From stockroom to boardroom: combating the $100 billion retail shrink crisis

With shrink’s rapid upward trajectory, retailers must take a data-driven approach to move beyond broad assumption and measure areas where losses actually occur.

In brief

• In an industry where margins and profitability are already under pressure, the rise in retail shrink requires a strategic, enterprise-wide approach.
• Retailers must develop their shrink profile to classify the various types of loss at a granular level and provide better insights into underlying causes.
• New technologies and predictive modeling tools make a holistic data-driven model that pinpoints individual origins of shrink not just possible, but pragmatic.

If you’ve paid attention to recent retailer earnings reports, you have undoubtedly heard the phrase retail shrink cited as a key factor contributing to margin erosion and poor earnings performance. Shrink is the industry term for inventory loss often attributed to theft and shoplifting, damage or errors. Once simply considered a cost of doing business, retail shrinkage resulted in profit losses exceeding a staggering $100 billion¹ in 2022.

What’s more problematic, the trend of shrink appears to be far from reversing course, with losses more than doubling over the past five years.²,³ In an industry where margins and profitability are already under significant pressure, the rise in retail shrinkage is capturing the attention of all levels within retail organizations; executives are signaling a crisis and investors are taking note. Despite recognition of the growing problem, retailers, in many cases, are struggling to devise a comprehensive approach to regain control over the current situation. The lack of a clear strategy to combat escalating shrinkage, nor a demonstrated confidence in understanding the discrete sources of inventory loss, is a troubling sign, suggesting that shrink losses will remain at heightened levels in the absence of a strategic, enterprise-wide approach.

You can’t manage retail shrinkage if you can’t measure it

Moving beyond broad assumptions of where inventory loss is likely coming from, retailers should undertake a data-driven approach to measure the areas where losses are actually occurring as doing so enables the deployment of targeted mitigation techniques with the greatest potential impact. Given that brands are distinct, with unique product offerings, geographic footprints, market dynamics, operational systems, data landscapes, etc., the root causes of shrink vary widely, often necessitating tailored solutions working in concert to effectively address the challenge.

Without specific insight into the numerous sources of shrink for their business, retailers are left to leverage a disconnected network of siloed, off-the-shelf shrink solutions that often have custom purposes. This patchwork of solutions is not clearly and deliberately linked to the underlying root causes, leading to diminishing effectiveness across an enterprise issue and limited transparency into a tool’s return on investment. Frequently employed tactics of today are often blunt instruments that may help reduce shrinkage, yet they may also result in unintended consequences. For example, select retailers are testing private label–only stores since it is a working supposition that national brands are more desirable for resale by organized retail crime groups. Shifting a high-shrink store to private label only may in fact reduce shrink in that location, but it also may drive paying customers to nearby competitors or the brand’s other local stores, converting a loss problem into a possible sales and customer loyalty problem. With a unified view from multiple data sources retailers already have at the ready, a retailer could not only pinpoint the national brands most frequently stolen, but also could collectively mine point-of-sale transaction data and employee schedules, along with unstructured data, such as camera footage to glean insights as to the time of day when intentional harm is most prevalent and the customer and employee traffic patterns around the most frequently stolen goods.

Putting more context around the problem through the lens of aggregated data allows for more precise solutions, such as moving, removing, replacing or locking specific products; identifying bad actors; increased coverage at specific times; or modified hours. With all of the new technologies available and predictive modeling tools to assist in decision-making, a holistic data-driven model that pulls information from across the organization to pinpoint individual origins of shrink is not just possible, its pragmatic.

Rethinking retail shrink by connecting the dots

The closer you get to diagnosing the distinct root cause issues at a granular level, the more precise and targeted your mitigation techniques can be, which translates into less disruption to the customer experience and less costly investments in extreme countermeasures. When EY teams work with clients on the complex problem of shrink, we first leverage structured and unstructured data that already exists across the organization and look at it in new ways.

Our recommended approach includes these four critical steps:

This article originally appeared on Fortune.com https://fortune.com/2024/01/09/why-retail-100-billion-shrink-crisis-may-shoplifting/

This article was co-authored by Jennifer Fagan, EY Retail Partner and Katie Kyle, EY Forensic & Integrity Services Partner. Additional contributors include Brian Wolfe, Ryan Suntich and Ryan Collins.