Analytics is a word that elicits many different reactions based on who we are and what we do. Some of us visualize a sea of numbers, records, and formulas that can drown us in an overwhelming wave of statistics. Some picture “techies” with white shirts and pocket protectors, pushing theories and designing algorithms that most of us need and few of us understand. Others simply think it’s complicated and difficult to comprehend and apply in everyday, real-world situations. Nearly all of us realize the value, but still some of us shy away from the term like it’s a four-letter word.
The truth is, analytics has always been a critical aspect of loss prevention and retail as a whole. We could never survive as businesses—or as professionals—without the use of analytics. Yet while the use of data analytics has made huge strides with the help of today’s technology, and in many ways the science has taken on new and different meanings, we can never afford to lose sight of just how important it is to everything that we do.
As we’ve matured as professionals, most of us grew from humble beginnings of dealing with shoplifters, to the challenges of dealing with internal theft and the development of interview skills, to the more global retail perceptions of controlling retail shrink and the overall profit enhancement of our businesses. Each step of the way, analytics have been part of our thought processes and our career development as much as it’s been part of our everyday job responsibilities. We might not have called it that, but it’s always been at the heart of who we are and what we do.
As the industry has matured, data analytics have been a cornerstone of our evolution from a security presence to a professional partner in asset and profit protection. The ability to use the information available to us to learn the business, identify issues and areas of opportunity, apply that data in meaningful and productive ways, and drive results remains essential to the ongoing progression of the profession. This has given us perspective as well as credibility, providing value beyond the numbers.
Applying the Information
Especially valuable in areas rich with recorded information, like retail, organizations may apply analytics to business data to describe, predict, and improve performance. It’s used in every aspect of the business, from purchasing and replenishment to identifying customer trends, shopping patterns, and customer-service needs. Sales performance, supply-chain analytics, store assortment and stock-keeping (SKU) optimization, marketing plans, price and promotion modeling, responding to new business trends, sales force optimization, even the temperatures maintained in our stores are all a product of data analytics.
Analytics is the process of uncovering meaningful patterns found in available information to help us make better, more meaningful decisions. It involves the careful research or examination of available information, discovering facts, interpreting the details, and helping us draw conclusions about the subject at hand. In loss prevention, isn’t that process at the core of every consequential investigation that we’ve ever conducted? And it’s so much more than that.
Analytics often involves studying past historical data to research potential trends, analyze the effects of certain decisions, and evaluate the performance of a given tool or scenario—the goal being to improve the business by gaining knowledge, which can be used to make improvements or changes. In loss prevention, isn’t this specifically the steps we take when we perform an audit? Shrink numbers are a product of data analytics, as are exception-based reporting and fraud analytics. We use analytics to monitor everything from our alarm systems to our safety programs.
Today It’s a Different World
Okay, so today it’s a much different world, with a more robust agenda. Retail isn’t what it used to be. Loss prevention isn’t what it used to be. Some people will correctly argue that this description of data analytics isn’t exactly what they had in mind. Rather, they turn to current methods and computer science—more advanced types of data analytics that include:
- Data Mining—sorting through large amounts of computerized data sets to identify meaningful and useful trends, patterns, relationships, and other information to enhance decision-making and other useful purposes.
- Predictive Analytics—a form of analytics using a number of advanced statistical techniques to conduct queries used to identify trends, patterns, relationships, behaviors, risks, and opportunities.
- Machine Learning—an application of artificial intelligence (AI) that provides software applications with the ability to automatically “learn” and improve from experience without being explicitly programmed. Focusing on the development of algorithms and computer programs that can access data and use it for statistical analysis, it allows computer systems to adapt to new data without human involvement.
- Text Mining—a process that involves analyzing text documents aided by software that can identify concepts, patterns, topics, keywords, and other attributes in the data. This requires sophisticated analytical tools that process text in order to glean specific keywords or key data points from what are considered relatively raw or unstructured formats.
- Big Data Analytics—the process of collecting, organizing, and analyzing large sets of data from a wide variety of sources (big data) to discover patterns and other useful information that might provide valuable insights into the business. Big data analytics can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions.
All of this may sound extremely complex and even a little intimidating, and all of these methods are being used in the retail industry. But the truth is, technology continues to impact all the tools of the trade, and analytics is just another piece of the puzzle. We have subject-matter experts in computer science, statistics, and mathematics that help us build programs and use these tools to help improve operational efficiency in just about every aspect of what we do, and this is a good thing. But we still have to do our part and continue to learn and grow with the needs of the business.
For example, some among us can still remember the days of searching through mountains of refund documents and miles of register tapes trying to uncover patterns and solve theft problems in our stores. Then somewhere along the way, we were given the gift of exception reporting, taking what once took hours and even days to review and turning it into a review of documents that took a few seconds to generate.
This by no means took away our jobs, but it did mean we had to modify the way we looked at how we manage the loss prevention process. We had to make adjustments to better fit the new standard of performance. Technology changed our approach and made our jobs much more efficient. It made us better, allowing us to focus on other things and be more productive. It has actually helped us gain additional credibility as we gain responsibilities and take on a more prominent role in managing the profitability of the business.
Let’s Move Forward
In the world of retail loss prevention, we will continue to apply analytics in order to describe, predict, and improve the performance of the company. But the world isn’t going to slow down for any of us. Not only won’t we see that new standard of performance ever go away, but also it’s only going to speed up as we move forward. Yet we shouldn’t pretend that this hasn’t been the case all along. The stakes might be higher and our roles are going to continue to change, but that’s only because they need to.
Just don’t be surprised if one day you look down and find you’re wearing a pocket protector. At least we’ll make wearing that pocket protector look good—if that’s possible (probably not).
This article originally appeared in Loss Prevention Magazine.
