SlideShare a Scribd company logo
1 of 39
Download to read offline
How to Choose and Use
Salary Data
Today's Presenters
Ruth Thomas
Chief Evangelist
Vicky Peakman
Director of Product, Data
Sara Hillenmeyer, PhD
Senior Director, Data
Science
Today's Agenda
• A deep dive into the various data
sources available
• Considerations when selecting data for
your organization
• Considerations for using and
communicating the data within your
organization
Welcome to the Data Age!
Data is everywhere,
and growth is
exponential
AI and technological
advancements are
fueling the power of
data
Demand for data
transparency
All this is impacting the data we use to support compensation management.
What data
sources are
available?
Market data available today
HR Reported
Data
Club/association
surveys
Annual Salary Survey
Data
HR aggregated
data (eg HRMA)
Closed network HR
reported data (eg Peer)
Employee
Reported Data
Payscale ERD
Publicly
Available Data
Free online data
Government data
Job posting data
Ad hoc data
Recruiter and
candidate insights
Compensation
consultant insights
Poll 1: From
which sources
do you obtain
market data
(select all that
apply)?
• Free or open online data
• Salary survey data from traditional publishers
• HR-reported aggregate market data in compensation software
(HRMA)
• Closed network HR-reported salary data (Peer / Club Surveys)
• Paid employee-reported salary data (ERD)
• Trade/industry association surveys
• Government data
• Salary data from job board postings
• Personalized competitor intelligence, including talking to
candidates or recruiters
1-99 ee: 26%
100-749 ee: 41%
1-99 ee: 65%
100-749 ee: 53%
1-99 ee: 31%
100-749 ee: 49%
1-99 ee: 36%
100-749 ee: 27%
1-99 ee: 19%
100-749 ee: 27%
56% of orgs use 2-4 data sources when pricing
jobs, with data specific to industry being the
most important characteristic
Which sources are used?
There is not one magic number!
Myth busting #1
Considerations
when selecting
data
Market data strategy
Do I need specific
industry data?
Do I have very
niche jobs?
How many data
sources can I buy?
How transparent
does it need to be?
Considerations when selecting data
What are the top three most important
considerations when selecting salary data to
use in market pricing? 51 percent say salary
data that is specific to industry.
Coming in second and third is salary data that
includes compensable factors valuable to the
organization and salary data that is
recent/timely.
A compensation data
strategy has three
components
Input Data
Internal Process
Communication
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
Compensation data strategy has three components
Questions to answer as you evaluate any data source
Coverage Can I find (or slot) my jobs?
Questions to answer as you evaluate any data source
Coverage
Repeatability
Can I find (or slot) my jobs?
How easy is it to repeat my methods, queries? Are the numbers noisy
over time?
Questions to answer as you evaluate any data source
Coverage
Repeatability
Methodology and
Explainability
Can I find (or slot) my jobs?
How easy is it to repeat my methods, queries? Are the numbers noisy
over time?
Where are the numbers from?
How are they calculated?
Are any numbers derived? By what methodology?
Questions to answer as you evaluate any data source
Coverage
Repeatability
Methodology and
Explainability
Freshness
Can I find (or slot) my jobs?
How easy is it to repeat my methods, queries? Are the numbers noisy
over time?
Where are the numbers from?
How are they calculated?
Are any numbers derived? By what methodology?
How old are the data?
Questions to answer as you evaluate any data source
Coverage
Repeatability
Methodology and
Explainability
Freshness
Biases
Can I find (or slot) my jobs?
How easy is it to repeat my methods, queries? Are the numbers noisy
over time?
Where are the numbers from?
How are they calculated?
Are any numbers derived? By what methodology?
How old are the data?
Are the data biased towards larger companies? Mostly from one location?
Questions to answer as you evaluate any data source
Coverage
Repeatability
Methodology and
Explainability
Freshness
Biases
Audience
Can I find (or slot) my jobs?
How easy is it to repeat my methods, queries? Are the numbers noisy
over time?
Where are the numbers from?
How are they calculated?
Are any numbers derived? By what methodology?
How old are the data?
Are the data biased towards larger companies? Mostly from one location?
Who can see these data? Will my employees also be able to access this
information?
Considerations
when using data
The priority of these factors varies based on how you
will use the data
Structures / annual reviews
Recruitment / promotion
Employee conversations
Poll 2: What are
you using data
for today (select
all that apply)?
• Recruitment
• Promotion or internal movement
• To develop pay structures
• For our merit review
• To support employee conversations
Coverage
Repeatability
Methodology and
Explainability
Freshness
Biases
Audience
May only need to apply to one job, if it’s a new role
Doesn’t necessarily need to be repeatable, but does need to agree with
your comp philosophy and internal equity
Need to be able to explain methodology for generating a range for a job
AND identify where in the range a new hire should land
Needs to be very fresh for hot markets or jobs
Could bias towards recent offers for similar jobs
Could be public, if you post salary ranges in job postings
Recruitment / promotion
$-$$$ Whichever dataset(s) you use for Structures/Annual Review
$$ Closed-network HR-reported data like Payscale’s Peer Dataset
0-$
Salaries from Job Postings (either in software or a one-off Google
search)
Typical Data Sources
Recruitment / promotion
Coverage
Repeatability
Methodology and
Explainability
Freshness
Biases
Audience
Single methodology should cover all jobs in the company or at least in a
job family
Needs to be repeatable each year
• Need to be able to explain methodology for generating a range for a job.
• Well matched jobs
Tolerant to slightly older data (within 1 year)
Better to bias towards datasets that report current incumbents (rather than
using only job postings or new offers)
HR sees the raw input data, others (managers, employees, exec) may
view the pricings for each job
Structures / annual reviews
Structures / annual reviews
$ Aggregated HR-reported data like HRMA
$$-$$$ Traditional Surveys
$$ Closed-network HR-reported data like Payscale’s Peer Dataset
Free Government Data (BLS)
Typical Data Sources
What do employees want to know?
What am I
paid?
How is my pay
determined?
How can I
progress my
pay?
Am I paid fairly
against my peers
and the market?
Poll 3: How
transparent are
you with your
data strategy?
• We communicate to HR or senior management only
• We communicate this to managers only
• We communicate this to all
• We want to move towards transparency
• We are not transparent currently
• I don't know
Coverage
Repeatability
Methodology and
Explainability
Freshness
Biases
Audience
Need to explain every employee, every job
Year over Year changes will be noticed
• Explain where ranges come from and explain employee’s position in range
• This may require understanding how factors like experience, education, and
specific skills contribute to pay
Could bias towards publicly viewable data sources
Could be public, if you post salary ranges in job postings
Employee conversations
Should be aware of recent public data like postings from competition
$-$$$ Whichever dataset(s) you use for Structures/Annual Review
0-$
Salaries from Job Postings (either in software or a one-off Google
search)
0-$ Payscale’s Employee Reported Data (ERD), Levels.fyi, Glassdoor, etc.
Typical Data Sources
Employee conversations
Myth busting #2
The market price isn’t the end of the story!
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
How to use the data
Aggregation
Integrate internal
data
Comparable jobs
Ranges
Job Postings
How do we aggregate multiple sources?
How do we handle the company’s “old” ranges or pay of current
incumbents when we make a new offer?
How do we ensure consistent pay ranges across comparable jobs?
How do we generate ranges?
How will we calculate what range we will list in a job posting?
Questions to answer as you develop your internal
processes and compensation strategy
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
How do I communicate the data
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?
Will we share methodology / ranges / market data / pay?
Questions to answer as you develop your internal
processes and compensation strategy
Will we tell employees where they sit against the range / market / their
colleagues?
Will we talk about which data sources we use, why and how?
Will we talk about how we aggregate data?
What regulations apply to us?
What are our competitors for talent doing?
Recommendations
Aim for consistency of methodology
Aim for transparency so you can explain each number
Be willing to explore the full market data landscape
Develop a market data strategy for each situation
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!

More Related Content

Similar to Webinar - How to Choose and Use Salary Data

HR analytics mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm
HR analytics mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmHR analytics mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm
HR analytics mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm
onalijayathilaka
 
Abc Of Hr Metrics 144
Abc Of Hr Metrics 144Abc Of Hr Metrics 144
Abc Of Hr Metrics 144
adeelbukhari
 
Running Head Team Project .docx
Running Head Team Project                                        .docxRunning Head Team Project                                        .docx
Running Head Team Project .docx
jeanettehully
 
9 Salary Surveys A Snapshot Market data obtained from salary surv.docx
9 Salary Surveys A Snapshot Market data obtained from salary surv.docx9 Salary Surveys A Snapshot Market data obtained from salary surv.docx
9 Salary Surveys A Snapshot Market data obtained from salary surv.docx
sleeperharwell
 

Similar to Webinar - How to Choose and Use Salary Data (20)

HR Analytics
HR AnalyticsHR Analytics
HR Analytics
 
HR analytics mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm
HR analytics mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmHR analytics mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm
HR analytics mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm
 
Abc Of Hr Metrics 144
Abc Of Hr Metrics 144Abc Of Hr Metrics 144
Abc Of Hr Metrics 144
 
6 Cutting-Edge HR Metrics to Measure in 2019
6 Cutting-Edge HR Metrics to Measure in 20196 Cutting-Edge HR Metrics to Measure in 2019
6 Cutting-Edge HR Metrics to Measure in 2019
 
What's New in Payfactors
What's New in PayfactorsWhat's New in Payfactors
What's New in Payfactors
 
How to prepare for pay equity analyis
How to prepare for pay equity analyisHow to prepare for pay equity analyis
How to prepare for pay equity analyis
 
Running Head Team Project .docx
Running Head Team Project                                        .docxRunning Head Team Project                                        .docx
Running Head Team Project .docx
 
Using Data to Build an Effective Talent Acquisition Strategy
Using Data to Build an Effective Talent Acquisition StrategyUsing Data to Build an Effective Talent Acquisition Strategy
Using Data to Build an Effective Talent Acquisition Strategy
 
Workforce Intelligence: How HR Can Make Data-Driven Decisions That Move the N...
Workforce Intelligence: How HR Can Make Data-Driven Decisions That Move the N...Workforce Intelligence: How HR Can Make Data-Driven Decisions That Move the N...
Workforce Intelligence: How HR Can Make Data-Driven Decisions That Move the N...
 
Using Performance Management Data to drive strategic decisions and company pe...
Using Performance Management Data to drive strategic decisions and company pe...Using Performance Management Data to drive strategic decisions and company pe...
Using Performance Management Data to drive strategic decisions and company pe...
 
Webinar - Is It Time to Modernize Your Compensation Management Practices
Webinar - Is It Time to Modernize Your Compensation Management PracticesWebinar - Is It Time to Modernize Your Compensation Management Practices
Webinar - Is It Time to Modernize Your Compensation Management Practices
 
Data-driven HR reshaping the business landscape
Data-driven HR reshaping the business landscapeData-driven HR reshaping the business landscape
Data-driven HR reshaping the business landscape
 
Flip the Script: Winning Tactics for Recruiting High-Quality Candidates
Flip the Script: Winning Tactics for Recruiting High-Quality CandidatesFlip the Script: Winning Tactics for Recruiting High-Quality Candidates
Flip the Script: Winning Tactics for Recruiting High-Quality Candidates
 
Webinar - How Organizations are Closing the Gender Pay Gap
Webinar - How Organizations are Closing the Gender Pay GapWebinar - How Organizations are Closing the Gender Pay Gap
Webinar - How Organizations are Closing the Gender Pay Gap
 
Marcus Baker: People Analytics at Scale
Marcus Baker: People Analytics at ScaleMarcus Baker: People Analytics at Scale
Marcus Baker: People Analytics at Scale
 
Using Data to Build an Effective Talent Acquisition Strategy
Using Data to Build an Effective Talent Acquisition Strategy Using Data to Build an Effective Talent Acquisition Strategy
Using Data to Build an Effective Talent Acquisition Strategy
 
Are You Using Data To Drive Your HR Strategy
Are You Using Data To Drive Your HR StrategyAre You Using Data To Drive Your HR Strategy
Are You Using Data To Drive Your HR Strategy
 
9 Salary Surveys A Snapshot Market data obtained from salary surv.docx
9 Salary Surveys A Snapshot Market data obtained from salary surv.docx9 Salary Surveys A Snapshot Market data obtained from salary surv.docx
9 Salary Surveys A Snapshot Market data obtained from salary surv.docx
 
What is a salary survey_ .pdf
What is a salary survey_ .pdfWhat is a salary survey_ .pdf
What is a salary survey_ .pdf
 
Data-Driven HR: Redefining Business Success | Exela HR Solutions
Data-Driven HR: Redefining Business Success | Exela HR SolutionsData-Driven HR: Redefining Business Success | Exela HR Solutions
Data-Driven HR: Redefining Business Success | Exela HR Solutions
 

More from PayScale, Inc.

More from PayScale, Inc. (20)

Webinar - Fundamentals of Compensation
Webinar  -  Fundamentals of CompensationWebinar  -  Fundamentals of Compensation
Webinar - Fundamentals of Compensation
 
Webinar - Maximize the efficiency of your merit increase cycle
Webinar - Maximize the efficiency of your merit increase cycleWebinar - Maximize the efficiency of your merit increase cycle
Webinar - Maximize the efficiency of your merit increase cycle
 
Webinar - How to set pay ranges in the context of pay transparency legislation
Webinar - How to set pay ranges in the context of pay transparency legislationWebinar - How to set pay ranges in the context of pay transparency legislation
Webinar - How to set pay ranges in the context of pay transparency legislation
 
Webinar - Payscale Innovation Unleashed: New features and data evolving the c...
Webinar - Payscale Innovation Unleashed: New features and data evolving the c...Webinar - Payscale Innovation Unleashed: New features and data evolving the c...
Webinar - Payscale Innovation Unleashed: New features and data evolving the c...
 
Webinar - Q2 2024: What’s New in MarketPay
Webinar - Q2 2024: What’s New in MarketPayWebinar - Q2 2024: What’s New in MarketPay
Webinar - Q2 2024: What’s New in MarketPay
 
Webinar - Pay Transparency Legislation Series, Ep. 11: Best Practices Revealed
Webinar - Pay Transparency Legislation Series, Ep. 11: Best Practices RevealedWebinar - Pay Transparency Legislation Series, Ep. 11: Best Practices Revealed
Webinar - Pay Transparency Legislation Series, Ep. 11: Best Practices Revealed
 
Webinar - 2024 Compensation Best Practices Panel
Webinar - 2024 Compensation Best Practices PanelWebinar - 2024 Compensation Best Practices Panel
Webinar - 2024 Compensation Best Practices Panel
 
Navigating the Now: Pay Transparency, Equity and Opportunity in 2024
Navigating the Now: Pay Transparency, Equity and Opportunity in 2024Navigating the Now: Pay Transparency, Equity and Opportunity in 2024
Navigating the Now: Pay Transparency, Equity and Opportunity in 2024
 
Webinar-The Future of Job Description Management.pdf
Webinar-The Future of Job Description Management.pdfWebinar-The Future of Job Description Management.pdf
Webinar-The Future of Job Description Management.pdf
 
Webinar - Closing the Gap: How Organizations are Making Fair Pay a Reality
Webinar - Closing the Gap: How Organizations are Making Fair Pay a RealityWebinar - Closing the Gap: How Organizations are Making Fair Pay a Reality
Webinar - Closing the Gap: How Organizations are Making Fair Pay a Reality
 
Webinar - Unlock the Power of Payfactors
Webinar - Unlock the Power of PayfactorsWebinar - Unlock the Power of Payfactors
Webinar - Unlock the Power of Payfactors
 
Webinar - Paving the Way: Reflections and Insights for Year-Long Compensation...
Webinar - Paving the Way: Reflections and Insights for Year-Long Compensation...Webinar - Paving the Way: Reflections and Insights for Year-Long Compensation...
Webinar - Paving the Way: Reflections and Insights for Year-Long Compensation...
 
Webinar - Panel: Navigating the Nuances of Compensation Communications
Webinar - Panel: Navigating the Nuances of Compensation CommunicationsWebinar - Panel: Navigating the Nuances of Compensation Communications
Webinar - Panel: Navigating the Nuances of Compensation Communications
 
Webinar - Compensation Best Practices Report Secrets Revealed: Play the Early...
Webinar - Compensation Best Practices Report Secrets Revealed: Play the Early...Webinar - Compensation Best Practices Report Secrets Revealed: Play the Early...
Webinar - Compensation Best Practices Report Secrets Revealed: Play the Early...
 
Webinar - Planning for Pay Transparency Legislation in the New Year
Webinar - Planning for Pay Transparency Legislation in the New YearWebinar - Planning for Pay Transparency Legislation in the New Year
Webinar - Planning for Pay Transparency Legislation in the New Year
 
Webinar - How to Create and Maintain a Practical Compensation Calendar
Webinar - How to Create and Maintain a Practical Compensation CalendarWebinar - How to Create and Maintain a Practical Compensation Calendar
Webinar - How to Create and Maintain a Practical Compensation Calendar
 
Webinar - Turning the Page: HR and Compensation Trends and Predictions for th...
Webinar - Turning the Page: HR and Compensation Trends and Predictions for th...Webinar - Turning the Page: HR and Compensation Trends and Predictions for th...
Webinar - Turning the Page: HR and Compensation Trends and Predictions for th...
 
Webinar - Getting the Right Data Mix to Market Price Your Jobs
Webinar - Getting the Right Data Mix to Market Price Your JobsWebinar - Getting the Right Data Mix to Market Price Your Jobs
Webinar - Getting the Right Data Mix to Market Price Your Jobs
 
Total rewards managing expectations
Total rewards managing expectationsTotal rewards managing expectations
Total rewards managing expectations
 
Pay Transparency Legislation Series, Ep. 10:   Planning for the New Year
Pay Transparency Legislation Series, Ep. 10:   Planning for the New YearPay Transparency Legislation Series, Ep. 10:   Planning for the New Year
Pay Transparency Legislation Series, Ep. 10:   Planning for the New Year
 

Recently uploaded

Recently uploaded (11)

Module 3 - Onboarding Course Outline.pptx
Module 3 - Onboarding Course Outline.pptxModule 3 - Onboarding Course Outline.pptx
Module 3 - Onboarding Course Outline.pptx
 
International Tech Talent in Finland 2024
International Tech Talent in Finland 2024International Tech Talent in Finland 2024
International Tech Talent in Finland 2024
 
Acing Performance Management - Harjeet Khanduja
Acing Performance Management - Harjeet KhandujaAcing Performance Management - Harjeet Khanduja
Acing Performance Management - Harjeet Khanduja
 
Unlocking Employee Potential with the Power of Continuous Feedback
Unlocking Employee Potential with the Power of Continuous FeedbackUnlocking Employee Potential with the Power of Continuous Feedback
Unlocking Employee Potential with the Power of Continuous Feedback
 
Emotional Intelligence and You - Gauri Das
Emotional Intelligence and You - Gauri DasEmotional Intelligence and You - Gauri Das
Emotional Intelligence and You - Gauri Das
 
Your Office Showstopper OR Eyesore: Make your office a place people want to b...
Your Office Showstopper OR Eyesore: Make your office a place people want to b...Your Office Showstopper OR Eyesore: Make your office a place people want to b...
Your Office Showstopper OR Eyesore: Make your office a place people want to b...
 
Module 2 - LO3 Human Resources Roles and Functions.pptx
Module 2 - LO3 Human Resources Roles and Functions.pptxModule 2 - LO3 Human Resources Roles and Functions.pptx
Module 2 - LO3 Human Resources Roles and Functions.pptx
 
Module 2 - LO3 Human Resources Roles and Functions
Module 2 - LO3 Human Resources Roles and FunctionsModule 2 - LO3 Human Resources Roles and Functions
Module 2 - LO3 Human Resources Roles and Functions
 
BHOLENDRA SINGH RESUME - Sr. Software Engineer at India Today Group
BHOLENDRA SINGH RESUME - Sr. Software Engineer at India Today GroupBHOLENDRA SINGH RESUME - Sr. Software Engineer at India Today Group
BHOLENDRA SINGH RESUME - Sr. Software Engineer at India Today Group
 
CI or FS Poly Cleared Job Fair Handbook | May 22
CI or FS Poly Cleared Job Fair Handbook | May 22CI or FS Poly Cleared Job Fair Handbook | May 22
CI or FS Poly Cleared Job Fair Handbook | May 22
 
Boost Efficiency with an Inventory Tracking Spreadsheet.docx
Boost Efficiency with an Inventory Tracking Spreadsheet.docxBoost Efficiency with an Inventory Tracking Spreadsheet.docx
Boost Efficiency with an Inventory Tracking Spreadsheet.docx
 

Webinar - How to Choose and Use Salary Data

  • 1. How to Choose and Use Salary Data
  • 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 • A deep dive into the various data sources available • Considerations when selecting data for your organization • Considerations for using and communicating the data within your organization
  • 4. Welcome to the Data Age! Data is everywhere, and growth is exponential AI and technological advancements are fueling the power of data Demand for data transparency All this is impacting the data we use to support compensation management.
  • 6. Market data available today HR Reported Data Club/association surveys Annual Salary Survey Data HR aggregated data (eg HRMA) Closed network HR reported data (eg Peer) Employee Reported Data Payscale ERD Publicly Available Data Free online data Government data Job posting data Ad hoc data Recruiter and candidate insights Compensation consultant insights
  • 7. Poll 1: From which sources do you obtain market data (select all that apply)? • Free or open online data • Salary survey data from traditional publishers • HR-reported aggregate market data in compensation software (HRMA) • Closed network HR-reported salary data (Peer / Club Surveys) • Paid employee-reported salary data (ERD) • Trade/industry association surveys • Government data • Salary data from job board postings • Personalized competitor intelligence, including talking to candidates or recruiters
  • 8. 1-99 ee: 26% 100-749 ee: 41% 1-99 ee: 65% 100-749 ee: 53% 1-99 ee: 31% 100-749 ee: 49% 1-99 ee: 36% 100-749 ee: 27% 1-99 ee: 19% 100-749 ee: 27% 56% of orgs use 2-4 data sources when pricing jobs, with data specific to industry being the most important characteristic Which sources are used?
  • 9. There is not one magic number! Myth busting #1
  • 11. Market data strategy Do I need specific industry data? Do I have very niche jobs? How many data sources can I buy? How transparent does it need to be?
  • 12. Considerations when selecting data What are the top three most important considerations when selecting salary data to use in market pricing? 51 percent say salary data that is specific to industry. Coming in second and third is salary data that includes compensable factors valuable to the organization and salary data that is recent/timely.
  • 13. A compensation data strategy has three components Input Data Internal Process Communication
  • 14. 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 Compensation data strategy has three components
  • 15. Questions to answer as you evaluate any data source Coverage Can I find (or slot) my jobs?
  • 16. Questions to answer as you evaluate any data source Coverage Repeatability Can I find (or slot) my jobs? How easy is it to repeat my methods, queries? Are the numbers noisy over time?
  • 17. Questions to answer as you evaluate any data source Coverage Repeatability Methodology and Explainability Can I find (or slot) my jobs? How easy is it to repeat my methods, queries? Are the numbers noisy over time? Where are the numbers from? How are they calculated? Are any numbers derived? By what methodology?
  • 18. Questions to answer as you evaluate any data source Coverage Repeatability Methodology and Explainability Freshness Can I find (or slot) my jobs? How easy is it to repeat my methods, queries? Are the numbers noisy over time? Where are the numbers from? How are they calculated? Are any numbers derived? By what methodology? How old are the data?
  • 19. Questions to answer as you evaluate any data source Coverage Repeatability Methodology and Explainability Freshness Biases Can I find (or slot) my jobs? How easy is it to repeat my methods, queries? Are the numbers noisy over time? Where are the numbers from? How are they calculated? Are any numbers derived? By what methodology? How old are the data? Are the data biased towards larger companies? Mostly from one location?
  • 20. Questions to answer as you evaluate any data source Coverage Repeatability Methodology and Explainability Freshness Biases Audience Can I find (or slot) my jobs? How easy is it to repeat my methods, queries? Are the numbers noisy over time? Where are the numbers from? How are they calculated? Are any numbers derived? By what methodology? How old are the data? Are the data biased towards larger companies? Mostly from one location? Who can see these data? Will my employees also be able to access this information?
  • 22. The priority of these factors varies based on how you will use the data Structures / annual reviews Recruitment / promotion Employee conversations
  • 23. Poll 2: What are you using data for today (select all that apply)? • Recruitment • Promotion or internal movement • To develop pay structures • For our merit review • To support employee conversations
  • 24. Coverage Repeatability Methodology and Explainability Freshness Biases Audience May only need to apply to one job, if it’s a new role Doesn’t necessarily need to be repeatable, but does need to agree with your comp philosophy and internal equity Need to be able to explain methodology for generating a range for a job AND identify where in the range a new hire should land Needs to be very fresh for hot markets or jobs Could bias towards recent offers for similar jobs Could be public, if you post salary ranges in job postings Recruitment / promotion
  • 25. $-$$$ Whichever dataset(s) you use for Structures/Annual Review $$ Closed-network HR-reported data like Payscale’s Peer Dataset 0-$ Salaries from Job Postings (either in software or a one-off Google search) Typical Data Sources Recruitment / promotion
  • 26. Coverage Repeatability Methodology and Explainability Freshness Biases Audience Single methodology should cover all jobs in the company or at least in a job family Needs to be repeatable each year • Need to be able to explain methodology for generating a range for a job. • Well matched jobs Tolerant to slightly older data (within 1 year) Better to bias towards datasets that report current incumbents (rather than using only job postings or new offers) HR sees the raw input data, others (managers, employees, exec) may view the pricings for each job Structures / annual reviews
  • 27. Structures / annual reviews $ Aggregated HR-reported data like HRMA $$-$$$ Traditional Surveys $$ Closed-network HR-reported data like Payscale’s Peer Dataset Free Government Data (BLS) Typical Data Sources
  • 28. What do employees want to know? What am I paid? How is my pay determined? How can I progress my pay? Am I paid fairly against my peers and the market?
  • 29. Poll 3: How transparent are you with your data strategy? • We communicate to HR or senior management only • We communicate this to managers only • We communicate this to all • We want to move towards transparency • We are not transparent currently • I don't know
  • 30. Coverage Repeatability Methodology and Explainability Freshness Biases Audience Need to explain every employee, every job Year over Year changes will be noticed • Explain where ranges come from and explain employee’s position in range • This may require understanding how factors like experience, education, and specific skills contribute to pay Could bias towards publicly viewable data sources Could be public, if you post salary ranges in job postings Employee conversations Should be aware of recent public data like postings from competition
  • 31. $-$$$ Whichever dataset(s) you use for Structures/Annual Review 0-$ Salaries from Job Postings (either in software or a one-off Google search) 0-$ Payscale’s Employee Reported Data (ERD), Levels.fyi, Glassdoor, etc. Typical Data Sources Employee conversations
  • 32. Myth busting #2 The market price isn’t the end of the story!
  • 33. 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 How to use the data
  • 34. Aggregation Integrate internal data Comparable jobs Ranges Job Postings How do we aggregate multiple sources? How do we handle the company’s “old” ranges or pay of current incumbents when we make a new offer? How do we ensure consistent pay ranges across comparable jobs? How do we generate ranges? How will we calculate what range we will list in a job posting? Questions to answer as you develop your internal processes and compensation strategy
  • 35. 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 How do I communicate the data
  • 36. 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? Will we share methodology / ranges / market data / pay? Questions to answer as you develop your internal processes and compensation strategy Will we tell employees where they sit against the range / market / their colleagues? Will we talk about which data sources we use, why and how? Will we talk about how we aggregate data? What regulations apply to us? What are our competitors for talent doing?
  • 37. Recommendations Aim for consistency of methodology Aim for transparency so you can explain each number Be willing to explore the full market data landscape Develop a market data strategy for each situation
  • 38. 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!
  • 39. Q&A Feel free to ask any questions in the chat!