Why the fashion industry is solving the wrong problem

New research reveals that the solution to rising return costs isn’t better fit – it’s better control.

Fashion brands have spent years chasing the “perfect fit.”
From AI size tools to virtual try-ons, the industry has poured millions into predicting what customers won’t return. But what if that’s the wrong problem to solve?

A new Swedish study from The Journal of Innovation & Knowledge (Patel, Karlsson & Oghazi, 2025) shows that even when shoppers used an advanced size-finder tool — designed to match body shape, height, and measurements — their likelihood to return slightly increased.

In fact, users of the tool were 0.65% more likely to return their purchases compared to those who didn’t use it.

So if prediction doesn’t lower returns, what does?

The myth of the “perfect size”

The study’s machine-learning analysis found that the “size-finder variable” was one of the least important factors in predicting returns.
That’s a critical insight: returns are not driven by fit alone.

Seasonality, buying behavior, delivery speed, assortment strategy, and expectations all play a role. And no algorithm can predict those nuances perfectly.

Instead, the researchers found something far more powerful:
When customers did use the size-finder, their Customer Lifetime Value (CLV) increased by 7.51% the following quarter and 5.53% the next.

So even if they returned more, they bought again — faster and for more money.

That changes the narrative completely.

Returns don’t kill profit. Inefficiency does.

The same paper highlights that “managing return and repair activities accounts for around 10% of total supply chain costs – but if inefficiencies build up, that share can grow, cutting profits by up to 30%.”

Let that sink in:
The problem isn’t that customers return. It’s that retailers still handle returns like it’s 2010, manual processes, siloed data, disconnected systems.

In other words: Returns reveal how much control you really have.

The post-purchase blind spot

Most retailers have fine-tuned their pre-purchase funnel: Ads, UX, checkout, conversion.
But what happens after the buy button still receives far less attention.

And that’s where profit quietly slips away:

• Credits take too long.
  
• Restocks miss selling windows.
  
• Manual dispositioning eats up hours every day.
  

While marketing keeps optimising for clicks, operations are left managing complex return flows with limited tools.

It’s time to bring the same precision to what happens after the purchase — where real efficiency and customer loyalty are built.

The real growth lever: Automation and control

In the article, Zappos is highlighted as a leading example of how a brand can turn returns into a competitive advantage.
As their VP of Operations explained:

“Our best customers have the highest return rates, but they are also the ones that spend the most and are our most profitable customers.”

It illustrates a key insight: returns can be a sign of strong customer engagement – not a loss.
The difference lies in how efficiently they’re managed.

That’s the next frontier in fashion retail:
automating the post-purchase chain.
From return initiation to inbound logistics and crediting, every step optimized, tracked, and fed back into the business.

From prediction to precision

The takeaway from this research is clear:
Chasing fewer returns is like chasing perfection — it sounds good, but it’s not scalable.

The brands that will win are the ones who can handle returns better, faster, and smarter than anyone else.

At Inretrn, we’re helping them do exactly that.
By automating return handling from initiation to inbound, we give retailers full visibility, faster crediting, and lower operational costs — so they can finally get return on returns.