A holistic framework for mCRM – data mining perspective

Abstract

Purpose

The purpose of this paper is twofold. First, in order to understand mobile customer relationship management (mCRM) and data mining application in the mCRM, this paper aims to present a conceptualization of mCRM in respect of data mining. Second, the paper also aims to develop the empirically grounded framework of the mCRM from data mining perspective.

Design/methodology/approach

The empirical paper is used to gain a conceptual view of mCRM. Semi‐structured interviews and contact methodology is used to form the main data source through which the major concerns and issues of mCRM are identified. This lead to holistic framework of mCRM. The paper followed the paradigm of natural science research on information technology by March and Smith and Hervner et al.

Findings

The framework identified three critical issues that are categorized as customer care information center, data store and data access systems, and mobile services and technology. The paper on various existing literatures in mCRM strategies and data mining leads to the development of the mCRM framework. The applications of methodology in data mining helped in identifying and exploring mCRM processes. The data mining based framework identifies issues related to customer attrition, customer life time value analysis and customer churn analysis while moving towards mCRM.

Originality/value

The suggested framework would serve as a guideline to all mCRM product vendors and will be considered as a structured consistent procedure for applying mCRM using data mining tools and techniques. The paper explored various studies in the area of mCRM and data mining and shed light on emerging issues in mCRM area. The suggested framework would give an organization, product developers, and management thinker's valuable insights on application of data mining tools and techniques in mCRM application.

Citation

Ranjan, J. and Bhatnagar, V. (2009), "A holistic framework for mCRM – data mining perspective", Information Management & Computer Security, Vol. 17 No. 2, pp. 151-165. https://doi.org/10.1108/09685220910964018