A MODEL OF PREDICTING CUSTOMER DEFECTION WITH SATISFACTION AND INTERPURCHASE TIME IN MARTECH FRAMEWORK
Keywords:
Customer defection, Satisfaction, Interpurchase time , MarTech(marketing technology)Abstract
Customer defection is a major challenge for companies to remain competitive and profitable. This paper combines the variables of customers satisfaction and their interpurchase time to propose a statistic model of customer defection rate. The Bayer’s rule is used for MarkTech application. The empirical data of customer purchase in E-commerce online platform are conduct for both parameters estimation and model calibration. The results show acceptable good fitness of proposed model and the practical data. The conclusion provides the suggestions for applying data combination with MarkTech.Downloads
References
Bisset, C. (2022).A stochastic programming approach for marketing campaign optimization. Dissertation in Master of Engineering in Industrial Engineering at the North-West University.
Buhmann, A. and White, C.L. (2022), “Artificial intelligence in public relations: role and implications”, in Lipschultz, J.H., Freberg, K. and Luttrell, R. (Eds), The Emerald Handbook of ComputerMediated Communication and Social Media, Emerald Publishing, Bingley, pp. 625-638.
Buhmann, A., Maltseva, K., Fieseler, C. and Fleck, M. (2021), “Muzzling social media: the adverse effects of moderating stakeholder conversations online”, Technology in Society, Vol. 64, pp.256-278.
Carter, N. T. and Dalal, D. K.(2010). An ideal point account of the JDI Work satisfaction scale.Personality and Individual Differences,49(7), pp.743-748.
Einhorn, H.J. and Gonedes, N.J. (1976). An Exponential Discrepancy Model for Attitude Evaluation. In: Mathematical Models in Marketing. Lecture Notes in Economics and Mathematical Systems, vol 132. Springer, Berlin, Heidelberg.
Freberg, K. (2022), Social Media for Strategic Communication: Creative Strategies and Research-Based Applications, 2nd ed., Sage, Thousand Oaks, CA.
Hasumoto, K. and Goto, M. (2022). Predicting customer churn for platform businesses: using latent variables of variational autoencoder as consumers’ purchasing behavior. Neural Comput & Applications https://doi.org/10.1007/s00521-022-07418-8.
Huang, H. H.(2018). Taking a View of Input-Output Process to Modeling Customer Purchase Behavior: A Case Study for Hotel Accommodation. International Journal of Hospitality & Tourism Management. 2(1), pp. 8-12.
Kim, S., Chang, Y., Wong, S.F., Park, M.C. (2020). Customer resistance to churn in a mature mobile telecommunications market. International Journal of Mobile Communications, 18(1), 41-66.
Lalwani, P., Mishra, M.K., Chadha, J.S. and Sethi, P. (2022). Customer churn prediction system: a machine learning approach. Computing 104, 271–294.
Lutrell, R., Emerick, S. and Wallace, A. (2021), Digital Strategies: Data-Driven Public Relations, Marketing, and Advertising, Oxford University Press, Oxford.
Matuszelański, K. and Kopczewska, K.(2022). Customer Churn in Retail E-Commerce Business: Spatial and Machine Learning Approach. Journal of Theoretical and Applied Electronic Commerce Research, 17(1), 165-198.
McLean, M.J., Madden, S. and Pressgrove, G. (2021), “Complexity theory as a new lens for digital social advocacy”, Public Relations Review, Vol. 47 No. 3,pp.43-55.
Meire, M.(2021). Customer comeback: Empirical insights into the drivers and value of returning customers. Journal of Business Research, 127(2), 193-205.
Mesak,H.I., Scott, C.P., Bari, A.(2022).On the Diffusion of Subscription-Based Services: The Roles of Price, Advertising, and Customers' Defection. IEEE Transactions on Engineering, pp1-14.
Mirkovic, M., Lolic, T., Stefanovic, D. and Anderla, A.(2022). Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data. Applied Sciences, 12(10), 5001.
Moore, S. and Hubscher, R. (2022), Strategic Communication and AI. Public Relations with Intelligent User Interfaces, Routledge, London.
Saura, J. R.(2021).Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics. Journal of Innovation & Knowledge, 6(2), pp. 92-102.
Simon, L. and Adler, J. (2022). Worth the effort? Comparison of different MCMC algorithms for estimating the Pareto/NBD model. Journal of Business Economics, 92, pp.707-733.
Somosi, A., Stiassny, A., Kolos,K., Warlop, L. (2022).An Interrogation of Causes of Customer Defection in Zimbabwean Mobile Telecommunication Industry. International Journal of Advanced Multidisciplinary Scientific Research,5(2),1-9.
Somosi,A, Stiassny, A., Kolos, K. and Warlop, L.(2021). Customer defection due to service elimination and post-elimination customer behavior: An empirical investigation in telecommunications. International Journal of Research in Marketing, 38(4), 915-934.
Wilson, C., Brubaker, P. and Smith, B. (2020), “Cracking the snapcode: understanding the organizational and technological influences of strategic social media adoption”, International Journal of Strategic Communication, Vol. 14 No. 1, pp. 41-59.
Worthington, S. L. S. (2022). Navigating the Marketing Technology Landscape. IEEE Engineering Management Review, 50(1), pp. 20-23.
Xiahou,X. and Harada, Y.(2022). B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM. Journal of Theoretical and Applied Electronic Commerce Research, 17(2), 458-475.
Zhang, T., Moro, S., & Ramos, R. F. (2022). A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation. Future Internet, 14(3), 94.
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