From Customer Value to Campaign Response: An Explainable Marketing Analytics Framework Using UK Online Retail and Portuguese Bank Marketing Data
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This study examines how linking customer value analysis to campaign response prediction can improve marketing decisions. The paper uses two historical public business datasets. The first contains online retail transactions from a UK-based, registered, non-store retailer between 1 December 2010 and 9 December 2011. The retailer sells unique all-occasion gifts, and many customers are wholesalers. The second dataset records direct phone marketing campaigns by an anonymized Portuguese banking institution, where the outcome is customer subscription to a term deposit. The datasets are not merged at the row level because they do not share customer identifiers. Instead, they are linked through one decision logic: identify where customer value is concentrated, then rank campaign contacts by their probability of response. The online retail analysis produces 397,884 clean transactions, 4,338 customers, and total revenue of 8,911,407.90. The top 20% of customers generate 74.6% of revenue. In the bank marketing data, the base response rate is 11.7%. A random forest model without call duration reaches a ROC AUC of 0.801, and the top 20% model-ranked contacts respond at 36.2%, about 3.09 times the base rate. The findings show that marketing return is uneven in both settings. Firms can improve targeting by protecting high-value customers and prioritizing campaign contacts with higher estimated response probability.
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[1]. Khajvand, M., Zolfaghar, K., Ashoori, S., & Alizadeh, S. (2011). Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study. Procedia Computer Science, 3, 57-63. https://doi.org/10.1016/j.procs.2010.12.011
[2]. Chen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing & Customer Strategy Management, 19(3), 197-208. https://doi.org/10.1057/dbm.2012.17
[3]. Migueis, V. L., Camanho, A. S., & e Cunha, J. F. (2012). Customer data mining for lifestyle segmentation. Expert Systems with Applications, 39(10), 9359-9366. https://doi.org/10.1016/j.eswa.2012.02.133
[4]. Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New insights into churn prediction in the telecommunication sector: A profit-driven data mining approach. European Journal of Operational Research, 218(1), 211-229. https://doi.org/10.1016/j.ejor.2011.09.031
[5]. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503
[6]. Germann, F., Lilien, G. L., & Rangaswamy, A. (2013). Performance implications of deploying marketing analytics. International Journal of Research in Marketing, 30(2), 114-128. https://doi.org/10.1016/j.ijresmar.2012.10.001
[7]. Leeflang, P. S. H., Verhoef, P. C., Dahlstrom, P., & Freundt, T. (2014). Challenges and solutions for marketing in a digital era. European Management Journal, 32(1), 1-12. https://doi.org/10.1016/j.emj.2013.12.001
[8]. Moro, S., Cortez, P., & Rita, P. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22-31. https://doi.org/10.1016/j.dss.2014.03.001
[9]. Germann, F., Lilien, G. L., Fiedler, L., & Kraus, M. (2014). Do retailers benefit from deploying customer analytics? Journal of Retailing, 90(4), 587-593. https://doi.org/10.1016/j.jretai.2014.08.002
[10]. Fan, S., Lau, R. Y. K., & Zhao, J. L. (2015). Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32. https://doi.org/10.1016/j.bdr.2015.02.006
[11]. Safari, F., Safari, N., & Montazer, G. A. (2016). Customer lifetime value determination based on RFM model. Marketing Intelligence & Planning, 34(4), 446-461. https://doi.org/10.1108/MIP-03-2015-0060
[12]. Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413
[13]. Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904. https://doi.org/10.1016/j.jbusres.2015.07.001
[14]. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420
[15]. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778
[16]. Kannan, P. K., & Li, H. A. (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1), 22-45. https://doi.org/10.1016/j.ijresmar.2016.11.006
[17]. Grewal, D., Roggeveen, A. L., & Nordfalt, J. (2017). The future of retailing. Journal of Retailing, 93(1), 1-6. https://doi.org/10.1016/j.jretai.2016.12.008
[18]. Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and predictive analytics in retailing. Journal of Retailing, 93(1), 79-95. https://doi.org/10.1016/j.jretai.2016.12.004
[19]. Kunz, W. H., Aksoy, L., Bart, Y., Heinonen, K., Kabadayi, S., Ordenes, F. V., Sigala, M., Diaz, D., & Theodoulidis, B. (2017). Customer engagement in a Big Data world. Journal of Services Marketing, 31(2), 161-171. https://doi.org/10.1108/JSM-10-2016-0352
[20]. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In I. Guyon et al. (Eds.), Advances in Neural Information Processing Systems 30 (pp. 4765-4774). Curran Associates.
[21]. Ascarza, E., Neslin, S., Netzer, O., Anderson, Z., Fader, P. S., Gupta, S., Hardie, B. G. S., Lemmens, A., Libai, B., Neal, D., Provost, F., & Schrift, R. (2018). In pursuit of enhanced customer retention management: Review, key issues, and future directions. Customer Needs and Solutions, 5(1-2), 65-81. https://doi.org/10.1007/s40547-017-0080-0
[22]. Ascarza, E. (2018). Retention futility: Targeting high-risk customers might be ineffective. Journal of Marketing Research, 55(1), 80-98. https://doi.org/10.1509/jmr.16.0163
[23]. De Caigny, A., Coussement, K., & 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), 760-772. https://doi.org/10.1016/j.ejor.2018.02.009
[24]. Adadi, A., & Berrada, M. (2018). Peeking inside the black box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138-52160. https://doi.org/10.1109/ACCESS.2018.2870052
[25]. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), Article 93. https://doi.org/10.1145/3236009
[26]. Reinartz, W., Wiegand, N., & Imschloss, M. (2019). The impact of digital transformation on the retailing value chain. International Journal of Research in Marketing, 36(3), 350-366. https://doi.org/10.1016/j.ijresmar.2018.12.002
[27]. Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42. https://doi.org/10.1007/s11747-019-00696-0
[28]. Hossain, M. A., Akter, S., & Yanamandram, V. (2020). Revisiting customer analytics capability for data-driven retailing. Journal of Retailing and Consumer Services, 56, 102187. https://doi.org/10.1016/j.jretconser.2020.102187
[29]. Verhoef, P. C., Broekhuizen, T. L. J., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889-901. https://doi.org/10.1016/j.jbusres.2019.09.022
[30]. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50. https://doi.org/10.1007/s11747-020-00749-9
[31]. Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131-151. https://doi.org/10.1177/0022242920953847
[32]. UCI Machine Learning Repository. (2015). Online Retail dataset. https://archive.ics.uci.edu/dataset/352/online+retail
[33]. UCI Machine Learning Repository. (2012). Bank Marketing dataset. https://archive.ics.uci.edu/dataset/222/bank+marketing