Everyone wants an apples-to-apples comparison when thinking about their financial institution’s productivity compared to others, but most think benchmark metrics are like comparing apples to oranges. How could you compare a $5 billion financial institution to a $500 million financial institution? Many organizations think that they can’t be compared whatsoever. However, benchmark data is a reliable source for financial institutions to use as a signifier to their overall health and productivity, as well as how they measure up to other financial institutions.
The Myth: Benchmark Data isn't Helpful
Many community banks and credit unions believe benchmark data does not provide realistic expectations or numbers that designate their institution’s productivity because it is not an accurate comparison.
The Truth: Scaling Benchmark Data Provide Valuable Insights
The truth is that benchmark metrics can yield meaningful insight for financial institutions because in most cases, the data can be scaled downward or upward appropriately.
For example, asset size per full-time employees (FTE) is a common way to measure and see the scalability of benchmark data. We want to point out that there is some truth when we hear that my $500 million-dollar bank or credit union can’t be compared to a $5 billion institution. However, an experienced benchmark analyst understands that some functions or activities require a minimum number of FTE, but scale across time allowing a smaller institution to continue to grow while not requiring additional staff in these functions. Benchmarks help to define these types of metrics for an organization.
An example of a scalable metric is digital banking accounts per FTE. The difference between the median, or midpoint of the data, and the 75th percentile or “best in class,” is nearly twice as much. Smaller institutions may need one or two FTE to support this function, but based on data from industry benchmarks, some smaller institutions may be able to double in size before requiring additional staff within that function.
Other data is easily compareable across institutions no matter the size. For instance, teller transactions per FTE per month is a metric financial institutions of any size can compare themselves. Teller processes are largely the same across institutions making this metric easy to compare across institutions of all sizes.
The Misconception of Benchmark Data
The misconception that benchmark data is not comparable prevents many organizations from participating in benchmark studies that could otherwise provide meaningful data. Part of this misunderstanding is a lack of knowledge about how specific data points translate into meaningful information. Raw information is not always valuable, but through the use of specific formulas, experienced data analysts can derive meaningful information that scales upwards and downwards across asset classes. Using a third party that has intimate knowledge of the banking industry and understands how benchmarks work provides even greater meaning to an organization.
We recognize that there are differences between large and small financial institutions, both from a scale and an organizational perspective. However, there is always a common denominator that we can use to cover virtually every function of the organization.
Benchmark metrics that are scaled appropriately can provide enough of the apples-to-apples comparison financial institutions are looking for, providing relevant information for staffing and productivity. The greatest benefit of a benchmark analysis is to understand how your financial institution compares to others. Couple this information with an analysis of processes that drive your benchmark numbers, and your organization can build a roadmap for better performance across time.
If you want to learn more about the common myths and misconceptions surrounding benchmark metrics and operational efficiency, make sure to check out Step 2 in our “Profitability Enhancement Playbook.”