Picture this: You ordered a shirt that you really liked from your favorite retailer. Then, when it finally arrives, it doesn’t fit correctly, and you must now navigate the returns process. This may include jumping through hoops to meet the retailer’s return policy requirements: Do you have to return in-store? Is returning the shirt free or do you have to pay shipping charges? This all-too-familiar scenario can significantly inhibit the customer experience.
What’s more, this is a growing issue in the retail industry. According to the Returns as an Engagement Strategy report published by Appriss Retail and Incisiv, returns increased by 78 percent since 2020. While retailers are looking to invest in fit and sizing technology, and they encourage customer reviews to reduce the overall number of returns, these strategies may not recover the relationship that was impacted by the example outlined above.
The solution to this immediate problem is a customized purchase-after-return incentive program. Sometimes, returns are inevitable, but retailers have the opportunity to take this often-unpleasant experience and re-engage the shopper with personalized offers to save the sale. This program uses artificial intelligence (AI) and machine learning (ML) to automatically determine the best way to ensure the customer relationship doesn’t miss a beat. Here’s how you can establish this program in just three easy steps.
1. Determine your business objectives.
When implementing AI or ML technology, it’s critical to understand the expectations set for the tools. What metrics are you most interested in improving? For some retailers, the incentive program will be designed to enhance margins or traffic, while others may focus on customer loyalty or buy-online-return-in-store profitability. These goals may vary slightly by store or region, but you should also consider overarching goals and key metrics across all sales and returns channels within your organization.
2. Identify the variables that drive your sales.
Once you’ve set your goals, it’s time to develop the incentive program. The program should be driven by individualized past behavior of your customers — e.g., their loyalty program enrollment, transaction dates/times, product category preferences, returns frequency, etc. Then, the AI and ML solution can determine which shoppers should be offered an incentive for their patronage and which might not need an incentive to save the sale after a return experience.
3. Select the right incentive for the individual shopper.
Now, you’re ready to use AI and ML to optimize purchase-after-return incentives. The best incentives will foster customer engagement without overly decreasing margins. This isn’t a one-size-fits-all approach and AI and ML will help strike this balance.
You can rely on the data-driven program to match incentives with specific shopping behaviors to meet the established objectives in step one. The program will predict the shopper’s chance of making another purchase after the sale with and without an incentive and choose the best action.
For some shoppers, a discount may be needed to keep them in-store after a return, while others may already be intrigued by new products on their way to the counter. For customers who do need an incentive to save the sale, some may respond stronger to a percent discount, a BOGO deal or a short-term offer to double loyalty program points earned with a purchase. The AI and ML will help the retailer choose the optimal approach to restoring and maintaining the customer relationship after a return while minimizing the impact on the bottom line.
Despite the availability of this technology, only 22 percent of retailers believe they have an effective purchase-after-return incentive program. By implementing this technology, you have the opportunity to stand out and create lasting, positive customer experiences while protecting your margins from costly returns.
Peter Barker is the director of product for Engage at Appriss Retail.
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