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According to recent statistics provided by the National Retail Federation, $45 billion dollars in inventory “goes out the door” every year in the United States.
And that number isn’t getting smaller any time soon. 48.1%—nearly half—of retailers surveyed by the NRF reported increases in overall inventory shrink.
Yet loss prevention budgets remain flat, and more than two-thirds of those surveyed said only loss prevention staff are allowed to make apprehensions.
In other words, retailers are finding themselves forced to do more with less… And they’re at risk of losing the war against shrink.
But a new weapon is emerging that might just even the odds and help companies like yours win the battle.
That weapon? Predictive analytics.
Reports detailing shrinkage on a monthly, quarterly or yearly basis have their place, but they’re backward-looking.
They don’t provide any insights you could use to determine what your inventory shrink will look like in the future. They’ll tell you what products were shoplifted, for example, but if you’re searching for information about what to protect during the upcoming season, you’re out of luck.
Enter predictive analytics—the science of using big data to examine patterns and anticipate how those patterns will play out in the future.
Predictive analytics can help you compile and understand thousands of unrelated data points and boil them down to intelligence you can act on. And by combining that kind of data analysis with the loss prevention work you’re already doing, you can do a better job of identifying—and reducing—shrinkage.
A PwC study reports that 90% of loss prevention professionals still use Microsoft Excel to analyze data, with 72% using analytics tools such as SAS and SPSS. These, obviously, are not predictive analytics tools.
Only a third of the PwC respondents reported using kinds of sophisticated business intelligence tools that could help them to identify, predict, and prevent shrink.
What if you could identify theft, fraud, and shrinkage trends—and project them out to next quarter or next year?
Predictive analytics isn’t just about trend-spotting; with enough time and dedication, you can do that on your own.
But where data might tell you “These products, in these settings, and at these price points frequently get stolen by organized retail crime,” predictive analytics opens up a whole new world.
With predictive analytics, you could examine those theft patterns and determine previously unrealized relationships—for example, between apprehended shoplifters and a larger organized retail crime ring, or between one type of theft and another.
Knowing all this information in advance could help you display merchandise in a way that would entice a suspected gang to make their move—but allow you to prepare ahead of time to better track and surveil suspected members after they fall for your trap. Suddenly you’re not just stopping one incidence of shoplifting, but busting open an organized crime ring.
Reduce the time it takes to find root causes of shrinkage, use data to develop strategies to stamp it out, and then measure the effectiveness of those strategies faster than you ever thought you could. Predictive analytics makes it all possible.
While 97% of respondents in the PwC survey used software to monitor internal and external theft—and 86% used it to enhance their loss prevention strategy—only about half were doing anything that looked like predictive analytics.
Which half are you? Take a hard look at your organization and ask yourself whether you’re adequately prepared with predictive analytics tools to meet the ever-increasing threats posed by internal and external theft, administrative errors and vendor fraud.
Consider the controls and policies you have in place to mitigate shrink. Do they reduce it to a level that your leadership and stakeholders consider acceptable? Could your efforts be improved by greater visibility into patterns and trends—and more effective strategies for the future?
If so, predictive analytics could be a powerful tool to help you get ahead of shrinkage before it happens.