Predictive Analytics and HR: Is it Right for Your Company?

Predictive Analytics and HR: Is it Right for Your Company?

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Analytics. Big Data. Two corporate buzzwords that have made their way into board rooms, business meetings and of course, the world of HR and recruiting.

An extension of those buzzwords is related to predictive analytics. What is predictive analytics?

According to predictiveanalyticsworld.com: “Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization. Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer’s predictive score informs actions to be taken with that customer — business intelligence just doesn’t get more actionable than that.”

Who uses predictive analytics?
According to fico.com “predictive analytics is widely used to solve real-world problems in business, government, economics and even science – from meteorology to genetics.”

How does predictive analytics relate to HR?
According to Jason Roberts, Vice President, Operations Americas for Randstad Sourcelight, HR and business leaders looking to improve strategic workforce planning and create talent-ready organizations will find talent and workforce analytics a powerful enabler. Roberts stated this in the article 5 tips for using talent analytics and predictive workforce intelligence.

Organizations large and small, private and public, can leverage vast amounts of data that can help them predict, find, engage and keep the most talented employees. Unfortunately, most don’t, writes Roberts.

According to Randstad Sourceright’s 2015 Talent Trends Survey of global HR leaders, 56% indicated they use talent analytics and insights to inform their workforce planning process. Of those who do, the number one reason for using analytical data was to make the workforce planning process more efficient (73%), followed closely by accurate mapping and addressing of skills gaps (69%), and the ability to clearly identify high-potential employees for development (65%). Respondents saw a number of uses for workforce data and analytics, including to improve alignment of people and company strategy, linking performance to compensation, and deeper access to external talent pools.

Roberts provides 5 tips on using talent analytics and predictive workforce intelligence:

  1. Assemble the internal team: Whether its recruitment leaders, HR business partners, or hiring managers, identify the team members who can best determine what intelligence you need — and bring in a technology expert to facilitate ease of reporting and a data expert to help you turn numbers into business recommendations.
  2. Establish a baseline: You can’t know where you’re going without knowing where you’ve been. Capturing a snapshot of the current status will provide the critical baseline to benchmark performance against.
  3. Identify critical metrics: You don’t want to analyze everything, so choose the data that will contribute real value to your workforce and business planning and can help you to look ahead.
  4. Share the findings: Reach out to the appropriate stakeholders who can benefit from the data you’ve collected. Collaborate on a plan of action in a defined period of time. Follow up on the data to ensure you’ve measured and interpreted the information correctly. Make adjustments if necessary.
  5. Upskill the HR team: As big data becomes the prevalent resource and tool, HR professionals need to develop skills and comfort with data, statistics, and analytics.

In the Society of Human Resources Management article titled HR Moves toward Wider Use of Predictive Analytics, experts weighed in:

“We’re moving into a predictive analytics world,” said Gene Pease, founder and CEO of Vestrics, a Carrboro, N.C.-based company that applies predictive analytics to workforce learning programs. In such a world, data is forward-looking, used to identify the traits that make for successful performance in a particular job, or the most effective method for delivering training to employees of a certain age in a specific working group.

No longer will it be enough to report on a department’s turnover rate, said Rishi Agarwal, national leader for workforce analytics at PricewaterhouseCoopers (PwC) Saratoga, a provider of workforce analytics services. HR will have to identify why turnover is high, and come armed with recommendations to address the root cause.

For example, San Francisco analytics startup Evolv helped Xerox reduce call center turnover by gathering and studying data on the characteristics and job performance of front-line employees, then applying what it learned to the hiring process. Evolv found that employees without call center experience were just as successful as those who had it, allowing Xerox to broaden its candidate pool. Creative personalities stayed longer than those with inquisitive personalities, as did candidates who belonged to at least one but not more than four social networks. Armed with such detailed information on what made a successful hire, Xerox was able to reduce attrition by 20 percent.

In an article written by Mark Lukens, founding partner of Method3 for The Recruitment Process Outsourcing Association, Lukens looked at the potential value of predictive analytics in HR:

Recruitment
traditionally recruitment has used past performance as an indicator of future performance. However, there is more and more evidence that this is not a reliable indicator, especially when the candidate will be moving into a significantly different role. This is particularly risky at the top – a Harvard Business Review study found that two out of five new CEOs fail in their first 18 months.

Predictive analytics can identify more reliable indicators, and help pick the mix of skills, experience and competencies that will make the right candidate.

Learning and development
Predictive analytics can be used to identify wide trends, such as which competencies are easiest to learn, and which skills might benefit a company in the near future. It can also be used on a micro scale, to identify the approaches to training that will most suit a particular employee, and when they will next be ready for a significant learning opportunity.

Talent management and retention
People often don’t have a good grasp of what will really make them happy in a job and what will provide incentives for them to stay. Predictive analytics can identify trends in when employees leave, factors leading to retention, and how accurate particular answers from employees are likely to be. It can also use existing data to identify how likely particular groups of employees are to resign at any given time, allowing HR and managers to plan interventions.

Organizational change
Habits are the biggest barriers to organizational change. It can be incredibly hard to convince someone, however rational they might be, that the way they have been working is not the best way. But marketing has shown that analytics can identify not just habits but the points when people are most receptive to changing them. This information could be invaluable in planning significant organizational reorganization and business improvement work.

The article Predictive human resources asks the question: Can math help improve HR mandates in an organization?

Authors Abhijit Bhaduri and Atanu Basu concluded with this: “Mistakes in people decisions can be costly for the business. Being successful in HR is about making correct decisions about critical people matters. Any foresight before making a people decision – be it in recruitment, retention, development or succession planning – is a powerful weapon in HR’s arsenal. Do predictive decisions guarantee success for HR? No. However, studies show that most people can’t intelligently process more than eight variables at a time. Predictors that humans come up with inherently embed some kind of a cause-and-effect relationship – our brains are just not equipped to identify and quantify predictors that may not have cause-and-effect synergy. On the other hand, advancements in mathematical sciences and computer science have enabled algorithms to take into account thousands of related and unrelated data points (numerical, text, audio, video, etc.) and business rules, process them computationally and come up with far better future decisions than human judgment and traditional methods can.”

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