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

TL;DR

This paper presents a machine learning approach using random forest node embeddings to personalize email communication for pension participants, improving segmentation and classification accuracy across large datasets.

Contribution

The paper introduces a novel method combining random forest node embeddings with classification for personalized communication and segmentation in pension marketing.

Findings

Achieved an AUC of 0.948 on pension data.

Demonstrated competitive performance on benchmark datasets.

Enabled effective customer segmentation for targeted marketing.

Abstract

Communicating effectively with customers is a challenge for many marketers, but especially in a context that is both pivotal to individual long-term financial well-being and difficult to understand: pensions. Around the world, participants are reluctant to consider their pension in advance, it leads to a lack of preparation of their pension retirement [1], [2]. In order to engage participants to obtain information on their expected pension benefits, personalizing the pension providers' email communication is a first and crucial step. We describe a machine learning approach to model email newsletters to fit participants' interests. The data for the modeling and analysis is collected from newsletters sent by a large Dutch pension provider of the Netherlands and is divided into two parts. The first part comprises 2,228,000 customers whereas the second part comprises the data of a pilot study, which took place in July 2018 with 465,711 participants. In both cases, our algorithm extracts features from continuous and categorical data using random forests, and then calculates node embeddings of the decision boundaries of the random forest. We illustrate the algorithm's effectiveness for the classification task, and how it can be used to perform data mining tasks. In order to confirm that the result is valid for more than one data set, we also illustrate the properties of our algorithm in benchmark data sets concerning churning. In the data sets considered, the proposed modeling demonstrates competitive performance with respect to other state of the art approaches based on random forests, achieving the best Area Under the Curve (AUC) in the pension data set (0.948). For the descriptive part, the algorithm can identify customer segmentations that can be used by marketing departments to better target their communication towards their customers.