Ahead of the curve: The future of performance management

Here is a brief excerpt from an article written by Boris Ewenstein, Bryan Hancock, and Asmus Komm for the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.

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What happens after companies jettison traditional year-end evaluations?

The worst-kept secret in companies has long been the fact that the yearly ritual of evaluating (and sometimes rating and ranking) the performance of employees epitomizes the absurdities of corporate life. Managers and staff alike too often view performance management as time consuming, excessively subjective, demotivating, and ultimately unhelpful. In these cases, it does little to improve the performance of employees. It may even undermine their performance as they struggle with ratings, worry about compensation, and try to make sense of performance feedback.

These aren’t new issues, but they have become increasingly blatant as jobs in many businesses have evolved over the past 15 years. More and more positions require employees with deeper expertise, more independent judgment, and better problem-solving skills. They are shouldering ever-greater responsibilities in their interactions with customers and business partners and creating value in ways that industrial-era performance-management systems struggle to identify. Soon enough, a ritual most executives say they dislike will be so outdated that it will resemble trying to conduct modern financial transactions with carrier pigeons.

Yet nearly nine out of ten companies around the world continue not only to generate performance scores for employees but also to use them as the basis for compensation decisions.1 The problem that prevents managers’ dissatisfaction with the process from actually changing it is uncertainty over what a revamped performance-management system ought to look like. If we jettison year-end evaluations—well, then what? Will employees just lean back? Will performance drop? And how will people be paid?

Answers are emerging. Companies, such as GE2 and Microsoft,3 that long epitomized the “stack and rank” approach have been blowing up their annual systems for rating and evaluating employees and are instead testing new ideas that give them continual feedback and coaching. Netflix4 no longer measures its people against annual objectives, because its objectives have become more fluid and can change quite rapidly. Google transformed the way it compensates high performers at every level.5 Some tech companies, such as Atlassian,6 have automated many evaluation activities that managers elsewhere perform manually.

The changes these and other companies are making are new, varied, and, in some instances, experimental. But patterns are beginning to emerge.

  • Some companies are rethinking what constitutes employee performance by focusing specifically on individuals who are a step function away from average—at either the high or low end of performance—rather than trying to differentiate among the bulk of employees in the middle.
  • Many companies are also collecting more objective performance data through systems that automate real-time analyses.
  • Performance data are used less and less as a crude instrument for setting compensation. Indeed, some companies are severing the link between evaluation and compensation, at least for the majority of the workforce, while linking them ever more comprehensively at the high and low ends of performance.
  • Better data back up a shift in emphasis from backward-looking evaluations to fact-based performance and development discussions, which are becoming frequent and as-needed rather than annual events.

How these emerging patterns play out will vary, of course, from company to company. The pace of change will differ, too. Some companies may use multiple approaches to performance management, holding on to hardwired targets for sales teams, say, while shifting other functions or business units to new approaches.

But change they must.

Rethinking performance

Most corporate performance-management systems don’t work today, because they are rooted in models for specializing and continually optimizing discrete work tasks. These models date back more than a century, to Frederick W. Taylor.

Over the next 100 years, performance-management systems evolved but did not change fundamentally. A measure like the number of pins produced in a single day could become a more sophisticated one, such as a balanced scorecard of key performance indicators (KPIs) that link back to overarching company goals. What began as a simple mechanistic principle acquired layers of complexity over the decades as companies tried to adapt industrial-era performance systems to ever-larger organizations and more complicated work.

What was measured and weighted became ever more micro. Many companies struggle to monitor and measure a proliferation of individual employee KPIs—a development that has created two kinds of challenges. First, collecting accurate data for 15 to 20 individual indicators can be cumbersome and often generates inaccurate information. (In fact, many organizations ask employees to report these data themselves.) Second, a proliferation of indicators, often weighted by impact, produces immaterial KPIs and dilutes the focus of employees. We regularly encounter KPIs that account for less than 5 percent of an overall performance rating.

Nonetheless, managers attempt to rate their employees as best they can. The ratings are then calibrated against one another and, if necessary, adjusted by distribution guidelines that are typically bell curves (Gaussian distribution curves). These guidelines assume that the vast majority of employees cluster around the mean and meet expectations, while smaller numbers over- and underperform. This model typically manifests itself in three-, five-, or seven point rating scales, which are sometimes numbered and sometimes labeled: for instance, “meets expectations,” “exceeds expectations,” “far exceeds expectations,” and so on. This logic appeals intuitively (“aren’t the majority of people average by definition?”) and helps companies distribute their compensation (“most people get average pay; overperformers get a bit more, underperformers a bit less”).

But bell curves may not accurately reflect the reality. Research suggests that talent-performance profiles in many areas—such as business, sports, the arts, and academia—look more like power-law distributions. Sometimes referred to as Pareto curves, these patterns resemble a hockey stick on a graph. (They got their name from the work of Vilfredo Pareto, who more than a century ago observed, among other things, that 20 percent of the pods in his garden contained 80 percent of the peas.) One 2012 study concluded that the top 5 percent of workers in most companies outperform average ones by 400 percent. (Industries characterized by high manual labor and low technology use are exceptions to the rule.7 ) The sample curve emerging from this research would suggest that 10 to 20 percent of employees, at most, make an outsized contribution.

Google has said that this research, in part, lies behind a lot of its talent practices and its decision to pay outsized rewards to retain top performers: compensation for two people doing the same work can vary by as much as 500 percent.8 Google wants to keep its top employees from defecting and believes that compensation can be a “lock-in”; star performers at junior levels of the company can make more than average ones at senior levels. Identifying and nurturing truly distinctive people is a key priority given their disproportionate impact.

Companies weighing the risks and rewards of paying unevenly in this way should bear in mind the bigger news about power-law distributions: what they mean for the great majority of employees. For those who meet expectations but are not exceptional, attempts to determine who is a shade better or worse yield meaningless information for managers and do little to improve performance. Getting rid of ratings—which demotivate and irritate employees, as researchers Bob Sutton and Jeff Pfeiffer have shown—makes sense.

Many companies, such as GE, the Gap,9 and Adobe Systems,10 have done just that in a bid to improve performance. They’ve dropped ratings, rankings, and annual reviews, practices that GE, for one, had developed into a fine art in previous decades. What these companies want to build—objectives that are more fluid and changeable than annual goals, frequent feedback discussions rather than annual or semiannual ones, forward-looking coaching for development rather than backward-focused rating and ranking, a greater emphasis on teams than on individuals—looks like the exact opposite of what they are abandoning.

The point is that such companies now think it’s a fool’s errand to identify and quantify shades of differential performance among the majority of employees, who do a good job but are not among the few stars. Identifying clear overperformers and underperformers is important, but conducting annual ratings rituals based on the bell curve will not develop the workforce overall. Instead, by getting rid of bureaucratic annual-review processes—and the behavior related to them—companies can focus on getting much higher levels of performance out of many more of their employees.

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Here is a direct link to the complete article.

Boris Ewenstein, Bryan Hancock, and Asmus Komm are expert principals in McKinsey’s Johannesburg, Atlanta, and Hamburg offices, respectively.

The authors would like to thank the People & Organization team at Zalando SE for their valuable collaboration and contributions to this article.


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