Fix Your Returns Problem with Continuous Improvement

Continuous improvement is a lofty goal in tightly controlled environments like a factory floor or a logistics operation. Quality guru Dr. J. Edward Deming, the father of continuous improvement, defined a cycle of plan-do-check-action to help organizations achieve Kaizen, Japanese for “change for the better.”  

Nowhere are retail businesses riper to change for the better than in product returns, which are particularly fast-growing in ecommerce. According to the National Retail Federation, return rates in the US average 8.6% but can range as high as 18% in some categories. Kurt Salmon Associates says online returns can amount to as much as a third of sales in some industries.  

Unfortunately for most retailers, the handling and processing of returns hasn’t changed much despite this increase in volume.  

Product returns are long overdue for change. And they must change for retailers to win the race to get closer to customers. Applying continuous improvement practices to returns can help retailers select the right products, merchandise them effectively, and promote them well, so customers get the right product the first time and come back for more.  

How Returns Help Retailers

Continuous improvement isn’t a term you hear thrown around in retail circles. Unlike a production environment, a retail business depends on a huge and uncontrollable factor: consumers. But as retailers increasingly rely on advanced analytics, the idea of continuous improvement in retail – getting better and better at understanding and predicting consumer behavior – has legs.  

Advisory firms see advanced analytics as essential, particularly in ecommerce. In a recent report, Gartner said, “retailers will not be able to compete in the digitalized marketplace without advanced analytic capabilities.”  

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When retailers start collecting better data about returns, they can apply these analytics to gain critical insights into why products come back.  

Let’s apply these insights from product return data for a new style of sneaker:  

1. The retailer adds the image and specs and starts selling, but soon the shoes start coming back in unexpected numbers.
2. Consumers tell them they loved the new lightweight material, but the laces come untied too easily.  
3. The retailer can immediately get the manufacturer to send new laces to insert into the boxes currently in stock to prevent next week’s returns. They can then include that new lace type in all future orders for all vendors to ensure the problem doesn’t re-emerge.  
4. They can better highlight the style’s lightness in the description to attract more buyers since customers loved the material.  
5. Then they can use the feedback on any additional returns to make more changes.  

Over time, the retailer can build a database of features consumers like or dislike in their products, shaping what customers like and thus what to sell – using the same plan-do-check-action strategy used in factories and distribution centers. Eventually, they get rewarded with happier, more loyal customers and lower return rates – a real competitive advantage. Deming would be proud.