In the loss prevention field, we can’t afford to focus exclusively on fraud. Are fraud investigations a necessary part of the job? Sure. Is the satisfaction of removing a dishonest associate from the business exciting? Absolutely. But at the end of the day, it’s critically important to make sure we are not forgetting about another significant source of loss: transactional non-compliance.
Losses from transactional non-compliance may not have quite the same motivations or implications as fraud—but often times their financial impact can be greater. Everyone knows fraud is wrong, but in a naïve cashier’s mind, non-compliance may just register as “creative problem solving,” “a service to the customer,” or the dreaded “I didn’t see anything that said I couldn’t do it.”
Even if your leadership wants you laser-focused on fraud, you can continue to monitor for non-compliance within your data by deploying an advanced analytics solution like prescriptive analytics. The right solution will deliver course-correcting actions to your team, instructing them exactly how to respond to any detected violations.
Here are three subtle, yet damaging examples of transactional non-compliance that an advanced solution can identify using data.
We all know someone who deserves the title “Extreme Couponer,” but even the most diehard couponer can’t justify using more manufacturer coupons than there are items in an order. Ideally point-of-sale (POS) systems are programmed to disallow such actions, but retail rarely operates in an ideal world and cashiers can also figure out workarounds for nearly anything. A common source of coupon-related losses I’ve seen is via the hand-keyed coupon function, which (when used properly) is for entering the value of a coupon that won’t scan.
To circumvent the restrictions, often in the name of “customer service” for cost-conscious shoppers, some cashiers will combine the value of multiple coupons using the hand-key function—thus adding the cumulative value as a single coupon. This methodology enables excessive coupon stacking, which can lead the customer to get items for free, or worse, getting an item for free plus additional cash if your policy allows such transactions. As long as the customer’s coupons are (or at least look) applicable, poorly trained or misguided cashiers may think nothing of this violation.
Data presents the easiest way to identify this type of non-compliance. With the right analytics solution, you can identify a variety of anomalies that indicate excessive coupon use, such as:
- More coupons than items in an order (the most direct route).
- Excessive coupon value, especially when considered as a percentage of the total value of the transaction.
- Excessive manual coupon entry.
Better yet, an actionable analytics solution like prescriptive analytics can both identify suspicious activity like the above and alert the right member of your team to investigate.
Single-use Bag Fees
Many localities have instated fees on the use of single-use paper or plastic bags. The fees typically are around $0.10 per bag and must be entered manually by the cashier at checkout. It’s not unheard of for cashiers to fail to charge this fee. There could be a variety of motivations for this, such as not wanting to argue with an agitated customer, not thinking it matters in the grand scheme of things, or simply forgetting. Either way, this non-compliance needs to be corrected before the retailer faces consequences for not adhering to the law.
An advanced analytics solution can help you identify bag fee non-compliance and intervene quickly. One of the best ways to do so is by configuring the solution to monitor the occurrence of bag fees charged per cashier. This analysis can determine a benchmark average and flag when cashiers’ bag fees fall significantly below it. Now, it’s entirely possible that a cashier’s low bag fees are a coincidence, caused by an unusually high number of environmentally conscious consumers. But if a cashier has gone a significant period of time without a single bag fee compared to their peers? That certainly warrants a conversation.
Long waits for returns are typically high on the list of in-store shoppers’ frustrations. While store operations would likely jump at the chance to eliminate a step or two from the return process, we in LP need to continue to help the business manage risk. Is customer service important? Of course it is. But so is preventing fraud – and given the amount of return fraud that slips through the cracks already, we need to be smart about our return rules. Unfortunately, associates may skip some steps in the return process such as capturing proper original receipt or customer information in the name of quickness. These types of shortcuts require quick intervention in the form of retraining, or the involved cashiers may conclude that the behavior is acceptable. Worst-case, they may even make the leap to fraud after finding that there are no consequences for policy shortcuts.
So how can we quickly catch cashiers taking these shortcuts? The answer is simpler than you might think. Of course, we can look for anomalies in the frequency of customer information or original receipt capture. However, one additional interesting method in use by a Zebra Prescriptive Analytics customer involves looking at the time duration of returns. Some transactions are so short they could never legitimately be completed, and in other instances the cashier in question has a pattern of return durations significantly below the benchmark of similar cashiers. This customer has used this methodology to identify multiple cases of both non-compliance and fraud alike.