December 18 , 2017

7 Metrics That the Most Data-Savvy Recruiting Teams Are Tracking

As seen on LinkedIn Talent Blog (April 18, 2017).

If you closely research (as I have) the top five global companies with the highest market value (Apple, Google, Microsoft, Amazon and Facebook) you’ll realize that they all have three things in common. First, they are all serial-innovation firms. Second, they are all recruiting powerhouses. And third, an important lesson for recruiting leaders, they all excel at innovation and recruiting because they rely on a data-driven decision making model.

So, if you want to match their growth, recruiting domination and innovation rates, you must shift to their data-driven recruiting approach. Without that shift, not only will you consistently fail to recruit top talent, but also you will have literally no chance of landing the more difficult-to-recruit innovators.

This powerful data-driven recruiting approach requires you to shift from the traditional intuitive and past practice-based approach and start tracking metrics beyond the popular quality of hire and time to fill.

Here are seven areas where meticulous measurement and data analysis has made a significant difference for some well-known brands.

1. What parts of the application process are turning candidates away

Data-driven recruiting functions already know that your company’s “speed of hire” is critical, because data reveals that the top 10% of candidates are gone by the 10thday. But few also know that having a complex and time-consuming application processes will cause you to lose as many as 9 out of 10 of your qualified applicants. And as a result, the data-driven recruiting functions at companies like Netflix, Salesforce, Intel and Apple have all reduced their application time in some cases to fewer than five minutes.

Other negative factors will also dramatically reduce applications. Data reveals that many potential applicants would be deterred “if they encountered tech hurdles (60%)”, “if they couldn’t upload their résumé (55%)”, “if they couldn’t follow up on the application’s status (44%)” or “if they “couldn’t complete the application on a mobile device (20%)” (Jibe).

So if you’re not tracking “time to apply” or “applicant drop off rates” you may be losing a large percentage of your potential applicants without even knowing it. And if you assume that the best applicants have multiple choices, these high levels of frustration may mean that the very best applicants will simply apply elsewhere (at your competitors).

2. The tremendous cost of excess position vacancy days

Executives at a Midwestern bank were complaining that they were losing a great deal of money whenever one of their high revenue-generating loan officer positions was vacant. So their recruiting function went to work with the CFO’s office and began calculating the revenue loss.

Well, it turned out to be a $5000 per day revenue loss for every day a position was vacant. Their world-class recruiting leader then demonstrated to management that the total dollar loss for all vacant revenue-generating positions reached into the tens of millions of dollars each year. After “dollarizing” the cost of these excess “vacant position days,” executives subsequently gave recruiting sufficient funds and resources to cut the number of vacancy days in these key positions in half.

Applying data to every aspect of hiring decisions also had a secondary impact, which was identifying that “new hire referrals” produced the highest quality hires from any source. And subsequently emphasizing these referrals measurably increased the bank’s overall quality of hire.

3. What selection criteria matters for finding the best candidates

The sportswear chain Footlocker used data to improve its candidate slate selection. Unhappy with the results that standard resume screening process was producing, they implemented a pilot online skills assessment test. Adding this new step to the process had a huge business impact, including a double-digit increase in sales-per-hour and a double-digit reduction in new hire turnover. The valuable time of their managers was also saved because they had to review many fewer applicants, as few as 3 per opening, down from as many as 300. Rolling this process out across the entire organization will save the company millions.

Other companies have improved the accuracy of their candidate screening process. For example, Google used internal correlation data to determine that many selection factors that they had been using for years (i.e. grades, degrees, test scores and brainteaser questions) had no predictive value.

4. Why new hires leave

If you calculate hiring failures at your firm, you undoubtedly know the cost associated with the early turnover of new hires. And data reveals that one of the frequent causes of new hire turnover is their frustration because they have failed to quickly reach their expected level of productivity.

Google is a benchmark company because they expect HR decision-making to reach the same level of rigor as engineering decisions. Well, Google utilized data to prove that a simple five-step just-in-time onboarding reminder to managers improved onboarding to the point where new hires reached their minimum productivity levels 25% faster. Google also has data in a variety of other areas including predicting which regular employees are likely to quit, how to improve the performance of managers, whether promotions will succeed and how to increase collaboration and innovation.

5. The performance differential of innovators and top performers

Almost everyone agrees that it’s more time-consuming to recruit innovators. Well, one of the ways that companies like Apple, Google, and Facebook justify their focus on recruiting only “A level players” is to calculate what is known as “the performance differential.” This is the measurable increase in output between an innovator and an average performer in the same job.

Steve Jobs at Apple, for example, found the differential to be 25x and Google found it to be an astonishing 300x. Your company should calculate its own differential number. However, I recommend that you save yourself a great deal of work by focusing exclusively on jobs whose output is already measured in dollars (i.e. sales, business development, and collections). When you show executives the tremendous value added by top performers and innovators, you will easily be able to justify the extra budget expenditure that is needed to land them.

This differential is not just calculated by high-tech companies. For example, in the retail industry, the Container Store has found that a single great employee produces the same value as nine OK employees.

6. Employer brand strength

Your employer brand is a firm’s only long-term recruiting approach, so it’s essential that you measure it. Unfortunately, most companies measure the wrong things, like the number of followers, the number of hits or how high you appear in search rankings. But be wary because those are not recruiting results.

Instead, focus exclusively on the recruiting results created by your employer brand. And that means that you should be exclusively measuring the number of job applications that you receive each year. Google is the benchmark firm with the #1 brand, which produces nearly 3 million applications per year. Yahoo, for example, set their benchmark brand impact number to be equal to receiving applications each year that reach 52 times your number of employees. At the very least, measure the increase in applications year-over-year.

7. Recruiting process efficiency

When you examine recruiting processes, they are seldom very businesslike. LinkedIn took note of that fact and they reengineered their process. They strengthened their new hire forecasting capability and improved communications with their business leaders. They also implemented more accountability and performance management within the recruiting organization. As a result of these process improvements and their prioritization they “were able to save 15% of the overall Talent Acquisition budget by dynamically shifting recruiters’ time to the areas of highest need”.

Final thoughts

The latest annual survey from the prestigious Conference Board ranks Human Capital on top challenges facing global CEOs. Initially, you might think that top ranking as a positive thing. But that would be a mistake because Human Capital was classified as a strategic challenge (not an asset). And HR has unfortunately remained as the #1 CEO issue for the last four years running. And since recruiting has the highest business impact of all HR functions, it’s time for recruiting leaders to realize that we need to make a significant contribution to the overall change in HR.

In my research, I have found that the highest impact action that a recruiting function can take is to shift to a data-driven model. So if you are planning to make or continue the transition towards a focus on data, I recommend that you benchmark against the metric driven practices at Google. Fortunately, that’s quite easy to do because Laszlo Bock, their former SVP of HR has written a popular book entitled “Work Rules.” And in the book, he clearly outlines Google’s data-driven practices in detail.

You should also benchmark data-driven recruiting functions at lesser-known firms like Sodexo and the outstanding data-driven work done by Jim D’Amico now at Celanese. I have also been impressed by some of the recruiting metrics now being provided by various vendors, including Visier. And one final thought about metrics, always remember that “you can’t be a champion if you don’t keep score, and real champions keep score on everything”.

Dr. John Sullivan is an internationally known HR thought-leader from the Silicon Valley who specializes in providing bold and high business impact; strategic Talent Management solutions to large corporations. You can follow him on LinkedIn

Image: Pixabay

About Dr John Sullivan

Dr John Sullivan is an internationally known HR thought-leader from the Silicon Valley who specializes in providing bold and high business impact; strategic Talent Management solutions to large corporations.