Analysis of customer churn behavior in digital libraries. Issue 4 (27th August 2014)

• Record Type: Journal Article
• Authors: Lai, Yuangen; Zeng, Jianxun
• Is Part Of: Program. Volume 48:Issue 4(2014)
• Page Start: 370
• Page End: 382
• Publication Date: 2014-08-27
• Subjects: Customer relationship management -- Digital library -- Survival Analysis -- Customer churn -- Survival time; Libraries, University and college -- Great Britain -- Automation -- Periodicals; 025.30285
• DOI: 10.1108/PROG-08-2011-0035
• Languages: English
• ISSNs: 0033-0337

Abstract

Abstract : Purpose: – The purpose of this paper is to discuss issues related to customer churn behavior in digital libraries (DLs) and demonstrate the successful application of Survival Analysis for understanding customer churn status and relationship duration distribution between customers and libraries. Design/methodology/approach: – The study applies non-parametric methods of Survival Analysis to analyze churn behaviors of 8, 054 customers from a famous Chinese digital library, and a cluster method to make customer segmentation according to customer behavioral features. Findings: – The customer churn rate of the given library is very high, so as to the churn hazard in early three months after customer's registration on the web site of the library. There is clear difference in both customer survival time and churn hazard among customer groups. It is necessary to strengthen customer churn analysis and customer relationship management (CRM) for DLs. Research limitations/implications: – The studied samples are mainly based on customers from one digital library and some hypotheses have not been strictly proven due to the absence of relevant empirical researches. Practical implications: – This study provides a reasonable basis for decision making about CRM in DLs. Originality/value: – Most previous researches about information behavior concentrate on information seeking behavior in DLs, seldom discuss customer switching behavior. The paper discusses issues related to customer churn analysis and illustrates the adaptation of Survival Analysis to understand customer churn status and relationship duration distribution in DLs.