In predictive modeling, firms often deal with high-dimensional data that span multiple channels, websites, demographics, purchase types, and product categories. Traditional customer response models rely heavily on feature engineering, and their performance depends on the analyst's domain knowledge and expertise to craft relevant predictors. As the complexity of data increases, however, traditional models grow exponentially complicated. In this paper, we demonstrate that long-short term memory (LSTM) neural networks, which rely exclusively on raw data as input, can predict customer behaviors with great accuracy. In our first application, a model outperforms standard benchmarks. In a second, more realistic application, an LSTM model competes against 271 hand-crafted models that use a wide variety of features and modeling approaches. It beats 269 of them, most by a wide margin. LSTM neural networks are excellent candidates for modeling customer behavior using panel data in complex environments (e.g., direct marketing, brand choices, clickstream data, churn prediction).

在预测建模中，公司经常处理跨多个渠道，网站，人口统计，购买类型和产品类别的高维数据。传统的客户响应模型严重依赖于功能工程，其性能取决于分析师的领域知识和专业知识，以制定相关的预测变量。但是，随着数据复杂性的增加，传统模型也呈指数级增长。在本文中，我们证明了完全依赖原始数据作为输入的长期短期记忆（LSTM）神经网络可以非常准确地预测客户的行为。在我们的第一个应用程序中，模型的性能优于标准基准。在第二个更现实的应用程序中，LSTM模型与使用各种功能和建模方法的271个手工模型竞争。它击败了其中的269个，大多数都远远超过了。LSTM神经网络是在复杂环境中使用面板数据（例如，直接营销，品牌选择，点击流数据，客户流失预测）对客户行为进行建模的极佳候选者。