Curious how your recruitment game measures up against the competition? Last decade, benchmarking became an HR industry standard for measuring aggregate performance for all aspects of HR—from components like salary to overtime, to an organization’s diversity. Benchmarks in years past were often published on a yearly basis and were based on surveys. But now, comparison data available is more comprehensive and up-to-date than ever before.
Many vendors, including Pandologic, now provide benchmarks embedded in their tech to track performance within an organization and beyond. With access to this up-to-date data, there are several key practices (and caveats) to keep in mind when you consider benchmarks in your recruiting strategy.
Use Benchmarks As Starting Points
Every organization is different. While the comparison data may be reliable and accurate (i.e., it is comprehensive and up-to-date) that doesn’t mean the numbers should be taken as gospel. Organizations have to be able to challenge the benchmark data, interpret their meaning, and contextualize it for themselves. You should not simply adopt benchmarks as a given standard; rather, you should use them to gain a better understanding of your internal performance data. It’s important to consider internal data, historical data, and your KPIs to understand what truly makes your organization tick.
So what happens when the benchmark misses the mark for you? One example, cited in Visier’s HR Trends report for 2020, looks at optimal sales performance in relation to sales team size. When the pharmaceutical company Merck investigated the relationship between these two data points, they found sales teams comprised of a smaller number than the benchmark standard drove greater performance for their company. The takeaway here is that while benchmarks can provide insight, you also need to establish ways to test the benchmarks against internal data and find the best practices and standards personalized to your organization. This includes training HR and company leaders on how to understand the data— both cautiously and critically—and to apply appropriate standards in a thoughtful manner.
Expand Your Performance Markers
With increased accessibility, HR departments can regularly utilize real-time benchmarks and both respond to changes and share information with relevant stakeholders. When you combine accessibility with the broadened capability of current technologies to collect more types of data, you simply have better access to better information—you get a broader set of benchmarks and more specialized benchmarks, and you can use them all. You can then create a greater depth to your internal data and go beyond measuring the basics like salary tracking to better understand the parts and particulars of the engine of your organization.
Furthermore, you can also deep dive into the numbers in certain areas. If you want to increase diversity at your company, benchmarks for diversity and inclusion don’t simply ask: How diverse is your company? Exploration can go deeper: How diverse is your company at the level of leadership? With the broader range of comparison data available, organizations have the opportunity to widen the scope of their recruitment strategy and enhance the overall experience of their workforce.
Progress Beyond The Benchmark
KPIs go hand in hand with goal-setting. Once your organization has met the benchmark, now what? Again, you don’t want to accept benchmarks at face-value. The benchmark is not the limit—it’s the average. When you over-perform in one business objective, it is good to investigate the whys and hows using analytics to further drive your next steps. Striving to enhance performance beyond the average and sharing the data and goals with employees can further motivate everyone in your organization to engage in goal-setting practices and help drive business outcomes.
When it’s time to consider benchmarks for your next job ad campaign, look toward options like Pandologic’s pandoIQ, which helps HR teams drive KPIs like cost-per-hire and quality of hire using the power of AI combined with Big Data.