Preview. This page currently shows the analysis skeleton. The full worked walkthrough — sample dataset, connected steps, runnable code, interpretation — is being built.

HR / People Analytics · how-to

Predict Employee Churn from Organizational Network — in DAX (Power BI)

A network-driver analysis. Every step below is the same analysis rendered for DAX (Power BI) — grounded in the source, honest where DAX (Power BI) can't do a step cleanly.

Same analysis, your tool

DERIVE

convert the network into quantitative signals per person

Derived = <expr>   -- calculated column

JOIN · mechanical

link network position to who stayed or left

RELATED(Other[key])   -- via model relationship

CORRELATE

identify which network signals predict staying

-- Pearson r has no native DAX fn; build a measure: DIVIDE(SUMX(d,(x-x̄)(y-ȳ)), SQRT(SUMX(d,(x-x̄)²)*SUMX(d,(y-ȳ)²)))  [driver] vs [outcome]

FIT-CLASSIFIER

model resignation odds from connectedness and context

DAX (Power BI) can’t do this cleanly

FIT-CLASSIFIER has no clean dax form —

PREDICT

flag well-connected people worth retaining

Score = [b0] + [b1]*[x1] + [b2]*[x2]   -- apply the fitted coefficients (calc column)

Honest note: DAX (Power BI) can’t do 1 of these steps cleanly — the switcher shows which tools can.

Grounded in

Predictive HR Analytics with Excel — §17.8/17.9 ONA graph metrics & churn

Doing HR / People Analytics work?

The full HR / People Analytics guide covers this and the whole workflow around it — reconciled from the field’s best books.

Want to run it, not read it?

Run this analysis live on your data, in DAX (Power BI) or any tool — powered by the Stepcode engine.