6 Ways That Prescriptive Analytics Can Help Retailers and Grocers Ensure Policy Compliance by Employees and Customers Alike
A couple months ago I shared some of the ways that retailers can optimize inventory performance in periods of unexpected surge demand. However, keeping shelves stocked has not been the only challenge to emerge amidst the COVID-19 outbreak.
Compliance has always been a huge priority for retail. The very nature of the industry – low margins, high risk, high complexity – mandates setting protocols and other rules at every level of the business. But, amid the uncertainty of the current global climate, those protocols are now more important than ever. Retailers must prioritize the implementation of measures that ensure store compliance with local social distancing mandates, cleaning recommendations and curfews.
When employees follow the regulations and rules you set for them, your business is better poised to weather the impacts of COVID-19. When they don’t, severe problems can occur.
You see, there’s a key reason why compliance has traditionally been very difficult to ensure. Employees sometimes (mistakenly) believe that a single act of non-compliance (their own) is insignificant in the grand scheme of things.
What that philosophy fails to take into account is that retailers fail by death of a thousand cuts. In other words: yes, that one employee’s act of non-compliance may only have a small impact (unless of course that non-compliance breaks a law, which could potentially result in tens millions of dollars’ worth of fines). But that same, small act of non-compliance will amount to huge losses if the employee continues it – or worse, if others do the same.
Of course, some might say that guaranteeing 100% compliance is impossible. That all you can do is reinforce policies frequently and then hope that employees understand the significant implications of even a single, small instance of non-compliance – and that may be true. However, sometimes non-compliance is purely accidental. For example, the store associate may have been distracted helping customers and forgot to disinfect the bathroom at the designated time, or the cashier was rushing to checkout a customer and didn’t count the number of restricted-quantity items in the cart. Alternatively, the manager may have thought a task was low on the priority list because headquarters never told him otherwise. In other words, it is completely possible to improve compliance if you can see when non-compliance violations occur and prompt an immediate investigation and resolution action. That’s where prescriptive analytics comes in.
How Prescriptive Analytics Enables Retailers and Grocers to See, Analyze and Act on Non-Compliance Issue to Improve Store Safety and Shopper Confidence
Numerous retailers around the world who used Zebra Prescriptive Analytics pre-pandemic to monitor everyday compliance issues (like improper use of manager codes, safety violations, etc.) are now using it to monitor coronavirus-related health and safety measures as well as transactional limitations implemented to protect supply chains and operational integrity. Every violation leaves a subtle, but very distinct, anomaly in a retailer’s data whether it’s an excessive transaction rate indicating non-compliance with social distancing or a suspicious birthday input suggesting an illegal alcohol sale. Even a manual input/override can be a red flag that an associate is trying to bypass quantity limits and scan additional sanitizer or disinfecting wipes for a customer. In every instance, Zebra Prescriptive Analytics uses artificial intelligence and machine learning to comb through data, identify those anomalies and alert retailers with information about how to resolve them quickly and accurately before losses can increase any further.
Here are some specific non-compliance examples that retailers are addressing with Zebra Prescriptive Analytics:
Excessive quantities
In times of peak demand, it may be necessary to impose quantity limits on essential items to prevent “panic buying” from wiping out your entire inventory. But how do you enforce it? Your first thought might be to reconfigure your point-of-sale (POS) terminals to recognize these limits and disallow any excess purchases, but that can take weeks. Many retailers have deployed this pattern to quickly identify violations while they wait for the POS change. This allows their asset protection teams to intervene with retraining or disciplinary action, if necessary. The pattern can be scaled up or down to monitor violations per item, per cashier, per store or per district to identify the worst offenders.
Improper returns
As a precaution during unique health situations such as COVID-19, retailers may suspend returns to avoid spreading disease. Because reconfiguring every POS chainwide to ban returns takes time (assuming it’s even possible), this pattern flags any associates who process returns while the POS fix is pending. By identifying violators in real time, managers can intervene before the practice can continue, thereby decreasing risk.
Repetitive/suspicious birthdays on alcohol and tobacco sales
When controlled substances like alcohol and tobacco are in high demand, cashiers tend to try and save time by neglecting security protocols; for example, failing to check IDs. That cannot be allowed to happen, as the financial penalties can be severe. This pattern was configured for a retailer who was facing millions of dollars in Food and Drug Administration (FDA) fines for its cashiers selling alcohol to minors. It analyzes the customers’ birthdays that the cashiers enter after checking IDs. Any cashier who enters the same date multiple times per shift, a clearly fake date (like 11/11/11), or their own birthday, among other suspicious entries, is flagged as an opportunity. The retailer identified over 500 cashiers meeting this criteria within just 24 hours, and it’s still identifying many more. The retailer also presented this pattern in court to show the judge they were trying hard to correct the problem. The judge agreed and significantly reduced the retailer’s fines in acknowledgement of its efforts.
Items with above-average damage rates
Every item counts when surge demand hits, and it’s crucial to know what is happening to each and every SKU within the walls of your stores. If an item is being written off as damaged more so than others, associates may not be complying with handling procedures for delicate items. You need to know about unusually high damages rate right away and intervene before your losses increase. This pattern first looks at damage rates across all your stores, not just one specific store, to determine a benchmark average. It then alerts the right people to investigate when damage rates on any given item exceed this average at a given location, empowering them to stop the losses quickly.
Stores operating outside posted hours
Sometimes, high demand forces retailers to shorten their operating hours to allow more time for restocking. Other times, the government may impose curfews due to public health and safety orders or other events. Either way, retailers need to ensure their associates are adhering to these restrictions. This pattern flags stores who disarm their alarms outside of posted operating hours, which can indicate a training gap, payroll fraud or theft.
In one case, the pattern alerted an asset protection investigator to a store whose alarm had been disarmed at 4:00 a.m., three hours before opening time. Checking CCTV footage, the investigator saw an assistant manager entering the store with several family members. They left 20 minutes later with nearly $1,000 in merchandise, after which the assistant manager simply re-armed the alarm and locked the door. The assistant manager and her family members were prosecuted, and all the stolen product was recovered.
Failure to enforce social distancing
When public health is a concern, governments may impose “social distancing” mandates requiring people to stay a certain physical distance apart. With the right pattern of behavior data, you can help enforce this rule and keep your customers and employees safe. This pattern calculates average hourly transaction rates and alerts managers to any associates or stores that exceed the average. A cashier with a high transaction rate may not be reminding customers to obey social distancing as required or sanitizing the register belt between each customer. With this information, your managers can respond accordingly.
Zebra Prescriptive Analytics can also synchronize with data from locating devices or mobile computers to ensure employees practice social distancing themselves. For example, if two or more devices remain within six feet of each other for an excessive amount of time (as determined by a public health authority or your organization) and users don’t respond to audible alerts warning them to separate, the users’ information is anonymously captured and stored in a database as a “proximity event”. Should an employee be diagnosed with COVID-19, a manager could then login to the database to securely identify any associates who had high-risk contacts with the sick individual (i.e. a recorded “proximity event”) and alert them to quarantine and get tested, if recommended.
In other words, prescriptive analytics gives you a wide – yet targeted – set of tools to strategically identify non-compliance violations, investigate why they are happening and then take proper action to prevent a re-occurrence. This, in turn, will help you ensure compliance no matter how many policies are in place or how frequently they may have to change.
If you’re interested in learning more about how prescriptive analytics can help improve compliance in your stores, please contact my team here.
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