Bayesian Survival Analysis with PyMC: Modelling Customer Churn
The article discusses the application of Bayesian survival analysis using PyMC to model customer churn in subscription businesses. It emphasizes the importance of accounting for right-censored data, where active customers who haven't yet churned provide valuable information about minimum survival times. The author demonstrates how to build a Bayesian accelerated failure time (AFT) model with Weibull and Log-Logistic distributions and generate individual survival curves for different customer profiles.
- ▪Right-censored data includes customers who are still active and must be included in churn analysis to avoid bias toward shorter lifetimes.
- ▪The Kaplan-Meier estimator is used to compute the empirical survival curve while properly handling censored observations.
- ▪A Weibull accelerated failure time (AFT) model is implemented in PyMC using synthetic data for 1,000 customers with covariates like monthly spend and support tickets.
- ▪The article provides code to generate synthetic churn data, fit Bayesian models, and visualize survival probabilities over time.
- ▪Standardized covariates and administrative censoring at 24 months are applied to simulate realistic customer churn scenarios.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3843317) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Berkan Sesen Posted on Apr 29 • Originally published at sesen.ai Bayesian Survival Analysis with PyMC: Modelling Customer Churn #bayesian #probabilistic #survivalanalysis #pymc Every subscription business lives or dies by churn. Whether it is a B2B SaaS platform tracking annual contracts or a consumer app watching monthly renewals, the question is the same: how long will this customer stay? The data seems straightforward.
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