B2B Customer Conversion Prediction: A Document Representation, Graph Theory, and CatBoost Driven Methodology

Abstract:In the one-time selling B2B context, the buying cycle may last months or even years. During the long process, targeting customers that have a high potential to make purchases and recommending personalized campaigns accordingly are important for effective marketing. For this goal, we study the following problems, B2B customer data aggregation, customer feature generation, and prediction of whether a B2B customer would show interest in making a purchase (i.e., prediction of conversion into sales funnel). We propose an algorithm to aggregate individual contacts to the B2B customer level based on multiple keys. For non-standardized keys such as company names, we propose a novel architecture to cluster them in a domain encompassing irregularities such as spelling mistakes and spelling variants. We then define and generate a set of features and apply the CatBoost model for customer conversion prediction. Our framework achieves 91\% prediction accuracy. Based on the prediction results and analysis of the model, we then discuss personalized campaign recommendations to foster conversion.

10 pages, 6 figures. Presented at the 2nd Workshop on End-End Customer Journey Optimization at KDD 2023

Machine Learning (cs.LG)

arXiv:2609.03239 [cs.LG]

(or arXiv:2609.03239v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.03239 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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