Predicting Customer Lifetime Value Using Recurrent Neural Net

Huigang Chen, Edwin Ng, Slawek Smyl, Gavin Steininger
12/28/2024
stat.APcs.LGstat.ML

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Abstract

This paper introduces a recurrent neural network approach for predicting user lifetime value in Software as a Service (SaaS) applications. The approach accounts for three connected time dimensions. These dimensions are the user cohort (the date the user joined), user age-in-system (the time since the user joined the service) and the calendar date the user is an age-in-system (i.e., contemporaneous information).The recurrent neural networks use a multi-cell architecture, where each cell resembles a long short-term memory neural network. The approach is applied to predicting both acquisition (new users) and rolling (existing user) lifetime values for a variety of time horizons. It is found to significantly improve median absolute percent error versus light gradient boost models and Buy Until You Die models.

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Cite this paper

@article{chen2024predicting,
  title  = {Predicting Customer Lifetime Value Using Recurrent Neural Net},
  author = {Huigang Chen and Edwin Ng and Slawek Smyl and Gavin Steininger},
  year   = {2024},
  eprint = {2412.20295},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.20295v2}
}

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