RETEL TECHNOLOGIES

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As the world economy continues its weak recovery, internal shrink and fraud continues to plague both retailers and restaurants as the biggest source of loss for these organizations. The combination of a high volume of cash transactions, valuable and useful product inventories, cash-strapped employees and insufficient utilization of existing deterrent mechanisms has served to increase both the frequency and significance of these loss events.

Fortunately, there are many tools available to managers now to help curtail these losses, starting with understanding the employee motivation to steal at its psychological core, and then understanding how to replace these motivations to steal with motivations to perform.

To explain these motivations, our latest white paper explores the management concept of Expectancy Theory as it relates to employee theft and employee performance.

In this white paper, we will explore:

• The three psychological factors that determine whether or not an employee is likely to steal
• One simple change you can make today to instantly transform high-theft employees into high-performing employees
• How the same forces that hamper employee performance can also hinder management attempts to reduce shrink and increase revenues.

To request this white paper, click here now.

To learn more about Expectancy Theory, click here.

QUICK LINK: Download the whitepaper here – no registration is required.

Within the retail industry, it’s commonly known that internal fraud – that is, losses that occur because of employees – account for the majority of thefts and losses suffered by retailers. With the one exception of organized retail crime, these internal losses are typically the biggest concern for retailers’ loss prevention (LP) and asset protection (AP) departments.

Internal loss comes in a variety of forms. At the simplest level, asset misappropriation activities such as skimming (taking cash before it hits the books) and larceny (stealing cash and product that is already on the books) can be pervasive throughout the organization, from the stores to the warehouse to corporate HQ. At a more complicated level, corruption activities such as embezzlement are often more isolated to senior management levels, and are often very difficult to detect.

In their latest Report To The Nations, the Association of Certified Fraud Examiners (ACFE) surveyed corporations and independent CFEs worldwide to discover three key sets of data:

1. The types of fraud events that organizations typically experience
2. The total dollar amount associated with each type of fraud event
3. The average amount of time it takes to detect a fraud event

The report was both fascinating and sobering. Overall, most organizations lose five percent of their annual revenues to fraud. Factor in the low margins of retailers, and this becomes an extremely significant hit to the bottom line. Even more troubling, however, is that fraud events often go undetected for as long as two years. Early detection, and technology that enables early detection, therefore becomes paramount to organizations that suffer regularly from these losses. It can make the difference between profitability and significant losses.

We have taken the 2010 Report To The Nations and analyzed the key points that are relevant to retail LP and AP professionals. In particular, we look at the impact that early detection has on reducing losses suffered as a result of fraud. The white paper is available for download by clicking on this link, and no registration is required. Please feel free to distribute this white paper, and your feedback is appreciated.

In our last blog post, we spoke briefly about The Hawthorne Effect and how ReTel’s surveillance auditing services can be used as a mechanism to trigger it. In the post, we’ll dig a little bit deeper into the origins of The Hawthorne Effect, how it works, and case studies that reveal its power in operationally-driven environments, such as quick serve restaurants (QSRs) and convenience stores.

The History Of The Hawthorne Effect

At its simplest, The Hawthorne Effect can be described as a change in the performance of subjects under observation, simply because they are aware that they are being observed. In studies performed in the 1920s, researchers were baffled when upticks in performance during a study suddenly disappeared when the study was complete. As it turns out, the test changes made to the observed participants environments had only a nominal effect on their behavior; rather, it was the observation itself that truly had an impact on their performance.

How It Works & Why It Works

In many instances prior to observation, participants in studies were either unaware of their performance and therefore unable to understand whether it was good or bad, or they were aware of their performance but made no special effort to improve it because there was no means of measuring it, and therefore no incentives or punishments based on that performance. Once observation was established, however, participants became more aware of their behaviors, modifying it either explicitly or unknowingly to a higher level of performance. As those early studies showed, as soon as the observation or measurement mechanism was removed, performance soon slipped back to previous, lower levels.

Examples of The Hawthorne Effect in the QSR and Convenience Store Industries

Perhaps the most well known use of The Hawthorne Effect in these industries is with drive thru timers. Prior to the existence of drive thru timers, franchisors and franchisees had no way of understanding speed of service at the drive thru. By default, then, they had no way of providing employees with timing benchmarks or awareness of their performance at the drive thru.

With the installation of drive thru timing devices, certain chains saw an overall reduction of up to 29 seconds per order during peak rush times. Chains such as McDonald’s estimated that for every 6 seconds saved at the drive thru, unit sales increased by as much as 1%. It’s easy to see the impact that an improvement like this can have on a high-volume business such as a QSR.

What is interesting to note about these drive thru timers is that they do nothing else but provide highly visible evidence that the drive thru is under observation for speed of service. It is simply by knowing that they are being measured that the drive thru crews increase performance, which therefore increases sales.

Applying the Power of The Hawthorne Effect Elsewhere

ReTel’s advanced auditing technologies allow organizations to put the power of The Hawthorne Effect to work anywhere in their organization. Similar to the above example, ReTel’s customers have been able to realize significant gains in performance simply by measuring and providing awareness of measurement.

You may be familiar with the classic tale of the boy who cried wolf. As a shepherd, he spent all day watching his sheep graze peacefully, yet on occasion, would cry “Wolf!” to get a villager or two to run down to help. When they arrived, he’d yell “Gotcha!”, laugh, and the poor villager would sulk back to the village, angry that they wasted their time on his joke. Of course, we all know what happened next. By the time an actual wolf came around, and the boy cried “Wolf!”, no one came to his aid. The result? No more sheep, one very satisfied wolf, and an out-of-work shepherd.

If this boy was a video analytics system, he would have what we call a poor signal-to-noise ratio.

Signal-to-noise is one of the general terms used to describe how often an automated alerting system returns a true event versus a false event. It’s also what typically fails these systems in the real world, as system operators learn to ignore alerts because the majority of those received tend to be false. In the surveillance world, automated alerting has primarily been used for video analytics and surveillance system “health check” programs that check assets for operation and uptime. Here’s an example of how they fail because of signal-to-noise issues.

Let’s assume I manage an installation of 100 cameras in a medium-sized corporate facility. I assign one guard per shift to sit in an on-site central monitoring station to watch screens, get alerts, and be prepared as a first responder in case of an event.

First and foremost, I have a health check running on my surveillance system. If a camera goes down, the guard protocol is to receive the alert, check the monitor, verify the problem, and fix the problem (or call the surveillance installer to fix it). I have one of the best in breed health check systems. That means that each camera only generates 2 false positives a day. Over 100 cameras, that translates to 200 false positives a day. Let’s also assume that there are 5 real problems mixed in there. This translates to a signal to noise ratio of 1:40. For every 40 false events, there is 1 real event. The result? When the health check cries “Wolf!”, no one responds.

Next, I have video analytics on 20 cameras outside monitoring a virtual tripwire to indicate a perimeter violation. Here’s where it gets even more challenging. Next to and around the areas that these cameras monitor, I have branches swaying in the wind, animals (maybe even an occasional wolf) going to and fro, and the occasional early morning jogger who seems to like jogging along our fence. Therefore, on a daily basis, each camera is sending 50 alerts of a tripwire violation – that’s 1000 a day! And, on a daily basis, the perimeter is never violated. Not even on a weekly basis. Or monthly. Actually, last I remember, there was a break-in…two years ago? You get the picture.

So what is the solution to the problem of “The Camera That Cried ‘Wolf!’”? Unfortunately, many industry experts will tell you that even the best analytics and health check systems are still a ways off from effectively lowering the rate of false positives that they generate. That’s why ReTel is developing applications that will work right now with our proprietary two-layer auditing system to solve these problems and more. The broadest description of what we are developing would be a video noise filter that separates bad from good, returning only signal to the end user.

And our goal is not to replace analytics or health checks, but to make them better and more usable, so that they become acceptable features of an organization’s security and surveillance system. Bias is already creeping into end users’ opinions on analytics and health checks, which can make future adoption difficult – even after all the wrinkles are ironed out. That is unfortunate, because they can be truly useful tools to help manage an organization’s security and surveillance.

After all, when that real wolf comes, you want to make sure that there is someone there to hear the warning!