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Analyze another fileIdentifies top churn drivers using logistic regression for interpretable directional coefficients and random forest for non-linear variable importance, with ROC evaluation and optimal threshold confusion matrix
Use when you need to understand which customer attributes most strongly predict cancellation and want both interpretable coefficients and non-linear importance rankings
Do not use if the target variable is continuous rather than binary, or if you need causal inference rather than predictive modeling
Built for: Customer success managers, retention analysts, ecommerce data analysts, CRM managers, product managers at subscription-based companies
Typical data source: Customer records with a churn label (Yes/No), tenure in months, monthly charges, contract type, and optional service add-on or demographic columns
Dataset with 12 columns
Minimum 100 rows
Logistic regression for interpretable coefficients plus random forest variable importance for non-linear effects. Binary target Churn (Yes/No converted to 1/0). Side-by-side comparison of the two methods reveals both directional and ranked drivers.
Churn rate by contract type showing month-to-month vs annual retention
Churn rate by internet service type comparing Fiber optic vs DSL
Churn rate by payment method highlighting electronic check vs automatic payment risks
Logistic regression log-odds coefficients showing directional impact of each predictor on churn probability
Random forest variable importance by mean decrease in Gini impurity for non-linear churn prediction
Heatmap of churn rate by tenure bucket and contract type revealing early-tenure month-to-month risk
ROC curve showing model discrimination ability with AUC score
Confusion matrix at optimal Youden threshold showing true vs predicted churn classifications
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Tf038 Live Ttest — When you only need to test whether a single metric such as tenure or monthly charges is significantly different between churned and retained customers, without building a full predictive model.
Similar: Anova Factorial
Identify which customers are at highest risk of cancellation
Identify which customers are at highest risk of cancellation
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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