Predicting Customer Lifetime Value Using Recurrent Neural Net
It addresses customer value prediction for SaaS businesses, but it is incremental as it builds on existing neural network methods.
This paper tackles predicting customer lifetime value in SaaS applications by introducing a recurrent neural network approach that accounts for three time dimensions, and it significantly improves median absolute percent error compared to existing models like light gradient boost and Buy Until You Die models.
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.