View from Academia — E-commerce returns, policy changes, and gender (in) differences

By Patricia J. Daugherty, Robert E. Overstreet, Tyler R. Morgan ,

E-commerce shoppers are returning more products than ever. That’s the trade-off that e-retailers face. While return rates are higher than with brick-and-mortar sales, e-commerce is too big of a revenue opportunity to pass up. As a result of increased (sometimes questionable) returns, some retailers have tightened returns policies in an attempt to reduce the volume of returns. We conducted research to better understand the implications of differentiated return policies.

We used a crowdsourcing website, and focused on participants over the age of 18 who had purchased at least one product online in the last six months. The 462 usable responses were evenly split between male and female participants. To better understand our participants, we asked multiple questions about their feelings regarding the environment as well as their purchase and return behaviors. Our results indicated that female participants were significantly more likely to have a pro-environmental self-identity, which was related to a lower amount of returns. In most other areas the patterns were remarkably homogeneous with respect to gender with no significant differences observed in the following areas:

• The number of online purchases made last month.
• The number of times in the last year [he/she] purchased multiple variations (e.g., different size, multiple colors) online with the intent of keeping one and returning the others.
• The amount of purchases that [he/she] typically return.
• The amount spent buying clothing online versus in a retail location.

However, our primary focus was the impact of differentiated returns policies. In the research, differentiated (more restrictive) returns policies limited the time allowed to return purchases. Two scenarios were presented: either the time restriction applied to all customers or the time restriction applied only to customers with a history of “excessive” returns. Many retailers now use commercially available data to track consumer purchase/return habits. We assessed the respondents’ “reactions” to the differentiated policy as reflected in their intention to spread word-of-mouth about a retailer. Interestingly, females were significantly more likely to spread positive word of mouth about an e-commerce retailer when the returns management policy change targeted specific customers based on his/her return habits. Because word-of-mouth has been shown to be more influential than information from commercial sources (e.g. company advertisements), these findings have significant implications for retailers contemplating a change to their returns management policy. Targeted policies effecting individuals with a history of excessive returns may not only address issues with problem customers, but may also generate positive discussions among current and future customers.