Some surveyors still chisel or affix “benchmarks” into stable structures to mark elevations. These fixed positions are useful because they can be assumed to maintain an unchanging relationship with a known quantity of interest, which may not be as easy to observe—like the potentially distant sea level. Once established, one may think that a benchmark only provides a basis for comparison with other positions whose properties are not as well known. But, in reality, benchmarks do more than this, or rather, people do more with benchmarks. They enable maps to be made, routes to be redirected, plans to be plotted, and ultimately, smarter decisions.
In the 20th century, benchmarking came to signify the act of measuring performance or quality versus a standard on the same scale. Applied to an organization, it’s meant to identify best (internal or external) practices and reveal strategies which drive superior results. Over all of these applications, the objectives remain the same: awareness and improvement.
What’s a meaningful difference that separates success from falling short? Or between outperforming, improving on the last measurement, being average, or executing poorly? Or, in simply knowing how you’re trending and where you stand compared to others?
Reference points for surveyors’ benchmarks are useful because they’re fairly stable; geography tends to change at a very slow pace. However, in most other things that concern us, rapid change has become a constant. The ground in this landscape can shift before the seismograph detects its first quiver.
The speed, flexibility and convenience of modern data analysis tools—along with the breadth and depth of data that drives them—allows savvy analysts to construct benchmarks well-suited for a volatile world.
Technology-focused enterprises were among the first to adopt benchmarking under its new meaning, with other businesses following in due course. Now all types of lenders—not just tech-savvy, FinTech lenders—have the ability to quickly access market insights and derive appropriate benchmarks. Today, an ever-growing number of benchmarking tools, models and standards make this happen. The adaptability and utility of the process has increased, while the cost and effort required to gather insights has decreased.
Organizations which embrace benchmarking as part of their standard toolkit tend to benefit from more rapid innovation cycles, increased business awareness, and more unified, focused goals. It’s an easy concept to understand and communicate, promoting greater understanding of an organization’s direction among broad constituencies. Visualization of data via dashboards, canned analytics, charts, and tables make the information easier to absorb and interpret. Knowing what’s needed, where deficiencies are and what has been learned is critical, and reduces confusion and conflict within an organization. One can either see the big picture or focus upon a few elements with minimal translation required. It also makes it possible to communicate with partners in a much more meaningful way.
Let’s take lending as an example.
When deciding how best to allocate resources, you need data which describe how lending activities and portfolio status (number of loans, delinquency rates, roll rates, utilization, etc.) compare to the competition. Knowing where you stand on key metrics compared to lenders of similar size, geography and concentration, or the like, provides proper context and allows for a deeper understanding of the market.
This in turn enables action, whether that involves identifying the root cause of a problem or defining an entirely new strategy.
Different levels of granularity can then be looked at to identify competitive gaps in the marketplace or investigate risk tier performance versus expectations. Lenders can optimize risk-return in a number of ways, including investigating risk tier distributions, evaluating risk among new accounts, understanding credit performance by originating risk tier, or creating new risk thresholds to guide future policy. Ongoing trends can be explained and further understood by lenders as well as their current and prospective investors. New product types for existing customers can be substantiated, and economic opportunities in specific markets or geographies can be more easily sized. Enabled by multiple data sources, various categories (financial—margins, fee income; customer satisfaction—market share, complaint rate; process—service levels, cycle times; product—quality, complexity; environment—market dynamics, changing demographics; technology—cost, service level; people—turnover, morale, skills) can be examined and more educated decisions made.
The value of benchmarking is dependent upon how well appropriate internal and external data can be combined and evaluated, and the resources required to accomplish that. Since the risk and cost associated with a full-scale buildout would be prohibitive for most, it makes sense to consider a multi-phase, long-term commitment to partner with a company which combines the necessary data science expertise, business segment experience, and desired functionality and support in a configurable, on-demand environment.
As with surveyors, lenders use benchmarking to map the present environment and determine a desirable future; its value follows from doing this efficiently, and avoiding the risk of not knowing. And though no mark was chiseled into you, dear reader, I hope this post easily left a lasting and stable impression.