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.

翻译：本文提出了一种用于预测软件即服务（SaaS）应用中用户终身价值的循环神经网络方法。该方法考虑了三个相互关联的时间维度：用户队列（用户加入日期）、用户在系统内的存续时长（用户加入服务后的时间）以及用户在特定存续时长时所处的日历日期（即同期信息）。该循环神经网络采用多单元架构，其中每个单元类似于长短期记忆神经网络。该方法被应用于预测不同时间范围内的获取型（新用户）与滚动型（现有用户）终身价值。研究发现，相较于轻量梯度提升模型和“购买至流失”模型，该方法能显著降低中位数绝对百分比误差。