用RNN预测SaaS用户生命周期价值，提升预测精度。
Predicting Customer Lifetime Value Using Recurrent Neural Net
- 构建多时间维度的RNN模型，融合用户加入时间、使用时长与当前日期。
- 相比轻量梯度提升与Buy Until You Die模型，中位绝对百分比误差显著降低。
- 适合需要精准用户价值评估的SaaS企业或增长团队使用。
本文提出一种用于预测软件即服务（SaaS）应用中用户生命周期价值的循环神经网络方法。该方法综合考虑三个关联的时间维度：用户分群时间（用户加入日期）、用户系统年龄（自加入以来的时间）以及用户当前所处的时序位置（即同期信息）。采用多单元结构的循环神经网络，每个单元类似于长短期记忆网络。该方法被应用于预测不同时间跨度下的新用户获取价值和现有用户的滚动生命周期价值。实验表明，其在中位绝对百分比误差方面显著优于轻量梯度提升模型和Buy Until You Die模型。
原文摘要 · Abstract (English)
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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