We find ourselves living in a time when nearly every decision we make is recorded. Where we drive, what we buy–all this data is collected somewhere. We permit it in the vague hope that somebody on the other end is being responsible with our information.
The loss prevention industry is now a “somebody” on the other end. By nature a risk-averse beast, the LP industry is well suited to the task of playing steward to this enormously valuable set of big data. LP is also well suited to lead others toward responsible use of retail data analytics.
Analytics is the side of big data that doesn’t have to worry as much about risks. Analytics plays with the data, extracting patterns and putting them to useful ends. But guarding the data—cyber security—is outside the scope of analytics. Nevertheless, it’s important to keep in mind during any discussion of analytics that all of the data comes from somewhere and could have wide-ranging, possibly calamitous effects if not well protected.
The modern data-focused trajectory of retail has accelerated its own pace of change and with it the rate at which a response is necessary in order for a retailer to remain competitive. “The traditional LP person is focused on tangible things in brick and mortar,” said Tom Meehan, CFI, Bloomingdale’s corporate manager of data, systems, and central investigations. “But today, by the time your loss is tangible, it’s already out of control.”
With the ongoing shift from conventional channels to multichannel, omni-channel, and now unified commerce, staying on top of the data—data that’s coming in from the many disparate avenues of the business—is essential to serving the multitude of platforms that consumers have come to use. “If we follow suit with the industry, we’re going to need to utilize data more to be able to make better business decisions in real time, especially given the omni-channel environment,” said Meehan. “If I didn’t utilize data, I wouldn’t be able to make business decisions. Behavior analytics is, in my opinion, the key to success in our future. I’m a big proponent of focusing on deviant behavior to identify anomalies. It’s looking at the behavior that’s driving the data. What our data guy says is, ‘There’s a lot of noise, but it’s all we have.’”
How Retail Data Analytics Works
Using computers to search for patterns is all analytics really is. When an LP professional on the floor in a brick-and-mortar store notices certain tells common to bad actors in their stores, or gains a sixth sense when it comes to intuiting an investigation, he is taking inputs of data from his eyes and ears and, consciously as well as subconsciously, finding patterns in that data. Analytics does the same thing, just using computers instead of the human brain and using hundreds or thousands of data feeds from all across the retail organization instead of human eyes and human ears.
Analytics has gained a certain mystique, surrounded by esoteric terminology like “Markov chain Monte Carlo sampling” or “traversing million-dimensional Bayesian solution space.” And, true, the actual methods behind the approach—what goes on under the hood—is something that requires an advanced understanding of mathematics and statistical methods to really get. But what analytics does is quite simple. What analytics does is automatically, and in real time, look at the cornucopia of data streams available and search for patterns that have an impact on profit.
Retail data analytics supports the eyes and ears of an LP floor team member or investigator with computer files: point-of-sale (POS) feeds, employee time clocks, direct-store-delivery (DSD) manifests, inventories, and other kinds of data central to the running of the business. It can also use less obvious data sources. Video analytics can pull out information like average group size of customers entering the store or heat maps of which aisles or displays customers spend the most time around. Floorplans can be overlaid with all sorts of information pertaining to the within-store geographic centers of profit and loss. Even social media can be used. Crawling Twitter and Facebook posts looking for mentions of the company or of key brands can be used to predict when spikes of sales or loss occur and help illuminate why they occur.
These data are usually available as time series, that is, as data points connected to a timeline. So by lining up the timelines of different kinds of data beside the timelines of profitability, you can see which variables impact shrink as well as which don’t matter as much. Analytics just takes this concept to an extreme of sophistication, comparing thousands of variables, seeing if co-variance between a group of variables can explain profitability fluctuations if a single variable cannot, and searching for patterns that might not be immediately apparent, like if an indicator from six months ago is impacting shrink today. Watching these leading indicators closely can help ensure that the day six months from now where the loss would have occurred never actually comes. Retail data analytics enables the business to act predictively and proactively, rather than descriptively and reactively.
Some indicators may not be direct in their influence on shrink. “We find that high cash over and short activity points to high shrink,” said Johnny Custer, LPC, CFI, director of analytics at Sears, “not necessarily because of direct effects, but because it shows there’s not attention to detail. And if there’s not attention to detail at the register, then there’s not going to be attention to detail for the more complicated things.” In such cases, addressing that one issue won’t fix the shrink problem since the root cause is being ignored. But they are still valuable for the information they provide.
“Look at the data points found throughout your enterprise, such as POS, inventory management, and others to see which ones truly have the highest impact on shrink and profit erosion,” said Custer. “Sometimes things like an employee morale test can act sort of like a profitability mood ring. In instances like that, where you can isolate and measure some of these more ‘obscure’ data points, it can show you specifically where to focus your efforts.”
When constructing an analytical model, you’re trying to isolate which variables have the most explanatory power when it comes to the factors you’re interested in—profit and loss. But these things change over time, as do the contributing factors. Furthermore, the algorithmic methods and the loss prevention software tools used are continually improving. What this means is that model building in retail data analytics is not a one-time thing. It’s not like constructing a building; it’s more like the inventory on the shelves inside the building. You’re constantly adding things and taking things away to see what’s most successful.
“One of the main pieces that is important to communicate is that you’re building a predictive model,” said Custer, “It’s a growing, organic report. We’re always adding to it, taking things away, and modifying. What’s important in December might not be important in August. After a few years, when your loss has stabilized, it becomes as much or more about sustaining those results.”
Keeping Up with Retail Technology Trends
As the LP function continues to morph into something more integrated with the digital world and the vast and varied streams of data associated with that world, the skillset required of LP professionals also changes. “In the past,” said Meehan, “we’ve always taken LP people and tried to make them analysts. We’d take a guy who was great in the field and put him with a guy who was great at Excel. I’ve found that it’s a lot harder to teach a field LP guy statistics or advanced mathematics than it is to take a stats guy and teach him how we catch bad guys.”
Each organization will find a different balance between training field LP professionals in analytics and training analysts in fieldwork. “The older generation is very much about LP fundamentals—investigations, catching bad guys, being safety-minded,” said Custer. “Then we have a very new crop of people who are 100 percent data-centric. We have to meet in the middle. I worry that if we go too far to the analytics side, then we lose what we’re really good at. There’s always going to be a need for that ‘gut feeling,’ a need for good investigations. I worry what happens when the new crop of LP personnel are only about analytics. I think anyone entering the field, whether straight from school with a statistics degree or coming from law enforcement or military, or from elsewhere in retail, they need the fundamental, foundational LP view. And they also need to get a comprehensive understanding of the company as a whole, not just catching bad guys.”
The opportunity now also exists for enterprising people either entering the profession or already on the LP path to aim toward exposure and training in as many facets of the job as possible, knowing the role that retail data analytics will be playing in the future as well as the importance of the stock-in-trade in the field. Likewise, the opportunity exists for forward-looking companies to train the next generation of industry leaders holistically, creating opportunities for employees to have exposure to the role through a variety of lenses. Firsthand experience on each side could strengthen the position of the employee in all roles they perform, setting up the organization for future success.
For the full article, check out “Analytics,” which was originally published in 2016. This article was updated February 21, 2017.