Personalizing communication and segmentation with random forest node embeddings

Abstract

Communicating effectively with customers is a challenge, especially in a context requiring long-term planning such as the pension sector. Engaging individuals to obtain information on their expected pension benefits, by personalizing the pension providers’ email communication, is a first and crucial step. We describe a machine learning approach to model email newsletters to fit individual interests. The data for the analysis is collected from newsletters sent by a pension provider of the Netherlands and is divided into two parts (N= 2,228,000 participants in total of which 465,711 participants were part of the pilot study). Our algorithm calculates node embeddings over the nodes of a random forest, which are then used as features for the machine learning task. We illustrate the algorithm’s effectiveness in classification tasks using multiple benchmark data sets, and in a data mining task where segmentation rules can be inferred from the node embeddings. The proposed model demonstrates competitive performance with respect to other approaches based on random forests, achieving the best area under the curve (AUC) in the pension data set (0.948), while also identifying customer segments that can be used by marketing departments to better target their communication towards their customers.