XAI for Churn Prediction in B2B Models: A Use Case in an Enterprise Software Company

Gabriel Marín Díaz (), José Javier Galán and Ramón Alberto Carrasco
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Gabriel Marín Díaz: Faculty of Statistics, Complutense University, Puerta de Hierro, 28040 Madrid, Spain
José Javier Galán: Faculty of Statistics, Complutense University, Puerta de Hierro, 28040 Madrid, Spain
Ramón Alberto Carrasco: Faculty of Statistics, Complutense University, Puerta de Hierro, 28040 Madrid, Spain

Mathematics, 2022, vol. 10, issue 20, 1-29

Abstract: The literature related to Artificial Intelligence (AI) models and customer churn prediction is extensive and rich in Business to Customer (B2C) environments; however, research in Business to Business (B2B) environments is not sufficiently addressed. Customer churn in the business environment and more so in a B2B context is critical, as the impact on turnover is generally greater than in B2C environments. On the other hand, the data used in the context of this paper point to the importance of the relationship between customer and brand through the Contact Center. Therefore, the recency, frequency, importance and duration (RFID) model used to obtain the customer’s assessment from the point of view of their interactions with the Contact Center is a novelty and an additional source of information to traditional models based on purchase transactions, recency, frequency, and monetary (RFM). The objective of this work consists of the design of a methodological process that contributes to analyzing the explainability of AI algorithm predictions, Explainable Artificial Intelligence (XAI), for which we analyze the binary target variable abandonment in a B2B environment, considering the relationships that the partner (customer) has with the Contact Center, and focusing on a business software distribution company. The model can be generalized to any environment in which classification or regression algorithms are required.

Keywords: churn detection; XAI; interpretability; B2B; RFM; RFID (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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