@inbook{79e7cfd0493448f999030741bbb31243,
title = "Utilizing customers{\textquoteright} purchase and contract renewal details to predict defection in the cloud software industry",
abstract = "This study aims to predict customer defection in the growing market of the cloud software industry. Using the original unstructured data of a company, we propose a procedure to identify the actual defection condition (i.e., whether the customer is defecting from the company or merely stopped using a current product to up/downgrade it) and to produce a measure of customer loyalty by compiling the number of customers{\textquoteright} purchases and renewals. Based on the results, we investigated important variables for classifying defecting customers using a random forest and built a prediction model using a decision tree. The final results indicate that defecting customers are mainly characterized by their loyalty and their number of total payments.",
keywords = "Cloud software industry, Customer defection, Decision tree, Machine learning, Random forest",
author = "Martono, {Niken Prasasti} and Katsutoshi Kanamori and Hayato Ohwada",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.",
year = "2014",
doi = "10.1007/978-3-319-13332-4_12",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "138--149",
editor = "Kim, {Yang Sok} and Kang, {Byeong Ho} and Deborah Richards",
booktitle = "Knowledge Management and Acquisition for Smart Systems and Services - 13th Pacific Rim Knowledge Acquisition Workshop, PKAW 2014, Proceedings",
}