Predicting Customer Lifetime Value Using Recurrent Neural Net
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913636069408768 |
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| author | Chen, Huigang Ng, Edwin Smyl, Slawek Steininger, Gavin |
| author_facet | Chen, Huigang Ng, Edwin Smyl, Slawek Steininger, Gavin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20295 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting Customer Lifetime Value Using Recurrent Neural Net Chen, Huigang Ng, Edwin Smyl, Slawek Steininger, Gavin Applications Machine Learning 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. |
| title | Predicting Customer Lifetime Value Using Recurrent Neural Net |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2412.20295 |