Title: Recommendation system for improving churn rate based on action rules and sentiment mining

Authors: Yuehua Duan; Zbigniew W. Ras

Addresses: Department of Computer Science, University of North Carolina, Charlotte, NC 28223, USA ' Department of Computer Science, University of North Carolina, Charlotte, NC 28223, USA; Polish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warszawa, Poland

Abstract: It is well recognised that customers are one of the most valuable assets to a company. Therefore, it is of significant value for companies to reduce the customer outflow. In this paper, we focus on identifying the customers with high chance of attrition and provide valid and trustworthy recommendations to improve their customer churn rate. To this end, we designed and implemented a recommender system that can provide actionable recommendations to improve customer churn rate. We used both transaction and survey data from heavy equipment repair and service sector from 2011 to 2017. This data was collected by a consulting company based in Charlotte, North Carolina. In the survey data, customers give their thoughts, feelings, expectations and complaints by freeform text. We applied aspect-based sentiment analysis on the review text data to gain insightful knowledge on customers' attitudes toward the service. Action rule mining and meta-action triggering mechanism are used to recognise the actionable strategies to help with reducing customer churn.

Keywords: action rule mining; meta-actions; aspect-based sentiment analysis; recommender system; reduct.

DOI: 10.1504/IJDMMM.2022.126665

International Journal of Data Mining, Modelling and Management, 2022 Vol.14 No.4, pp.287 - 308

Received: 07 Nov 2020
Accepted: 22 Mar 2021
Published online: 01 Nov 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article