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HR / People Analytics · how-to
Engagement Drivers — what moves sales/income — in Python
A key-driver analysis. Every step below is the same analysis rendered for Python — grounded in the source, honest where Python can't do a step cleanly.
Same analysis, your tool
CORRELATE
see which outcomes engagement tracks with before claiming impact
df["AvgEngagementScore"].corr(df["CustomerSatisfaction,RevenuePerStaff,OperatingIncomePerStaff"])REGRESS
estimate how much engagement moves sales holding advertising constant
smf.ols("y ~ x1 + x2", data=df).fit()VALIDITY · mechanical
confirm the driver model is statistically sound
df["measure"].corr(df["criterion"])RANK · mechanical
order drivers so the strongest lever is obvious
df.reindex(df["coefficient magnitude"].abs().sort_values(ascending=False).index)PREDICT
quantify the payoff of raising engagement (the Best Buy $100k logic)
model.predict(df)Grounded in
Predictive HR Analytics with Excel, Sec 15.4-15.5 Analyze Engagement (correlation + multiple regression)
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