A perfect storm is brewing in the workplace. There are now four generations in the workforce, each bringing differences in attitudes, expectations and values. At the same time, Baby Boomers are reaching retirement age, leaving organisations at risk of losing years of institutional knowledge and expertise.
These demographic shifts, along with new job roles that have emerged in today’s economy, have intensified the competition for talent as companies worry about finding and keeping the skill sets they need.
While finding the right talent is critical, keeping that talent can be even harder. Employee retention and turnover is the number one workplace challenge facing HR leaders today. The differences among demographics make it impossible to have any single one retention strategy, as what keeps people in their jobs can vary by generation. For example, Millennials – now the largest generation in the workforce – may care more about opportunities for working in different locations and are more likely to move from job to job than Baby Boomers, many of whom expect to remain at a company for decades or even a lifetime.
That’s why more organisations are looking to predictive and prescriptive analytics to help them answer and solve these tough workforce challenges. These capabilities, designed for use by HR and business managers, can be built into core human resource systems and predict who might be at risk of leaving, and then prescribe what actions to take.
This new form of HR analytics can be based on a variety of internal and external data.
For example, one company analysed years of its own aggregated historical data and discovered that the employees most likely to stay were those who had experience in multiple parts of the organisation, challenging its assumption that willingness to stay was more likely tied to how many years employees had been with the company. That insight prompted the company to revitalise a dormant rotational programme, which lowered attrition.
The importance of engagement
Losing great talent can have many repercussions. It can cost thousands of dollars and significant time to replace and train a new hire, and negatively impact productivity, customer satisfaction and workplace morale.
Many employers assume more money is the only way to stop an employee from seeking new opportunities, but that’s often not enough. Employees often leave due to a lack of engagement. People that are satisfied and connected to their employers are less likely to entertain headhunters and search outside for other opportunities. Now, more than ever before, employee engagement is paramount to attracting and retaining talent – and remaining competitive in the global market. Research shows a direct link between employee engagement and corporate performance.
A 2013 Gallup study revealed that employee engagement is an important predictor of company performance, even in a tough economy, with highly engaged companies outperforming the competition by 22% in profitability, 21% in productivity and 10% on customer ratings. Additional Gallup research showed that companies with engaged workforces have higher earnings per share (EPS), experiencing as much as 147% higher EPS compared with their competition.
A major part of engaging employees is understanding what motivates each individual and working with them on clear and direct career paths aligned with their goals and interests.
Yearly evaluations are not enough in today’s workplace. Employers need to continuously re-recruit employees with feedback and new opportunities to keep them engaged and invested in the business.
This is where predictive and prescriptive analytics can make a difference. It’s important to keep using everyday communications and the more subjective information – conversations with employees about career goals, evaluations from team members, and even our own instincts as managers – but the current war for talent tells us that’s no longer enough. With richer data and better analysis, managers can make much better informed decisions on the next steps to take and proactively engage with employees before they leave for more attractive opportunities.
Data science and machine learning
The science and technology behind these new capabilities in the enterprise often go under the names of data science and machine learning. These approaches have their roots in consumer web companies, which offer up books and movies based on what you’ve read or watched before.
Now these approaches are being applied to finding those employees at a high risk for leaving, and provide personalized recommendations for how to re-engage them before they get discouraged or look somewhere else. These insights are based on analysing a number of different factors about an employee, such as commute time, new life changes, time since a promotion, or stock vesting, to better understand how and when employees’ needs and interests may change.
Employers are not the only ones who stand to gain from this new evolution in analytics. Data science is also empowering employees with individualised insights and recommendations to help them create the ideal career path.
Analytics can show them “what other people like me” have done in the organisation, different career path options, and what skills or experience are needed.
While some may fear that data science makes relationships less personal, it can actually create greater transparency and a more personalised connection among HR, managers and employees. As the war for talent continues to intensify, the power of prediction will be a competitive advantage to engaging and retaining employees, and for helping individual employees map out a rewarding career.
The writer is Tim Young, vice-president, Workday, Asia Pacific and Japan.
This article was first published in Marketing Magazine Singapore’s Jan-Feb 2016 print edition. To read more views from senior marketers click here.