Just like the baseball owners of years past, many human resource professionals and line managers are making mistakes in assembling their teams -- by misvaluing their employees. Now, more than ever, a holistic approach is needed to ensure that the right data is being selected, tracked, reported on, compared and used to improve talent management.
The reason the book and movie Moneyball have won many fans is not just that they tell a great underdog sports story of a small-market baseball team battling higher-payroll behemoths. It's that the story is about how markets value people, as much as it is about sports.
"These baseball players who we all think we know and understand are misvalued," Moneyball author Michael Lewis told an interviewer. "And if they can be misvalued, who can't?"
Indeed, human resources professionals and line managers at many different types of companies have made similar mistakes, in assembling their teams -- and their outcomes have been the same: "misvalued" employees.
While tools and approaches for selecting and developing talent abound in today's marketplace, it continues to be all too easy to make mistakes -- and those mistakes cost organizations real money.
In many ways, today's overabundance of employee-assessment instruments, performance metrics and available data makes it even more challenging to ensure that the talent pool is aligned with strategic business goals and prepared to drive steadily toward continued growth and prosperity.
When employee skills, traits, motivations and knowledge are out of sync with the requirements of the job, everyone loses, including the employee, his or her team, the organization and markets served.
Now, more than ever, a holistic approach is needed to ensure that the right data is being selected, tracked, reported on, compared and used to improve performance. Rising commodity costs, fierce competition and fewer consumer dollars put pressures on profits. Global leaders are responsible for ever-larger portfolios of products and teams of people, and they need transformative approaches to sustain and grow their companies.
To complicate matters, changing global demographics are making it harder to find high-performing employees with the right fit for changing job requirements, and the most talented "stars" are becoming increasingly elusive.
In a competitive job market, candidates with the highest potential are like baseball's free agents, being repeatedly tempted by recruiters and their own career aspirations to leave for better compensation. Regardless of the size of their budget, HR leaders should be making sure they are developing and rewarding the skills and employee loyalty that will truly help achieve their organizations' business goals.
Harnessing the Data
Our data-driven approach, called Quantitative Talent Management, is designed to enable HR leaders to harness and integrate all of the relevant information for talent management. QTM involves three components: data gathering, data analysis and information transparency.
We advocate a data-gathering approach similar to the Oakland A's embrace of quantitative analysis on a much larger scale than any other team in baseball at the time, as featured in Moneyball.
The A's took advantage of a wealth of statistical data to use new, more effective metrics. For example, although baseball traditionalists valued "batting average" as a measure of offensive talent, "on-base percentage" (which also accounts for walks) was actually a far-better measure of a player's contribution to victory.
The team also ignored the traditional dismissal of college statistics as inconsequential and saw that statistical analysis indicated they were surprisingly good indicators of professional performance. And, conversely, they recognized that some hallowed numbers -- such as fastball velocity -- offered more glamor than substance.
In business, too, there has been a data revolution. Companies have available a wealth of sophisticated tools to gather and convert data into intelligence regarding the workforce. Yet how many HR executives -- as they power their way through days filled with firefighting and tactical issues -- feel confident that they are thoroughly evaluating talent through use of the most relevant and effective metrics?
To take an obvious example: Is a salesperson measured by the revenues she brings in or the profitability of her sales? Or, are upstream measures such as frequency of customer contact and sales-cycle times, which are tracked in the CRM system, more important?
In decades past, data for performance evaluation was minimal. But today, organizations can drill down through reams of data -- gathered by human resources, on the shop floor, within service organizations, and from integrated processes that span the organization -- to find better metrics for managing talent.
Any single piece of data is, of course, meaningless by itself. Data analysis is required, involving comparisons between relevant data, in order to derive meaningful interpretations.
The A's were drawn to on-base percentage because of its relationship to winning. They studied 70 years of statistics to build an entire model of success. It was a model based not upon the characteristics of people -- such as size, speed, athleticism and leadership abilities -- but on skills.
In seeking to properly value talent, the leaders of the A's took a different approach, carefully and methodically separating championship skills from the player. In their model, as described in Moneyball, success (i.e., a championship) could be predicted by monitoring certain statistics, such as on-base percentage.
In turn, on-base percentage was found to result from specific activities (e.g., hits and walks), which arose from specific skills (in this example, plate discipline). Because evidence suggested that plate discipline might be more innate than learned, it was adopted as a skill to recruit for, even at the lowest levels.
Some of Moneyball's best scenes showed scouts exasperated with the directive to discount athletic qualities that stirred their romantic imaginations in favor of wonkier measures. But the new data-driven, albeit, less emotionally stirring, approach worked.
Such analysis may be trickier in business than baseball because of the vast variety of skills, activities, metrics and even definitions of winning. But trickier doesn't mean impossible.
For example, in production processes, Statistical Process Control or Theory of Constraint approaches use an analytical framework for turning facts into insights that improve performance. And these approaches guide the user to interpretations based on a deeper analysis of skills, rather than just results.
For example, they might focus on the location of bottlenecks in an upstream process, rather than purely on outcome data, such as how many units are completed per hour. These more sophisticated approaches to operational performance management facilitate construction of models that link success back to certain activities and skills.
So, why aren't companies applying the same principles in the HR department? Quantitative analyses can show which skills lead to success -- and which skill deficits lead to bottlenecks.
HR executives need to determine which skills are important by breaking down accepted metrics (e.g., widgets produced, billable hours, occupancy rates) into more actionable, skill-based components.
The final component of QTM is information transparency. That's nothing new to baseball, where all players live in a world of constant feedback, with statistics that show their performance in real time. That's good. Top performers want to know what the goals are and how they are doing.
And most businesses can do a better job of compiling and sharing metrics that depict employee value. If your data gathering and analysis efforts provide the metrics, it only makes sense to be transparent with the information they provide. Let your employees see the metrics in real time.
Benefits of a Quantitative Approach
For the Oakland A's, the Moneyball approach lowered talent acquisition and replacement costs. Oakland's cost-per-hire dropped significantly for both mid-career and emerging players. They could pay less for a mid-career free agent because plate discipline was undervalued in the marketplace. They could trade stars overvalued by the market, such as "closer" relief pitchers, for younger, less expensive, and/or better fitting talent.
And they could lower their risks in drafting emerging players because their advanced statistics could better predict future success.
The end result of the new focus was that the A's delivered baseball's highest ROI for talent, the most wins per payroll dollar.
Selecting Useful Metrics
We recently worked with a global retail company that operated on thin margins, and knew that in-person customer service was a key differentiator in competing with online rivals.
Traditionally, the company had recruited and rewarded store managers using a single metric: store sales. And, not surprisingly, employees responded to their incentives with excellent, yet sometimes counter-productive performance.
In short, although same-store sales grew, so did costs -- and profitability suffered. For example, purchases of back-office store supplies were not tracked, relative to depletions, and many managers ordered far more than they needed. One even leased an offsite storage facility because all of the extra supplies wouldn't fit in his back room.
An analysis of the store's data yielded information that was used to drive more effective performance. For example, stores with greeters had higher sales volumes. That was instructive, but , the QTM approach went deeper, to determine the activities that drove those sales.
In this case, the company realized the sales results were driven by customer engagement. Greeting was an important component of engagement, but so were other activities, such as initiating conversations and matching customer types to display areas.
The company also analyzed high-margin point-of-sale purchases, which revealed the importance of merchandising and suggestive selling skills.
In the area of transparency, the company revised its approach to incentive compensation by using individual data (rather than store data) to determine performance metrics. It identified managing costs and merchandise selling as valuable skills, and gave pay increases and public praise to employees with those skills.
These changes improved employee satisfaction, because managers now had tools to improve -- or at least recognize -- their limitations. The company was also able to lower its acquisition costs, because with better clarification of needed skill sets, it was more easily able to narrow its candidate pools.
Struggles with Attrition
In another situation, we worked with an inbound contact center interested in using QTM to improve its hiring processes. Like most contact centers, it struggled with employee attrition. Furthermore, new developments in the industry, such as an increasing focus on social media and a trend toward at-home employment, had the potential to change the skill set required for success.
Traditionally, the data used for talent management in this industry has been outcome-based: call time, hold time, calls per hour, resolution time and customer satisfaction.
But what really constitutes success in this arena?
Given the increasing adoption of self-service capabilities and an increasing focus on customer experience, a more important metric might be first contact resolution. And employees need soft skills to succeed at that. They must be able to listen and logically distill the call input into manageable resolution steps.
This client was using three tools in its interview process: a math assessment, a typing assessment and an in-person interview. After analyzing these tools against the employees' actual performance, we found that the in-person interview was the worst predictor of future success.
We also increased the organization's transparency. Although, like many call centers, it displayed site- or team-based metrics to ensure cultural continuity, it adopted the QTM approach by showing individual employees their own performance metrics. That gave each one a framework for improving performance and compensation.
The benefits of a carefully crafted, quantitative approach can improve hiring processes, employee engagement, and many other components of talent management similar to the way the use of "sabermetrics" transformed the Oakland A's.
Specifically, the increased focus on workplace analytics and transparency can result in:
* Lower cost-per-hire: Instead of overpaying for highly thought of, yet vague qualities such as an Ivy League education, a company can hire for specific traits, skills and competencies that may be undervalued in the marketplace.
* Better retention: Armed with knowledge of the abilities that predict future success ? whether success is defined as staying happily in the same job or moving up through the ranks -- companies can reduce hiring risks.
* Situational deployments: With a better understanding of which skills are needed in which situations, companies can better tap the right person for the right assignment at the right time.
* Improved Incentives: By showing individuals which skills contribute to corporate success, and how they are performing relative to those measures, companies can give employees the right incentives in real time.
And the Beat Goes On
The only constant is change. In a competitive marketplace that inevitably produces followers of winning approaches, the Oakland A's succumbed. After experiencing far greater success than their budget should have allowed, they saw the wealthier Boston Red Sox adopt a similar approach -- of assigning value for previously undervalued skills -- and go on to win their first championship in 86 years in 2004. The A's have still never won a championship since adopting this approach.
But there is no single championship in the corporate world. Some companies will lead, some will follow, and some will fail to stay in the game due to poorly structured performance goals and metrics.
The name of the game is change, and using a qualitative talent management approach allows companies to be flexible, dynamic and respond to volatile conditions in the marketplace. To stay on top of the game, companies must discover the most important information to track, which may be hidden within volumes of less relevant data.
And they must remain ever ready to adapt and make adjustments along the way.
Beth Bovis is a partner with A.T. Kearney, the global management-consulting firm. She has more than 18 years of global consulting experience with a focus on organizational transformation, talent management, SG&A functional excellence, offshore strategies and change management, and can be reached at (312) 223-6696. Adam Pressman is a principal at A.T. Kearney, where he is a member of the global retail industry practice and is one of the leaders of the firm?s Strategic Information Technology Practice. He can be reached at (312) 223-6103. Dan Gagne and Braxton Sisco are both managers at A.T. Kearney and members of the Organization & Transformation practice. Gagne has led engagements in multiple industries and specializes in restructuring and change management initiatives in both the commercial and public sector. Sisco has led engagements focusing on executive-level issues across strategy, organizational transformation and procedure for multiple industries, with significant depth in both the CPG and pharmaceutical/healthcare operations.