Three Traits That Characterize Workforce Analytics Success

by   |   August 27, 2013 10:38 am   |   0 Comments

It’s no coincidence that since rolling out its proprietary workforce analytics system, San Francisco-based executive search firm Riviera Partners has cut its search time for candidates by a third and has increased revenue per head by 52 percent over the past year. In fact, when research firm i4cp delved into how companies make the most of their workforce data, stark differences emerged between so-called high-performance and low-performance organizations.

Related Stories

San Francisco recruiter’s predictive analytics target tech talent.
Read the story »

VoloMetrix analyzes enterprise efficiency using email, calendar data.
Read the story »

Four tips for evaluating workforce analytics software.
Read the story »

Focus on: analytics skills.
Read the story »

When asked about the secret of its success, Riviera Partners cited unique factors such as a proprietary workforce analytics system and an impressive database of candidates that enable recruiters to match techies with some of the Bay Area’s most highly sought-after employers liked LinkedIn, Zappos and Dropbox. But, according to Carol Morrison, a senior research analyst with Institute for Corporate Productivity (i4cp) and author of its “Workforce Planning: Data Choices for High Performance” report, there are common traits among companies like Riviera that enable them to achieve workforce analytics success.

Here are the top three data management capabilities demonstrated by companies with an enviable workforce analytics track record:

1. A handle on the right metrics. According to Morrison, drilling down when measuring performance is a key advantage. She says that high-performance organizations “track workforce metrics that speak to performance at the individual level” rather than the performance of an entire department. What’s more, Morrison says particular metrics are more commonly used by these organizations than others. For example, the i4cp report reveals that high performers are three times more likely than low performers to measure new-hire attrition rates—“red flags” that often point to problems with recruitment, onboarding, training and employer branding.

“If you have high turnover among people you’ve just hired, an organization needs to ask itself, ‘Are we sourcing the right people? Does our recruitment process accurately represent what the employee value proposition is? Is there a problem with our selection process?’,” warns Morrison.

That’s no surprise to Riviera which has long been measuring new-hire attrition rates among its clients. By tracking how long the candidates it places stay with a company, Riviera’s recruiters are able to determine “how good we are at the match-making process,” says Ali Behnam, Riviera’s managing partner and co-founder.

2. A refined ability to spot relevant trends. The i4cp report reveals that high-performing organizations understand the value in looking beyond simple numbers in order to identify trends that can impact workforce costs and pinpoint causes for employee attrition. “It’s important to not only collect data and manipulate it but to be able to understand what that data is telling you and then be able to effectively communicate that story within the organization,” says Morrison.

Ali Behnam of Riviera Partners

Ali Behnam of Riviera Partners

For instance, Riviera’s Behnam says, “We have learned simply looking at data points doesn’t tell us the whole story. Rather, analyzing trends allows us to understand over time if we are heading in the right direction.”

Riviera recently crunched preliminary data around its search for talent and discovered “that nearly two-thirds of our placed executives were put in front of our clients within the first 30 days of a search,” says Behnam. By identifying this trend, Behnam discovered that “getting the right people in early and on board makes the experience better for everyone; it expedites the process and minimizes the burden on the candidates and client.”

3. The ability to overcome data management challenges. High-performing organizations in I4cp’s report were more likely to encounter difficulties around data integration and data quality than low performers.  Just as rapper Notorious B.I.G. lamented “Mo Money Mo Problems,” many analytics professionals are singing “Mo Data Mo Challenges.”

“We found high performing organizations are much more likely than lower performers to have issues related to workforce data,” says Morrison. “Lower performing companies are often still in the early stages of trying to gain senior leadership support for workforce planning. Higher performers, however, are looking at, ‘How do we conduct supply and demand analysis? How do we conduct gap analysis?’ They’re looking at problems that significantly involve data. It makes sense that their issues are more complex.”

In fact, the report indicates that 46 percent of high performers cite technologies that do not share data effectively as the number one data issue challenge they face.

To combat this problem, Riveria built a data warehouse that combines internal data such as historical information on candidates and recruiter feedback with external data such as market research. Next, “we take all this information and boil it down to a simple score for an individual that tells us how likely they would be interested in hearing about new jobs,” says Behnam. This “trending score” is then used to target the right people and make better matches – a formula that has significantly helped the company hone its workforce analytics strategy.

Cindy Waxer, a contributing editor who covers workforce analytics and other topics for Data Informed, is a Toronto-based freelance journalist and a contributor to publications including The Economist and MIT Technology Review. She can be reached at or via Twitter: @Cwaxer.

Tags: , ,

Post a Comment

Your email is never published nor shared. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>