Your Engagement Survey Is a Decent Turnover Predictor. It's Not Enough.
A synthetic-data walkthrough of building a turnover risk score — and why the metric most companies already have needs company, not replacement.
HR Analytics, Done Differently
Most HR analytics tells you what happened. I help you predict what will happen — and act on it with appropriate confidence.
Planning around point estimates — the average turnover rate, the typical engagement score — ignores the range of outcomes you actually face. I work with distributions, not just means. Uncertainty isn't a problem to eliminate; it's information to quantify.
If it matters to your organization, it can be measured — perhaps not perfectly, but well enough to reduce uncertainty and improve decisions. That's the standard.
Dashboards tell you where you've been. Good models tell you where you're going. I build models designed to predict outcomes — turnover, performance, fit — not just report on them.
The goal isn't certainty — it's calibrated uncertainty. Knowing you're 70% confident in an outcome, and knowing what would change that, is more valuable than a false sense of precision.
Turnover risk, performance classification, promotion readiness — built and validated against your own data.
→Define what you actually need to measure, build instruments that measure it, and validate that they do.
→Frame people decisions under uncertainty. Quantify what you know, identify what's worth learning, choose with clarity.
→Rigorous psychometric design for employee surveys, assessments, and selection instruments.
→A synthetic-data walkthrough of building a turnover risk score — and why the metric most companies already have needs company, not replacement.
A synthetic-data walkthrough of turning "high potential" into a measured construct — and what happened when two independent raters scored the same people.
A synthetic-data walkthrough of building a promotion-readiness model — and why the two inputs most companies rely on turned out to be the weakest ones in it.