2012 metrics and analytics_patterns of use and value

A report by WorldatWork and Mercer

July 2012

2012 Metrics and Analytics: Patterns of

Use and Value

rese

arch

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About Mercer

Mercer is a global leader in human resource consulting and related services. The firm works

with clients to solve their most complex human capital issues by designing and helping

manage health, retirement and other benefits. Mercer’s 20,000 employees are based

in more than 40 countries. Mercer is a wholly owned subsidiary of Marsh & McLennan

Companies (NYSE: MMC), a global team of professional services companies offering

clients advice and solutions in the areas of risk, strategy and human capital. For more

information, visit www.mercer.com.

2012 Metrics and Analytics: Patterns of Use and Value 1

Table of Figures Table 1: Frequency of analytics used within the compensation function ............................................................ 8

Figure 1: Impact on compensation decisions ..................................................................................................... 8

Table 2: Who is requesting workforce analytics ................................................................................................. 9

Table 3: Organizations hold an adequate level of skill within the compensation function to perform the following

analytics ..................................................................................................................................................... 9

Figure 2: HR analysis performed by organizations ............................................................................................. 9

Figure 3: Number of FTEs required for HR-related analytics ........................................................................... 10

Table 4: Organizations with an agreed-upon and single source definition of headcount .................................. 10

Table 5: Raw data and tools exist to perform the following .............................................................................. 11

Figure 4: Raw data and/or tools exist for the following types of data ................................................................ 12

Table 6: Usable and/or reliable data types ....................................................................................................... 13

Figure 5: Usable and/or reliable data types...................................................................................................... 14

Table 7: Perceptions of data within the organization ........................................................................................ 14

Figure 6: Future of analytics ............................................................................................................................ 15

Figure 7: Total number of employees .............................................................................................................. 15

Table 8: Industry .............................................................................................................................................. 16

Table 9: Organization type ............................................................................................................................... 16

Figure 8: Voluntary turnover ............................................................................................................................ 17

Figure 9: Country of residency ......................................................................................................................... 17


Introduction & Methodology

This report summarizes the results of a February 2012 survey of WorldatWork members to gather

information about current trends in metrics and analytics. The focus of this research is to better

understand what types of analytics are conducted and what technologies are used within

organizations.

On Feb. 15, 2012, survey invitations were sent electronically to 5104 WorldatWork members.

Members selected for participation specifically noted compensation or HR generalist in their title

and/or area of responsibility. The survey was open to all members— domestic, Canadian and foreign

—meeting specific criteria.

The survey closed on March 2, 2012, with 693 responses, a 14% response rate. The final dataset was

cleaned, resulting in 560 responses.

In order to provide the most accurate data possible, data were cleaned and analyzed using statistical

software. Any duplicate records were removed. Data comparisons with any relevant, statistically

significant differences are noted with this report.

The demographics of the survey sample and the respondents are similar to the WorldatWork

membership as a whole. The typical WorldatWork member works at the managerial level or higher in

the headquarters of a large company in North America.

The frequencies or response distributions listed in the report show the number of times or percentage

of times a value appears in a dataset. Due to rounding, frequencies of data responses provided in this

survey may not total exactly 100%.


Too Focused on Benchmarks

New survey shows that many organizations may not be using all of the analytical tools available in

making the most effective pay decisions, continuing to rely on external/internal benchmarking

techniques while not utilizing the more advanced analytical methods such as simulations and

predictive modeling.

Survey highlights:

� The “2012 Metrics and Analytics: Patterns of Use and Value Survey,” conducted February 2012

by WorldatWork and Mercer, asked compensation leaders at more than 560 North American

organizations how metrics and analytics are used in their decision making.

� Within the compensation function, organizations are more likely to use ongoing reports and

benchmarking among internal and external peer groups to guide their decisions, as opposed to

more sophisticated analytical techniques such as projections, simulations and predictive

modeling. Furthermore, there is a higher degree of faith that these less sophisticated analytics

make for better decision making.

� Respondents, primarily compensation practitioners, say they lack access to and confidence in

data regarding education, competencies/capabilities and training investments — data that are

often at the heart of modern workforce analytics.

In the survey of more than 500 organizations, 95% say they use analytics to externally benchmark

(and 78% of them use it often), yet only 43% use it for predictive modeling (and 12% of them use it

often). In fact, use of advanced tools trails off significantly as they become more sophisticated, as

outlined in Exhibit 1. Whichever type of analytics is used, its use leads to better decisions, albeit at

varying degrees, according to respondents.

Compensation professionals may be falling behind their peers in other HR functional areas in the use

of increasingly sophisticated analytics methodologies.

2012 Metrics and Analytics: Patterns of Use and Value 4 Exhibit 1

Range of

analytical

strength

Types of analytics used

today within the

compensation function

…

… perceived by

participants to lead to

better compensation

decisions (Participants who did not use

the specified analytic were

excluded from this figure.)

Less

powerful

More

powerful

Ongoing reporting 87% 75%

External benchmarks 95% 94%

Internal benchmarks 89% 87%

Projections 80% 71%

Simulations 64% 61%

Predictive modeling 43% 52%

Source: WorldatWork and Mercer “2012 Metrics and Analytics: Patterns of Use and Value Survey”

What accounts for less usage of more powerful analytics?

Inadequate skill level? Maybe. Two out of every three respondents (67%) — many of whom are

compensation practitioners — indicate that they have an adequate level of skill to perform

sophisticated analytics such as projections, simulations and predictive modeling.

Limited staffing resources? Perhaps. Almost half of respondents (47%) have one to two full-time

equivalent (FTE) employees responsible for HR-related analytics, which would equate to five to 10

people spending 20% of their time on analytics. Given that half of the organizations have between

1,000 and 10,000 employees, one to two FTEs sounds about right for organizations that are just

starting to delve into deeper workforce analytics.

Uninterested leadership? No. According to the survey, three-fourths of respondents indicate

their top/C-suite executives and their HR leaders have requested workforce projections, simulations

or predictive modeling. Furthermore, 74% say C-suite leadership has confidence in the accuracy and

reliability of the data.


Limited availability and poor quality of data? Very likely according to respondents who

indicate that some data are simply not available. See Exhibit 2. Moreover, 75% of respondents say

they are undergoing developments to improve the consistency of their global data. And, 52% say it’s

unclear who has responsibility for data integrity.

Frankly, we have reason to be skeptical; unavailable data may signal more of a lack of interest in the

data than an ability to access it. While such data elements are often less complete or accurate than pay

data, it is noteworthy that those in other parts of human resources, such as talent management and

workforce planning, routinely rely on such data to determine their policies and practices.

In a true total rewards environment, key variables reflecting workforce capability — for example,

education levels, competencies, and training and development investments — should be available to

track outcomes. However, there is a sharp dropoff relating to existence of key total rewards-related

data and tools. This may signal a continued preoccupation of the rewards community with the

behavioral or motivational side of rewards, as in assessments of the pay-performance relationship,

and neglect of the “asset side” of the equation, that is, the effect of rewards on the ability of the

organization to secure the right kinds of people. If they are not asking questions about the latter, it is

no wonder they would not be insisting on acquiring these kinds of data.

Exhibit 2

0% 20% 40% 60% 80% 100%

Employee utilization of learning opportunities

Training and development investments

Employee competencies

Employee’s prior work experience

Employee’s educational attainment

Reporting structure

Salaries, incentives, benefits

Grade/band

Performance ratings

Internal promotions and transfers

Retirement eligibility

Termination

Manager f lag, supervisor ID

Job function, families, roles

Contingent and contract employees

Headcount, FTE, staff ing ratios

Raw data exists within the organization

Tools exist to generate reports with this data

2012 Metrics and Analytics: Patterns of Use and Value 6 Given all of the above, it is not surprising that the wish list of tasks compensation professionals would

like to better explore includes at the top the following two choices (57% of respondents indicated one

or both of the following):

√ Whether our rewards strategy effectively motivates and engages our best-performing employees

√ Which elements of our rewards strategy (e.g., compensation, benefits, work-life, careers) effectively motivate our best-performing employees.

What does it all mean? Today, the employment deal consists of much more than the competitiveness of pay or how suitably

rewards are motivating employees to perform well. External analysis to gauge market competitiveness

of course remains vitally important to any rewards assessment. But gauging the market

competitiveness of rewards requires assessment of the value of a host of factors related to career

advancement, learning and development, work environment, culture, etc. — factors that cannot be

fully captured in market or competitor surveys.

To get at these increasingly important elements requires a deeper look within, the kind of insight that

can only come from empirical analyses of the actual workforce and the business impact of specific

practices. This is where high-end analytics like predictive modeling come into play. To get there,

practitioners must push their thinking to look at a broader set of factors and implications for pay.

They must determine what employees really value as well as how careers unfold, and how they affect

an organization’s workforce and drive business performance.

Compensation professionals may be falling behind other HR functions in this era of big data as

internal labor market data analysis and fact-based decision-making become the norm throughout

human resources. This is likely not the consequence of inadequate analytical know-how — indeed,

historically, the compensation function has been a leader in analytical orientation and capability

within human resources — but more a reflection of rewards professionals focused on too narrow a set

of questions concerning market competitiveness and pay-performance sensitivity, and not thinking

sufficiently about the role of rewards in driving human capital development as well as current

business performance.

2012 Metrics and Analytics: Patterns of Use and Value 7 As a result, professionals may want to consider:

• Rebuilding a culture of analytics by examining a broader set of data and utilizing more

sophisticated analytical processes for critical decision making.

• Living up to the total rewards philosophy by tracking career velocity and movement, and

accounting for important elements, such as training, education, developmental moves within

an organization and lateral transfers.

2012 Metrics and Analytics: Patterns of Use and Value 8 Table 1: Frequency of analytics used within the compensation function “How frequently does your organization use the following types of analytics within the compensation function?”

Mean

Never (4)

Seldom (3)

Sometimes (2)

Often (1)

A. Ongoing reports (e.g., headcount reports, turnover reports) (n=551)

1.51 4% 9% 21% 66%

B. External benchmarking (e.g., data comparisons to a standard external point of reference) (n=550)

1.27 1% 4% 17% 78%

C. Internal benchmarking (e.g., data comparisons to an internal reference such as another division or line of business) (n=547)

1.47 3% 8% 23% 67%

D. Projections (e.g., future forecasts based on current data) (n=535)

1.85 4% 17% 41% 39%

E. Simulations (e.g., “what-if” scenarios) (n=543)

2.21 9% 26% 40% 24%

F. Predictive modeling (e.g., statistical or regression analysis of current and historical data to make predictions about future events under multiple scenarios) (n=533)

2.67 22% 35% 31% 12%

Figure 1: Impact on compensation decisions “Our organization makes better compensation decisions as a result of our … ” Participants who answered “Never” in Table 1 were excluded from this analysis.

0% 20% 40% 60% 80% 100%

F. Predictive modeling (n=338)

E. Simulations (n=408)

D. Projections (n=441)

C. Internal benchmarking (n=476)

B. External benchmarking (n=493)

A. Ongoing reports (n=457)

10%

7%

5%

3%

1%

6%

39%

33%

24%

10%

4%

19%

52%

61%

71%

87%

94%

75%

Strongly disagree/disagree Neutral Strongly agree/agree

2012 Metrics and Analytics: Patterns of Use and Value 9 Table 2: Who is requesting workforce analytics “Please rate your level of agreement with the following statements:”

Strongly

disagree/disagree Neutral

Strongly agree/agree

A. Our top/C-suite executives have requested workforce analytics (e.g., projections, simulations, predictive modeling) (n=468)

13% 11% 76%

B. Our divisional business leaders have requested workforce analytics (e.g., projections, simulations, predictive modeling) (n=461)

16% 16% 68%

C. Our line managers have requested workforce analytics (e.g., projections, simulations, predictive modeling) (n=452)

35% 31% 34%

D. Our HR leaders have requested workforce analytics (e.g., projections, simulations, predictive modeling) (n=473)

13% 11% 77%

Table 3: Organizations hold an adequate level of skill within the compensation function to perform the following analytics “Within the compensation function, we have an adequate level of skill to conduct ... ”

Strongly



A. Basic analytics such as ongoing reports and benchmarks (n=490)

3% 1% 96%

B. More sophisticated analytics such as projections, simulations and predictive modeling (n=483)

17% 16% 67%

Figure 2: HR analysis performed by organizations “HR analysis is completed by the following in your organization (choose one):” (n=495)

0% 20% 40% 60%

Don’t know

We are decentralized but plan on creating an analytics CoE within 12

months

Analysis is centralized in an analytics center of expertise (CoE)

Analysis is decentralized throughout various parts of the organization

9%

5%

39%

47%


Figure 3: Number of FTEs required for HR-related analytics “In your organization, the full-time equivalent of people responsible for HR-related analytics (e.g., would be 1 full-time equivalent [FTE] if 5 people spend 20% of their time in analytics):” (n=495)

0% 10% 20% 30% 40% 50%

Don’t know

More than 10 FTEs

6-10 FTEs

3-5 FTEs

1-2 FTEs

0 FTEs

11%

4%

5%

19%

47%

13%

Table 4: Organizations with an agreed-upon and single source definition of headcount “Please rate your level of agreement with the following statement: ”

Strongly



A. Our organization (including finance, IT and HR) has an agreed-upon and single source for the definition of headcount (n=459)

26% 10% 64%

2012 Metrics and Analytics: Patterns of Use and Value 11 Table 5: Raw data and tools exist to perform the following “Please respond regarding the following types of data … ”



Yes Responses Yes Responses

A. Headcount, FTE, staffing ratios 99% 463 93% 459

B. Contingent and contract employees 88% 417 71% 395

C. Job function, families, roles 93% 460 81% 450

D. Manager flag, supervisor ID 94% 440 90% 438

E. Terminations and termination type/reason 98% 466 92% 459

F. Retirement eligibility 88% 371 80% 365

G. Internal promotions and lateral transfers 91% 455 77% 446

H. Performance ratings 95% 459 86% 455

I. Grade/band 94% 448 89% 445

J. Salaries, incentives, benefits 98% 468 91% 458

K. Reporting structure (e.g., organizational unit) 93% 467 85% 453

L. Employee’s educational attainment and background

65% 434 51% 414

M. Employee’s prior work experience 50% 427 32% 408

N. Employee competencies 45% 422 39% 401

O. Training and development investments 62% 412 53% 398

P. Employee utilization of learning opportunities 59% 394 53% 387

2012 Metrics and Analytics: Patterns of Use and Value 12 Figure 4: Raw data and/or tools exist for the following types of data “Please respond regarding the following types of data … ” (n varies)

0% 20% 40% 60% 80% 100%





Employee’s educational attainment

Reporting structure


Grade/band

Performance ratings



Termination

Manager f lag, supervisor ID


Contingent and contract employees

Headcount, FTE, staffing ratios



2012 Metrics and Analytics: Patterns of Use and Value 13 Table 6: Usable and/or reliable data types “Please respond regarding the following types of data … ”

Generally speaking, I trust this data

This data is used in decision making

Yes Responses Yes Responses

A. Headcount, FTE, staffing ratios 91% 463 97% 447

B. Contingent and contract employees 70% 387 76% 357

C. Job function, families, roles 84% 440 84% 424

D. Manager flag, supervisor ID 86% 428 84% 395

E. Terminations and termination type/reason 89% 460 88% 426

F. Retirement eligibility 85% 354 78% 332

G. Internal promotions and lateral transfers 77% 441 82% 407

H. Performance ratings 89% 429 90% 430

I. Grade/band 92% 440 91% 426

J. Salaries, incentives, benefits 95% 459 96% 454

K. Reporting structure (e.g., organizational unit) 83% 446 90% 428

L. Employee’s educational attainment and background

49% 349 56% 332

M. Employee’s prior work experience 45% 312 52% 318

N. Employee competencies 42% 310 51% 308

O. Training and development investments 54% 321 54% 313

P. Employee utilization of learning opportunities 54% 320 52% 302

2012 Metrics and Analytics: Patterns of Use and Value 14 Figure 5: Usable and/or reliable data types “Please respond regarding the following types of data …” (n varies)

0% 20% 40% 60% 80% 100%





Employee’s education and background

Reporting structure


Grade/band

Performance ratings



Termination type/reason

Manager flag, supervisor ID


Continget/ contract employees

Headcount, FTE, staffing ratios

Generally speaking, I trust this data

This data is used in decision making

Table 7: Perceptions of data within the organization “Please rate your level of agreement with the following statements:”

Strongly



A. Our C-suite leadership has confidence in the accuracy and reliability of our data (n=439)

7% 20% 74%

B. Our organization is undergoing data audits/cleanup in relation to our data (n=434)

17% 15% 69%

C. Our organization is undergoing developments to improve the consistency of our global/international data (n=345)

9% 17% 75%

D. It is clear what roles in the organization are responsible for maintaining data integrity (n=456)

26% 22% 52%

E. On average, the user interface of our analytics technology and tools is intuitive and easy to use (n=432)

44% 30% 26%

2012 Metrics and Analytics: Patterns of Use and Value 15 Figure 6: Future of analytics “What would you like to better explore within your organization that you are unable to do today? (Choose top three.)” (n=452)

0% 20% 40% 60%

Other

How we can effectively segment our workforce to identify critical talent segments

How our rewards strategy needs to adjust to changing demographics or generational trends

Where we can reduce or reallocate workforce costs (e.g., headcount, benefits, compensation) without diminishing the quality of output

Whether our current sources of talent will fulf ill our future business needs

The critical drivers of employee retention in our organization

Which elements of our rewards strategy (e.g., compensation, benefits, worklife) effectively motivate our best-performing employees

Whether our rewards strategy effectively motivates and engages our best-performing employees

1%

22%

32%

34%

40%

46%

57%

57%

Figure 7: Total number of employees “Please choose the total number of employees your organization employs worldwide:” (n=466)

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

2%

4%5%

15% 16%

19%

14%

9% 9%

6%

2012 Metrics and Analytics: Patterns of Use and Value 16 Table 8: Industry “Please choose one category that best describes the industry in which your organization operates:” (n=466)

Industry Percent

Finance & Insurance 14%

All Other Manufacturing 12%

Healthcare & Social Assistance 11%

Utilities, Oil & Gas 7%

Consulting, Professional, Scientific & Technical Services 6%

Information (includes Publishing, IT Technologies, etc.) 6%

Public Administration 5%

Retail Trade 5%

Educational Services 4%

Computer and Electronic Manufacturing 4%

Transportation 3%

Pharmaceuticals 2%

Other Services (except Public Administration) 2%

Agriculture, Forestry, Fishing & Hunting 1%

Wholesale Trade 1%

Real Estate & Rental & Leasing 1%

Arts, Entertainment & Recreation 1%

Mining 1%

Construction 1%

Other 15% Table 9: Organization type “Your organization is:” (n=461)

Type Percent

Public sector (local, state, federal government) 20%

Private sector — publicly traded 38%

Private sector — privately held 29%

Nonprofit/Not-for-profit (educational organizations, charitable organizations, etc.)

14%

2012 Metrics and Analytics: Patterns of Use and Value 17 Figure 8: Voluntary turnover “What is the approximate annual voluntary turnover for employees in your organization?” (n=442)

0% 10% 20% 30% 40%

41% or more

27-40%

21-26%

16-20%

11-15%

6-10%

0-5%

2%

2%

4%

9%

18%

39%

26%

Figure 9: Country of residency “In which country do you reside?” (n=242)

0%

10%

20%

30%

40%

50%

60%

70%

80%73%

16%

2% 2%1% 1%

2012 metrics and analytics_patterns of use and value

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