performance and valuation

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CHAPTER 19An Economic View of the Impact

of Human Capital on FirmPerformance and Valuation

Mark C. UbelhartPractice Leader, Value-Based Management, and Architect,

Human Capital Foresight, Hewitt Associates

Everyone knows that human capital and intellectual property form thecore drivers of our global economic growth today. Consequently, how

best to measure these driver effects become paramount to shareholderwealth creation. This chapter explains the measurement of the movementof pivotal employees between firms and their effect on shareholder valuereturn.

In order to examine business valuation implications from a human cap-ital standpoint, cross-company, longitudinal data becomes essential. Whilemaking use of such data forms the empirical underpinnings of modern cor-porate finance, that usage is new to human resources (HR) as a function;consequently, it is unavailable to investors in general.

Of course, investors can obtain a glimpse of the highest-level pay prac-tices from proxy disclosures, but see virtually nothing below or beyondthose disclosures. Hewitt possesses an exceptionally rich database. Thatdatabase includes well over 1,000 companies and 20 million employees de-rived from compensation and benefit surveys. It also includes the out-sourced administration of many HR activities to Hewitt Associates as well.For our research, we gathered and used all the data possible, while carefullypreserving company confidentiality and individual privacy. Our intent mir-rored accomplishments using data and associated measurements in otherdisciplines like marketing and finance.

508

E1C19 08/14/2009 Page 509

CREAT ING AND STANDARD I Z I NG METR I CS

Once we de-identified individual data covering the 20 million employees bygiving them unique numbers, we discovered that some employees in onecompany one day arose in another company the next. In other words, theychanged jobs. We could observe the workings of a microcosm of the U.S.labor market, as employees transitioned between companies.

Knowing we now had the capability to measure the flow of employeesinto and out of organizations, our next step was to devise a metric—calledthe Talent QuotientTM (TQTM). TQ quantified these employee transitionsby measuring the relative proportion of employees leaving or joining thecompany who are pivotal employees. Simply put, pivotal employees pro-duce a disproportionate impact on the business. TQ reveals the propor-tional magnitude of pivotal employees leaving the organization who arecritical to the firm’s success.

The standardized definition of pivotal employees relies on incremen-tal investment measured by percentage pay progression, adjusted for age,pay, and tenure. This definition captures management decisions regard-ing individual employees in a systematic manner applicable to cross-company analysis and linkage to business performance and valuecreation. The top quartile percentage pay progressors are identified aspivotal. Consequently, a standard TQ score of 100 means 25 out ofevery 100 departing employees were considered pivotal—equal to thepercentage of total employees so defined within the company. A higherscore means fewer than 25 out of 100 departing employees were piv-otal—that is, a better retention rate for such critical employees—and thereverse, that a lower score means less retention of them. The TQ score iscompensation-dollar weighted so that a lower-paid departing person hasless impact in the calculation. It is not a head-counting turnover statisticbut a financial one.

Of course, one can take issue with the definition; for example:

& What if pay decisions are made poorly such that those so identified aspivotals really are not? The law of large numbers helps out, as somelevel of poor pay decisions or other anomalies characterize most com-panies, but with many companies and over time, an element of self-correction occurs. In some cases, we went back 10 years in applyingthis definition, and for all companies in our database we went back atleast five years.

& And even if these pay decisions are poorly made, what does it say abouta company where the people receiving the highest pay increases areleaving?

An Economic View of the Impact of Human Capital 509

E1C19 08/14/2009 Page 510

Certainly some companies may have separate internal lists of highpotentials and people with the highest performance ratings, but these listsare not available in cross-company data and are not consistently developed.Moreover, in our piloting process, clients encouraged us to rely on pay pro-gression rather than internal lists because they believed their own internallists were suspect.

PRED I CT ING FUTURE F I NANC IA L R ESULTS

With a measure of the flow of pivotal employees in and out of a company,testing its relationship to business value creation became possible. Well-per-forming companies are likely to retain more of their pivotal employees, andat the same time the retention of more pivotal employees is likely to contrib-ute to subsequent financial performance and value creation. We had to sep-arate these effects. We removed reverse casualty by handicapping companyperformance—that is, whether a company was performing well was takeninto account at the start of the time period analyzed, which used prior-period TQs together with subsequent performance. The process used is il-lustrated in Exhibit 19.1, and greater detail is available in the article

EXHIBIT 19.1 Linkage to Business Results and Cash Flow Return on Investment(CFROI)

510 THE VALUATION HANDBOOK

E1C19 08/14/2009 Page 511

‘‘Optimizing Human Capital Investments for Superior Shareholder Re-turns’’ by Samir Raza (Valuation Issues, February 2006).

Most readers of this publication will be at least somewhat familiarwith cash flow return on investment (CFROI

11) and variations of it. Forthose less familiar, please refer to Rawley Thomas and Robert Atra’sChapter 11, ‘‘The LifeCycle Returns Valuation System.’’ Also, refer toBartley Madden’s book (Madden 1999) and his Chapter 3, ‘‘Applying aSystems Mind-Set to Stock Valuation.’’ We incorporated CFROI into theanalysis because of its measurement advantages—both over time and acrossindustry. CFROI’s conceptual validity as an economic return measure ofbusiness performance and the prevalence of its use by analysts and inventorsadd to its advantages.

Research suggests that stock price level links to CFROI level, whilestock price change correlates to the market’s expectations of CFROIchange.

Exhibits 19.2 and 19.3 summarize the results, namely that a 10-pointdifference in TQ predicts a 0.7 percent and 1.6 percent difference in CFROIfor standard industrial companies and financial services, respectively.

Exhibit 19.4 shows a more dramatic picture by comparing the bestto worst in TQ and their subsequent financial results on measures otherthan CFROI.

Two individual company case studies are represented in Exhibits 19.5and 19.6, to complement and reinforce the results found in the multicom-pany analysis.

EXHIBIT 19.2 What Is the Impact of Loss or Gain of Talent on Future BusinessPerformance?


E1C19 08/14/2009 12

D i agnos t i c Benchmark i ng

A crucial characteristic of financial reporting is its standardization throughgenerally accepted accounting principles (GAAP). Credit Suisse HOLT andLifeCycle Returns go beyond conventional reporting to recast results so thatthey are much more reflective of economic cash flow performance and moreaccurate for comparison over time and company to company. No suchequivalent exists in the domain of human capital. This fact alone stifles ac-countability and reporting, and that, in turn, stifles HR’s use of data in adecision science framework, as there are no external anchors creating pres-sures on firms to more effectively manage their pivotal employees.

In fact, the development of TQ is a step in the direction of measuringand managing pivotal employees, as any company can report it in thestandardized manner used in our analysis. At the same time, any com-pany can compute its own customized TQ, using its own internally

EXHIBIT 19.4 TQ Impacts Business Results—Cross-Industry Study

Average2004TQ

Sales Growth 3Years Annualized(Ending 7/07)

Total Return 3years Annualized(Ending 7/07)

Price toBook (at7/07)

Worst10 inTQ

84 Median 5.8% 7.1% 1.8

Best10 inTQ

141 Median 8.0% 13.2% 2.9

EXHIBIT 19.3 Is There a Greater Talent Leverage in Industries That Are RelativelyMore Dependent on Human and Intellectual Capital?


E1C19 08/14/2009 Page 513

defined pivotal employees, just like it can define and report non-GAAPfinancial metrics.

Exhibit 19.7 reveals TQ results for various industries. Please note thatmost companies score above 100, meaning they do a better job of retainingtheir own critical employees than retaining all other employees. However, awide range does exist, and where on this spectrum any individual company(or business unit within the company) lies is telling in an overall human cap-ital performance context. Since the TQ metric predicts future financial per-formance, its importance is underlined.

ORGAN I ZAT I ONAL DECOMPOS I T I ON

Like financial metrics, in order to understand where and what is driving re-sults, drilling down becomes necessary. The top half of Exhibit 19.8 por-trays the parallel between financial and human capital metrics.

Just as firms decompose earnings and economic profit by organizationunit, so can the Talent Quotient. As an example, Exhibit 19.9 reveals theTQs broken into pay levels—broad employees, management, and executive

Relative Sales/Sq. Ft.

Growth in Sales/Sq. Ft.

Controllable Margin

Econometric Methods to

Normalize Store Performance for External Factors

Regression Model: Store Performance vs. TQ

Future Sales Sq Ft or Controllable Margin = Function of Human Capital Metrics (e.g., TQ)

(adjusted for reverse causality)

For each 10-point improvement in TQ…

1.5 to 2.0% improvement in sales per square foot

EXHIBIT 19.5 Case Study—Big Box Retail Organization


E1C19 08/14/2009 Page 514

Clinical Trials—A Business Case for TQTo determine if the flow of talent matters for a particular pharmaceutical sales force, a detailed analysis was undertaken to measure the impact of TQ on sales performance across 22 sales units over four years. A two-step econometric model was developed to first capture the effect of past sales results on TQ, and then measure the impact of TQ on future sales performance. Not only arethe results consistent with the intuition of seasoned HR professionals, but for the first time, this landmark effort quantifies what was previously unmeasurable.

KEY FINDINGS

• TQ reliably predicts future sales performance. In other words, retainingcritical talent is a leading indicator ofhigher sales, and vice versa.

• A 10-point increase in TQ within this salesforce translates into approximately $40–$110 million per year in additional pretax operating income.

• Sales unit TQs were startlingly different,making a compelling business case tomanage TQ.

EXHIBIT 19.6 Case Study—Major Pharmaceutical Company

TQ retention values are typically over 100, but vary widely fromcompany to company

14013012011010090807060

Consumer Discretionary

Energy

Financials

Health Care

Industrials

Information Technology

TQ100

= Interquartile range: 25th–75th percentile= Median for industry

EXHIBIT 19.7 Industry TQ Retention Ranges


E1C19 08/14/2009 Page 515

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515

E1C19 08/14/2009 Page 516

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516

E1C19 08/14/2009 Page 517

groups. Here the particular company is assessed as being deficient in benchstrength, as it is disproportionately losing pivotal employees at the level justbelow executives.

Interestingly, analysts’ comments about this particular company raisethe same issue by conjecturing that it may not be able to grow organically—but only by acquisition—as it lacks internal management depth. Humancapital metrics can be decomposed in many ways, such as according to thedemographics of age, pay, tenure, location, ethnicity, and gender. They canalso be grouped in categories, such as those who participate in certain train-ing programs or other initiatives and those who do not.

Prescr i p t i v e I n s i g h t s

Once TQ is recognized as a linchpin metric that predicts future financialresults, stepping back to examine what drives TQ becomes not only possiblebut essential to decision making in the human capital arena. All of the di-mensions depicted in the octagon in Exhibit 19.8, and many more, can belinked to TQ using data available internally within a company or cross-company information where it exists.

The fundamental building block of such analysis is retention risk at theindividual level, as illustrated in Exhibit 19.10.

Each individual is assigned a score representing the risk of leaving theorganization within the next 12 months. These proprietary calculations uti-lize a neural network model continuously trained on:

TQ

TQ RETAIN

BROAD($50K–$125K)

MANAGEMENT($125K–$200K)

EXECUTIVE($200K+)

TOTAL($50K+)

120

110

90

100

80

100102

83

106

Example of Talent Quotient Retain by pay level, highlighting critical futureleadership gap for this company

TQ> 100RETAINING

PIVOTALEMPLOYEES

TQ< 100LOSINGPIVOTAL

EMPLOYEES

EXHIBIT 19.9 Organizational TQ Retention


E1C19 08/14/2009 Page 518

& Human capital data on the behavior of participants.& Employee demographics such as age, tenure, and gender.& Employee history, including pay progression and performance.& Company history of hiring and attrition.& National economic data such as housing statistics and price inflation or

deflation.& General labor market data such as regional unemployment.& Local market data such as household income and hiring trends.

This is precisely the methodology used to detect credit card fraud andstop transactions before they occur. It also utilizes concepts applied to de-velop FICO credit scores, which range from 0 to 800. In this case (as op-posed to FICO scores), a higher number means greater risk. Exhibit 19.11shows the probability that an individual employee with a particular reten-tion risk score will leave the company during the coming year.

Exhibit 19.12 employs the same individual scoring methodology toidentify prominent risk clusters of people and their characteristics. Such in-formation can inform talent management strategies to:

& Identify and target at-risk talent.& Target segments of risk for group interventions.& Discover retention performance differences across units.& Inform talent sourcing strategies.& Benchmark the company.

Jeff Singh

Anand Gupta

Carol Yu

Jim Smith

Employee

280

462

610

726

Retention Risk Score

Yes

No

Yes

Yes

Critical Talent

8002 1QerocS ksiR noitneteRIT Department: Terry Brown

HighestRisk

To help companies understand and manage their critical talent risk,the basic building block is the individual retention risk score.

Risk of leaving the organization in the next 12 months

Scores range from 0 to 800

EXHIBIT 19.10 Individual Retention Risk Score


E1C19 08/14/2009 Page 519

A Por t f o l i o V i ew

Mitigating risk is a good thing, but not if it costs too much. The cost ofintiatives aimed at improving retention rates can be compared to the result-ing improvement in TQ and its estimated financial benefit.

Exhibit 19.13 shows a portfolio view of retention risk incorporating thescoring methodology just described, applied to 75 companies in HewittAssociates’ database. It displays two different charts. The one on the leftsimply counts people, while the one on the right is compensation-dollarweighted and illustrates greater human capital risk. From the standpoints

EXHIBIT 19.11 Attrition Probabilities

Retention Risk ScoreProbability of Attrition(within 12 Months)

800 95%700 85%620 65%520 32%455 16%400 8%345 4%300 2%

“Builders”1% of population

1% of compensation investment

• Young, perhaps first job• Modest pay• Skewed toward Business Unit A• Tend to live in suburban-like

areas

“Hired Young Guns”3% of population


• Young, though likely not first job• High performing, relatively high pay• Tend to live in suburban-like areas

(educated, mid-to-high income, homeowners, married with children)

“Reaching for More”3% of population


• Average performers• Recently changed roles/

promoted, but with relatively small pay increase

• Tend to live in economically depressed areas

“Solid Opportunists”2.5% of population


• Average performing• Tend to move between roles• Have relatively high pay• Tend to live in suburban-like

areas

“Midcareer Misfires”3.5% of population


• Not first job• Low performers, slow pay

progression• Tend to live in low-income,

relatively uneducated areas

“Struggling Starters”2.5% of population


• Young, likely first job out of school

• Average to modest performers• Likely living in relatively

low-income areas

Most ConcernPivotal/Key Talent Concentration

Secondary Concern Modest Concern

RRS480

RRS500

RRS450

RRS470

RRS490

RRS460

EXHIBIT 19.12 Talent GuardianTM—Actual Client, Prominent Risk Clusters


E1C19 08/14/2009 Page 520

of both business valuation and the board of directors’ fiduciary responsibil-ity to manage the risk of pivotal employee loss, these retention assessmentsmatter to shareholders.

C l amor f or D i s c l o sure

No one denies the impact that human capital has on business valuation. De-spite its obvious importance, the absence of commonly accepted measure-ment and reporting standards creates a vacuum on the availability ofhuman capital metrics that matter. As mentioned previously, that same vac-uum inhibits a groundswell of external pressure from focusing its energy onchanging these conditions. A perfect storm is gathering and inevitable.Exhibit 19.14 represents just one indication.

In answer to the question posed during a live webcast by the HumanCapital Institute, 76 percent of the respondents stated they believed stan-dardized human capital metrics are coming within five years.

Another indication occurred in May 2008 at the joint Credit Suisse/Hewitt Associates conference—the first-ever joint conference of investmentprofessionals and corporate managements focused on human capital and itsimpact on valuation.

The Impact of Human Capital on Investment CapitalTuesday Evening, May 13, 2008

Hewitt and HOLT: Examining human capital metrics thatdrive corporate performance

Please accept this invitation for this joint conference of HewittAssociates and Credit Suisse HOLT. This program offers new

Number of People

Low Risk57%

High Risk22%

In Between

21%

Investment in People

Low Risk47%

High Risk24%

In Between

29%

EXHIBIT 19.13 Retention Risk Complexion Benchmarks


E1C19 08/14/2009 Page 521

insights on human capital management and the impact on CFROI1

levels.Hewitt Associates has a research database for nearly 20 million

individuals, a microcosm of the U.S. labor force. Credit SuisseHOLT’s database provides detailed cash flow and valuation analy-sis for more than 19,000 equities through the lens of the HOLTCFROI

1

Performance and Valuation Framework. Hewitt’s HumanCapital ForesightTM (HCFTM) initiative used the HOLT CFROI

1

framework and database in combination with their extensiveemployee data to examine the relationships between people-relatedmeasures, corporate performance, and valuation.

The results of this research project have been quite insightful:

& Industry-by-industry analysis and trends of the U.S. labor mar-ket, and the relevance of key human capital indicators to in-dustries in the HOLT CFROI

1

framework.& The Talent QuotientTM metric, a measure that stood out as a

leading indicator of future changes in business cash flowreturns.

& Research results from examining the corporate-level impact ofother metrics such as pay differentiation, pay at risk, and re-wards mix.

& Determining when issues such as employee turnover begin todramatically impact corporate cash flows.

When will standardized human capital metrics become a visiblepractice for leading companies? — 2007 Human Capital Institute Poll:

Accountability Reporting

28%

3% 2%

22%

45%

0%

10%

20%

30%

40%

50%

NeverBeyond 5 YearsFive YearsNext 2–3 YearsThis Year

EXHIBIT 19.14 Human Resources Measures . . . Management and InvestmentInformationSource:Human Capital Institute.


E1C19 08/14/2009 Page 522

& What companies are beginning to monitor internally, whatactions can they take as a result, and what investors may beginrequesting from management teams.

This program provides insights from two perspectives: Fromthe point of view of the investor analyzing company fundamentalswith an eye on stock price and from the planning and actions ofmanagement looking to improve business performance andvaluation.

The goal of business strategy is clear—to invest capital in a waythat maximizes shareholder value. Traditional capital budgetingand financial planning frameworks offer very little to guide humancapital investment decisions; yet pay and benefits typically consti-tute 30% to 70% of operating expenses. Hewitt’s Human CapitalForesightTM offers factual analysis grounded in data representingmore than 20 million people—in effect, a microcosm of the U.S.labor market.

The research based on this data has yielded HR metrics andinsights quantitatively linked to business results.

Credit Suisse Conference Center, 11 Madison Avenue, Level2B, The Club Room, New York, NY 10010

There are two sides to this story. Certainly, investors are clamoring formore disclosure. They seek an enhanced ability to both understand andvalue the human capital risks and associated circumstances of companies inwhich they invest. At the same time, managements—particularly leading-edge human resources (HR) functions—seek to apply decision scienceframeworks to the vast amount of data now available. They know that HRdata will provide their firms with a competitive advantage and they wel-come the external pressure that will reinforce their efforts.

MATHEMAT I CAL MODE LS GU I D I NGPRACT I CAL ACT I ON

At the core of Talent GuardianTM is a mathematical model that predicts thelikelihood an individual employee will quit within a specified time. Hewittdesigned the rest of Talent Guardian to achieve the greatest practical benefitfrom this basic insight of quitting behavior.

Predicting individual human behavior with a computer represents a no-toriously difficult challenge. The Human Capital Foresight team at HewittAssociates and partner Global Analytics overcame this challenge by a


E1C19 08/14/2009 Page 523

flanking attack: Rather than write a program to mimic the complexity ofhuman decisions, the Talent Guardian team developed a program—a math-ematical model—that learns by example from exhaustive trial and error tobehave as humans actually do in millions of actual employment histories inHewitt databases and in histories from subscribing employers.

To capture the complexity of human actions, Global Analytics andHewitt constructed Talent Guardian models using a proprietary, evolvedneural network structure. This approach differs from more traditionalmodel structures like those used in regression models. The model-buildingprocess analyzes the impact of both individual variables and the many com-binations of variables interacting with one another thereby changing eachother’s impact on a final outcome. The resulting models recognize complexpatterns of behavior and their most probable outcomes.

By dealing directly with the complex relationships in employment deci-sions, these models avoid misleading, oversimplified, single-cause explana-tions for complex human outcomes. As a consequence, their predictiveperformance is superior. (Models for each client vary in technical perform-ance, but are usually in the high 20s to low 30s on the Komogorov-Smirnov—K-S—test statistic.) Exhibit 19.15, based on real-world examplesfor over 200,000 employees, shows that Talent Guardian retention riskscores accurately predict subsequent actual attrition.

EXHIBIT 19.15 Predicted Retention Risk Scores versus Subsequent Actual Attrition


E1C19 08/14/2009 Page 524

Talent Guardian not only locates attrition risk in employee popula-tions, it also differentiates between those risks due to employee character-istics and those due to employer actions or policies. It guides interventionby identifying high-risk groups with common characteristics and by accu-rately monitoring the retention impact of specific interventions and policyactions.

Talent Guardian avoids the guesswork on whether retention successesor problems are due to employee characteristics or to employer actions;whether a group’s retention problems are inherent or created; or whetherapparent patterns across similar employees are real or random.

Talent Guardian is the most sensitive way to identify retention risks. Itrepresents the best tool to help overcome those risks.

Global Analytics supplies Talent Guardian’s predictive analytics. Glob-al’s management includes the inventor of neural network fraud detectionfor bank payment systems and other pioneers in the application of advancedanalytics to financial services. One pioneering executive became most re-sponsible for the widespread adoption of consumer credit scores and theway financial services firms manage consumer credit accounts today. Learnmore about Global Analytics at www.global-analytics.com.

NOTE

1. CFROI1

is a registered trademark in the United States and other countries(excluding the United Kingdom) of Credit Suisse or its affiliates. Credit SuisseHOLT is a division of Credit Suisse. CFROI is adjusted for asset age/life/mix,and allows for comparisons across companies.

RE F ER ENCE

Madden, Bartley J. 1999. CFROI valuation: Cash flow return on investment—Atotal system approach to valuing the firm. Woburn, MA: Butterworth-Heinemann.


performance and valuation

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