Systematic Literature Review on Customer Switching Behaviour from Marketing and Data Science Perspectives
Keywords:
customer switching, marketing, data science
Abstract
This paper systematically examines the literature review in the field of customer switching behavior. Based on the literature review, it can be concluded that customer switching behavior is a topic that has been widely researched, with a focus on various industries, particularly banking and telecommunications. Research trends in this area have shown a positive direction in recent years, and the amount of research being done in marketing and data science is relatively balanced. In marketing, correlational studies are predominant, with a focus on identifying relationships between customer satisfaction, price-related variables, attractiveness of alternatives, service failure, quality, and switching costs to switching behavior. The PPM model is also gaining popularity as an important development for switching behavior because it considers both push and pull factors. Data science research has shown promising results in predicting customer switching behavior, with each research paper achieving good predictive accuracy. However, research gaps spanning the fields of marketing and data science need to be addressed to provide a comprehensive understanding of the drivers of customer switching behavior. Overall, the literature review shows that customer switching behavior is an important concern for businesses, and further research in this area is essential to gain a better understanding of customer behavior and develop effective strategies to retain customers.
References
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Bansal, H.S., Taylor, S.F. and James, Y.S. (2005) ‘“Migrating” to New Service Providers: Toward a Unifying Framework of Consumers’ Switching Behaviors’, Journal of the Academy of Marketing Science, 33(1), pp. 96–115.
Brockett, P.L. et al. (2008) ‘Survival Analysis of a Household Portfolio of Insurance Policies: How Much Time Do You Have to Stop Total Customer Defection?’, Journal of Risk and Insurance, 75(3), pp. 713–737.
Buckinx, W. and Van den Poel, D. (2005) ‘Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting’, European Journal of Operational Research, 164(1), pp. 252–268. Available at: https://doi.org/10.1016/j.ejor.2003.12.010.
Burez, J. and Van den Poel, D. (2009) ‘Handling class imbalance in customer churn prediction’, Expert Systems with Applications, 36(3, Part 1), pp. 4626–4636. Available at: https://doi.org/10.1016/j.eswa.2008.05.027.
Chigwende, S. and Govender, K. (2020) ‘Corporate brand image and switching behavior: case of mobile telecommunications customers in Zimbabwe’, Innovative Marketing, 16(2), pp. 80–90. Available at: https://doi.org/10.21511/im.16(2).2020.07.
Chou, S.-Y. et al. (2016) ‘Multichannel service providers’ strategy: Understanding customers’ switching and free-riding behavior’, Journal of Business Research, 69(6), pp. 2226–2232. Available at: https://doi.org/10.1016/j.jbusres.2015.12.034.
Chuang, Y.-F. and Tai, Y.-F. (2016) ‘Research on customer switching behavior in the service industry’, Management Research Review, 39(8), pp. 925–939. Available at: https://doi.org/10.1108/MRR-01-2015-0022.
Clemes, M.D., Gan, C. and Zhang, D. (2010) ‘Customer switching behaviour in the Chinese retail banking industry’, International Journal of Bank Marketing, 28(7), pp. 519–546. Available at: https://doi.org/10.1108/02652321011085185.
Coussement, K., Lessmann, S. and Verstraeten, G. (2017) ‘A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry’, Decision Support Systems, 95, pp. 27–36. Available at: https://doi.org/10.1016/j.dss.2016.11.007.
Dawes, J. (2004) ‘Price changes and defection levels in a subscription‐type market: can an estimation model really predict defection levels?’, Journal of Services Marketing, 18(1), pp. 35–44. Available at: https://doi.org/10.1108/08876040410520690.
De Bock, K.W. and Poel, D.V. den (2011) ‘An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction’, Expert Systems with Applications, 38(10), pp. 12293–12301. Available at: https://doi.org/10.1016/j.eswa.2011.04.007.
De Caigny, A., Coussement, K. and De Bock, K.W. (2018) ‘A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees’, European Journal of Operational Research, 269(2), pp. 760–772. Available at: https://doi.org/10.1016/j.ejor.2018.02.009.
Dettori, J.R., Norvell, D.C. and Chapman, J.R. (2022) ‘Fixed-Effect vs Random-Effects Models for Meta-Analysis: 3 Points to Consider’, Global Spine Journal, 12(7), pp. 1624–1626. Available at: https://doi.org/10.1177/21925682221110527.
Du, Y. et al. (2020) ‘Visual analysis of customer switching behavior pattern mining for takeout service’, Journal of Computer Languages, 57, p. 100946. Available at: https://doi.org/10.1016/j.cola.2020.100946.
Evanschitzky, H. et al. (2012) ‘Success Factors of Product Innovation: An Updated Meta-Analysis: Success Factors of Product Innovation’, Journal of Product Innovation Management, 29, pp. 21–37. Available at: https://doi.org/10.1111/j.1540-5885.2012.00964.x.
Ganesh, J., Arnold, M.J. and Reynolds, K.E. (2000) ‘Understanding the Customer Base of Service Providers: An Examination of the Differences between Switchers and Stayers’, Journal of Marketing, 64(3), pp. 65–87. Available at: https://doi.org/10.1509/jmkg.64.3.65.18028.
Ghasrodashti, E.K. and Link to external site, this link will open in a new window (2018) ‘Explaining brand switching behavior using pull–push–mooring theory and the theory of reasoned action’, Journal of Brand Management, 25(4), pp. 293–304. Available at: https://doi.org/10.1057/s41262-017-0080-2.
Ghufran, M. et al. (2022) ‘Impact of COVID-19 to customers switching intention in the food segments: The push, pull and mooring effects in consumer migration towards organic food’, Food Quality and Preference, 99, p. 104561. Available at: https://doi.org/10.1016/j.foodqual.2022.104561.
Glady, N., Baesens, B. and Croux, C. (2009) ‘Modeling churn using customer lifetime value’, European Journal of Operational Research, 197(1), pp. 402–411. Available at: https://doi.org/10.1016/j.ejor.2008.06.027.
Han, H., Kim, W. and Hyun, S.S. (2011) ‘Switching intention model development: Role of service performances, customer satisfaction, and switching barriers in the hotel industry’, International Journal of Hospitality Management, 30(3), pp. 619–629. Available at: https://doi.org/10.1016/j.ijhm.2010.11.006.
Hino, H. (2017) ‘Does switching-intention result in a change in behaviour? Exploring the actual behavioural shopping patterns of switching-intended customers’, British Food Journal, 119(12), pp. 2903–2917. Available at: https://doi.org/10.1108/BFJ-12-2016-0622.
Huang, B., Buckley, B. and Kechadi, T.-M. (2010) ‘Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications’, Expert Systems with Applications, 37(5), pp. 3638–3646. Available at: https://doi.org/10.1016/j.eswa.2009.10.027.
Hussain, S. et al. (2022) ‘Determinants of switching intention in the electricity markets - An integrated structural model approach’, Journal of Retailing and Consumer Services, 69, p. 103094. Available at: https://doi.org/10.1016/j.jretconser.2022.103094.
Keaveney, S.M. (1995a) ‘Customer Switching Behavior in Service Industries: An Exploratory Study’, Journal of Marketing, 59(2), pp. 71–82. Available at: https://doi.org/10.1177/002224299505900206.
Keaveney, S.M. (1995b) ‘Customer Switching Behavior in Service Industries: An Exploratory Study’, Journal of Marketing, 59(2), pp. 71–82.
Kim, K., Jun, C.-H. and Lee, J. (2014) ‘Improved churn prediction in telecommunication industry by analyzing a large network’, Expert Systems with Applications, 41(15), pp. 6575–6584. Available at: https://doi.org/10.1016/j.eswa.2014.05.014.
Kotler, P., Bowen, J.T. and Makens, J.C. (2009) ‘Marketing for Hospitality and Tourism’.
Landsman, V. and Nitzan, I. (2020) ‘Cross-decision social effects in product adoption and defection decisions’, International Journal of Research in Marketing, 37(2), pp. 213–235. Available at: https://doi.org/10.1016/j.ijresmar.2019.09.002.
Lee, Jonathan, Lee, Janghyuk and Feick, L. (2001) ‘The impact of switching costs on the customersatisfaction‐loyalty link:mobile phone service in France’, Journal of Services Marketing, 15(1), pp. 35–48. Available at: https://doi.org/10.1108/08876040110381463.
Lees, G., Garland, R. and Wright, M. (2007) ‘Switching banks: Old bank gone but not forgotten’, Journal of Financial Services Marketing, 12(2), pp. 146–156. Available at: https://doi.org/10.1057/palgrave.fsm.4760070.
Lin, C.-N. and Huang, H.-H. (2023) ‘Exploring users’ switching intention and behavior on social networking sites: Linear and nonlinear perspectives’, Computer Standards & Interfaces, 83, p. 103660. Available at: https://doi.org/10.1016/j.csi.2022.103660.
Maldonado, S., López, J. and Vairetti, C. (2020) ‘Profit-based churn prediction based on Minimax Probability Machines’, European Journal of Operational Research, 284(1), pp. 273–284. Available at: https://doi.org/10.1016/j.ejor.2019.12.007.
Moon, B. (1995) ‘Paradigms in migration research: exploring “moorings” as a schema’, Progress in Human Geography, 19(4), pp. 504–524. Available at: https://doi.org/10.1177/030913259501900404.
Narayandas, D. (1998) ‘Measuring and Managing the Benefits of Customer Retention: An Empirical Investigation’, Journal of Service Research, 1(2), pp. 108–128. Available at: https://doi.org/10.1177/109467059800100202.
Nassaji, H. (2015) ‘Qualitative and descriptive research: Data type versus data analysis’, Language Teaching Research, 19(2), pp. 129–132. Available at: https://doi.org/10.1177/1362168815572747.
Nguyen, A.T.V., McClelland, R. and Thuan, N.H. (2022) ‘Exploring customer experience during channel switching in omnichannel retailing context: A qualitative assessment’, Journal of Retailing and Consumer Services, 64, p. 102803. Available at: https://doi.org/10.1016/j.jretconser.2021.102803.
Nugroho, A. and Wang, W.-T. (2023) ‘Consumer switching behavior to an augmented reality (AR) beauty product application: Push-pull mooring theory framework’, Computers in Human Behavior, 142, p. 107646. Available at: https://doi.org/10.1016/j.chb.2022.107646.
Page, M., Pitt, L. and Berthon, P. (1996) ‘Analysing and reducing customer defections’, Long Range Planning, 29(6), pp. 821–834. Available at: https://doi.org/10.1016/S0024-6301(97)82819-1.
Pick, D. and Eisend, M. (2014) ‘Buyers’ perceived switching costs and switching: a meta-analytic assessment of their antecedents’, Journal of the Academy of Marketing Science, 42(2), pp. 186–204. Available at: https://doi.org/10.1007/s11747-013-0349-2.
Postigo-Boix, M. and Melús-Moreno, J.L. (2018) ‘A social model based on customers’ profiles for analyzing the churning process in the mobile market of data plans’, Physica A: Statistical Mechanics and its Applications, 496, pp. 571–592. Available at: https://doi.org/10.1016/j.physa.2017.12.121.
Pustokhina, I.V. et al. (2021) ‘Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms’, Information Processing & Management, 58(6), p. 102706. Available at: https://doi.org/10.1016/j.ipm.2021.102706.
Quoquab, F. et al. (2018) ‘Antecedents of switching intention in the mobile telecommunications industry: A partial least square approach’, Asia Pacific Journal of Marketing and Logistics, 30(4), pp. 1087–1111. Available at: https://doi.org/10.1108/APJML-06-2017-0121.
Rockmann, K.W. and Northcraft, G.B. (2008) ‘To be or not to be trusted: The influence of media richness on defection and deception’, Organizational Behavior and Human Decision Processes, 107(2), pp. 106–122. Available at: https://doi.org/10.1016/j.obhdp.2008.02.002.
Sands, S. et al. (2020) ‘How small service failures drive customer defection: Introducing the concept of microfailures’, Business Horizons, 63(4), pp. 573–584. Available at: https://doi.org/10.1016/j.bushor.2020.03.014.
Santonen, T. (2007) ‘Price sensitivity as an indicator of customer defection in retail banking’, International Journal of Bank Marketing, 25(1), pp. 39–55. Available at: https://doi.org/10.1108/02652320710722605.
Swedberg, R. (2020) ‘Exploratory Research’, in C. Elman, J. Mahoney, and J. Gerring (eds) The Production of Knowledge: Enhancing Progress in Social Science. Cambridge: Cambridge University Press (Strategies for Social Inquiry), pp. 17–41. Available at: https://doi.org/10.1017/9781108762519.002.
Vo, N.N.Y. et al. (2021) ‘Leveraging unstructured call log data for customer churn prediction’, Knowledge-Based Systems, 212, p. 106586. Available at: https://doi.org/10.1016/j.knosys.2020.106586.
Vyas, V. and Raitani, S. (2014) ‘Drivers of customers’ switching behaviour in Indian banking industry’, International Journal of Bank Marketing, 32(4), pp. 321–342. Available at: https://doi.org/10.1108/IJBM-04-2013-0033.
Wiebach, N. and Hildebrandt, L. (2012) ‘Explaining customers’ switching patterns to brand delisting’, Journal of Retailing and Consumer Services, 19(1), pp. 1–10. Available at: https://doi.org/10.1016/j.jretconser.2011.08.001.
Williams, P., Khan, M.S. and Naumann, E. (2011) ‘Customer dissatisfaction and defection: The hidden costs of downsizing’, Industrial Marketing Management, 40(3), pp. 405–413. Available at: https://doi.org/10.1016/j.indmarman.2010.04.007.
Wirtz, J. et al. (2014a) ‘Contrasting the Drivers of Switching Intent and Switching Behavior in Contractual Service Settings’, Journal of Retailing, 90(4), pp. 463–480. Available at: https://doi.org/10.1016/j.jretai.2014.07.002.
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Zhang, X. et al. (2012) ‘Predicting customer churn through interpersonal influence’, Knowledge-Based Systems, 28, pp. 97–104. Available at: https://doi.org/10.1016/j.knosys.2011.12.005.
Amin, A. et al. (2019) ‘Customer churn prediction in telecommunication industry using data certainty’, Journal of Business Research, 94, pp. 290–301. Available at: https://doi.org/10.1016/j.jbusres.2018.03.003.
Bansal, H.S., Taylor, S.F. and James, Y.S. (2005) ‘“Migrating” to New Service Providers: Toward a Unifying Framework of Consumers’ Switching Behaviors’, Journal of the Academy of Marketing Science, 33(1), pp. 96–115.
Brockett, P.L. et al. (2008) ‘Survival Analysis of a Household Portfolio of Insurance Policies: How Much Time Do You Have to Stop Total Customer Defection?’, Journal of Risk and Insurance, 75(3), pp. 713–737.
Buckinx, W. and Van den Poel, D. (2005) ‘Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting’, European Journal of Operational Research, 164(1), pp. 252–268. Available at: https://doi.org/10.1016/j.ejor.2003.12.010.
Burez, J. and Van den Poel, D. (2009) ‘Handling class imbalance in customer churn prediction’, Expert Systems with Applications, 36(3, Part 1), pp. 4626–4636. Available at: https://doi.org/10.1016/j.eswa.2008.05.027.
Chigwende, S. and Govender, K. (2020) ‘Corporate brand image and switching behavior: case of mobile telecommunications customers in Zimbabwe’, Innovative Marketing, 16(2), pp. 80–90. Available at: https://doi.org/10.21511/im.16(2).2020.07.
Chou, S.-Y. et al. (2016) ‘Multichannel service providers’ strategy: Understanding customers’ switching and free-riding behavior’, Journal of Business Research, 69(6), pp. 2226–2232. Available at: https://doi.org/10.1016/j.jbusres.2015.12.034.
Chuang, Y.-F. and Tai, Y.-F. (2016) ‘Research on customer switching behavior in the service industry’, Management Research Review, 39(8), pp. 925–939. Available at: https://doi.org/10.1108/MRR-01-2015-0022.
Clemes, M.D., Gan, C. and Zhang, D. (2010) ‘Customer switching behaviour in the Chinese retail banking industry’, International Journal of Bank Marketing, 28(7), pp. 519–546. Available at: https://doi.org/10.1108/02652321011085185.
Coussement, K., Lessmann, S. and Verstraeten, G. (2017) ‘A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry’, Decision Support Systems, 95, pp. 27–36. Available at: https://doi.org/10.1016/j.dss.2016.11.007.
Dawes, J. (2004) ‘Price changes and defection levels in a subscription‐type market: can an estimation model really predict defection levels?’, Journal of Services Marketing, 18(1), pp. 35–44. Available at: https://doi.org/10.1108/08876040410520690.
De Bock, K.W. and Poel, D.V. den (2011) ‘An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction’, Expert Systems with Applications, 38(10), pp. 12293–12301. Available at: https://doi.org/10.1016/j.eswa.2011.04.007.
De Caigny, A., Coussement, K. and De Bock, K.W. (2018) ‘A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees’, European Journal of Operational Research, 269(2), pp. 760–772. Available at: https://doi.org/10.1016/j.ejor.2018.02.009.
Dettori, J.R., Norvell, D.C. and Chapman, J.R. (2022) ‘Fixed-Effect vs Random-Effects Models for Meta-Analysis: 3 Points to Consider’, Global Spine Journal, 12(7), pp. 1624–1626. Available at: https://doi.org/10.1177/21925682221110527.
Du, Y. et al. (2020) ‘Visual analysis of customer switching behavior pattern mining for takeout service’, Journal of Computer Languages, 57, p. 100946. Available at: https://doi.org/10.1016/j.cola.2020.100946.
Evanschitzky, H. et al. (2012) ‘Success Factors of Product Innovation: An Updated Meta-Analysis: Success Factors of Product Innovation’, Journal of Product Innovation Management, 29, pp. 21–37. Available at: https://doi.org/10.1111/j.1540-5885.2012.00964.x.
Ganesh, J., Arnold, M.J. and Reynolds, K.E. (2000) ‘Understanding the Customer Base of Service Providers: An Examination of the Differences between Switchers and Stayers’, Journal of Marketing, 64(3), pp. 65–87. Available at: https://doi.org/10.1509/jmkg.64.3.65.18028.
Ghasrodashti, E.K. and Link to external site, this link will open in a new window (2018) ‘Explaining brand switching behavior using pull–push–mooring theory and the theory of reasoned action’, Journal of Brand Management, 25(4), pp. 293–304. Available at: https://doi.org/10.1057/s41262-017-0080-2.
Ghufran, M. et al. (2022) ‘Impact of COVID-19 to customers switching intention in the food segments: The push, pull and mooring effects in consumer migration towards organic food’, Food Quality and Preference, 99, p. 104561. Available at: https://doi.org/10.1016/j.foodqual.2022.104561.
Glady, N., Baesens, B. and Croux, C. (2009) ‘Modeling churn using customer lifetime value’, European Journal of Operational Research, 197(1), pp. 402–411. Available at: https://doi.org/10.1016/j.ejor.2008.06.027.
Han, H., Kim, W. and Hyun, S.S. (2011) ‘Switching intention model development: Role of service performances, customer satisfaction, and switching barriers in the hotel industry’, International Journal of Hospitality Management, 30(3), pp. 619–629. Available at: https://doi.org/10.1016/j.ijhm.2010.11.006.
Hino, H. (2017) ‘Does switching-intention result in a change in behaviour? Exploring the actual behavioural shopping patterns of switching-intended customers’, British Food Journal, 119(12), pp. 2903–2917. Available at: https://doi.org/10.1108/BFJ-12-2016-0622.
Huang, B., Buckley, B. and Kechadi, T.-M. (2010) ‘Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications’, Expert Systems with Applications, 37(5), pp. 3638–3646. Available at: https://doi.org/10.1016/j.eswa.2009.10.027.
Hussain, S. et al. (2022) ‘Determinants of switching intention in the electricity markets - An integrated structural model approach’, Journal of Retailing and Consumer Services, 69, p. 103094. Available at: https://doi.org/10.1016/j.jretconser.2022.103094.
Keaveney, S.M. (1995a) ‘Customer Switching Behavior in Service Industries: An Exploratory Study’, Journal of Marketing, 59(2), pp. 71–82. Available at: https://doi.org/10.1177/002224299505900206.
Keaveney, S.M. (1995b) ‘Customer Switching Behavior in Service Industries: An Exploratory Study’, Journal of Marketing, 59(2), pp. 71–82.
Kim, K., Jun, C.-H. and Lee, J. (2014) ‘Improved churn prediction in telecommunication industry by analyzing a large network’, Expert Systems with Applications, 41(15), pp. 6575–6584. Available at: https://doi.org/10.1016/j.eswa.2014.05.014.
Kotler, P., Bowen, J.T. and Makens, J.C. (2009) ‘Marketing for Hospitality and Tourism’.
Landsman, V. and Nitzan, I. (2020) ‘Cross-decision social effects in product adoption and defection decisions’, International Journal of Research in Marketing, 37(2), pp. 213–235. Available at: https://doi.org/10.1016/j.ijresmar.2019.09.002.
Lee, Jonathan, Lee, Janghyuk and Feick, L. (2001) ‘The impact of switching costs on the customersatisfaction‐loyalty link:mobile phone service in France’, Journal of Services Marketing, 15(1), pp. 35–48. Available at: https://doi.org/10.1108/08876040110381463.
Lees, G., Garland, R. and Wright, M. (2007) ‘Switching banks: Old bank gone but not forgotten’, Journal of Financial Services Marketing, 12(2), pp. 146–156. Available at: https://doi.org/10.1057/palgrave.fsm.4760070.
Lin, C.-N. and Huang, H.-H. (2023) ‘Exploring users’ switching intention and behavior on social networking sites: Linear and nonlinear perspectives’, Computer Standards & Interfaces, 83, p. 103660. Available at: https://doi.org/10.1016/j.csi.2022.103660.
Maldonado, S., López, J. and Vairetti, C. (2020) ‘Profit-based churn prediction based on Minimax Probability Machines’, European Journal of Operational Research, 284(1), pp. 273–284. Available at: https://doi.org/10.1016/j.ejor.2019.12.007.
Moon, B. (1995) ‘Paradigms in migration research: exploring “moorings” as a schema’, Progress in Human Geography, 19(4), pp. 504–524. Available at: https://doi.org/10.1177/030913259501900404.
Narayandas, D. (1998) ‘Measuring and Managing the Benefits of Customer Retention: An Empirical Investigation’, Journal of Service Research, 1(2), pp. 108–128. Available at: https://doi.org/10.1177/109467059800100202.
Nassaji, H. (2015) ‘Qualitative and descriptive research: Data type versus data analysis’, Language Teaching Research, 19(2), pp. 129–132. Available at: https://doi.org/10.1177/1362168815572747.
Nguyen, A.T.V., McClelland, R. and Thuan, N.H. (2022) ‘Exploring customer experience during channel switching in omnichannel retailing context: A qualitative assessment’, Journal of Retailing and Consumer Services, 64, p. 102803. Available at: https://doi.org/10.1016/j.jretconser.2021.102803.
Nugroho, A. and Wang, W.-T. (2023) ‘Consumer switching behavior to an augmented reality (AR) beauty product application: Push-pull mooring theory framework’, Computers in Human Behavior, 142, p. 107646. Available at: https://doi.org/10.1016/j.chb.2022.107646.
Page, M., Pitt, L. and Berthon, P. (1996) ‘Analysing and reducing customer defections’, Long Range Planning, 29(6), pp. 821–834. Available at: https://doi.org/10.1016/S0024-6301(97)82819-1.
Pick, D. and Eisend, M. (2014) ‘Buyers’ perceived switching costs and switching: a meta-analytic assessment of their antecedents’, Journal of the Academy of Marketing Science, 42(2), pp. 186–204. Available at: https://doi.org/10.1007/s11747-013-0349-2.
Postigo-Boix, M. and Melús-Moreno, J.L. (2018) ‘A social model based on customers’ profiles for analyzing the churning process in the mobile market of data plans’, Physica A: Statistical Mechanics and its Applications, 496, pp. 571–592. Available at: https://doi.org/10.1016/j.physa.2017.12.121.
Pustokhina, I.V. et al. (2021) ‘Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms’, Information Processing & Management, 58(6), p. 102706. Available at: https://doi.org/10.1016/j.ipm.2021.102706.
Quoquab, F. et al. (2018) ‘Antecedents of switching intention in the mobile telecommunications industry: A partial least square approach’, Asia Pacific Journal of Marketing and Logistics, 30(4), pp. 1087–1111. Available at: https://doi.org/10.1108/APJML-06-2017-0121.
Rockmann, K.W. and Northcraft, G.B. (2008) ‘To be or not to be trusted: The influence of media richness on defection and deception’, Organizational Behavior and Human Decision Processes, 107(2), pp. 106–122. Available at: https://doi.org/10.1016/j.obhdp.2008.02.002.
Sands, S. et al. (2020) ‘How small service failures drive customer defection: Introducing the concept of microfailures’, Business Horizons, 63(4), pp. 573–584. Available at: https://doi.org/10.1016/j.bushor.2020.03.014.
Santonen, T. (2007) ‘Price sensitivity as an indicator of customer defection in retail banking’, International Journal of Bank Marketing, 25(1), pp. 39–55. Available at: https://doi.org/10.1108/02652320710722605.
Swedberg, R. (2020) ‘Exploratory Research’, in C. Elman, J. Mahoney, and J. Gerring (eds) The Production of Knowledge: Enhancing Progress in Social Science. Cambridge: Cambridge University Press (Strategies for Social Inquiry), pp. 17–41. Available at: https://doi.org/10.1017/9781108762519.002.
Vo, N.N.Y. et al. (2021) ‘Leveraging unstructured call log data for customer churn prediction’, Knowledge-Based Systems, 212, p. 106586. Available at: https://doi.org/10.1016/j.knosys.2020.106586.
Vyas, V. and Raitani, S. (2014) ‘Drivers of customers’ switching behaviour in Indian banking industry’, International Journal of Bank Marketing, 32(4), pp. 321–342. Available at: https://doi.org/10.1108/IJBM-04-2013-0033.
Wiebach, N. and Hildebrandt, L. (2012) ‘Explaining customers’ switching patterns to brand delisting’, Journal of Retailing and Consumer Services, 19(1), pp. 1–10. Available at: https://doi.org/10.1016/j.jretconser.2011.08.001.
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Published
2021-11-30
How to Cite
Amsal, A., Albar, B. and Yeni, Y. (2021) “Systematic Literature Review on Customer Switching Behaviour from Marketing and Data Science Perspectives”, AMAR (Andalas Management Review), 5(2), pp. 95-123. doi: 10.25077/amar.5.2.95-123.2021.
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