LSTM Response Models for Direct Marketing Analytics: Replacing Feature Engineering with Deep Learning

Abstract

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 handcrafted 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).

Keywords: Long-short term memory neural network (LSTM), Recurrent neural network (RNN), Feature engineering, Response model, Panel data, Direct marketing