The number of choices for benchmarking pay data and pricing jobs is growing fast. Compensation professionals are faced with a dizzying array of benchmarking surveys, online databases, economists’ services, government statistics, and proprietary reports from recruiters, staffing companies, consultants, and even the media.  There are a number of exciting possibilities for using more powerful market intelligence, but the choices are confusing and it is challenging to determine what source to trust.    Join us as Greenwich.HR CEO Cary Sparrow shares how to take advantage of new data capabilities so your compensation team can create even more strategic value. Cary will discuss how compensation professionals are evolving to become the experts of talent economics and share practical examples of how you can adjust your compensation practices to reduce the time you spend on administration. In this webinar you will learn: • Current trends in pay & benchmarking data • Pros and cons of various types of data services • How to use your data to build a competitive benchmarking model • How to use big data to save time on your salary & comp planning process

1. TalentTakeaways
webinar & podcast series
How To Use Big
Data For Better
Compensation
Benchmarking
Guest Presenter: Cary Sparrow
Founder & CEO, Greenwich.HR

2. AGENDA
The Series
TalentTakeaways
webinar & podcast series
Talent Takeaways Series

3. AGENDAAGENDA
The Sponsor
Talent Takeaways Series
Talent Management Made for Managers
Compensation Planning
Total Rewards
Stay Interviews

4. Introduction
Solutions that bring next-generation labor market intelligence to all
audiences
Personal
• CEO and Founder Greenwich.HR
• VP (HR, IT) at Cargill, Inc.
• Global Practice Leader at Towers Perrin
(now Willis Towers Watson)
• Submarine Officer
• Engineer and Geek Dad

5. Today’s Discussion
• Setting The Stage
• How the growing array of powerful data options is impacting how we need
to approach pay benchmarking
• A framework for segmenting talent markets based on economic drivers
• What’s Out There
• The current landscape for pay data, including trends, risks, and
opportunities
• Getting The Most Value
• Approaches to help you get the most value from new and more powerful
data (for the business and your comp team)

6. Understanding Pay
Markets
Then
(Focal Point)
Well-defined sources
and processes
(surveys and
benchmarking)
Owned by
Compensation
Department
Highly manual
Now
(Many Silos)
Explosion of ‘market
data’ from online
sources
Many stakeholders
owning processes that
control the supply and
pay of talent
Still very manual
Future
(Integrated)
A suite of data sources
best suited to specific
situations
Tighter collaboration
across stakeholders
Stronger automation

7. Talent Markets With
Distinct Economics
• Talent At Rest – Current Employees
• Talent In Motion – Employees Changing Jobs
(typically to a different company)
• Talent For Rent – Contract, Temporary, and
Freelance workers

8. Based On A True Story
• A large company was expanding operations into a new country,
requiring them to add about 300 positions locally
• Key management and technical positions were sourced from
existing employees – pay, benefit, relocation decisions were based
on existing policies
• All other positions were intended to be recruited locally and paid
according to published market data
• 2-3 years ago, this would be the end of the discussion. And the
company would have failed to launch its new business.

9. Based On A True Story
BUT In This Case….
• Analysis of the local talent supply indicated the number of
recruiters with requisite skills employed in the market was 30, and
this company needed to fill 7 recruiting positions
• The company adopted a more aggressive compensation approach
for these positions
• To manage risk, they also engaged a local staffing company to fill
interim talent and recruiting needs, expecting a more challenging
talent situation than they originally planned
• The new site launched on time.

10. Talent At Rest
Who Are They: Current employees
Economics: Internal labor market; often very stable
• Exceptions: Hot Skills, talent segments
experiencing high turnover, and countries with
high wage inflation or key skills shortages
Typical Rewards Goals: Retention, Alignment
Data Sources: Compensation surveys and online
databases usually administered through compensation
departments
• Large survey firms
• Boutique survey shops specializing in specific
markets/industries
• Associations
• Online data sources (e.g., salary.com,
Payscale.com)
Strengths:
• Robust analysis
• Range of providers to fit budgets and desired
precision
Limitations:
• Time delay between initial collection and final
outcomes
• Administrative burden
• Inconsistent sampling/precision
• Limitations with lower-cost providers
• Excess data in some countries (e.g., US), but
limited data in others
Emerging Trends:
• Online data services are increasingly marketing
themselves for this segment
• Use of Survey Aggregators (e.g., MarketPay)
• Removing data complexity

11. Maximizing Value Of Data
For Talent At Rest
Main Themes
1. Reduce Complexity
2. Leverage Power From New ’Talent In Motion’ Data
Simpler More Powerful Processes Simpler More Powerful Data Structures

12. Maximizing Value Of Data
For Talent At Rest
Developer
Median 105K
Developer – Full Stack
Median 110K
Developer – Backend
Median 100K
Developer – Frontend
Median 102K
Job (75th-25th)/Median △Median
Full Stack Developer 31.4% 4.5%
Backend Developer 38.0% 4.5%
Frontend Developer 34.3% 2.9%
Developer 36.2% 0%
Reduce complexity of job pricing and the number of benchmark
jobs
Source: Greenwich.HR

13. Maximizing Value Of Data
For Talent At Rest
Reduce Complexity In Job Pricing Through A Job Catalog
Job Catalog: A synchronized framework that defines a standard list of benchmark jobs
that have been aligned to the company’s salary structure
Impact: - Significantly reduces the complexity of market pricing
- Reduces complexity of systems administration and data maintenance
- Serves as a framework for other talent management processes
- Breaks down silos across the organization
Job CatalogJob CatalogJob CatalogJob Catalog
Leveling
Framework
Job Family
Taxonomy
Salary
Structure
Job Catalog
Fit
Position
Requirement
s
Market Data
Position
Pricing and
Banding
Catalog Structure Position Pricing Process

14. Talent In Motion
Who Are They: Employees changing jobs (typically by
changing companies)
Economics: External labor market
• More dynamic and subject to localized
supply/demand conditions
• Has a built-in premium for switching cost and in-
demand experience
Typical Rewards Goals: Attraction
Data Sources: Online services that measure job
posting data and online recruiting profile data
Emerging Trends:
• Significant investment in data providers for this
segment
Typical Data Providers:
• Burning Glass
• CEB
• Job Boards
Strengths:
• Dynamic and real-time
• Solutions geared for talent sourcing requirements
• Powerful business intelligence capabilities
Limitations:
• Market data is often ‘inferred’ from posting data
• Some providers use self-reported data from
individuals
• Very limited predictive usefulness

15. Maximizing Value Of Data
For Talent In Motion
Source: Greenwich.HR
• Become familiar with the new
strengths of online sources
that can be applied more
broadly (e.g., ability to show
sensitivity of pay to skills, etc.)
• For any business situation,
recognize the labor market
that needs to be considered
and choose the data source
that best fits
• Strengthen partnerships with
recruiting organizations and
foster data transparency

16. Talent For Rent
Who Are They: Contractor, temporary, and freelance
workers
Economics: External labor market for temporary
talent
• Very dynamic
• Has premiums for administration and non-
employee status
• Supply chain for talent can be very complex
Typical Rewards Goals: Control spending while
maintaining access to critical skills
Data Sources: In general the data picture for this
space is immature and highly fragmented
Emerging Trends:
• Larger providers deliver market data based on
their own client base
• Vendor Management System (VMS) providers are
beginning to develop their own data solutions
Typical Data Providers:
• Large contract recruiting firms
• Large temporary staffing firms
Strengths:
• Larger providers are investing in their
data/analytics capabilities
Limitations:
• Very limited data sources
• No standards – therefore many competing views
• Very limited matching precision
• Very limited visibility – audience is typically
procurement manager or operations leader and is
shared situationally

17. The Talent Economist
• Compensation professionals in particular have the opportunity to
be the stewards of talent economics across the organization, e.g.,
• Building awareness of the ‘whole picture’ of the labor market
• Advising on the interplay of decisions for each market segment
on near-term and longer-term business outcomes
• Integrating labor market data
• Fostering collaboration across talent process owners
• Demonstrating the power of an expanded set of tools

18. Recap
• Bring perspective and use data more powerfully across all
three talent segments
• Talent at Rest
• Talent In Motion
• Talent For Rent
• Become savvy accessing and appropriately using data for
each of these segments
• Adjust data structures and processes to simplify
administration
• Serve as the stewards for the ’whole picture’ and
collaborate closely with other process owners

19. “Big Data” and Compensation
Benchmarking
Cary Sparrow
Founder and CEO
Greenwich.HR

20. Thank You
Questions

21. AGENDAAGENDA
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22. COMPview | Compensation Planning Software
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Automate, Align & Simplify Complex Compensation Planning