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’ 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.
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Martono, N.P., Kanamori, K., Ohwada, H. (2014). Utilizing Customers’ Purchase and Contract Renewal Details to Predict Defection in the Cloud Software Industry. In: Kim, Y.S., Kang, B.H., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2014. Lecture Notes in Computer Science(), vol 8863. Springer, Cham. https://doi.org/10.1007/978-3-319-13332-4_12
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DOI: https://doi.org/10.1007/978-3-319-13332-4_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13331-7
Online ISBN: 978-3-319-13332-4
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