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Communicating effectively with customers is a challenge for many marketers,but especially in a context that is both pivotal to individual long-termfinancial well-being and difficult to understand: pensions. Around the world,participants are reluctant to consider their pension in advance, it leads to alack of preparation of their pension retirement [1], [2]. In order to engageparticipants to obtain information on their expected pension benefits,personalizing the pension providers' email communication is a first and crucialstep. We describe a machine learning approach to model email newsletters to fitparticipants' interests. The data for the modeling and analysis is collectedfrom newsletters sent by a large Dutch pension provider of the Netherlands andis divided into two parts. The first part comprises 2,228,000 customers whereasthe second part comprises the data of a pilot study, which took place in July2018 with 465,711 participants. In both cases, our algorithm extracts featuresfrom continuous and categorical data using random forests, and then calculatesnode embeddings of the decision boundaries of the random forest. We illustratethe algorithm's effectiveness for the classification task, and how it can beused to perform data mining tasks. In order to confirm that the result is validfor more than one data set, we also illustrate the properties of our algorithmin benchmark data sets concerning churning. In the data sets considered, theproposed modeling demonstrates competitive performance with respect to otherstate of the art approaches based on random forests, achieving the best AreaUnder the Curve (AUC) in the pension data set (0.948). For the descriptivepart, the algorithm can identify customer segmentations that can be used bymarketing departments to better target their communication towards theircustomers.

That looks interesting! Personalizing Communication and Segmentation with Random Forest Node Embeddings