Designing Executive Correlation Heatmaps

Do These Metrics Move Together? The Correlation Heatmap Your stakeholder asks: "If discount goes up, does revenue go up or down? And does that hold across order count, tenure, and average order value?" You have 5 metrics and that is 10 pairwise correlations to check. A heatmap answers all 10 in one glance — but only if you build it so a non-technical reader can read it. This article builds that heatmap from a tiny table you can verify by eye.

1. The Data: Same Customers, 5 Metrics One row per customer — the kind of summary you computed after the groupby in Module 3:

customer_id revenue order_count avg_order_val discount_rate tenure_months C001 1,274 3 425 0.08 8 C002 1,380 3 460 0.05 14 C003 90 2 45 0.25 2 C004 680 5 136 0.12 6 C005 2,100 7 300 0.04 18 You can already suspect: customers with high discount_rate (C003: 0.25) have the lowest revenue. A correlation matrix makes that suspicion a number.

2. What Pearson r Means — Three Sentences Pearson r measures how linearly two columns move together, on a scale from −1 to +1 :

r Meaning Example from the table +1.0 Perfect uphill line — when one goes up, the other always goes up revenue vs order_count — more orders → more revenue (positive) 0.0 No linear relationship — knowing one tells you nothing about the other tenure_months vs discount_rate — roughly 0 in this data −1.0 Perfect downhill line — when one goes up, the other always goes down discount_rate vs avg_order_val — higher discount → smaller basket (negative) In practice most r values land between −1 and +1: |r| > 0.7 strong, 0.3–0.7 moderate, . An r of 0.85 (Quiz 3 Q08) is strong positive — higher spend strongly associated with higher revenue, but association is not causation .

3. From Table to Matrix: What df.corr() Gives You df[['revenue','order_count','avg_order_val','discount_rate','tenure_months']].corr() returns a 5×5 grid. The diagonal is always 1.00 (a column correlates perfectly with itself). The grid is symmetric — r of (revenue, discount) equals r of (discount, revenue). That symmetry is why we hide half of it next.

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