Adapting Data Analytics Techniques to Combat Organized Retail Crime
Organized Retail Crime (ORC) is more than just petty theft — it’s a coordinated criminal enterprise responsible for billions of dollars in losses each year and a rising tide of violence.
But how to combat this problem effectively?
In this article, we show how Loss Prevention (LP) teams can help teams act with more speed, precision and impact, supported by AI, smart metrics and collaboration with authorities.
Understanding ORC: Scope, Tactics and Threats
In addition to significant financial losses, organized retail crimes also bring risks to the public for reselling expired products, consumer electronics without warranties or violent acts against store employees and customers.
And it doesn’t stop there.
These gangs operate in several regions at the same time, exploiting logistical and legal loopholes. In some states, such as California, ORC is already treated as a serious crime.
The good news? It is possible to react intelligently.
With centralized data, automation and collaboration between retail and authorities, you can transform chaos into control.
Key Metrics That Drive Smarter Loss Prevention Analysis
Preventing retail losses requires more than vigilance. Data-based intelligence is a must.
At Hubstream, we believe metrics aren’t just about explaining the past. They must provide actionable insights that help loss prevention teams put safety measurements around high-risk items or areas.
Below are key data points loss prevention teams can use to produce better outcomes:
Key Product Metrics
To identify where your losses are coming from, start by asking a few key questions:
- Which products are stolen most often?
- What’s the average loss per incident?
- How easy are these items to sell online?
By analyzing these metrics, you can focus your efforts on easy-to-sell items or popular products—like electronics, cosmetics, and luxury goods—that are frequently targeted due to their high value and strong resale potential on illegal online marketplaces.
Location-Based Insights
Knowing where crimes happen most often—or most recently—is key to allocating resources effectively.
Regional heat maps can highlight high-incident stores or areas, helping you direct security efforts where they’ll have the greatest impact—through targeted surveillance, additional staffing, or time-specific interventions.
Repeat Offenders
In organized retail crime, repeat activity is often the rule—not the exception. Identifying these offenders requires connecting behavioral patterns across time, locations, and incidents.
Systematic tracking can reveal key trends and expose coordinated theft. Useful indicators include:
- Frequency of thefts by location or region
- Recurring suspect descriptions or MO (method of operation)
- High-shrink stores with similar loss patterns
- Timing patterns—such as repeated incidents on specific days or shifts
- Unusual return or refund activity tied to known hotspots
These data points help flag offenders operate in crime rings and support proactive intervention strategies.
Loss and Recovery Metrics
To understand the effectiveness of your loss prevention efforts, it’s important to ask the right questions:
- How many cases have been resolved?
- How much merchandise or value was recovered?
- How long does it typically take to close a case?
These key performance indicators (KPIs) help measure what’s working and highlight areas that need improvement. By tracking them consistently, LP teams can respond more precisely, reinforce security at high-risk moments, and prevent future losses.
From Data to Action: Modern Loss Prevention in Practice
For LP teams to be truly effective in combating ORC, they need to be able to transform these metrics into actions.
Here are some strategies and tools that can be leveraged:
Integrating LP Analysis Tools With AI
Modern AI and data analysis tools help LP teams identify theft trends as they develop. These systems analyze large volumes of data to detect suspicious patterns—like coordinated group activity or unusual return behavior—making it easier to recognize and respond to emerging threats in real time.
Combining E-Commerce and Physical Store Data
ORC groups operate in both physical and online stores, so integrating data from these two worlds is essential.
A product can be stolen in a store and, hours later, sold on a marketplace. By cross-referencing information from transactions, e-commerce, and resellers, teams identify fraud that would otherwise go unnoticed.
Adapting A Dynamic Reporting System
Static reports aren’t enough in fast-moving environments. Prevention teams need dynamic, flexible reporting tools that allow them to drag and drop data fields, adjust time frames, and filter by store, product type, or offender profile—all in real time.
With this level of adaptability, teams can track key indicators like product recovery rates, ORC-related arrests, and loss reduction, then quickly pivot strategies as new patterns emerge. The result: faster decisions, smarter resource allocation, and more effective crime prevention.
Collaborating with Law Enforcement
Fighting organized retail crime effectively requires more than just internal action—it demands strong coordination with law enforcement.
By providing detailed reports, surveillance footage, and behavioral trends, LP teams empower law enforcement to act more strategically—targeting repeat offenders and high-theft zones.
Whether it’s real-time alerts from store systems or structured reports built from flexible data tools, this partnership helps shift the balance from reactive to proactive crime prevention.
Case Studies: When Data Drives Results
When data turns into results, the impact is visible and measurable. Here’s are some real examples:
Case Study 1: Philadelphia’s Data-Driven Approach to ORC
In 2024, Philadelphia saw a 33% rise in retail theft over the previous year. In response, city officials and law enforcement launched a data-driven strategy to counter organized retail crime. This included deploying more officers to high-theft areas, assigning additional detectives to ORC investigations, and focusing efforts on identifying repeat offenders. One standout initiative—modeled after a successful program in nearby Bensalem—involved publishing mug shots and case details online as a deterrent.
These coordinated efforts, supported by data-sharing between retailers and police, resulted in multiple arrests across Philadelphia and Cherry Hill. The case underscores how collaboration and intelligence-sharing between retailers and law enforcement can accelerate investigations and help dismantle organized theft networks.
Case Study 2: Data-Driven Collaboration Dismantles ORC Ring in Northern California
In Northern California, a coalition of retailers and law enforcement agencies formed a working group to address a surge in organized retail crime. By sharing detailed incident reports, surveillance footage, and theft patterns, the group identified coordinated thefts targeting high-value items like perfumes and alcohol. These thefts often involved teams moving along the California coast, executing rapid, large-scale shoplifting operations.
The collaborative effort enabled law enforcement to recognize patterns across jurisdictions, leading to more effective investigations and prosecutions. Retailers benefited from improved communication with police, resulting in better guidance reporting incidents to support legal action. This partnership exemplifies how data sharing and coordinated strategies can significantly enhance the fight against organized retail crime.
Building a Data-Driven LP Management System
Organized crime in retail has already exceeded the limits of what cameras and patrols can contain. Combating ORC today requires centralized data, intelligent automation and agile partnerships between retailers and authorities.
Is your strategy ready for 2025?
With Hubstream, your team can identify patterns, connects all the dots, and turns complex investigations into actionable insights. Schedule a demo and see how we can strengthen your response to ORC.