Network competition and consumer churn

被引:0
|
作者
Gans, JS [1 ]
机构
[1] Univ Melbourne, Sch Business, Carlton, Vic 3053, Australia
关键词
network competition; connection costs; customer churn; up-front fees;
D O I
10.1016/S0167-6245(00)00002-0
中图分类号
F [经济];
学科分类号
02 ;
摘要
This paper examines the nature of connection and churn charges that would arise in a context of network competition. Connection charges are incurred when customers initially connect to a network while churn charges are imposed on customers that disconnect. It is demonstrated that in a competitive equilibrium, the weighted sum of connection and churn charges, rather than their individual levels, is determined. While connection costs are always a factor in determining network prices, chum costs are only relevant when chum is actually expected; but even then only on an expected basis. It is also demonstrated that chum charges do not perform a useful role in encouraging consumers to switch only when it is efficient to do so. Finally, the inter-temporal nature of connection charges is examined and it is demonstrated that such charges will be of an on-going or 'rental' nature rather than up-front. (C) 2000 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:97 / 109
页数:13
相关论文
共 50 条
  • [1] A Feature Interaction Network for Customer Churn Prediction
    Tang, Qi
    Xia, Guoen
    Zhang, Xianquan
    Li, Yaxiang
    ICMLC 2020: 2020 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING, 2018, : 242 - 248
  • [2] Analytical Model of Customer Churn Based On Bayesian Network
    Sun, Peng
    Guo, Xin
    Zhang, Yunpeng
    Wu, Ziyan
    2013 9TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS), 2013, : 269 - 271
  • [3] Case based models of the relationship between consumer resistance to innovation and customer churn
    Sun, Yang
    JOURNAL OF RETAILING AND CONSUMER SERVICES, 2021, 61
  • [4] Combining Local and Social Network Classifiers to Improve Churn Prediction
    Backiel, Aimee
    Verbinnen, Yannick
    Baesens, Bart
    Claeskens, Gerda
    PROCEEDINGS OF THE 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2015), 2015, : 651 - 658
  • [5] Making Customer Intention Tactics with Network Value and Churn Rate
    Liu, Rong
    Li, Yuanquan
    Qi, Jiayin
    2009 5TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-8, 2009, : 5339 - 5342
  • [6] Predicting Banking Customer Churn based on Artificial Neural Network
    Zaky, Amany
    Ouf, Shimaa
    Roushdy, Mohamed
    5TH INTERNATIONAL CONFERENCE ON COMPUTING AND INFORMATICS (ICCI 2022), 2022, : 132 - 139
  • [7] Social network analytics for churn prediction in telco: Model building, evaluation and network architecture
    Oskarsdottir, Maria
    Bravo, Cristian
    Verbeke, Wouter
    Sarraute, Carlos
    Baesens, Bart
    Vanthienen, Jan
    EXPERT SYSTEMS WITH APPLICATIONS, 2017, 85 : 204 - 220
  • [8] A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry
    Oskarsdottir, Maria
    Bravo, Cristian
    Verbeke, Wouter
    Sarraute, Carlos
    Baesens, Bart
    Vanthienen, Jan
    PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, 2016, : 1151 - 1158
  • [9] ABC Based Neural Network Approach for Churn Prediction in Telecommunication Sector
    Paliwal, Priyanka
    Kumar, Divya
    INFORMATION AND COMMUNICATION TECHNOLOGY FOR INTELLIGENT SYSTEMS (ICTIS 2017) - VOL 2, 2018, 84 : 343 - 349
  • [10] A New Neural Network Based Customer Profiling Methodology for Churn Prediction
    Tiwari, Ashutosh
    Hadden, John
    Turner, Chris
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2010, PT 4, PROCEEDINGS, 2010, 6019 : 358 - 369