Customer Churn Prediction — Confusion Matrix & Metrics Calculator
Compute accuracy, precision, recall, F1, specificity, MCC and more for customer churn prediction from TP/FP/FN/TN counts.
Churn models guide retention spend, so the relevant question is whether an intervention's cost is justified by the precision of your 'will churn' flags and the recall of at-risk customers you actually reach. This computes both alongside the lift that retention teams report to finance.
Formula
Disclaimer: This tool is for general informational and estimation purposes only and is not professional financial, tax, accounting or legal advice. All figures are estimates — verify with a qualified professional before making decisions. Read the full disclaimer.
About Customer Churn Prediction — Confusion Matrix & Metrics Calculator
Churn models guide retention spend, so the relevant question is whether an intervention's cost is justified by the precision of your 'will churn' flags and the recall of at-risk customers you actually reach. This computes both alongside the lift that retention teams report to finance. Enter the four confusion-matrix counts and this calculator returns every standard metric — accuracy, precision, recall (sensitivity), F1, specificity and the Matthews correlation coefficient — recomputed live. MCC is highlighted because it is the most honest single number for imbalanced problems: it only scores high when the model does well across all four quadrants, unlike accuracy or F1 which can be gamed.
How to use Customer Churn Prediction — Confusion Matrix & Metrics Calculator
- 1Enter your values into Customer Churn Prediction — Confusion Matrix & Metrics Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Customer Churn Prediction — Confusion Matrix & Metrics Calculator?
- ✓Computes Customer Churn Prediction instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: precision = TP/(TP+FP).
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Should a churn model optimize precision or recall?+
It depends on intervention cost. Cheap interventions (an email) favor recall — reach everyone at risk. Expensive ones (a human call, a discount) favor precision — only spend on likely-churners. The right operating point is where intervention cost equals retained customer value.
What is 'lift' in churn modeling?+
Lift measures how much better your model targets churners than random selection. If 15% of customers churn but your top-decile flags churn 60% of the time, that's 4× lift — the number retention teams use to justify the model's ROI versus blanket campaigns.
Why is MCC considered the most reliable single metric?+
MCC uses all four confusion-matrix cells and behaves like a correlation coefficient (−1 to +1): it is high only when predictions track reality across both classes. On imbalanced data where accuracy and even F1 can mislead, MCC stays informative — which is why it's increasingly the recommended summary statistic.
What's the difference between recall and specificity?+
Recall (sensitivity) is the fraction of actual will churn cases the model catches — TP/(TP+FN). Specificity is the fraction of actual will stay cases it correctly clears — TN/(TN+FP). A model can have high recall and low specificity (flags everything) or vice versa; you need both to judge it.
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