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Retail shrink — and theft in particular — has become one of the most talked-about problems in the industry.
It dominates earnings calls. It fuels headlines. And it’s driving a wave of new technology focused on detection and alerting.
But there’s a more important question we’re not asking often enough:
Why did shrink suddenly become a crisis in 2022–2023 — after decades of existing in retail?
The answer isn’t that theft is new. It’s that something else changed first.
When shrink made headlines
Shrink has always been part of retail. It’s the gap between what inventory systems say should be on the shelf and what’s actually there.
What is new isn’t shrink itself – it’s the intensity of the conversation around it.
In 2022, the National Retail Federation reported retail shrink at over $112 billion, up sharply in absolute dollars from prior years. That number — and the stories behind it — pushed shrink into mainstream business news and onto earnings calls across the industry.
By 2023, industry estimates suggest total retail shrink reached well over $130 billion — turning what had long been a known issue into a balance-sheet-level concern.
What happened right before that? A labor shift
The timing isn’t a coincidence.
From 2021 through 2022, the U.S. labor market experienced what’s been widely called the Great Resignation — millions of workers quit or changed jobs, especially in frontline service sectors.
At the same time, many retailers:
- cut store staffing to reduce costs
- expanded self-checkout and unattended models
- reduced cashiers and floor associates
Stores became less staffed before shrink and theft narratives spiked.
The connection of theft to labor shortages is intuitive. The challenge is treating labor as an investment rather than a cost.
Retailers didn’t want empty stores — they were responding to labor pressures.
But then we tried to solve the consequences with technology that alerts instead of visibility that prevents.
Lock up products. Add panic buttons. Install more cameras.
Meanwhile, employees are spread thin — or not present at all.
An alert tells a retailer:
“Something might be happening here.”
But it often happens where:
- no one is present
- no one can intercept
- liability prevents engagement
That’s not prevention — that’s late documentation.
What actually prevents theft? People.
Here’s the insight we hear from retailers who are getting this right:
If an employee engages a shopper — even just makes eye contact — within the first minute of entering the store, incidents of theft drop significantly.
And this isn’t just about loss.
Customer experience improves. Conversion rises. Revenue grows.
The retailers who excel at this understand something fundamental:
Labor isn’t just a cost center. It’s a revenue driver.
Measure What Matters
This is where most of the industry still struggles.
Retailers know human interaction matters — but it’s rarely measured, managed, or rewarded. We track shrink after it happens, but we don’t consistently measure the behaviors that prevent it.
How quickly is a shopper acknowledged? Where are employees actually spending time? Which layouts encourage engagement — and which eliminate it?
These are leading indicators. And leading indicators are what change outcomes.
This is why at Standard AI, we avoided building theft alerts — and instead built metrics around human engagement:
- time to first customer contact
- employee presence relative to traffic
- how interaction correlates with conversion and loss
As an operator, I’ve learned that you don’t change behavior with hindsight.
You change it by measuring what matters – and making it visible.
It’s time to connect the dots
Retailers didn’t create today’s environment intentionally. They responded rationally to unprecedented constraints.
But now we have to be honest about what will actually fix the outcomes we care about.
Shrink isn’t primarily a surveillance problem. It’s not solved by alerts. And it’s not fixed by more hardware.
It’s a human presence problem.
And the solution that reduces loss is the same one that improves customer experience and drives revenue.
Measure what matters. Demand real intelligence
Written by Angie Westbrock CEO at Standard AI