Retailers who have made the jump to modern data analytics solutions know that the retail industry has moved past being overwhelmed by too much data and instead is now entering a time where this important data needs to become actionable. To remain competitive, it is vital that retailers use the data at their disposal intelligently, and data analytics is making this easier than ever.
To see how loss prevention departments are effectively using their data to drive profits and reduce losses, I reached out to three thought leaders in the space—Rick Beardsley, director of loss prevention, safety, and risk management at At Home; Gregg Smith, senior director of asset protection at Five Below; and Scott Ziter, director of asset protection at Price Chopper.
Our discussions included life before data analytics, how their current applications are used, what business impact they have seen, and how they see data analytics evolving over the next few years.
Life before Data Analytics
“For a long time, we used what was built into the point of sale,” stated Beardsley. “Those built-in tools would call themselves exception reporting, but there was no real reporting behind them. If we knew what we were looking for, we could write a query and go look for it, but that’s not exception reporting; that’s just you looking for something based on a tip or a known fraudulent tactic like line voids.”
Smith added that their system, in the beginning, consisted of reports that came in the form of Microsoft Excel sheets from their IT department. “Excel can be useful if you already know what you are looking for, but it is not an efficient way to analyze information. That takes time,” Smith noted.
Both Smith and Beardsley pointed out how labor intensive their methods were before data analytics. Beardsley’s company, At Home, employed a time-consuming, manual process that involved creating queries that they expected would identify various events or exceptions. If nothing came back, they would then have to brainstorm new exception situations and run queries on them to see if their assumptions were right. “That took up a lot of our time,” reflected Beardsley.
Smith noted that he was hired as the first asset protection employee at Five Below. For him, being tasked with building a team from the ground up while also performing the daily activities of an AP department was difficult due to the considerable amount of time he had to dedicate toward mining through the Excel sheets produced by IT.
Things were not much better for those with existing exception-reporting tools either. “From the time I started here at Price Chopper, we have always had access to some form of exception reporting,” said Ziter. He went on to add that even though their exception-reporting tool had its uses, it was outdated and not user-friendly. Price Chopper personnel were limited to the reports that came from their vendor, as well as a few reports that were generated by their IT team. These reports focused on fraud tactics and exceptions that were known to produce some results. “If a new issue arose, it was difficult to research the cause and who or what was contributing to the loss,” Ziter added. This often led to manual manipulation of the data, which resulted in a drain on time.
Though their departmental goals are slightly different, all three loss prevention professionals unanimously agreed that their old systems and processes were not sustainable. For them to grow as departments and companies, they needed something powerful, something user-friendly, and something that would do the legwork of analytics for them so that their teams could focus on solutions and results. They needed a modern data analytics application.
Once the decision was made to start vetting data analytics solutions, all three had to comply with corporate directives as well as their individual departmental needs. Working for a rapidly expanding retailer, Smith knew that he needed to invest in a product that was scalable. On top of this, Smith added his own requirement. “Finding a solution that would allow a small team to make a big impact was imperative,” said Smith. Ziter and Beardsley both echoed that sentiment.
All three had to meet specific financial parameters and include the needs of other departments such as operations, IT, finance, and legal in their search criteria as well. In other words, the product selected had to work for more than just loss prevention and had to allow teams across the business to work smarter, not harder.
A New Era in Data Analytics
The first thing the At Home team noticed about their new data analytics solution was how light it was on their IT department. “The biggest challenge was getting the IT group to move up the priority of our project. They provided us with their requirements and set a limit on man-hours to be spent,” said Beardsley. Thanks to the simplicity of the solution’s infrastructure and the help of the support staff, “we were able to get the system up and running three months ahead of projections,” he added.
Ziter and Smith both had similar stories, and once the solution was up and running in all three organizations, the impact was instant. “Our team became far more efficient at identifying and correcting issues,” noted Ziter. The ability to easily create complex queries and develop personalized 
reports had a big impact on their team’s performance. Ziter also added, “The new solution allows us to focus on resolving root causes of loss, not just individual cases.”
Beardsley added to Ziter’s observations. “Everything we did became much more efficient. Everything the new software presented to us had a reason behind it. The question during analysis changed from ‘Is this actually an exception?’ to ‘How do we resolve this?’ Not only are we able to go through our data extremely quickly, but we are identifying fraudulent employees faster too. This is the most important thing to us, because catching problems sooner is the easiest way to minimize the impact of whatever they’re doing on our financials,” noted Beardsley.
While improvements were being made in their own departments, all three began to notice that the impact of data analytics was reaching far beyond loss prevention. “Not only has the application helped us to identify fraud and fraudulent activity quickly, but it has also helped us to identify operational opportunities,” said Smith. He went on to add that by allowing cross-functional departments like finance, sales audit, and store operations to use the tool, they have 
been able to identify additional sources of loss.
“Our operations team uses the software as well,” said Beardsley. “With operations we have built dashboards for district managers that let them see what is going on in their stores at a glance. Many of them use these dashboards as a report card to rank an individual store’s performance.”
Beardsley goes on to add that the new application is one of the better platforms in their company. It provides quick access to vital information for LP, operations, and even finance and accounting, who use it to find specific data that would never appear in their own applications, such as the utilization and impact of employee discounts.
Of the three, the broadest adoption of data analytics comes from Price Chopper. Ziter noted that throughout the organization there are over 400 users of their data analytics application, which started out as an asset protection-specific software. “Along with asset protection, our legal department, 
internal audit team, front-end store operations, and our field teams all use the application now,” said Ziter.
One of the surprises of our conversation came from Ziter. “Our front-end operations team used a homegrown system to monitor and balance common daily POS activities like refunds, coupon usage, postage stamp purchases, and charity donations,” said Ziter. “The flexibility and ease of use of our new data analytics application won them over, and they opted to use it to replace their homegrown system.”
Ziter went on to add that the other departments who use the application also take advantage of the flexibility and put it to work for their own needs. “Our internal audit team uses it to prepare for required cash and pharmacy audits. Our legal department uses it to research accidents. And our field team uses it to keep a close eye on store performance,” remarked Ziter. The flexibility, power, and ease of use of the application has allowed it to spread throughout the organization.
The Future of Data Analytics
“As data analytics continues to evolve, our ability to use it will as well,” said Beardsley. “As our ability to pull in more and more external data grows, usage at At Home will take off.” He added that data analytics takes the guesswork out of translating raw data because the interface presents their data in a more useful way. “As other departments take notice of that, more will jump on board.”
Ziter added that he sees usage at Price Chopper expanding as well. “We are always looking for new ways that the application will help us perform 
better,” said Ziter. “We are currently revising some of our existing queries and reports to better target issues and opportunities. We are also working to take more advantage of new functionality as it comes out, such as alerts that let specific users know when something needs their attention.” Ziter went on to note that as long as new functionality continues to be added, the application’s use at Price Chopper will continue to expand.
Smith noted that Five Below sees a slightly different future for data analytics within their organization. Of course, they see other departments jumping on board and using data analytics, but Smith plans to integrate the application deeper into the organization by opening it up to store users. “We plan on evolving the process by giving our stores direct access to the application. This way they can get firsthand access to analyze their own data, rather than hearing it from somewhere else in the chain.” Smith added that this will allow decisions to be made even faster and on a local scale.
Smith wrapped up the interview with this remark: “We have had success in sharing our data analytics capabilities with partners throughout our organization. By aligning our goals across the chain, we can equip ourselves with a broader brush to positively impact overall business operation.”
Final Thoughts
After speaking with these three executives and watching what has taken place in retail over the last few years, one thing is clear—the data analytics revolution is here, and loss prevention is at the center of it.
Ziter, Beardsley, and Smith all pointed out multiple times during our discussion that for data analytics to have a real impact, collaboration is key. Loss prevention professionals have always excelled at collaborating with each other on concepts that help them do their jobs better. Now it is time to 
spread that collaboration skill to other departments within your organizations so that you can use the data at your disposal to not only prevent loss, but also improve efficiencies and increase sales.
Beardsley put it best when he said, “There is so much data out there to look at, and you can spend a lot of time going through it. But if it doesn’t help you accomplish a goal, or increase profits, or reduce losses, it’s a waste of time. And that’s where I see the ultimate benefit of analytics. Modern data analytics applications take your data and present it in the form of actionable items that help improve efficiencies, improve profits, and prevent loss.”
Data analytics is no longer just another buzzword. It is the next big thing for success in loss prevention and throughout the retail enterprise.