Choosing the Right Compensation Data

This episode explains how compensation professionals and HR leaders can pick, judge, and use salary benchmark data when pricing jobs, especially in mid-market companies that have outgrown spreadsheets but are not ready for enterprise software. You will learn why pay data goes out of date so quickly, how to benchmark the full scope of a role instead of its title, how to handle title inflation, and how to price hybrid roles where one person does several jobs. The conversation also compares the strengths and weaknesses of traditional salary surveys, employee-reported pay data, and aggregated or predictive data sets, and covers what to do when a leader brings you a pay number generated by an AI tool. It closes with why executives still struggle to understand compensation, even though pay is often their largest expense. Chapters

03:49 – Compensation tools for companies outgrowing spreadsheets
13:09 – Why salary benchmark data goes stale so quickly
14:47 – Benchmarking the full job scope instead of the title
17:15 – Pricing the job versus fitting a job to a price
19:59 – Handling title inflation in smaller organizations
21:55 – How to price hybrid and blended roles
26:25 – Pros and cons of traditional salary surveys
28:52 – What employee-reported salary data is useful for
33:29 – Trusting aggregated and predictive pay data
39:37 – Why AI pay recommendations still need human review