Good merchandising is a beautiful thing: A customer gets excited about the product, recognizes the value the retailer has added (rewards, high service levels, special privileges,etc.) and makes a purchase. Repeat a few times, and the result is a customer with a high lifetime value.
According to the Adobe Digital Index Report, engaged, repeat customers are so valuable to a business that, on average, they account for 40 percent of total revenue and, per visit, drive three to seven times the revenue one-time buyers do. The report estimates that for each 1 percent of shoppers who return for a subsequent visit, overall revenue will increase by approximately 10 percent.
Yet, when a newly purchased product doesn’t live up to expectations, that cycle can be irretrievably broken: Excitement turns to disappointment, and the customer's likelihood of having a positive feeling toward the retailer, much less making a repeat purchase, drops precipitously.
A good customer experience has gone bad, maybe irretrievably. The result is customer churn.
It's important, then, to decide on your company's best strategy for fostering repeat customers and creating a virtuous shopping cycle. And, clearly the best strategy is to make sure your products live up to or exceed expectations in the first place, so they don’t get returned.
Returns data is a critical source of such insights, and that’s what can make returns a great thing for a retailer. The sooner the retailer spots and mitigates a product issue, the less the likelihood of derailing that path to lifetime customer value. For the retailer, mining returns data to prevent problems is an unexplored but potentially huge driver for a better customer experience.
Applying analytics to returns can reveal all sorts of insights that retailers wouldn't get any other way. For example:
- Spotting a spike in the return of blue shirts because they were placed in a warehouse slot where the green shirts were supposed to go
- Hearing that the fabric used in a new style of sweater is scratchy and irritates the skin
- Finding out the drawstring is missing from an entire shipment of one style of athletic shorts
- Learning that the 32-inch inseam listed on the online product description is really 30 inches
Sometimes, returns information can drive a quick fix, like a product description or a warehouse error. Other returns can impact the next order a vendor fulfills, by your 1) asking the vendor to improve quality control; and 2) taking the information received into account for future, similar orders -- noting, for example, that you need to avoid that fabric. All of these actions may save future sales by increasing quality and the likelihood of customer satisfaction.
Gaining these insights takes surprisingly little additional effort. Here are three easy steps:
1. Improve your returns data.
Reason codes are often inaccurate. Retool your reason codes so they better fit the merchandise, and add a comment box to returns forms so customers can volunteer details. Collect social media comments that mention returns or a poor customer experience.
2. Inspect the returned product.
Front-line employees are a treasure trove of untapped product information. Build a process for receivers to quickly inspect at least a percentage of the returned product. Note not just the reason codes, but also your vendors, product categories, etc., as well as the vendors' own observations on the product condition. Also consider capturing images of returned goods and tagging them with reason codes such as poor packaging.
3. Spot patterns quickly, using returns analytics.
Incorporate returns analytics tools into your process to gain previously unattainable insights into real customer experiences with your products. Use this knowledge to shape future orders.
Many retailers are implementing more liberal returns policies; some are even offering free returns, to reduce customer perceived risk and drive more revenue. Those are great moves. But the fact is, a return always represents a disappointment, no matter how comfortable the returns process.
Ultimately, retailers stand to earn a much bigger payoff from returns prevention. Rapidly identifying the root cause of returns and making changes to prevent them in the future not only saves money, but more importantly preserves the customer relationship.
The customer who delivers the highest "lifetime customer value" is one who was satisfied the first time, and therefore keeps coming back. This is the lifeblood for all retailers, yet few do everything they can to nurture and protect it.
An efficient data-driven returns process is a strategic weapon that smart retailers can leverage in the war against product returns and decreasing margins. Those that fail to do this may well find it increasing difficult to compete.