Toward a hybrid data mining model for customer retention

Chu, Bong-Horng; Tsai, Ming-Shian; Ho, Cheng-Seen

doi:10.1016/j.knosys.2006.10.003

Cited by 92 publications

(42 citation statements)

References 7 publications

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“…In this spirit, a common churn management process involves constructing a churn prediction model using past churn data, and determining key variables, which influence churn. The churn model is then used to identify and classify a list of customers with potentially high risk of churn (potential churners) from existing customer data and to perform the appropriate retention activities (Ngai, Xiu & Chau., 2009;Coussement & Van den Poel., 2008;Chu, Tsai & Ho., 2007;Au, Chen & Yao., 2003;Berson et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Improved Customer Churn and Retention Decision Management Using Operations Research Approach

Adebiyi

Oyatoye

Amole

2017

emaj

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The relevance of operations research cannot be overemphasized, as it provides the best possible results in any given circumstance, through analysis of operations and the use of scientific method. Thus, this paper explores the combination of two operations research models (analytic hierarchy process and Markov chain) for solving subscribers' churn and retention problem peculiar to most service firms. A conceptual model for unraveling the problem customer churn and retention decision management was proposed and tested with data on third level analysis of AHP for determining appropriate strategies for customer churn and retention in the Nigeria telecommunication industries. A survey was conducted with 408 subscribers; the sample for the study was selected through multi-stage sampling. Two analytical tools were proposed for the analysis of data. These include: Expert Choice/Excel Solver (using Microsoft Excel) and Windows based Quantitative System for Business (WinQSB). This paper plays important role in understanding various strategies for effective churn and retention management and the ranking of churn and retention drivers in order of importance to stakeholders` decisionmaking. The study provided a framework for understanding the application of AHP and Markov chain for modeling, analysing and proffering solution to problem of churn and retention. The study recommends organizational strategies (corporate, business and functional) that reverse the churn alternatives with high priority and equally strengthen service delivery on high priority retention alternatives in order to ensure firms sustainable competitive advantage.

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Section: Introductionmentioning

confidence: 99%

Improved Customer Churn and Retention Decision Management Using Operations Research Approach

Adebiyi

Oyatoye

Amole

2017

emaj

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“…The approach is based on a novel combination of existing approaches, as noted by a number of researchers [11]. This work is motivated by the obvious limitations of classical paradigms for data mining that have been used in isolation.…”

Section: Hybrid Mining Approachmentioning

confidence: 99%

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

Tan

Turel

Dohan

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…Price is the most important factor for customer churn, followed by customer service, service quality and coverage quality [5]. However, social influence is another key driver to customer churn in the mobile services industry [25,31].…”

Section: Industrymentioning

confidence: 99%

Data-Driven Customer Behaviour Model Generation For Agent Based Exploration

Mgbemena

Bell

2016

49th Annual Simulation Symposium (ANSS 2016)

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Customer retention is a critical concern for most mobile network operators because of the increasing competition in the mobile services sector. This concern has driven companies to exploit data as an avenue to better understand customer needs. Data mining techniques such as clustering and classification have been adopted to understand customer retention in the mobile services industry. However, the effectiveness of these techniques is debatable due to the increasing complexity of the mobile market itself. This study proposes an application of Agent-Based Modeling and Simulation (ABMS) as a novel approach to understanding customer retention. A dataset provided by a mobile network operator is utilized to automate decision trees and agent based models. The most popular churn modeling techniques were adopted in order to automate the development of models, from decision trees, and subsequently explore customer churn scenarios. ABMS is used to understand the behavior of customers and detect possible reasons why customers churned or stayed with their respective mobile network operators. Data analysis is able to identify that location and choice of mobile devices were determinants for the decision to churn or stay with their mobile network operator -with word of mouth as an important factor. Importantly, agent based simulation is able to explore further the determinants in the wider marketplace.

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Toward a hybrid data mining model for customer retention

Cited by 92 publications

References 7 publications

Improved Customer Churn and Retention Decision Management Using Operations Research Approach

Improved Customer Churn and Retention Decision Management Using Operations Research Approach

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

Data-Driven Customer Behaviour Model Generation For Agent Based Exploration

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