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How to Implement a Data-Driven
Compensation Strategy
Today's Presenters
Ruth Thomas
Chief Evangelist
Vicky Peakman
Director of Product, Data
Sara Hillenmeyer, PhD
Senior Director, Data
Science
Today's Agenda
• How to build the best data strategy
for your organization
• Other considerations:
• Data transparency
• Data bias
• Data and AI
• Q&A
A compensation data strategy has three components
Communication
Input Data Internal Process
How transparent will we
be?
What will we provide to
employees?
How will we explain the
data sources?
What more do we need to
know?
Communication
Coverage
Repeatability
Methodology and
Explainability
Freshness
Biases
Input Data
Aggregation
Integrate internal data
Comparable jobs
Ranges
Job Postings
Internal Process
A compensation data strategy has three components
How transparent will we
be?
What will we provide to
employees?
How will we explain the
data sources?
What more do we need to
know?
Communication
Coverage
Repeatability
Methodology and
Explainability
Freshness
Biases
Input Data
Aggregation
Integrate internal data
Comparable jobs
Ranges
Job Postings
Internal Process
A compensation data strategy has three components
How transparent will we
be?
What will we provide to
employees?
How will we explain the
data sources?
What more do we need to
know?
Communication
Coverage
Repeatability
Methodology and
Explainability
Freshness
Biases
Input Data
Aggregation
Integrate internal data
Comparable jobs
Ranges
Job Postings
Internal Process
A compensation data strategy has three components
Data
transparency
Data Transparency
• Pay transparency is on the rise.
• The primary goal of pay transparency is to
promote fairness, equity, and trust by
ensuring that employees understand how
their pay is determined and how it
compares to that of their peers.
• In practice this means openly sharing
information about employee compensation-
including how it is determined.
• This can extend to transparency of data,
including sources and data strategy.
The pay transparency continuum
Pay
Secrecy
Pay
Transparency
No information
shared with
employees
except their
own pay
Share salary
range data for
current role with
employee
All salary range
data shared
internally
Salaries
shared
publicly
Individual
employee
salaries shared
internally
Share with
individuals how
their pay is
determined
The pay transparency continuum – data strategy
Pay
Secrecy
Pay
Transparency
No information
on data strategy
shared with
employees or
managers
Share with
employees pay
philosophy
Share data
strategy with
managers that
is used to
create range
All data is
shared with
managers
Share data
strategy with
employees that
is used to
create range
Share with
managers pay
philosophy
All data is
shared with
employees
Poll 1: How
transparent are
you with your
data strategy?
• We communicate to HR or senior management only
• We communicate this to managers only
• We communicate this to all
• We want to move towards transparency
• We are not transparent currently
• I don't know
Data bias
All market data is biased
• Surveys/Peer are collected from companies that participate (generally skews toward larger
companies)
• ERD and other employee-reported data (glassdoor, levels.fyi, etc) often skews towards
people who look up their pay information online (younger, more technical)
• Aggregated data from job postings are biased towards states that have pay transparency
legislation
• Bureau of Labor Statistics tries to “even out” their data to get make it a representative
sample of labor in the US. Most compensation data providers do not do this! (None, that I
know of.)
Peer, like many HR-participation-based data sources, is
biased toward larger companies
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
<5 5-9 10-19 20-49 50-99 100-249 250-499 500-999 1000
%
of
Total
Incumbents
in
Dataset
Number of Employees at Company
% Incumbents working for a company with X Employees
Bureau of Labor Statistics ERD Peer
Peer is biased toward Retail companies;
ERD toward Professional, Scientific, and Technical Services
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
Health
care
and
social
assistance
Retail
trade
Accommodation
and
food
services
Manufacturing
Professional,
scientific,
and
technical
services
Construction
Finance
and
insurance
Transportation
and
warehousing
Information
Arts,
entertainment,
and
recreation
Real
estate
and
rental
and
leasing
Utilities
Wholesale
Trade
%
Incumbents
in
Industry
Group Data Product Industry Bias versus Bureau of Labor
Statistics
Bureau of Labor Statistics ERD Peer
What should you do? It depends!
• You may WANT biased data! (if it
matches your pay market)
• Sometimes the bias doesn’t matter
• Sometimes you can counter-act the
overall bias by sub-setting the data
(using a scope)
• Sometimes you may want to counter-
act the bias with an adjustment factor
Where can bias creep into the process?
Inconsistency
in eligibility for
Incentive Plans
Matching /
pricing one
by one
Inconsistency in
survey use
Differences in
treatment of Part-
Time vs Full-Time
roles
Ad-hoc pay
adjustments
Inconsistency in
market reference
points / weightings
/ aging factors
Differences in
applying differentials
(eg skills, education,
location)
Lack of
understanding
of comparable
jobs
Allowing manager
discretion
Use of
performance in
setting pay
Different people
matching / pricing
Matching /
pricing using
salary as a guide
Creating
structures using
salary as a guide
Poll 2: Do you
monitor your
data processes
for bias?
• Yes, continuously
• Yes, occasionally
• No, but we are planning to
• No, I was not aware of the issue
• I don't know
AI as part of a
data strategy
AI sentiment
Payscale CBPR 2024
Payscale CBPR 2024
AI as part of data strategy
Payscale CBPR 2024
0%
10%
20%
30%
40%
50%
60%
Undecided Cautiously
Optimistic
Against It Totally on board
What is your organization’s overall
sentiment around using AI in making
compensation decisions?
Forms of AI are already widely used:
• Aggregating data
• Aging data
• Calculating and applying
differentials for location, industry,
company size
Future:
• Improved precision/accuracy in
calculated market ranges
• Better de-biasing of underlying
data sources
Now and near-term use cases for AI in compensation
Survey
matching
automated
Benchmark
jobs and predict
pay ranges
Salary offer
acceptance and
ROI analysis
Geo & skills-
based pay
adjustments
recommended
Comp trends &
market monitoring
with alerts
Pay increase
recommendations
and pay equity
monitoring
Job descriptions
auto-generated
Total Rewards
Statement (TRS)
auto-generated
Pay communications
auto-generated
Things AI Cannot Do…
Eliminate bias
in pay
Eliminate
human review
and oversight
Determine comp
strategy
to achieve key goals
Poll 3: How are
you using AI
(select all that
apply)?
• Use AI to benchmark and price jobs or predict pay ranges
• Use AI to monitor for pay equity and/or suggest pay increases
• Use AI to collect intelligence on skills for recruiting,
education/upskilling, career pathing, and/or compensation
• Use AI to write offer letters and generate total rewards
statements
• Use AI to speed up survey participation and year-over-year
updates
• None of the above
• I don't know
Recommendations
Aim for consistency of methodology
Aim for transparency so you can explain each number
Understand the biases in the data and in your processes
Understand how AI can help you now and in the future
Intelligent streams of curated, validated, compensation data
Payscale’s Diverse & Dynamic Data Portfolio
Peer
A transparent & dynamic
HR reported data network
100 M salary profiles (all time)
40M salary profiles in use
350,000 new profiles/month
15,000 jobs
8,000 skills/certifications
1 billion+ data points
4,900 jobs
15 countries
2,400 organizations
7M employees
10,000 surveys
From 300+ publishers
Employee Reported
The world’s largest
real-time salary database
HR Market Analysis
A composite of analyst curated
employer reported survey data
Published Survey Data
Trusted data partner
4,500 jobs
100+ industries
Compensation Survey
A modern, quarterly
compensation survey
1,350 organizations
2.9M employees
6,111 jobs
Request a demo of
Payscale data in the polls
tab now!
Q&A
Feel free to ask any questions in the chat!

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Webinar - How to Implement a Data-Driven Compensation Strategy

  • 1. How to Implement a Data-Driven Compensation Strategy
  • 2. Today's Presenters Ruth Thomas Chief Evangelist Vicky Peakman Director of Product, Data Sara Hillenmeyer, PhD Senior Director, Data Science
  • 3. Today's Agenda • How to build the best data strategy for your organization • Other considerations: • Data transparency • Data bias • Data and AI • Q&A
  • 4. A compensation data strategy has three components Communication Input Data Internal Process
  • 5. How transparent will we be? What will we provide to employees? How will we explain the data sources? What more do we need to know? Communication Coverage Repeatability Methodology and Explainability Freshness Biases Input Data Aggregation Integrate internal data Comparable jobs Ranges Job Postings Internal Process A compensation data strategy has three components
  • 6. How transparent will we be? What will we provide to employees? How will we explain the data sources? What more do we need to know? Communication Coverage Repeatability Methodology and Explainability Freshness Biases Input Data Aggregation Integrate internal data Comparable jobs Ranges Job Postings Internal Process A compensation data strategy has three components
  • 7. How transparent will we be? What will we provide to employees? How will we explain the data sources? What more do we need to know? Communication Coverage Repeatability Methodology and Explainability Freshness Biases Input Data Aggregation Integrate internal data Comparable jobs Ranges Job Postings Internal Process A compensation data strategy has three components
  • 9. Data Transparency • Pay transparency is on the rise. • The primary goal of pay transparency is to promote fairness, equity, and trust by ensuring that employees understand how their pay is determined and how it compares to that of their peers. • In practice this means openly sharing information about employee compensation- including how it is determined. • This can extend to transparency of data, including sources and data strategy.
  • 10. The pay transparency continuum Pay Secrecy Pay Transparency No information shared with employees except their own pay Share salary range data for current role with employee All salary range data shared internally Salaries shared publicly Individual employee salaries shared internally Share with individuals how their pay is determined
  • 11. The pay transparency continuum – data strategy Pay Secrecy Pay Transparency No information on data strategy shared with employees or managers Share with employees pay philosophy Share data strategy with managers that is used to create range All data is shared with managers Share data strategy with employees that is used to create range Share with managers pay philosophy All data is shared with employees
  • 12. Poll 1: How transparent are you with your data strategy? • We communicate to HR or senior management only • We communicate this to managers only • We communicate this to all • We want to move towards transparency • We are not transparent currently • I don't know
  • 14. All market data is biased • Surveys/Peer are collected from companies that participate (generally skews toward larger companies) • ERD and other employee-reported data (glassdoor, levels.fyi, etc) often skews towards people who look up their pay information online (younger, more technical) • Aggregated data from job postings are biased towards states that have pay transparency legislation • Bureau of Labor Statistics tries to “even out” their data to get make it a representative sample of labor in the US. Most compensation data providers do not do this! (None, that I know of.)
  • 15. Peer, like many HR-participation-based data sources, is biased toward larger companies 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 <5 5-9 10-19 20-49 50-99 100-249 250-499 500-999 1000 % of Total Incumbents in Dataset Number of Employees at Company % Incumbents working for a company with X Employees Bureau of Labor Statistics ERD Peer
  • 16. Peer is biased toward Retail companies; ERD toward Professional, Scientific, and Technical Services 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% Health care and social assistance Retail trade Accommodation and food services Manufacturing Professional, scientific, and technical services Construction Finance and insurance Transportation and warehousing Information Arts, entertainment, and recreation Real estate and rental and leasing Utilities Wholesale Trade % Incumbents in Industry Group Data Product Industry Bias versus Bureau of Labor Statistics Bureau of Labor Statistics ERD Peer
  • 17. What should you do? It depends! • You may WANT biased data! (if it matches your pay market) • Sometimes the bias doesn’t matter • Sometimes you can counter-act the overall bias by sub-setting the data (using a scope) • Sometimes you may want to counter- act the bias with an adjustment factor
  • 18. Where can bias creep into the process? Inconsistency in eligibility for Incentive Plans Matching / pricing one by one Inconsistency in survey use Differences in treatment of Part- Time vs Full-Time roles Ad-hoc pay adjustments Inconsistency in market reference points / weightings / aging factors Differences in applying differentials (eg skills, education, location) Lack of understanding of comparable jobs Allowing manager discretion Use of performance in setting pay Different people matching / pricing Matching / pricing using salary as a guide Creating structures using salary as a guide
  • 19. Poll 2: Do you monitor your data processes for bias? • Yes, continuously • Yes, occasionally • No, but we are planning to • No, I was not aware of the issue • I don't know
  • 20. AI as part of a data strategy
  • 23. AI as part of data strategy Payscale CBPR 2024 0% 10% 20% 30% 40% 50% 60% Undecided Cautiously Optimistic Against It Totally on board What is your organization’s overall sentiment around using AI in making compensation decisions? Forms of AI are already widely used: • Aggregating data • Aging data • Calculating and applying differentials for location, industry, company size Future: • Improved precision/accuracy in calculated market ranges • Better de-biasing of underlying data sources
  • 24. Now and near-term use cases for AI in compensation Survey matching automated Benchmark jobs and predict pay ranges Salary offer acceptance and ROI analysis Geo & skills- based pay adjustments recommended Comp trends & market monitoring with alerts Pay increase recommendations and pay equity monitoring Job descriptions auto-generated Total Rewards Statement (TRS) auto-generated Pay communications auto-generated
  • 25. Things AI Cannot Do… Eliminate bias in pay Eliminate human review and oversight Determine comp strategy to achieve key goals
  • 26. Poll 3: How are you using AI (select all that apply)? • Use AI to benchmark and price jobs or predict pay ranges • Use AI to monitor for pay equity and/or suggest pay increases • Use AI to collect intelligence on skills for recruiting, education/upskilling, career pathing, and/or compensation • Use AI to write offer letters and generate total rewards statements • Use AI to speed up survey participation and year-over-year updates • None of the above • I don't know
  • 27. Recommendations Aim for consistency of methodology Aim for transparency so you can explain each number Understand the biases in the data and in your processes Understand how AI can help you now and in the future
  • 28. Intelligent streams of curated, validated, compensation data Payscale’s Diverse & Dynamic Data Portfolio Peer A transparent & dynamic HR reported data network 100 M salary profiles (all time) 40M salary profiles in use 350,000 new profiles/month 15,000 jobs 8,000 skills/certifications 1 billion+ data points 4,900 jobs 15 countries 2,400 organizations 7M employees 10,000 surveys From 300+ publishers Employee Reported The world’s largest real-time salary database HR Market Analysis A composite of analyst curated employer reported survey data Published Survey Data Trusted data partner 4,500 jobs 100+ industries Compensation Survey A modern, quarterly compensation survey 1,350 organizations 2.9M employees 6,111 jobs Request a demo of Payscale data in the polls tab now!
  • 29. Q&A Feel free to ask any questions in the chat!