically and proactively managed to boost customer profitability without impacting top-line growth.