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Advanced Recruiting Analytics: Creating Competitive Advantage in a Talent Economy

Despite interest in big data and the increasing sophistication of analytics, very few companies use insight-oriented measurement in the recruiting function.

The focus remains where it has for decades—on transactional metrics like time-to-fill, cost-per-hire, applicant-to-hire rate, and so on. In a talent economy where human potential is the key driver of change, there is a significant opportunity to extract more highly developed insights from talent and information functions to drive strategic objectives.

More advanced analytics can ultimately improve the predictive power of recruiting tools and create a stronger connection between employees (whether entry-level or executive level) and long-term strategic objectives. However, those responsible for talent acquisition and analytics need the resources and support necessary to realize the potential of metrics.

“Most of the metrics used today are transactional—they measure the efficiency of the recruiting function, but reveal very little about the people being recruited.”

The need for more advanced analytics is especially pressing because recruiting is now a high-stakes endeavor. ManpowerGroup’s 2015 Talent Shortage Survey found that 38% of companies have difficulty filling jobs due to lack of available talent. At the same time, and at least partly in response, companies are increasing their talent acquisition spend and their recruiting headcount.

Companies that prioritize higher-level recruiting analytics do better in this competition for talent. For example, when comparing organizations with very sophisticated talent acquisition functions versus those that are considered reactive, time-to-fill at all job levels has been found to be 20% lower and new hire turnover is 41% lower for the more advanced companies.

Despite spending more on recruitment, very few companies pursue strategy-level recruiting metrics when evaluating their approach. According to one recent global study, only 14% of companies have a talent analytics capacity in place. Another found that while 9% use predictive analytics in workforce decision making, nearly half use no data at all. Most of the metrics used today are transactional—they measure the efficiency of the recruiting function, but reveal very little about the people being recruited.  

A new model is needed to ensure higher return on investment. This involves a three-step sequence:

1. Understand the starting line. To move toward more sophisticated metrics, companies need a clear picture of the baseline. These are the transactional metrics that ensure the operation is running smoothly on a day-to-day basis. Information and talent functions (e.g., the CIO and CHRO) may already have systems in place to capture and report on this data. If not, expectations around delivering this data and the resources necessary to do so should be a priority.

2. Create expectations for optimized analytics at all levels. Once the baseline is evaluated, companies can move into a more mature, best-practice stage that looks at the impact of the recruiting function on issues such as employee engagement, productivity and brand stewardship. Establishing expectations is essential, but moving toward more optimized metrics also requires the technological and human resources to capture employee and candidate information as well as merge existing data sets—drawing the statistical line between new-hire data and productivity, for example.

3. Champion transformation. This is a visionary stage in which the recruiting function and related measurement is fully integrated across the enterprise. For example, measurement looks at strength of organizational culture and the cultivation of talent to drive a company forward. Commitment starts at the top and is likely to reach the board level in terms of support for system-wide investments and expectations for continually evolving data capture and analysis. In addition, new kinds of resources involving data science and analytics experts will likely be necessary to define metrics that contribute to long-term growth

4. Transformational metrics equate to higher expectations. Consider a government organization in Australia. The group recruits for 330 job roles with up to 10,000 annual placements. They are unable to compete with the private sector on salary and work environment, which is hardly considered progressive or innovative. At the same time, the hiring process involves rigorous physical and psychometric testing.

Reducing time-to-fill is simply not going to cut it. Instead, the recruitment strategy focuses on predictive measures of mental and physical preparedness and alignment with culture. It also heavily emphasizes the innovative brand-building approaches necessary to raise the profile of opportunities available. This is the kind of customized, ‘one-size-fits-one’ thinking companies will need going toward to compete.

We all operate in a data-driven world today where transactional metrics represent the floor, not the ceiling. The competition for talent is intense and there is a need for companies to develop and implement plans that raise the bar in talent acquisition. To do so, CEOs must champion and support much more advanced recruiting analytics that are aligned with long-term strategic goals. The data to do this already exists. Understanding how to capture, analyze and deploy is the key to finding competitive advantage in a talent-driven economy.



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