Predictive human resources
Can math help improve HR mandates in an organization?
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By Abhijit Bhaduri and Atanu Basu
Listen randomly to 10 interviews of Global Fortune 500 CEOs and you will hear them say, “it is all about the people” when accounting for the success of their respective companies. Human resource (HR) professionals have the important mandate of making a business successful by acquiring, engaging and developing talent. While some corporations are legendary for their people practices – GE and PepsiCo come to mind – most companies still go about the business of talent acquisition, retention and development the same way they did in the past. While there has been intent to change, in the absence of the appropriate tools, HR and business leaders have found it challenging to deliver on the HR mandates. Predictive analytics and related technologies have the ability to transform HR as we know it.
Let’s look at the HR mandates and explore how predictive decisions can deliver business value.
In a Harvard Business Review study, Ram Charan showed that two out of five new CEOs fail during their first 18 months. The essence of successful hiring at any level is to find someone who is willing, able and qualified for the vacancy. Traditionally, when HR comes across an interested candidate, it collects the pertinent data and makes the best possible judgment call. Examples of collected data include requirements for the role, candidate’s qualifications, past performance, interactions during the interview process and reference checks. In today’s dynamic business climate, companies can’t rely only on past performance and limited interactions during the interviews to predict future performance of a candidate.
Suppose we have to choose one from a pool of 10 similar candidates. Which one has the greatest likelihood to succeed in the role? Once hired, how long will it take this person to succeed, assuming the success metrics for the role are clearly defined, and how successful would she be? If HR acquires the ability to predict – to identify, quantify and rank – the future performance of a candidate before making a hiring decision, it would be a great value addition to the business. Enter predictive analytics. By taking into account datasets that are intuitively obvious – resumes, job descriptions, references and interviews – and some that may not be so obvious, mathematical algorithms can be used to answer questions about the future. These algorithms can also unearth predictors of success (in a role) that may not have been considered before, thus improving the entire recruiting process.
Attrition and Loyalty
Gone are the days when employees worked for decades for the same corporation. Today’s businesses have learned to live with employee churn, while evolving to minimize the impact of attrition on the overall health of the business. Attrition at all levels remains an issue that corporations would like to understand and get ahead of. While some businesses, such as insurance sales and business process outsourcing, are known for their high attrition, many other businesses also suffer regularly from attrition, especially in key leadership roles.
Who is going to leave, when and why? Where is she going to go? How can we keep her? Is she worth keeping this way? HR needs predictions, as well as decisions to take advantage of these predictions. Once the appropriate datasets, business rules, etc. are gathered, predictive analytics can answer the what, when and why questions, and operations research can answer the how and what-if questions for HR. The continuous learning that will follow, once these algorithms are in place, will help managers identify and quantify the leading indicators of attrition for their employees, plus help managers take preemptive actions to retain their human assets. Loyalty is the flip side of this coin. Similar methodologies can enable forward-looking decisions with respect to which incentives – and it doesn’t always have to be money – will generate loyalty from which employee and for how long.
Learning and Development
This learning and development part of the organization kicks in to bridge the capability gaps that the recruiting team is not able to close. What if we could predict which competencies would be easiest to learn – and which developmental approach would be the most effective – for which employee? Which training, if any, would be able to fill in the experience gap for an employee who is otherwise qualified for her next assignment? People develop their skills from a variety of experiences, interactions and relationships. Formal development plans try to shorten the time needed for developing these competencies.
Predictive decisions would help customize this methodology per employee per competency gap. So the individual development plan for Employee A would not only state that the development gap for Employee A is “decision-making,” it will also suggest having Employee X mentor A because that is the most effective mentor-mentee relationship for this particular skill. The plan may go on to say that Employee A should, however, learn about building “financial acumen” by watching a video tutorial. Dell used predictive training to increase the effective tenure of new call center agents so they can deliver better customer experience to the callers.
Many organizations that have a formal approach toward talent identification use a performance vs. potential matrix by classifying their employees into high-medium-low performers and with high-medium-low potential. The assumption is that past performance is an accurate predictor of success in future roles. To assist in potential identification, the companies use assessment centers to simulate scenarios that determine success in the next role. In a world that is rapidly changing, the assumptions about future roles that do not keep pace with the external shifts are not going to predict which employee to bet on. What if someone is a poor performer in the current role but has the competencies necessary for the next role, especially if the two roles demand different competencies?
Most succession planning processes today assume that a trend of strong performance is the best predictor of success in a future role, no matter how different the past roles are from the future role. Adaptive algorithms that produce updated predictions, and associated decisions, as more (and better) data becomes available can help. The U.S. Army is already using information from aptitude tests, medical tests, etc. to place applicants in the type of career path best suited for their skills and interests.
Is Predictive HR Necessary?
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.
Abhijit Bhaduri (firstname.lastname@example.org) is chief learning officer of Wipro. Prior to Wipro, he led HR teams at Microsoft, PepsiCo, Colgate and Tata Steel and has worked in India, Southeast Asia and the United States.
Atanu Basu (email@example.com) is chief executive officer of DataInfoCom, an analytics software company headquartered in Austin, Texas. He has 17 years of experience in the semiconductor and software industry.