It’s a fact of retail life: Not all sales are final. About one in three online purchases is returned; for apparel the rate is closer to 40%.
It’s often unclear whether the return policy is at fault, or issues with the merchandise, or changes in how people shop these days. In fact, nothing about returns is clear – including who within the retail organization owns the problem, or what can be done about it.
Some clues about how to solve returns problems comes from consumer surveys and academic research. From these, retailers have learned:
The value of liberal returns policy. Research by JDA Software found 62% of consumers are frustrated when asked to pay for return postage and packaging, and more than 50% view the ease of returning items to retailers as very important in where they make online purchase.
The biggest driver of returns: 67% of all returned online purchases are the fault of the retailer and not the customer, according to Chain Store Age and TrueShip
That frequent returners are also frequent purchasers, according to research by Andrew Petersen of the University of North Carolina at Chapel Hill and V. Kumar of Georgia State University.
That research can be invaluable in giving clues about what’s causing high return rates. But they don’t identify the specific issues going on at a particular retailer. Only one thing can do that: The retailer’s own data.
Locked inside the data retailers are already collecting about the products that come back are insights that point to specific problems across the organization that, when addressed, are very likely to reduce return rates.
Here are a few easy steps in three areas of your business that you can start on to improve returns prevention:
In distribution and shipping
Identifying slotting errors through a sudden spike in wrong size/color/model
Spotting quality control issues when customers return products they deem defective
Tracking a pattern of damages to substandard packing materials
In buying and merchandising
Finding out a product image or description is inaccurate and customers feel misled
Identifying fabrics or findings that are scratchy/flimsy/otherwise disappointing
Uncovering labeling issues such as inaccurate size markings
In customer relationship management
Determining if high returning customers are even higher buying customers
Seeing the impact of changes in returns policy terms on return rates and sales
Finding out customers find return processes too cumbersome
Improving the quality of the returns data collected along with the right returns analytics software can hlep you reveal even richer insights about every aspect of returns. Those insights can be applied across the entire retail organization, with each department taking steps that together drive return rates down.
The more retailers can learn about what causes a product to come back, the better they can head off those problems from the start. Discovering and acting on the issues uncovered by analyzing product returns data doesn’t just reduce return rates; it satisfies customers, and keeps them coming back.