Everyone knows by now that the global pandemic has changed retail forever. Online shopping exploded when physical stores were shuttered, and many retailers chose to institute free shipping, free return shipping, and lenient return policies to keep consumers buying from them during that extremely disruptive period.

Unfortunately, the rate of returns has also skyrocketed. The 2021 Consumer Returns in the Retail Industry Report indicates 20.8% of online sales were returned in the US during 2021 ($281 billion). Obviously, the lost revenue of the returned purchase is just a part of the impact on the retailer. Return shipping costs (if offered), labor and materials to examine and restock merchandise (if possible), or disposal costs (if necessary) all add up to negatively impact the retailer’s bottom line.

The data shows that about 89% of online returns are for legitimate reasons, made by valuable consumers whom you would like to keep happy and continue buying from you. However, the costs of lenient shipping and return policies are proving to be unsustainable. What’s a retailer to do?

Revised, one-size-fits-all shipping and returns policies are not the answer

Many retailers have implemented stricter policies to reduce the impact of returns on their bottom line. Unfortunately, they are finding that blanket return policies do not account for the lifetime value of individual consumers or their buying trends or patterns. Consider the example of two different consumers wishing to return an item valued at $50.

  • Consumer A has made a total of $500 in returns involving three different transactions at three different stores in a single day, all without a receipt.
  • Consumer B has made $500 in returns, too. But her returns are spread across the previous 5 months, all made with a receipt.

What standard blanket policy could you put in place to adequately evaluate the risk and value of accepting returns from these two consumers?

Use AI-powered data analysis to capitalize on your returns

Broad-based return policies affect every shopper in the same way. Loyal, valuable consumers are given the same treatment as the shoppers who initiate risky or abusive returns. The answer lies in a science-based, artificial intelligence-driven (AI) data analytics tool such as Appriss® Engage that gives you the power to easily aggregate and link your shopper data across all your buying channels in real time. Linking the individual consumer transactions together to create a holistic view of the consumer’s behavior gives you the ability to surprise and delight your valued consumers while minimizing the cost and risk of returns.

Instant incentives can be offered to high-value consumers, such as a 15% off coupon if an on-line purchase is returned to the store rather than shipped back to the warehouse. Not only do you save the return shipping/restocking costs of the return, you also increase the chance that the consumer will spend even more with you while in store. In the same way, predictive analytics can identify consumers purchasing multiple sizes of the same item and instantly offer up coupons to drive consumers to the store to try the items on, saving you the costs of the return for the sizes that don’t fit.

On the other hand, holistic consumer views can help you identify and deter behaviors of frequent returners or bad actors trying to defraud your company. When your system detects a new online or in-store purchase by a person who has exhibited extreme refund behaviors, it can instantly apply a customized return policy to the purchase to minimize the chance of a return

Dynamic return policies best serve you and your consumer

It’s important to keep in mind that most consumers find returning purchases to be quite stressful, no matter what the reason. They have a problem to solve, and they have turned to the retailer for help. Suddenly creating more restrictive blanket return policies only makes the situation worse, impacting the trust between the retailer and its consumers.

Using AI-driven data analytics, retailers can better serve the legitimate returners while reducing overhead. Appriss Engage connects the information in a retailer’s own data to provide dynamic return policy recommendations to minimize the financial impact of each return.  Download the whitepaper, AI-Driven Alternatives to Blanket Returns Policies: New Ways to Maximize Profitability to learn more.



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