PERFORMANCE REVIEWS ARE DEAD - OR ARE THEY?

Adopt a review system which provides feedback, holds employees accountable and encourages talent development and retention.

Performance reviews are a thing of the past in companies like IBM, OppenheimerFunds and General Electric. They cite compelling reasons: Widespread evidence shows traditional reviews do not work: They focus on an individual’s previous behaviour, rather than helping them to improve in the future; There is an assumption that poor performers will never change.

If companies are concerned about developing and retaining talent, whilst holding employees accountable and helping them to set and reach performance benchmarks, there are alternatives to the traditional annual review. There are a number of actions:

1.    Determine whether traditional Performance Reviews are achieving company goals. Do they provide accurate assessments of performance? Do they help employees improve and develop? Do the rewards which relate to superior reviews assist in developing and retaining talent?

 2.    Consider an increasingly informal review system. This will involve more regular interaction with employees and should include ways to quickly identify poor performers, so they can be monitored and coached. These reviews can include numerical rankings and assign employees with several numbers four times a year, to provide rolling feedback on different dimensions.

 3. Get support from senior management and re-inforcement from the organisational culture. Before rolling out any new review system, there has to be a clear, consistent message about its validity, particularly from HR.

These issues are not new. They have become increasingly transparent, as roles evolved over the last 15 years. More and more roles need employees with deeper expertise, more independent judgment and better problem-solving skills. Employees shoulder greater responsibilities in their interactions with customers and business partners, as well as pressure to create value in ways which industrial-era, performance-management systems would struggle to identify. 

Around the world, nine out of ten companies continue to generate performance scores for employees. Importantly, they also use these as the only basis for salary decisions. The issue which prevents Managers’ dissatisfaction with the process is uncertainty, in terms of how a revamped performance review system ought to look.

Answers are emerging. Netflix no longer measures its employees against annual objectives, because their objectives rapidly change. Google has transformed the way it remunerates high performers at every level. 

These changes are new, varied and experimental and patterns are emerging:

Companies are re-thinking 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 amongst the majority of employees in the middle.

Companies are also collecting more objective performance data, through systems which automate real-time analyses.

Performance data is used increasingly less as an instrument for setting remuneration levels. Some companies are now severing the link between evaluation and compensation, at least for the majority of the workforce, whilst linking them more comprehensively at the high and low ends of performance.

Better data supports a shift in emphasis from backward-looking evaluations to fact-based performance and development discussions, which are becoming frequent and on an as-necessary basis, rather than annual events.

 How these emerging patterns develop will vary from company to company. The pace of change will also differ. Some companies will use multiple approaches to performance management, holding on to hard-wired targets for sales teams, whilst moving other functions or business units to new approaches.

 RE-THINKING PERFORMANCE

The majority of performance-management systems do not work, as they are rooted in models for specialising and continually optimising discreet work tasks. 

Performance-management systems have evolved, however they have not fundamentally changed. A measure of the number of pins produced in a day could become a more sophisticated one, such as a balanced scorecard of key performance indicators (KPIs), which link back to over-arching company goals. What began as a simple mechanistic principle acquired layers of complexity over decades, as companies tried to adapt industrial-era performance systems, to ever-larger organisations and more complicated work.

What was measured and weighted became increasingly more micro. Many companies struggle to monitor and measure a proliferation of individual employee KPIs - a development which created two kinds of challenges. Firstly, collecting accurate data for 15 - 20 individual indicators can be cumbersome, often generating inaccurate information. (In fact, many organisations ask employees to report these data themselves.) Secondly, a proliferation of indicators, often weighted by impact, produces immaterial KPIs and dilutes the focus of employees. KPIs are regularly encountered which account for less than 5 percent of an overall performance rating.

Nevertheless, Managers attempt to rate employees as well as they can. The ratings are then calibrated against one another and, if necessary, adjusted by distribution guidelines which are typically bell curves. These guidelines assume the vast majority of employees cluster around the mean and meet expectations, whilst smaller numbers over and under-perform. This model typically manifests itself in three, five or seven-point rating scales, which are sometimes numbered and labelled: for instance, “meets expectations,” “exceeds expectations” or “far exceeds expectations.” This logic appeals intuitively (“aren’t the majority of people average by definition?”) and helps companies to distribute their remuneration (“most people get average salaries; over-performers get more, under-performers get less”).

Bell curves however, may not accurately reflect the reality. Research suggests that talent-performance profiles in many areas, such as business, sports, the arts and academe, look more like power-law distributions. Sometimes referred to as Pareto curves, these patterns resemble a hockey stick on a graph. In 2012, a study concluded that the top 5 percent of workers in most companies out-perform average ones by 400 percent.The sample curve emerging from this research would suggest that at most, 10 to 20 percent of employees make an out-size contribution.

Google has said that in part, this research lies behind much of its talent practices, as well as its decision to pay out-size rewards, in order to retain its top performers: remuneration for two employees doing the same work can vary by as much as 500 percent. Google wants to keep its top employees from defecting and believes remuneration can be a “lock-in”; star performers at junior levels in 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 bit better or worse yield meaningless information for Managers and do little to improve performance. Getting rid of ratings which demotivate and irritate employees makes sense.

 Adobe breaks projects down into ‘sprints’, which are immediately followed by de-briefing sessions. They emphasise principles of collaboration, self organisation, self direction and regular reflection on how to work more effectively. Regular check-ins to assess these principles replace annual appraisals. PWC moved to a scoreless review system, however after negative response by employees, particularly those focused on a Partner track, they re-instituted rankings in their client services practices. Employees do not still receive a single rating every year, they now receive scores on 5 competencies, as well as other development feedback.

The point is that such companies now think that it is a fool’s errand to identify and quantify shades of differential performance amongst the majority of employees, who do a good job but are not amongst the few stars. Identifying obvious over-performers and under-performers is important, however conducting annual ratings rituals based on the bell curve, will not develop the workforce overall. Instead, by getting rid of bureaucratic annual-review processes, as well as the behaviours related to them, enables companies to focus on getting much higher levels of performance out of many more employees.

MEANINGFUL DATA

Good data is crucial to the new processes, not least because so many employees think current evaluation processes are subjective. Rather than relying on an annual, inexact analysis of individuals, companies can get better information by using systems which crowd-source and collect data about the performance of people and teams. Continual, crowd-sourcing performance data throughout the year yields even better insights.

 Tools can automate activities, not simply to free up time which Managers and employees now spend inefficiently gathering information on performance, but also transform what feedback is meant to achieve. The quality of the data improves as well. Because they are collected in real-time from fresh performance events, employees find the information more credible, whilst Managers can draw on real-world evidence for more meaningful coaching dialogues. As companies automate activities, and also add machine learning and artificial intelligence to the mix, the quality of data will improve exponentially and will be collected much more efficiently.

Finally, performance-development tools can also identify top performers more accurately - everyone knows subjectively who they are.

Relatively easy and inexpensive to build, or to buy and customise, such performance-development applications are promising, yet challenging. Employees could attempt to game systems to land amongst the top 10 percent, or to ensure that a rival does not. Artificial intelligence and semantic analysis might conceivably distinguish genuine from manicured performance feedback, and raters could be compared with others to detect cheating. Some employees may also feel that Big Brother is watching and evaluating their every move. These and other real-life challenges must be addressed, as more and more companies adopt similar tools.

ANXIETY ABOUT REMUNERATION

The next step which companies can take to move performance management from the industrial to the digital era, is to remove anxiety about remuneration. This requires Managers to make some counter-intuitive decisions.

Conventional wisdom links performance evaluations, ratings and remuneration. This seems completely appropriate: most people think stronger performance deserves more pay, weaker performance less. To meet these expectations, mean performance levels would be pegged around the market average. Over-performance would beat the market rate, to attract and retain the top talent. Poor scores would bring employees below the market average, to provide a dis-incentive for under-performance. The distribution guide, with its target percentages across different ratings, gives companies a simple template for calculating differentiated pay, whilst helping them to stay within an overall remuneration budget. 

This approach does have a number of problems. Firstly, the cart sometimes goes before the horse: Managers use desired remuneration distributions to reverse-engineer ratings. To pay Tom X and Maggie Y, the evaluator must find that Tom exceeds expectations which Maggie merely meets. This kind of reverse-engineering often plays out over several performance cycles and can lead to cynical outcomes. These practices discredit the performance system and often drown valuable feedback. They breed cynicism, de-motivate employees and can make them combative, rather than collaborative.

Secondly, linking performance ratings and remuneration in this way ignores recent findings in the cognitive sciences and behavioural economics.Research suggests that employees may worry excessively about the pay implications of even small differences in ratings, so that the fear of potential losses, however small, should influence behaviour twice as much as potential gains. Although this idea is counter-intuitive, linking performance with pay can de-motivate employees, even if the link produces only small net variances in remuneration.

Few employees are outstanding, so it makes little sense to risk de-motivating the majority by linking pay to performance. More and more technology companies have done away with performance-related bonuses. Instead, they now offer a competitive base salary and bonuses, which are sometimes paid in shares or share options, to the company’s overall performance. Employees are able to focus on doing great work, to develop and make mistakes, without worrying about the implications of marginal ratings differences on their remuneration. However, most of these companies pay special rewards, including discretionary pay, to outstanding performers. So, companies can remove a major driver of anxiety for the majority of employees.

Finally, researchers say things which really motivate people to perform well are feelings like autonomy, mastery and purpose. These tend to increase, as workers gain access to assets, priority projects and customers and also receive displays of loyalty and recognition. Snapping the link between performance and remuneration allows companies to worry less about tracking, rating and their consequences and more about building capabilities and inspiring employees to stretch their skills and aptitudes.

 Leaders should not however, delude themselves into thinking that cutting costs is another reason for de-coupling remuneration from performance evaluations. Many of the companies which have moved in this direction use generous share awards, which makes employees feel not only well compensated, but also like owners. Companies which lack shares as currency, may find it harder to make the numbers work, unless they can materially boost corporate performance.

COACHING ON A SCALE TO GET THE BEST FROM THE MOST

The growing need for companies to inspire and motivate performance makes it critical to innovate in coaching, and to do so at scale. Without excellent, frequent coaching, it is difficult to set goals flexibly and to help employees stretch their jobs, or to give people greater responsibility and autonomy, whilst demanding more expertise and judgment from them.

 Companies in high-performing sectors, such as technology, finance and the media are ahead of the curve in adapting to the future of digital work. It is no surprise that organisations in these sectors are pioneering the transformation of performance management. More companies will need to follow, quickly.They ought to shed the old models of calibrated employee ratings based on normal distributions, and liberate large parts of the workforce to focus on drivers of motivation which are stronger than incremental changes in pay. Meanwhile, companies still have to keep a keen eye on employees who are outstanding and on those who struggle.

It is time to explore tools to crowd-source a rich, fact base of performance observations. Ironically, companies like GE are using technology to democratise and re-humanise processes, which have become mechanistic and bureaucratic. Others must now follow. 

(Original sources: Harvard Business Review, Boris Ewenstein, Bryan Hancock and Asmus Komm, with contributions from Wharton Business Executive, Peter Cappelli and George W Taylor.)

 Keith Mould

The thread running through Keith Mould’s career has been to find new ways to improve business performance. He has previously worked as a corporate executive, successfully leading teams tasked with innovation. In an entrepreneurial role, he has launched several start ups. As a senior business process consultant and as a post-graduate teacher, his focus has been on how to build successful and sustainable businesses.

For almost a decade, Keith has been working with business owners and corporate executives at a strategic level, to help improve their companies performance, achieve their strategic goals and to grow the bottom line.

He is passionate about what makes both businesses and people tick, incorporating elements from neuroscience into his leadership coaching.