Those familiar with exception based reporting systems (EBR), know that the purpose of the tool is to identify POS data that is an exception to normal business conditions. Once a qualified exception is identified it can be investigated to determine the cause of the anomaly. The result of an investigation is often theft, training, compliance and even systemic issues.
When it comes to calculating a return on investment (ROI) for this software, admission dollars is often the main determinant. The calculation is simply the comparison between the total admission dollars (confessed amount by a dishonest associate) identified through the EBR system to the total cost of implementing the tool. Although there is no argument that using admission dollars for an ROI is a legitimate number for your calculation, should it be the only number used regarding theft to help you calculate your ROI?
We recently worked with one of our clients to assist them in developing a stronger ROI for their exception-reporting system. The system has been in place for several years and they were interested in getting some upgrades to support new EBR initiatives. Having a successful program over the years it was difficult to determine an updated ROI on admission dollars alone. They have been detecting and resolving dishonesty faster with the system, which has led to lower admission dollars.
What we developed was an additional determinant in their ROI calculation, called the Predictive Loss Indicator. This indicator shows the potential future cost of a dishonest associate, had they remained with the company and their thefts not identified by the tool.
The Predictive Loss Indicator is a dollar figure determined by the average monthly theft dollars multiplied by the average remaining length of employment. Here is how it is calculated;
- Take the total admission amount from a dishonest associate and the number of months they admitted to theft (when did they start stealing).
- Using both figures, calculate the average monthly theft dollars (how much they averaged stealing in a month)
- Determine the average length of employment for that associate’s position (manager, assistant manager, key holders, associates, etc.). We recommend working with Human Resources to determine the average length of employment.
- Multiple the average monthly theft dollars by the remaining months of that associate’s average length of employment. The remaining dollar figure is the Predictive Loss Indicator.
Let’s look at an example of how this would work.
- A key holder admits to stealing $1,000 in a two (2) month timeframe of their three (3) months of employment.
- The average monthly theft dollars is $500. ($1000/2) The average length of a key holder with the company is twelve (12) months.
- Multiplying the average monthly theft ($500) by that associate’s remaining months of average employment (9 months), the Predictive Loss Indicator would be $4,500.00.
Using this calculation with the number of people detected through the EBR system, it can be alarming as to the total potential loss if a system was not in place, and continuing to provide loss prevention support. Ten key holders alone following this example would be an additional $45K potential loss to the company if no EBR system was in place or working properly to resolve theft.
Like admission dollars, the Predictive Theft Indicator is an estimated amount. Admission dollars are often conservative estimates as it is known that unless there is hard proof to the contrary, most often admitted dollars are less than actual dollars stolen. One may also argue whether or not an associate would continue to steal throughout their employment. Statistics show that it is not often a matter of them continuing to steal but rather how much would their thefts increase if still employed.
Like many ROI calculations, it is up to each company as to whether or not it can be utilized. In this particular situation our Predictive Theft indicator was designed with a strong history of dishonest case analysis. We also worked with human resources and finance to validate our figures, calculations and approach. In the end it was accepted as a number they could use in their ROI calculation for their exception-based reporting tool.
The “true value” of an EBR tool, like many loss prevention technologies and initiatives, can be difficult to measure. Being able to develop ROI measures like the Predictive Loss indicator can help to show a more expanded impact of the tool when fighting for budget dollars. In today’s belt-tightening retail environment, every dollar you can show to be saved can help get you additional dollars to do more.
Written By Tim Casey, Director of Corporate Services