Every year I attend multiple retail events focused on loss prevention and asset protection, and each year seems to have a new trend that dominates all conversations. In 2019, it was the evolution of organized retail crime (ORC). Everywhere I went in the last year – whether a large industry conference or a simple customer meeting – there seemed to be concern about ORC’s progressive modus operandi. Every discussion circle buzzed with a growing interest in how to best protect retail assets from theft, especially since ORC seems to be getting more subtle, more damaging and more difficult to catch each year.
When I’m asked by retailers for recommendations on how to identify and combat ORC, I always defer to what I know works: prescriptive analytics.
Prescriptive analytics solutions, such as the one Zebra offers, automatically analyzes data for any behaviors that could potentially indicate ORC-type behavior and then alert a retailer to the “opportunity” for resolution. As part of this alert, the system directly distributes both detailed findings from its analysis and one or more prescriptive actions to the appropriate stakeholder, informing them exactly how to respond to the opportunity. These actions may include instructions for launching an investigation in accordance with company policy, point the recipient to potential evidence, and/or direct the recipient to contact law enforcement for assistance.
Zebra's Prescriptive Analytics solution has been used to eliminate ORC on multiple occasions, so I thought it might be helpful to share a few examples:
A national grocer adopted our prescriptive analytics solution, specifically the Inventory module, to ensure better margin protection and improve inventory accuracy. Soon after deployment, the module’s machine learning and artificial intelligence (AI) technology alerted the grocer’s asset protection team to an inventory anomaly. A specific store’s meat department had begun the week with just 250 pounds of chicken parts on hand; by Wednesday of that same week, records showed it had sold 505, with no new deliveries. The prescriptive analytics solution also identified that beef was moving slowly based on the store’s typical ship-to-sales ratio. Interestingly, another store nearby showed similar behaviors. A prescriptive action directed the store’s District Asset Protection Manager to check pricing-sticker accuracy at the stores and interview the meat department employees that had been on duty over the past several days.
Under interrogation, several meat workers confessed their longtime involvement in an ORC ring with a local caterer. The caterer would come into the stores several times per week and order very large quantities of expensive beef cuts, like rib roasts or tenderloins. The colluding employees would attach price tags for chicken parts to the beef, allowing the caterer to purchase them at a fraction of their actual price. To avoid suspicion at the register, the caterer would ring up the beef at the self-checkout line. The caterer would later give the meat employees a kickback for their help.
The retailer pressed charges against all involved employees and the caterer, ultimately recovering $90,000 in losses. It also updated its self-checkout procedures to mitigate future risk, adopting additional Zebra technologies such as the MP7000 bioptic scanner to identify scanned products with biometric data such as color, volume and size to verify scanning compliance.
A hardlines retailer’s asset protection team wanted to identify more-subtle cases of employee fraud, and adopted the Sales & Exception-Based Reporting (EBR) module of the Zebra Prescriptive Analytics platform to support its initiatives. Shortly after going live, the solution identified a potential indicator of fraud.
After analyzing and comparing transactional and human resources data, the prescriptive analytics system had identified a store showing a strong correlation between sales transactions with the highest-value item voided and times of low supervisor presence on the sales floor. The module sent the asset protection team a list of these specific occurrences, along with CCTV footage and a prescriptive action directing them to investigate.
The CCTV footage showed numerous employees passing merchandise to their friends and family at the self-checkout registers. Each time, the employee waited for the customer to ring up the entire order, then voided the most expensive item and placed it in the customer’s bag. More than a dozen employees were terminated and prosecuted, and the retailers recovered more than $50,000 in stolen merchandise.
A fashion retailer adopted the Sales & EBR module of the Zebra Prescriptive Analytics platform as part of an initiative against shrink and fraud. After much success with in-store data, the retailer decided to upload its e-commerce data into the module as well. Within five minutes of deployment, the prescriptive analytics platform identified and alerted the asset protection team to some suspicious behavior within the retailer’s call center.
To “appease” dissatisfied customers and improve retention, the retailer allowed its customer service representatives (CSRs) to refund and/or replace certain products that customers complained about or claimed they never received. As standard practice, CSRs would also offer each customer a $20 gift card for the inconvenience. However, the prescriptive analytics system identified several CSRs who were appeasing many, many more e-commerce orders than average benchmark levels. The system further identified that the suspect CSRs were shipping the appeased orders to just five addresses — all of which were linked to their friends and families. The prescriptive action to the asset protection team was to investigate the suspect CSRs for potential ORC activity.
The prescriptive action led asset protection investigators to the root cause: these employees had indeed formed an ORC group. Working together, they would legitimately buy products online and, after receiving them, call the center to complain they never got the products. Thus, each participant ended up with a $20 gift card and two products (one purchased, one replacement) that could then be sold or returned for cash. With this information, the retailer terminated all involved, saving an average of $50,000 a month.
Facing a similar challenge with OCR?
These are just three of the many battles that retail and consumer packaged goods (CPG) customers have won against ORC after implementing prescriptive analytics. Its integrated machine learning and AI technologies can actually identify theft, fraud, risk and much more through various means.
If ORC is posing a threat to your organization, let’s chat about the different ways that a prescriptive analytics system may be able to help – and how it can benefit all areas of your business, including loss prevention and asset protection.
Editor’s Note: For more information on how you can use prescriptive analytics as a tool against ORC, visit our website or contact the Zebra Retail Analytics team.