Compensation Analytics and Insights
Compensation Analytics and Insights tests whether you can turn pay data into decisions - choosing the right metric, testing it properly, and explaining what the result does and does not prove.
Metrics and methods
Core metrics include compa-ratio, range penetration, pay equity ratios, turnover and regretted turnover, cost of turnover, offer acceptance, time to fill, variable pay spend as a percentage of target, benefits utilization, and total rewards cost per employee. Use medians and percentiles for skewed pay data, and segment before averaging. Regression can test whether pay differences persist after controlling for job level, tenure, performance, and location. Statistical significance (p-value) and confidence intervals indicate whether a result is likely due to chance. Dashboards should show trend and segmentation, not a single snapshot.
Worked example
A company has 100 employees, 15% annual turnover, an average salary of $70,000, and an estimated replacement cost of 50% of salary. Turnover cost = 15 × $70,000 × 0.50 = $525,000. A retention program costing $150,000 is proposed for a critical segment with 40% of that turnover. If it prevents three departures, avoided cost = 3 × $35,000 = $105,000, which does not cover the program for the whole population; targeting the program to the critical segment changes the math. This is why analytics must connect cost to a specific decision.
Pay equity analysis
A regression of salary on job level, tenure, and performance returns a gender coefficient of −$3,200 with p = 0.04. That suggests an unexplained gap worth investigating, not proof of intentional discrimination. If p = 0.20, the result is inconclusive. Always report effect size, sample size, and model limitations.
Traps that cost marks
- Confusing correlation with causation.
- Averaging already-averaged data.
- Ignoring Simpson's paradox when aggregating groups.
- Drawing conclusions from small samples or p-hacking.
- Using a chart that truncates the axis or obscures the comparison.
- Presenting a metric without the decision it supports.