In retail, returns will simply stay, so better deal with them in a smart way
Lately, it has been all over the news. The monster of on-line retail: Returns
The first question is if returns really are a monster or just a fact of life in a customer-centric on-line retail landscape?
It is not black and white
For sure, the truth is somewhere in the middle. In some way, e-commerce has created a monster and we need to fight it where possible. But we also need to accept returns as part of the business and manage the fact and its consequences. Returns are part of on-line retail so the retailer's business model should be prepared for the consequences of returns. Should we fight the monster or just accept our destiny? I believe that we need to fight the monster by minimizing the consequences (and leveraging the opportunities).
We’re all in this together
Different parties need to work together as they are all involved in the returns process. Customers, carriers, logistics providers, and retailers, each of these parties can contribute to making the returns process less problematic. That means less expensive and way more sustainable. Without compromising the ease-of-use of on-line shopping. If all parties put their eggs in the basket we can start looking for a joint approach that doesn’t break the eggs and makes everyone happy.
Smart differentiation is key
The solution to minimizing the consequences lies in stepping away from a “one-size-fits-all” approach. Not all returns are the same. Not all customers are the same. Not all orders are the same. Not all return reasons are the same. So why should the return policy be the same for all returns? Why not differentiate the return policy to minimize the consequences of returns without compromising the ease-of-use for the customer.
Differentiated return policies in different scenarios
Let’s have a look at different order scenarios and let’s consider if these scenarios might require or justify a differentiated return policy for the order. Let’s see if differentiation minimizes the consequences.
A customer has ordered one product. It is quite likely that the customer is buying a product that he or she likes and intends to keep.
A customer has ordered one product in 3 different sizes. It is quite likely that the customer is buying a product that he or she likes and intends to keep the size that fits. He or she just doesn’t know the size yet. It is likely that some sizes will be returned. Question yourself if it is legitimate that the customer pays for (part of) the return costs.
A customer has ordered 3 products in one size. It looks like the customer knows his or her size but isn’t sure yet what to buy. It is likely that he or she will simply keep all products or perhaps return one. Question yourself if it is legitimate that the customer pays for (part of) the return costs.
A customer has ordered 10 products in one size. It looks like the customer knows his or her size but isn’t sure yet what to buy. It is likely that he or she will keep some and will return some other products. Question yourself if it is legitimate that the customer pays for (part of) the return costs.
A customer has ordered 20 products in multiple sizes. It looks like the customer still has to make his or her mind up what to buy. Likely there will be some returns. Question yourself if it is legitimate if the customer pays for (part of) the return costs.
Anticipate for returns or react when there is a return
The second question is if we can use the insights in these different order scenarios to fight the monster or minimize the consequences of returns.
Fighting the monster
You can make above order scenario analyses in the checkout and present a unique return policy to the customer before hitting the buy button (e.g. free returns in scenario 1 and paid returns in scenario 4). In theory, this sounds great, managing customer expectations in real-time. But why risk an abandoned shopping cart when you don’t know the actual return scenario yet? Fighting the monster is jeopardizing conversion. That is a no go.
Minimizing the consequences
Instead, you can also use the returns process itself to minimize the consequences of the return. Hmmm? Yes, you can.
At the start of the returns process, when the customer registers a return on-line, you can dynamically define the return policy based on the return scenario for the order scenario. In a digital world, all data and computing power is available to make this real-time analysis. The return policy then defines how the consequences (read costs) of the return are shared between the customer and the retailer. In one scenario the retailer pays, in the other the customer pays, in another scenario costs are shared. We’re all in this together.