Retail Data Analytics: A Straightforward Breakdown of Types and Sources

Preliminary analysis of how LP teams are using retail data analytics suggests it is being used in at least three ways.

Analytics Data Loss Prevention Strategy Retail Asset Protection, retail data analytics

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 external systems for other elements. In addition, the location and availability of the human resource to perform the analysis and interpretation steps of data analytics varies considerably—this may reside in the IT department, it could be a specialist function within the loss prevention team, or it could be a service provided by a third party. It could also be a task carried out by a full-time specialist or be something tagged on to the duties of staff employed to perform a range of tasks.

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Analytics Uses

Preliminary analysis of how LP teams are using data analytics suggests it is being used in at least three ways, based on the type of analysis being performed, the frequency with which it is done, and the type of employees using it.

Operational data analytics—Typically, automated systems based on multiple data sources and computer algorithms designed to push results to a given audience to trigger organizational responses. This can take the form of data dashboards where the intended audience is guided both in the interpretation of the results and how they might best react to them in order to make business improvements. Frequency of use: daily or weekly. Typical user: store managers or LP associates in field or store-based roles.

Tactical data analytics—The use of data streams that can be interrogated by LP teams to resolve specific issues more quickly and efficiently, for instance the investigation of specific anomalies such as above-average rates of refund frauds or out of stocks. This type of analytics is typically pulling data from business systems. Frequency of use: on an ad hoc basis. Typical user: LP investigators or regional management.

Strategic data analytics—The assessment and review of multiple data sources to enable the LP senior management team to develop medium- and long-term strategic decisions for the business. This type of analytics is typically pulling data from business systems. Frequency of use: quarterly or yearly. Typical user: senior LP executives.

One of the key elements in these three types of analytics is the extent to which the systems employed are used to push or pull data to recipients. As can be seen, a functionality increasingly offered by third-party analytics providers is the collation, interpretation, and dissemination of operational data analytics, typically to store managers, that will not only give them data specific to their situation but also potential interventions that may improve their performance.

This data is largely generated automatically and is “pushed” out by the system. For some, this is increasingly being described as “prescriptive analytics”—in effect data analytics but with greater emphasis placed on helping the end user improve business performance. At the strategic data analytics level, where the data is more likely to be “pulled” from the system, some companies refer to the use of “predictive analytics”—another variation on data analytics where the various data streams are collated and analyzed with an end goal of making predictions about what future key data indicators are likely to be. For instance, stores may be offered estimates of what their levels of shrinkage, refunds, or cash losses are likely to be based upon historical data and other changes to their environment.

Data Sources

As mentioned earlier, the range and depth of data sources used in data analytics by LP teams can vary enormously depending on the scope and nature of the department, the data streams readily available within the company, and the type of IT hardware and software being used. Detailed below are various data streams most likely to be used in data analytics:

  • Security systems and operationsCCTV images; EAS data; access control and alarm records; investigation results; records of internal and external theft; safety incidents; workers compensation; general liability.
  • Store operations data—Stock audit results, stock adjustments; product damages data, spoilage data.
  • Point-of-sale data—Transaction data, refund information, self-scan audit data.
  • Payment data/cash control—Cash over/shorts; deposit shortages; check write-offs; gift card/SVC-losses or fraud; credit card write-off; card payment information; credit card charge backs.
  • Employees—Staff turnover; performance reviews; management and employee tenure; staff engagement/morale survey results; number of full-time versus part-time staff.
  • Crime data—Local police crime statistics; third-party risk models.
  • Store profile—Type; size; sales volume; mall, strip, or street; layout; number of entrances; age of store.
  • E-commerce data—Customers frauds; credit card charge backs; damages; errors; rate of returns.
  • Supply-chain data—Unknown losses; vendor short shipping; picking accuracy; in-transit losses.

As can be seen, the potential data sources are many and varied—the data lake is increasingly fed by myriad data tributaries. Undoubtedly, these suggestions are but a snap shot of what some companies are now using, but the power of data analytics is to enable retail executives to begin to make sense of this wealth of data to improve the quality of the decision-making in the business.

Check out the full article, “Understanding Data Analytics in Loss Prevention,” for more context on working in a data lake. The original article was published in 2017; this excerpt was updated June 5, 2018. 

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