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Not every type of return abuse is obvious. Some shoppers place frequent orders and appear honest on the surface. However, their post-purchase behavior tells a different story.These shoppers are known as ecommerce serial returners. Their activity might not raise red flags at checkout, but it often leads to strained operations, distorted data and lost revenue.Below, we explore what serial return behavior looks like, how to tell it apart from legitimate shopping habits and how to use smarter tools to protect your business.
What are ecommerce serial returners?
An ecommerce serial returner is a shopper who frequently purchases items online with the intent to return most of them, causing financial, operational, and data-related harm to retailers.
The cost of ecommerce serial returners
Ecommerce serial returners
are a significant burden on retailers, causing substantial financial, operational, and data-related harm. The costs associated with processing these returns include shipping, restocking, and potential loss of saleable goods, which directly impact a company’s bottom line. Additionally, the operational strain and inaccurate data created by this behavior disrupt inventory management and forecasting, making it difficult for businesses to optimize their strategies. Addressing this behavior through targeted return optimization not only protects margins but also relaligns policy to improve forecasting, inventory flow, and long-term strategic decision-making.
Financial losses
According to our 2025 State of Commerce Report, online return abuse and fraudulent returns cost retailers an estimated $46 billion last year in 2024. This figure represents the merchandise value alone and does not include added costs such as shipping, manual inspections, restocking labor, repackaging, resale markdowns, or waste from damaged products.
Operational inefficiencies
The ecommerce returns process is already a pain point for retailers, and serial returners just exacerbate things. Their high volume of send-backs can overwhelm support teams, clog up workflows and take up valuable warehouse space. The added strain also slows down fulfillment, inflates handling costs and creates inefficiencies that ripple across the supply chain.
Policy inflation
In an effort to curb abuse, many merchants turn to stricter ecommerce return policies. But applying blanket rules doesn’t always yield better outcomes. Doing so often creates additional hurdles for loyal shoppers, which hurts trust, disrupts the customer experience and makes it harder to convert new customers into repeat buyers. Over time, this friction can also lead to increased cart abandonment and a decrease in customer lifetime value (CLTV).
Skewed data
Though serial returners make up just 11% of shoppers, their return behavior quietly distorts trust in the data merchants rely on to make critical business decisions. Frequent, high-volume returns send false demand signals that lead to overstocking, inflated customer acquisition costs (CAC) and unreliable customer lifetime value (CLTV) projections.Even product performance models aren’t immune. Elevated return rates can be mistaken for sizing or quality issues, making a well-performing item look problematic when the real issue is actually behavioral.
Six types of ecommerce serial return abuse
Post-purchase fraud can take many forms. Here are some of the most common types of return abuse to look out for:
1. Staging
Staging involves intentionally purchasing products with no intent to keep them, often for social media content, product photography or one-time events. After using the item temporarily, the shopper returns it in like-new condition.
2. Wardrobing
Wardrobing is when a shopper wears an item, like a dress or accessory, once before returning it.
3. Bracketing
Bracketing refers to buying multiple sizes, colors or versions of the same product so the shopper can try the items on, keep what works and return the rest.
4. False defect claims
Some serial returners mark the products they receive as “defective” — even when nothing’s wrong with it — to avoid paying restocking or return shipping fees.
5. Open box fraud
Open box fraud refers to when a customer returns an item claiming it’s unused or in resellable condition. Upon inspection by the retailer, the product has generally been altered or degraded in some way.
6. Product switching
Product switching is when a shopper purchases a new item, only to send back a different, older or damaged version of the original product in its place.
Why is it important to understand a serial returner’s data?
Without visibility into the intent behind returns, it’s hard to tell the difference between nefarious and honest shoppers. By analyzing the right behavior data, you can uncover patterns that separate opportunistic abuse from genuine customer needs, helping to prevent post-purchase fraud.
Return frequency per customer
Analyzing how often a customer returns items can highlight patterns of returns that go beyond normal shopping behavior. High-frequency returners can be flagged for further review, while occasional returns from otherwise valuable customers may call for a different approach.
Reason codes over time
Tracking the reasons customers provide for returns, like “item not as described” or “fit issue,” can help you spot trends that indicate abuse or policy loopholes. For example, a serial returner may repeatedly use a vague or inconsistent reason code, which is a clear signal for potential fraud or manipulation.
SKU level trends
Monitoring which SKUs are most frequently returned helps separate product-related issues (i.e. materials quality) from abusive customer behavior. This stops you from mistakenly attributing high returns to product defects when the problem is actually the customer.
Timing patterns
Serial returners often show predictable timing patterns, like returning items just before the policy window closes or cycling purchases and returns around major sales events. Recognizing these patterns helps ecommerce merchants create more targeted rules.
Red flags vs. false positives
Not every customer who returns a lot is trying to game the system. Some are genuine shoppers who return items for valid reasons. For example, someone might frequently buy expensive items but return whatever doesn’t meet their expectations. On the surface, this behavior may resemble abuse, but the intent is completely different.That’s why context matters. The key is being able to distinguish between fraud and legitimate shopping behavior so you can protect your revenue without alienating valuable customers.
Customer segmentation
Data-driven segmentation lets merchants treat return requests differently based on the level of risk indicated by the data signals that accompany the return. Instead of applying strict policies across the board, you can tighten controls when requests show signs of abuse, while making the experience easier for requests that indicate a trusted customer is involved. That might mean offering store credit to protect against potential serial returners or offering instant refunds to responsible customers.But building that kind of precision into your return strategy requires the right infrastructure. It takes tools that can surface return patterns and segment requests by transaction and behavioral data to guide better decisions.
How to detect and combat ecommerce serial returners
Many fraud tools do a good job of catching suspicious activity before an order is approved. But anticipating abuse post-purchase is a trickier task and many solutions fall short there. They treat each transaction in isolation, lacking insights provided by a large network of merchants and the transactional and behavioral data needed to make a connection between how a shopper behaves at checkout and how they behave after the sale.
Why you need the whole picture to process returns
Take this example: A customer with a clean transaction history checks out without raising any red flags. But after each delivery, they return high-ticket items with “defect” claims that don’t hold upon inspection. Without tying together pre- and post-purchase behavior, traditional tools see only part of the picture and mistakenly label that shopper as trustworthy
To uncover these patterns and take meaningful action, retailers need a more connected approach. That means merging fraud, return and customer experience signals from a large merchant network into one system that supports smarter decisions. With advanced tools in place, like Signifyd’s Return Insights solution, teams can see the full picture, so they can prepare and respond more efficiently.
Get a centralized view of returns data
The foundation of any effective return strategy is visibility — and that’s often where merchants struggle most. Return data tends to live across disconnected systems, making it hard to get a clear view of what’s really happening post-purchase. Tools like Returns Insights can solve this challenge by centralizing and analyzing all return-related activity in one dashboard so you can make winning decisions about returns that protect profits.
Spot problems with segmentation
Looking at trends in aggregate can hide the outliers. By segmenting customers based on return frequency, timing and reason codes, you can pinpoint the behaviors that separate ecommerce serial returners from trustworthy shoppers. This level of granularity enables a more personalized approach that considers intent, past behavior and long-term value.
For example, you might discover that a high-return shopper is still profitable, or that certain profiles consistently return for the same solvable reason. These insights can inform how you engage with different customers, where you focus education and how to design better experiences.
Identify the real reasons behind returns
As we know, a spike in returns doesn’t always point to deliberate abuse. But when you take a step back and look at return patterns over time (like what segments are returning what, how often and when) a clearer picture starts to appear. Maybe customers are unhappy with product quality. Or perhaps certain shoppers are consistently testing policy limits. With the right context, you can separate the one-offs from the red flags and respond accordingly.
Use data to flag risky return requests and products
To identify risky return requests and products, look at how they intersect. A shopper might repeatedly return items from a specific category, or a product may show unusually high “defective” return rates. By layering SKU-level data with customer history, you can proactively flag risky patterns and adapt your return handling.
Get recommendations to improve efficiency and reduce serial abuse
Once your return strategy is grounded in real customer behavior and supported by centralized data, it’s easier to spot what’s working and what’s not. And with Returns Insights, you get visibility into specific areas where policies or processes may be falling short, enabling you to fine-tune weak areas before they snowball into bigger problems.
Build smarter return rules and policies with return behavior data
When you understand return behavior at a deeper level, you can move beyond one-size-fits-all rules and design targeted, behavior-driven ecommerce return policies. Instead of relying on assumptions, your guidelines can reflect how shoppers actually behave rather than how you hope they will.
Signifyd’s Returns Insights solution makes this shift possible by centralizing return, refund, exchange and appeasement data in one place. With a complete view across SKUs, customer segments and timeframes, you can spot avoidable trends, adjust policies by product category, assess how different customer types respond and evaluate the downstream impact of those changes over time.
Turn return data into a strategic advantage
Ecommerce serial returners may seem like a drain on resources. However, with the right data and centralized visibility, they become a source of insight instead of just loss. Their behavior reveals patterns, pressure points and opportunities to refine everything from product descriptions to return policies.
With Signifyd, merchants can understand the story behind every return. Return Insights helps you decode post-purchase behavior, see where your current strategy is winning and discover where it needs to improve. Instead of reacting to abuse after the damage is done, you can proactively build return processes that prevent fraud, boost revenue and strengthen trust with the customers who matter most.
Want to end your serial returner troubles? Let’s talk.
Frequently asked questions
How much do serial returners cost online merchants?
It’s hard to pinpoint exactly how much serial returners cost online merchants since there’s currently a lack of visibility across most systems. Fraudulent returns cost retailers about $46 billion in 2024, according to our 2025 State of Ecommerce Report, and the 2024 U.K. Retail Returns Study found that 11% of online shoppers qualify as serial returners.
Why do people become serial returners?
Serial returners are often motivated by convenience, lenient policies, poor sizing guides or temporary use. According to the 2024 Global Consumer Returns Survey, over 40% of shoppers admit to committing at least one type of return abuse. Gen Z and Millennials are particularly prone to this behavior due to factors like the demands of content creation (i.e., buying for an Instagram photo and returning) and social media influence.
How can I tell the difference between a loyal customer and a serial returner?
One way to spot the difference is by looking at behavior over time. Serial returners tend to follow suspicious patterns, like vague or inconsistent return reasons and repeated issues with the same types of products. Loyal customers, on the other hand, usually show steady, predictable habits.
How can technology help detect and prevent serial returners?
Technology helps detect serial returners by revealing patterns that aren’t obvious in isolated transactions. With the right tools, merchants can track return frequency, timing, product types, and reason codes across their customer base. Tools like Signifyd’s Returns Insights take this a step further by centralizing post-purchase data and surfacing trends that help merchants flag risky behavior early and apply smarter, more targeted return policies.
Does adding friction to returns hurt customer experience?
Yes, it does. Blanket return restrictions often alienate loyal shoppers and lower conversion. A better approach is to add selective friction for high-risk segments while keeping the process seamless for trusted customers.
