4 Data-Driven Tips To Optimize Your Return Policy For The Holidays
Generous return policies may drive sales, but will they hurt your bottom line? Contributor Jordan Elkind gives recommendations on making promotional decisions.
“Free shipping and free returns” has become the proud declaration of many online fashion retailers. These policies have contributed to the explosion of online fashion sales: customers appreciate the convenience and savings associated with flexible shipping and return policies.
Unfortunately for retailers, this consumer-friendly approach has an unwelcome side effect, a little-known fashion e-commerce secret: crippling return rates. This issue will be painfully relevant during the upcoming holiday season, when online shopping — and returns — reach peak volume.
What can savvy retailers do to woo online customers without giving away the farm? Leading e-commerce teams are leveraging data and analytics to lead the charge — here’s how.
(Note: The data points included are, unless otherwise specified, based on The Custora Pulse, a free e-commerce dashboard offered by my employer, Custora, aggregating data from over 100 U.S. retailers.)
Fact #1: Returns Are A Force To Be Reckoned With For Online Retailers
For brick-and-mortar stores, the average return rate was estimated to be 8.6% of sales in 2013 (with little fluctuation in the past years), according to The Retail Equation’s 2013 Consumer Returns in the Retail Industry report [PDF auto-download]. In contrast, return rates for online retailers can be as high as a third of sales.
Recommendation: If you’re an e-commerce retailer, be aware that generous return policies can add up — and take a big bite out of the bottom line. Before rolling out new policies, assess the projected impact not just on sales and new customer growth, but also on restocking and other logistical expenses.
Fact #2: The Average Industry Figure Can Be Misleading — Online Return Rates Vary Greatly Across Retailers, Mainly Based On Product Category
Custora’s analysis reveals a variance of almost 8x in return rates across different online retailers. Even for the same retailer, different product categories drive widely different return rates.
Within the fashion and lifestyle vertical, non-variation (or one-size) product categories like bags, jewelry, accessories and beauty products correspond with lower return rates, while variation categories like shoes and apparel correspond with higher return rates. Other factors like price point and the importance of the right fit also account for some of the variation in return rates.
Recommendation: Plan for the highest return rate in categories that are expensive, fit-sensitive or highly visible.
What’s “highly visible”? A beauty retailer selling skincare and cosmetics products, for example, had a 1.8% average return rate. Makeup products had the highest return rate at 4.1%; soap was the least returned product category, with only 1.7% of products returned.
Fact #3: Some Customers Are More Prone To Return Than Others
Again, the average return rate is misleading: Different customers have a different propensity to return their purchases.
Custora’s data analysis shows that for some retailers, the customers with the highest likelihood to return are fifteen times (15x) more likely to return their purchases than other customers. Interestingly, while the end result might be similar — a purchase is returned — the underlying reasons vary.
- Some customers are “wardrobers” — they buy items to wear once and return.
- Others are “try them ons” — shoppers that order clothes just for the fun of trying them on at home, without any intention of actually keeping the items.
- Many shoppers are “fitting room-ers”” — they replicate the brick-and-mortar shopping process of heading to the fitting room with different sizes and colors of the same item to eventually pick their favorite.
Recommendation: Segmenting your customers is key, and “return likelihood” is an important dimension for retailers to segment on. Data-driven retailers are leveraging automated algorithms to predict the return likelihood of each customer, to identify customer segments with a high likelihood of returning their purchases, and to communicate with them accordingly.
For example, a retailer might offer a Free Shipping promotion only to customers with a lower likelihood of returning purchases — and focus on different types of promotions for segments known to be heavily return-prone.
Fact #4: Retailers Can Influence The Average Time To Return
Perhaps unsurprisingly, Custora’s analysis revealed that customers typically wait until the very end of the return window to mail in their returned items. The average time until return was just two to three days before the end of the retailer’s return window.
Recommendation: Since return data is taken into account while forecasting inventory, budget and personnel plans, being aware of this data point can improve plans’ accuracy. Modifying the return window length is sure to have an impact on all of these factors.
Ultimately, by gaining visibility into the categories and products most likely to be returned at the highest rate — and identifying which customers are most likely to take advantage of generous returns policies — retailers can plan more effectively for the upcoming holiday season.
Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.