how to use big data for better compensation benchmarking

TalentTakeawayswebinar & podcast series

How To Use BigData For BetterCompensationBenchmarking

Guest Presenter: Cary SparrowFounder & CEO, Greenwich.HR

AGENDAThe Series

TalentTakeawayswebinar & podcast series

Talent Takeaways Series

AGENDAAGENDAThe Sponsor


Talent Management Made for Managers

Compensation Planning Total RewardsStay Interviews

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

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)

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

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

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.

Based On A True StoryBUT 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.

Talent At RestWho 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 precisionLimitations:• 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 othersEmerging Trends:• Online data services are increasingly

marketing themselves for this segment• Use of Survey Aggregators (e.g.,

MarketPay)• Removing data complexity

Maximizing Value Of DataFor Talent At Rest

Main Themes1. Reduce Complexity

2. Leverage Power From New ’Talent In Motion’ Data

Simpler More Powerful Processes Simpler More Powerful Data Structures


DeveloperMedian 105K

Developer – Full StackMedian 110K

Developer – BackendMedian 100K

Developer – FrontendMedian 102K

Job (75th-25th)/Median

△Median

Full Stack Developer

31.4% 4.5%

Backend Developer 38.0% 4.5%Frontend

Developer34.3% 2.9%

Developer 36.2% 0%

Reduce complexity of job pricing and the number of benchmark jobs

Source: Greenwich.HR


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

LevelingFramewor

kJob Family Taxonomy

Salary Structure

Job Catalog Fit

Position Requireme

nts

Market Data

Position Pricing and

Banding

Catalog Structure Position Pricing Process

Talent In MotionWho 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 BoardsStrengths:• Dynamic and real-time• Solutions geared for talent sourcing

requirements• Powerful business intelligence capabilitiesLimitations:• Market data is often ‘inferred’ from posting

data• Some providers use self-reported data from

individuals• Very limited predictive usefulness

Maximizing Value Of DataFor 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

Talent For RentWho 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 firmsStrengths:• Larger providers are investing in their

data/analytics capabilitiesLimitations:• 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

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

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

“Big Data” and Compensation Benchmarking

Cary SparrowFounder and CEOGreenwich.HR

Thank You

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