LP Magazine

SEP-OCT 2017

LP magazine publishes articles for loss prevention, asset protection, and retail professionals covering shrinkage, investigations, shoplifting, internal theft, fraud, technology, best practices, and career development.

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continued on page 38 BENCHMARKING Beck is a professor in the criminality department of the University of Leicester in the UK where he is primarily focused on research on retail crime and shrinkage issues. He can be reached at bna@le.ac.uk. Palmer is CEO/president of PCG Solutions, a loss prevention consulting, training, and education firm. He can be reached at wpalmer@pcgsolutions.com. Peacock is a visiting fellow at the University of Leicester and strategic coordinator for both the ECR Europe Shrinkage and On-shelf Availability Group and the Retail Industry Leaders Association Asset Protection Leaders Council in the US. He can be reached at colinpeacock@hotmail.co.uk. All are frequent contributors to both LP Magazine US and European editions. Understanding Data Analytics in Loss Prevention T he focus of our next benchmarking survey will be data analytics. In our survey of loss prevention practitioners, this topic was ranked second highest after emerging technologies in terms of interest. Prior to undertaking the survey, we thought it important to first map out what the term "data analytics" may mean to the loss prevention industry—it is certainly a phrase now widely used but is open to a wide range of interpretations and definitions. Equally, the way in which data analytics is performed can vary enormously, ranging from something as simple as an Excel spreadsheet sent to store managers to the development and use of customized systems that integrate data flows from across the entire retail business. Moreover, understanding the range and breadth of data sources that can be used as part of data analytics performed by the loss prevention function is also important. Working in a Data Lake In the not too distant past, loss prevention was often described as "living in a data desert" with inventory-driven shrinkage numbers, which were only available a few times a year, being the primary driver of most business intelligence. Fast forward to today, and instead of deserts, retailers now refer to having "data lakes," vast quantities and types of data covering many aspects of the operation. While undoubtedly preferable, moving from a state of data famine to data feast presents its own challenges in terms of prioritization, management, and control of the data. The term data analytics is typically used to describe the collection, interpretation, and dissemination of data in order to describe, predict, and improve the performance of a business. Analytics can be undertaken in many ways, but it is important to distinguish the difference between it and other forms of data-driven systems that provide routine alerts and responses, such as exception-based CCTV systems and EAS alarms. These types of systems routinely generate "data" upon which individuals may react, such as a security guard responding to an alarm at a store exit triggered by an active EAS tag or a member of staff approaching a customer who has triggered an alert at a smart shelf. However, the process typically lacks any form of analytical interpretation. Certainly though, the aggregation and subsequent analysis of this type of data would be data analytics—the key difference being the steps taken beyond simply responding or reacting to data-based prompts. It is also important to distinguish the difference between data analysis and data analytics—the former is the interrogation of data sets, while the latter is viewed more broadly as the analysis, interpretation, and use of data sets to make better informed business decisions. In addition, while data analytics can be used on single data sources, it is normally associated with multiple data sources and the use of advanced statistics and predictive models. In this respect, data analytics moves beyond simply answering simple questions from data sources—how many refunds store X performed yesterday—to using the data sources to enable the business to make better and more-informed decisions—is the current refund policy being applied correctly across the business, and if not, what changes need to be made to ensure that it is? How the process of data analytics is performed varies enormously across the retail industry. Some companies prefer to "build" their own analytics capability internally, using the expertise and resources available within their businesses, while others opt for using third-party technologies. A visit to any of the major retail loss prevention conferences and associated exhibit halls reveals a plethora of providers now offering a wide array of data-analytics packages. Others adopt a blended approach using internal resources for some analytical functionality and 36 SEPTEMBER-OCTOBER 2017 | LOSSPREVENTIONMEDIA.COM In the not too distant past, loss prevention was often described as "living in a data desert" with inventory-driven shrinkage numbers being the primary driver of most business intelligence. Fast forward to today, and instead of deserts, retailers now refer to having "data lakes," vast quantities and types of data covering many aspects of the operation.

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