Article

Going Granular: Seeing the ROI in ATM monitoring and transaction analytics

A pair of case study models can help ATM operators estimate the potential financial benefits to their business.

June 23, 2014

by Brad Zaystoff, director of marketing
INETCO

ATM availability is an important metric — and one that is likely to influence a channel manager's performance review. But even when an ATM management system shows green lights, all may not be well within the channel.

Measuring the number of minutes an ATM is down does not accurately reflect the impact of lost revenue opportunities or the inconvenience to the consumer when their interaction fails.

If customers can deposit and withdraw cash, but the check deposit service is down, is the ATM really "available"?

If transactions from a specific card bin range are consistently failing, does it even matter how available ATMs are?

What if customer transactions are completing, but at a snail's pace during peak period?

If a percentage of an ATM fleet suddenly becomes "unavailable," how long does it take to determine whether the problem is with the network, an application module, a third-party service provider, a switch, or the ATM hardware itself?

Transaction level visibility can answer any or all of these questions. But establishing a truly granular level of detail within an ATM network requires an additional investment in real-time transaction monitoring and analytics software. And with financial services budgets becoming ever more constrained, it takes a strong business case to justify new software that provides data that some decision-makers might be inclined to dismiss as "unnecessary minutiae."

But such a case can be built in two parts, each based on bona fide, real-world application.

The business case

The first part of a business case can be built around the ability to troubleshoot and resolve ATM network issues more quickly and with fewer resources.

Consider this scenario:

The current ATM management system has detected a pattern of failed consumer interactions, and the pressure is on to find the root cause and its remedy, and then to restore services quickly.

This can be a labor intensive, time-consuming, trial-and-error process. According to TRAC Research, a market research and analysis company that specializes in IT management, members of a typical IT organization spend 46 hours per month gathered in war rooms trying to determine the root cause of application performance issues.

The gap between problem discovery and problem resolution requires a coordinated effort involving numerous teams. Among others, these might include: ATM operations; network operations; applications support; and third-party service providers.

The inherently fractured nature of this system means that information about the problem is often incomplete. To better understand the issue, teams must either must wait for the issue to reoccur or attempt to reproduce it. All of these activities add significant latency to the problem-resolution process.

Part I

The model below shows the potential financial impact of improving mean-time-to-repair of failed consumer interactions by 75 percent, and reducing failed transactions by 25 percent. The numbers reflect real-world results reported by a provider that augmented its ATM management system with real-time ATM monitoring and transaction analytics software.

Part II

In addition to recovered revenue from improved MTTR, an FI can realize a reduction in the number of staff hours required to address performance issues. More effective troubleshooting results in fewer people involved in war room discussions and issue resolution. In turn, this leads to reduced support costs, as demonstrated in the model below:

Looking at the results from these two models, the FI can demonstrate the following annual ROI:

• revenue recovered from faster issue isolation, $78,000;
• reduction in operational costs, $225,000: and
• total improvement in annual profitability, $303,000.

Beyond creating new operational efficiencies, ATM analytics can also prove valuable to new business initiatives such as improving cash management and customer flow during peak periods.

These models present the ROI for one financial institution focused on a business case built around more efficient troubleshooting and fewer failed customer transactions. Other FIs can use these models, plus their own ATM availability data to make an initial determination whether ATM monitoring and transaction analytics software might provide an ROI worth pursuing.

photo: andrew gustar