Autor: Bruce Fecheyr-Lippens, Bill Schaninger, and Karen Tanner / McKinsey
The latest data and analytics buzz comes from the field of advanced HR analytics, where the application of new techniques and new thinking to talent management is becoming more mainstream. The implications are dramatic because talent management in many businesses has traditionally revolved around personal relationships or decision making based on experience—not to mention risk avoidance and legal compliance—rather than deep analysis. Advanced analytics provides a unique opportunity for human-capital and human-resources professionals to position themselves as fact-based strategic partners of the executive board, using state-of-the-art techniques to recruit and retain the great managers and great innovators who so often drive superior value in companies.
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Some leading organizations we know are already using advanced HR analytics successfully in certain talent-management areas. A leading healthcare organization, for example, has used these techniques to generate more than $100 million in savings while simultaneously improving the engagement of its workforce. The organization found that highly variable and unequal compensation levels were disturbing employees and driving high rates of attrition. Once the data analytics had identified an optimal minimum and maximum compensation threshold, the healthcare group increased the engagement and productivity of its employees—and reduced not only their rate of attrition but also its total compensation expenditures.
Another company we know reduced its retention bonuses by $20 million—and employee attrition by half—thanks to the use of predictive behavioral analytics. Through this process, and contrary to expectations, the company found that limited investment in management and employee training, and inadequate recognition, were the main drivers of staff defections. Expensive retention bonuses, to which the company had resorted in desperation, were simply an ineffective and costly Band-Aid. Many companies conventionally try to tackle retention issues by conducting in-depth exit interviews. The important advantage of the new analytics techniques over that approach is that they are predictive, rather than reactive, and they provide more objective information than the more qualitative findings of a one-on-one discussion.
At McKinsey, we’ve been developing our own approach to retention: to detect previously unobserved behavioral patterns, we combine various data sources with machine-learning algorithms. We first held workshops and interviews to generate ideas and a set of hypotheses. Over time, we collected hundreds of data points to test. Then we ran different algorithms to get insights at a broad organizational level, to identify specific employee clusters, and to make individual predictions. Last, we held a series of workshops and focus groups to validate the insights from our models and to develop a series of concrete interventions.
The insights have been surprising and at times counterintuitive. We expected factors such as an individual’s performance rating or compensation to be the top predictors of unwanted attrition. But our analysis revealed that a lack of mentoring and coaching and of “affiliation” with people who have similar interests were actually top of list. More specifically, “flight risk” across the firm fell by 20 to 40 percent when coaching and mentoring were deemed satisfying.
Our North American consultants who pursue a functional affiliation and capability-building program in areas such as operations, marketing and sales, or corporate finance were three times more likely to stay with the firm than those who don’t pursue such options. When consultants do, they receive specialized training, gain access to a community of colleagues who share the same passion, and get exposure to senior leaders. Subsequently, the data we retrieved helped us devise new programs to monitor and further strengthen our coaching and mentorship relationships, especially for our younger colleagues, and to intervene proactively to retain those “at risk.” Given our six-month review period and rapid engagement-cycle times, our predictive-retention algorithm is now refreshed every six months.
We’re still developing our understanding of how data analytics can drive better people decisions, but we’re already actively using these techniques beyond retention, to improve everything from talent acquisition to performance management to diversity. Our work confirmed that while top-notch technological capabilities are critical, they are not a silver bullet. Getting the right talent—be it experts in risk, marketing, or behavioral economics—to interpret and act on the data is just as important. So are leadership engagement and alignment. Moreover, an HR-analytics approach is no substitute for engaging directly with employees in an effort to understand their mind-sets, challenges, and needs. HR analytics, if done well, generates data-driven, organization-specific insights for executives and human-capital professionals to make more strategic decisions about their people.