# ROI of Video Surveillance Made Easy

By John Honovich, Published Jul 25, 2012, 12:00am EDT

"It's just an expense" and "it's a damn cost center" are two big negative perceptions that make it hard to justify purchasing surveillance systems. To overcome this, often buyers want to prove the Return on Investment (ROI) of a system. In this note, we examine the easiest way to do this analysis, looking at examples of breaking down cost and estimating returns.

Many Ways

Financial analysis offers many methods of calculating return on investment (DCF, IRR, breakeven period, etc.). Typically, though, surveillance buyers, even if they use a financial metric, use simple ones. While they lack the accuracy of the more powerful ones, they are easier to calculate and explain.

Key Components

All of them, though, contain two components:

• Costs: How much does the project (in our case the surveillance system) cost?
• Return: How much does the project return? Most often, this is revenue (e.g., a company buys a popcorn machine and then can sell x thousands of popcorn in the next few years). However, in surveillance, this comes more often in the form of cost savings (e.g., a company buys a surveillance system and eliminates 10 car thefts over the next few years).

Returns vs Costs

At the most simple level, the returns need to be more than the costs over a reasonable time period (usually a number of years - 3, 5, 7 etc.). For example, a project that cost \$5,000 but was only projected to return \$4,000 over 5 years would almost certainly be rejected.

However, the requirements are usually much tougher, with projected returns having to be far greater than cost. This is so for two reasons:

• Risk: Projected returns are just estimates and may not be met if conditions change or the estimates are too rosy, etc.
• Time: Money in the future is worth less than money now. If I promise you \$10 in 10 years that is worth a lot less than if I gave you \$10 today.

While it depends on the analyst and specific scenario, a project may need to return multiples of its cost to be justified.

Using the Months Method

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A simple way to calculate ROI in surveillance is to estimate a cost per month (e.g., divide the total cost of the system by the number of months you plan to keep it). For instance, let's say you are planning a 16 camera surveillance system that costs \$10,000 and will last 5 years (60 months). The cost for this system per month is \$167 (\$10,000/60).

For this 16 camera system, if you can justify savings/return of more than \$200 (or \$300) per month, you can build a fairly strong case for the system.

Estimating Savings / Return

The real challenge in surveillance ROIs is estimating how much cost savings or returns one will achieve. Breaking this down into 4 steps will help:

• Drivers: What problems or issues does the surveillance system solve? Make a list of them such as shoplifting incidents, break-ins, vandalism, car thefts, check fraud, employee issues, etc.
• Cost per Incident: Estimate how much each incident costs. For example, \$100 for shoplifting, \$200 for vandalism, \$500 for check fraud, etc. Typically this will be a guess best done by the end user themselves.
• Frequency of Incident: Track how often each incident occurs. For example, is it once per week, twice per month, three times per year, etc.
• Impact of Surveillance System: Estimate how many incidents the surveillance system will prevent. For example, will it reduce one shoplifting event per week or per month?

Pulling this together, you should have (1) a driver, (2) a cost per incident and (3) an impact. For example, shoplifting costs \$100 per incident and one incident can be reduced per week with a new surveillance system. On a monthly basis, this means \$400+ saving/return per month (\$100 per incident * 4 per month reduced).

To get the total projected savings/return just sum up all the drivers and their estimated impact.

Comparison

Once you have your costs per month and your estimated savings per month you can project the return on investment.

Let's say we have a grocery store with 16 cameras costing \$10,000 that we project will eliminate 1 shoplifting incident per week that costs \$100 per incident (these are the numbers we have been using throughout). Here's the analysis:

• Savings/return: \$400+ per month (based on reduced shoplifting)
• Cost: \$167 per month (based on \$10,000 over 60 months)

In this example, the return looks strong because it is more than twice the projected costs.

One can use this process to quickly sketch out and explain the magnitude of savings. You can try out different savings (more incidents impacted, greater dollar value, etc.) and various systems (cheaper, more expensive, etc.).

Rare But Costly Risks

While the above method works well for common security risks (shoplifting, thefts, vandalism, etc.), it works poorly with rare but more dangerous security risks. For instance, many large users deploy systems partially or primarily to stop catastrophes - whether it is a city trying to stop terrorism, a water supply trying to stop posioning, a utility trying to stop power plants from being blown up, etc. With these risks, the probability is so rare that estimating a return is very difficult. While the cost may be simple to calculate, the frequency of an incident is nearly impossible to determine. Is it once every 10 years, every 100 years, every 1000 years? Depending on what you assume, the 'ROI' will differ dramatically.

With such rare but costly risks, usually security spending is driven by regulation, perceived threat and available budget rather than ROI justification. The government may require a certain level of security or provide a subsidy to ensure organizations deploy the security. Equally importantly, funding may rise and fall as events occur (e.g., the difference in security spending in the US before 9/11, after 9/11 and today). ROI justifications are generally not even considered in such circumstances.

Disclaimers / Warning

All ROI projections, even the most sophisticated, are more art than science. The biggest risk is projecting returns/savings. Sales people will push hard to make speculative claims (e.g., if the system prevents one murder, it will pay for itself) or bump up the amount of incidents the system will reduce (e.g., overoptimistically estimating that 2 shoplifting incidents will be reduced per week, instead of just one per month, etc.). Beyond that, it is often hard to predict the impacts of surveillance systems at all, especially with unproven technologies (e.g., video analytics, PSIM, etc.)

A real CFO or comptroller will generally not accept this heuristic, tending to prefer IRR or DCF calculations. While the mechanics are more complex and do a better job of factoring in all costs, even with these methods, the key issue remains is estimating the potential savings or return. The CFO will typically have no idea how the surveillance system works or how likely the system will actually solve security or operational problems. So regardless of how you calculate, the key challenge will always be estimating the financial impact (savings) of the system.