CLVAE: A Variational Autoencoder for Long-Term Customer Revenue Forecasting
Predicting customers' long-term revenue from sparse and irregular transaction data is central to marketing resource allocation in non-contractual settings, yet existing approaches face a trade-off. Traditional probabilistic customer base models deliver robust long-horizon forecasts by imposing strong structural assumptions, while flexible machine-learning models often require substantial training data and careful tuning. We propose a variational-autoencoder-based model that preserves the process-based likelihood of established attrition-transaction-spend models conditional on customer heterogeneity, but replaces the restrictive parametric mixing distribution with a flexible latent represe...
| Authors | Jeffrey Näf, Riana Valera Mbelson, Markus Meierer |
|---|---|
| Primary category | stat.ML |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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