How to Tackle Fraudulent Returns
Consumer modelling and segmentation have never been more critical in retail, as the pandemic-driven e-commerce boom has put customer acquisition and retention under the spotlight like never before. With shopper behaviors under scrutiny, retailers are continuing to focus on fraud prevention and loss minimization, particularly when it comes to returns. Preventing fraud and minimizing excessive returns is obviously a reasonable goal. However, retailers need to be sure that their efforts are well-placed and prioritized.
Data from Appriss suggests the cost of both online and offline returns fraud was $27 billion in 2020. Clearly, that’s not an insignificant amount of money, but against total retail sales of $4.04 trillion, only 0.6 percent of the value of retail sales is lost to returns fraud.
That may seem like a small percentage upon quick consideration, but these national numbers may not ring true for individual businesses. It raises the questions: what price are retailers willing to pay to reduce the rate of excessive or fraudulent returns, and what obstacles do they face and capabilities do they need in order to achieve that goal?
The Data Issue
Most e-commerce businesses rely on traditional methods to assess their customer’s lifetime value (CLV) and returns profile. That’s fine, provided they understand the limitations of that data.
For example, how do they include returns in the assessment of customer value? Is the overall value of the customer calculated after all sales and returns have been made, and is there data showing the average impact of a return on future customer spending and frequency? Or are “serial returners” flagged as a “problematic” segment on the basis of return volume or percentage alone?
The risk is that retailers miss the bigger picture. Our experience suggests that customers who make a higher number of returns often have a higher lifetime value — they shop more often and spend more with their favorite brands, and while they may be selective and have high expectations, their loyalty is deeply valuable. Customers with high levels of returns are valuable sources of information and feedback, too. Are they having to compensate for a sizing issue or difficult-to-comprehend photography?
Understanding their returns behavior gives a retailer clear opportunity for improvement, after which those return rates may well drop.
It’s essential that returns behavior is both properly grasped (e.g., what's driving the returns) and put into a longer term context of CLV (e.g., the impact of making a return on future purchasing and frequency).
Approaches to Tackling Fraud
After retailers properly contextualize and understand their returns data, they can come back to the question of fraud and excessive returning. There are a variety of non-algorithmic approaches we see used.
Free returns are a useful acquisition tool, but for customers whose spending and frequency don’t compensate for their return rate, retailers have the option to introduce charges for returns. Careful segmentation and good data collection are essential to make this approach effective without punishing customers acting in good faith.
If returns data shows that most purchases are taking a long time to return to stock, retailers have the option of targeting their policy towards faster returns. For example, returning within a week is free, but there's a charge after the initial period. This could decrease wardrobing, cover some costs and bring stock back to sale faster, uplifting the resale potential value of returned items.
It’s still fairly common for retailers to require customers to obtain authorization from customer support in order to process a return. This approach ensures that items are eligible for return, helping to prevent fraud, and could be used to identify problematic returns behaviors.
However, this strategy encompasses every customer who wants to make a return, making returns significantly less convenient across the board, not just for those who may abuse the system. Thirty-six percent of shoppers surveyed by YouGov on behalf of Doddle in 2020 said they would like retailers to remove these authorization policies.
Retailers must be able to accurately estimate the current cost of returns fraud, the difference that any intervention would make and, most importantly, the other costs of altering the returns process or blacklisting customers — reduced loyalty and potentially reduced brand equity.
In order to fully understand and appreciate what's driving returns and their longer term impact on customer loyalty, as well as to measure the difference such a project actually makes, retailers need to have a digital returns journey that gives them proper visibility and the possibility of analysis. With the insight gleaned from the digital returns journey, retailers are better able to determine if there’s a significant gain to be made with all costs factored in and it’s worth investing time and energy pursuing reduced fraud and returns abuse.
Dan Nevin is the North American CEO for Doddle, a business that specializes in technology solutions for deliveries and returns, serving e-commerce and omnichannel retailers.
Related story: E-Commerce Returns Experience is Falling Short
Dan Nevin is the North American CEO for Doddle Inc, a business that specialises in technology solutions for deliveries and returns, serving e-commerce and omni-channel retailers.
Dan has held senior positions across retail, sales, and marketing over the past +15 years, and was part of the founding team of Groupon UK where he built out the Groupon Goods business to reach circa $1billion in annual sales. He since joined the senior leadership team at Pepperjam where he managed the sales team for North America.
Dan has a passion for retail, building successful partnerships, and delivering technology solutions that define a category.