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Webinar - How to Implement a Data-Driven Compensation Strategy
The document discusses implementing a data-driven compensation strategy, emphasizing the importance of data transparency, communication, and bias management. It highlights the need for consistent methodologies and understanding biases in data sources while integrating AI into compensation processes. Ultimately, the document provides insights and recommendations for organizations to enhance fairness and equity in employee compensation.
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Webinar - How to Implement a Data-Driven Compensation Strategy
- 1. How to Implementa Data-Driven Compensation Strategy
- 2. Today's Presenters Ruth Thomas ChiefEvangelist Vicky Peakman Director of Product, Data Sara Hillenmeyer, PhD Senior Director, Data Science
- 3. Today's Agenda • Howto build the best data strategy for your organization • Other considerations: • Data transparency • Data bias • Data and AI • Q&A
- 4. A compensation datastrategy has three components Communication Input Data Internal Process
- 5. How transparent willwe 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 willwe 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 willwe 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
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- 9. Data Transparency • Paytransparency 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 transparencycontinuum 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 transparencycontinuum – 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 transparentare 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
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- 14. All market datais 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 manyHR-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 biasedtoward 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 youdo? 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 biascreep 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: Doyou 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 partof a data strategy
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- 23. AI as partof 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-termuse 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 CannotDo… Eliminate bias in pay Eliminate human review and oversight Determine comp strategy to achieve key goals
- 26. Poll 3: Howare 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 consistencyof 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 ofcurated, 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 toask any questions in the chat!