Emerging from the Loss Prevention Data Desert
Emerging from the Loss Prevention Data Desert: The Value of Data Analytics

March 4, 2021
By Adrian Beck

As William Deming rather pithily put it: ‘without data, you’re just another person with an opinion’. Certainly, within the realm of retail loss prevention, there has been a history of having relatively little data to understand the way in which organisations are affected by loss. For the most part, stretching back over 150 years, one number has been the dominant cipher of all things retail loss – the ‘shrinkage’ number. This much guarded and rarely shared organizational statistic has been the foremost guiding light in illuminating the pathway to managing retail losses. As we now appreciate, this rather one-dimensional and overly crude number actually reveals very little about the root causes of retail losses – it merely offers a convenient bolt hole to record all the stock that a retailer thought they had.

Getting to grips with understanding the where, the when, the who and the why is rarely if ever gleanable from a shrinkage number. But as we have seen from decades of loss prevention surveys, its explanatory paucity is routinely burnished by judicious practitioner guesswork and the imposition of personal preference and prejudice – ‘what do you think causes shrink?’ is a question regularly deployed to generate industry data that transforms the lumpen shrinkage number into apparently investment grade data. Perhaps not surprisingly therefore, the old adage: ‘if you employ a hammer don’t be surprised if it finds nails’ readily springs to mind when reviewing ‘traditional’ approaches to managing retail loss. In many respects, it is perfectly understandable – when you have to operate in what has been called a ‘data desert’, then personal experience and guesstimates is generally all that is available – gut instinct gets deployed to drive corporate decision-making.

customer viewing merchandise

Thankfully, the data desert has increasingly been replaced by a multitude of oases, indeed for some organisations, the situation has moved rapidly from one of famine to feast. No longer is the problem not having enough data but instead having too much – data deserts have been replaced by data lakes and data warehouses, teeming with statistics and potential insights. In addition, the singular source of retail loss measurement – the variation in expected and actual stock holdings, is now but one of numerous indicators that can be employed to map the increasingly complex risk landscape. This has led to concepts such as Total Retail Loss – a typology encompassing over 40 different types of retail loss – becoming a more realistic proposition to operationalise. In addition, these data lakes are now fed by a multitude of anagram-rich tributaries – EPOS, RFID, CRM, IMS, together with video, and incident reporting systems to name but a few.

sales report

Of course, data in and of itself offers little reward, it is through its collation and interpretation that genuine value can be realised. Indeed, to push the analogy further, not only do you need a fishing rod to catch fish in the proverbial data lake, but you also need to know how to use it. This can be seen in the growing employment of data analysts within retail loss prevention teams, providing the capacity to begin to utilise the available data to enable more informed decision making. A key part of this development is the importance of being able to ask the ‘right’ questions of the data – what do we need to know that will help the business to prosper? This always reminds me of the loss prevention executive working in a UK grocery business who one day asked the deceptively simple question: ‘what do we sell through our checkouts for less than 10 pence (14 cents)?’. The answer should have been hardly anything, but over a year 14.8 million products were found to have been sold for less than 10p, generating unknown losses (shrink) of more than £12 million. A subsequent investigation unearthed a major process glitch that was generating these irregular sales, which was duly rectified. So, without asking the ‘right’ question and having the capacity to interrogate the sales data, these losses would have remained unknown and likely attributed to one of the stock ‘causes’ of shrinkage – external theft.

Undoubtedly, the role of the retail loss prevention practitioner is changing, moving away from a function primarily focussed upon thief catching to one that is increasingly oriented towards profit protection. This requires an acute awareness of how business profits are impacted by all forms of loss, and this in turn relies upon not only having access to a wide range of data sources, but also the capability to make sense of them. Perhaps then, we will be able to misquote William Deming and conclude that: ‘with data you are no longer just another person with an opinion, but somebody who can make informed decisions enabling the business to sell more and lose less!’.

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