How a DTC Footwear Brand Cut Return Fraud by 90%

Most DTC brands assume they’ll see fraud when it happens.

But what if the biggest losses don’t look like fraud at all?

This case study breaks down how one lean, high-margin footwear brand uncovered a hidden return fraud problem - one that wasn’t obvious in their reports, wasn’t flagged by their support team, and wasn’t caught by their tools.

They didn’t rebuild their tech stack or hire more people. Instead, with the help of Jordan at YoFi, they added one layer: risk scoring. And used it to quietly reshape how decisions got made across returns, CX, support, and marketing.

If you’ve ever had that gut feeling that something’s off in your returns process, but can’t quite prove it, this story is for you.

Let’s dive in. 👇

What’s Inside

• The Setup → The fraud they didn’t see coming

• The Diagnostic → A 20-min scan that changed everything

• The Fix → Risk scores, refund rules, and support routing

• The Results → 90% less fraud, 70% faster support

• The Playbook → How to replicate this in your ops

• Full Q&A → With Yofi’s co-founder, Jordan Shamir

The Fraud They Didn’t See Coming

The footwear brand wasn’t worried about fraud.

They had all the typical guardrails in place: no free shipping, minimal discounting, and a tight focus on margins. Unlike most DTC brands chasing volume at any cost, their whole strategy was about protecting the bottom line.

So when Yofi offered them a free “bad actor diagnostic,” the team didn’t jump. They assumed fraud wasn’t a major issue. A few edge cases here and there - sure. But nothing worth spinning up a new tool.

They were wrong.

What looked like healthy returns data was actually hiding a serious leak. Customers were abusing return windows, cycling discount codes, and even using fake tracking labels to get refunded without sending anything back.

And because it was all happening in the background, across support, shipping, and multiple brands, they couldn’t see the full picture. Until the numbers showed up in black and white.

Proving The Problem Exists

Jordan Shamir, Yofi’s co-founder, knew from experience that return fraud often flies under the radar. Most brands only catch it when it gets so bad that finance starts asking questions.

So instead of pitching features, he asked for data. 

Within 20 minutes, the Yofi team connected to the footwear brand’s Shopify store, Loop Returns account, and support tools. Then they ran a scan on historical return and support activity.

The result was a list no one expected.

• Some customers were returning multiple orders per month - often after obvious wear. 

• Others were using fake or rerouted shipping labels to trigger refunds. 

• A few high spenders looked like VIPs until the return behavior told a different story. One customer had been flagged as “loyal” for years. But once returns, discounts, and shipping costs were added up, they were deeply unprofitable.

Acting On The Signal

Once the footwear brand saw the scope of the problem, they didn’t debate it. They moved.

Implementation took less than 30 minutes. 

They installed Yofi’s Shopify app, plugged in their Loop API key, and immediately started generating real-time risk scores for every customer and return.

These scores weren’t generic. 

Each one pulled in dozens of signals - from order and return history to support interactions and even the specific language used in refund requests.

But the power wasn’t just in the score. It was in what they did with it:

1. Pausing refunds until verification

Before Yofi, the brand’s return system was built on speed. 

Refunds were issued the moment a return was marked “delivered” by the carrier - no questions asked.

That worked fine for honest customers. But it created a giant loophole for fraud.

Some people were sending back empty boxes. Others were manipulating labels so a package looked like it was headed to the right zip code - but actually went to a neighbor’s house or empty lot. 

As long as the tracking said “delivered,” the refund went through.

Yofi helped them change that.

Now, refunds are delayed until verification: either a warehouse scan, a condition check, or cross-referenced data from the carrier. That small change, adding friction only where it’s needed, prevented tens of thousands in losses during the holiday season alone.

2. Creating custom risk profiles

The scores also helped the team identify different types of risky behavior.

Yofi worked with the footwear brand to create separate profiles for fraud patterns they were seeing in the data. For example:

• “Bad resellers” were customers constantly using promo codes to cycle through new purchases

• “Cyclical returners” wore products during peak season, then sent them back

• “Ghost clusters” were networks of accounts tied to the same person - used to bypass bans or order limits
  

This went beyond blocking one email address. Yofi’s clustering system linked behavior across identities. So if someone got banned on one account, the others tied to that behavior were flagged too.

3. Routing support based on risk

Even the best return rules can be undone in one place: customer support.

Support agents are measured on speed and satisfaction. That means they’re incentivized to resolve complaints quickly - and fraudsters know it. 

One emotional ticket can override any policy.

To fix this, the team embedded Yofi’s risk scores directly into their support flow.

Now, support tickets from low-risk customers go to an AI agent. High-risk ones go to a human. The goal isn’t to slow things down - it’s to make sure that exceptions don’t get abused.

That simple routing change cut fraud-related investigation time by 70%.

4. Personalizing operations based on trust

Once the scores were flowing through Shopify as customer tags, the team started using them to shape customer experience more broadly.

They didn’t just block fraud. They built smarter, more contextual policies:

• High-risk customers were charged return fees and had refunds delayed

• Low-risk customers got waived fees and faster support

• Risky profiles were excluded from email and SMS campaigns entirely

This wasn’t just about protecting margins. It was about making better use of every customer interaction.

Instead of treating everyone the same, they calibrated trust - adding friction where it saved money, and removing it where it built loyalty.

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