Insights

Avoid this Common Recruitment Blunder with Big Data

When it comes to hiring in a place like Silicon Valley, quality trumps fast and cheap. In fact, top performers can deliver 40% to 67% more to the business than average performers, according to research conducted by McKinsey & Company.

But too often, recruitment success is gauged by time-to-fill quotas or cost-of-hire numbers. All these numbers do is tell organizations how quickly someone was hired at the lowest possible price.

An over-focus on these metrics leads to hiring that is faster, cheaper... and (from a talent quality perspective) worse.

Here are three key questions organizations need to ask of their workforce data to efficiently hire more top performers, along with an overview of how big data supports the uncovering of related insights:

Question #1: Which recruiting sources are the best?

Finding the right talent is no longer a straightforward process: While newspaper announcements used to be one of few methods available to widely publicize job openings, there are now several sources to consider, from third party job boards to LinkedIn. A key component of recruiting more cost effectively is to understand which recruiting sources are delivering the right candidates.

This means it is important to analyze both recruitment sources and the ratio of filled positions to applications at the same time. When the combined data is displayed as a dynamic visualization, business leaders can immediately understand how each recruitment source is performing. For example, once a recruiter sees that the ratio of filled positions to applications is really poor for a third party job board, they can make an informed decision to invest more in other sources.

Question #2: Where are the bottlenecks in the hiring process?

The recruitment process can stall out at multiple points. This requires HR to dig deeper into the data and review conversion rates at each stage in the process -- from applications received to offers extended -- to pinpoint where the problem exists. With in-memory analytics, organizations can slice and dice the analysis population in real-time, based on any attribute. This allows HR leaders to understand and compare bottlenecks for different positions, organizations, and locations. Ultimately, this can help hiring managers fill positions faster, without sacrificing quality.

Question #3: How can we hire more top performers?

A number of large organizations are analyzing workforce data to determine the characteristics of a top performer. This information can be used to identify people from the talent pool who share those same attributes. For example, a large manufacturer used predictive analytics to discover why some of its hires from top schools succeed while others fail. The company learned that its most successful MBA recruits had all worked earlier in their careers in fast food or held some other low-wage service job. They then changed the candidate selection criteria to reflect these findings.

When determining who from the candidate pool is likely to become a top performer, it is important to avoid looking at only a small handful of pre-selected employee attributes (e.g. tenure, age, or time in role). Hidden aspects like recruiting source or previous experience can all be used to find more fine grained profiles that identify patterns of who is likely to become high (or low) performers.

Bringing it all Together

Rarely in the dynamic environment of talent and people do a couple metrics tell the whole story. HR leaders and executives must ask questions of the data related to recruitment sources, bottlenecks in the process, and a candidate’s potential performance.

With the answers to these questions, HR leaders can make better decisions about hiring in a different location, for example, or bring forward an informed point of view about how to adjust the recruitment plans. Data helps to remove a lot of bias, enabling HR, executives, and other business leaders to make more informed decisions based on a single version of the truth.

Article written by Ian Cook
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