Cohort Revenue & Retention Analysis: A Bayesian Approach
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910916857036800 |
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| author | Orduz, Juan Camilo |
| author_facet | Orduz, Juan Camilo |
| contents | We present a Bayesian approach to model cohort-level retention rates and revenue over time. We use Bayesian additive regression trees (BART) to model the retention component which we couple with a linear model for the revenue component. This method is flexible enough to allow adding additional covariates to both model components. This Bayesian framework allows us to quantify uncertainty in the estimation, understand the effect of covariates on retention through partial dependence plots (PDP) and individual conditional expectation (ICE) plots, and most importantly, forecast future revenue and retention rates with well-calibrated uncertainty through highest density intervals. We also provide alternative approaches to model the retention component using neural networks and inference through stochastic variational inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16216 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cohort Revenue & Retention Analysis: A Bayesian Approach Orduz, Juan Camilo Applications We present a Bayesian approach to model cohort-level retention rates and revenue over time. We use Bayesian additive regression trees (BART) to model the retention component which we couple with a linear model for the revenue component. This method is flexible enough to allow adding additional covariates to both model components. This Bayesian framework allows us to quantify uncertainty in the estimation, understand the effect of covariates on retention through partial dependence plots (PDP) and individual conditional expectation (ICE) plots, and most importantly, forecast future revenue and retention rates with well-calibrated uncertainty through highest density intervals. We also provide alternative approaches to model the retention component using neural networks and inference through stochastic variational inference. |
| title | Cohort Revenue & Retention Analysis: A Bayesian Approach |
| topic | Applications |
| url | https://arxiv.org/abs/2504.16216 |