Online newspaper subscriptions: using machine learning to reduce and understand customer churn

References

• Abdollahpouri, H., Malthouse, E. C., Konstan, J. A., Mobasher, B., & Gilbert, J. (2021). Toward the next generation of news recommender systems. The Web Conference 2021 - Companion of the World Wide Web Conference, WWW (pp. 402–406). https://doi.org/10.1145/3442442.3452327
   Google Scholar
• Ahn, J., Hwang, J., Kim, D., Choi, H., Kang, S. (2020). A survey on churn analysis in various business domains. Institute of Electrical and Electronics Engineers Access, 8, 220816–220839. https://doi.org/10.1109/ACCESS.2020.3042657
   Google Scholar
• American Press Institute. (2018). Paths to Subscription: Why recent subscribers chose to pay for news. https://www.americanpressinstitute.org/publications/reports/survey-research/paths-to-subscription/single-page/
   Google Scholar
• Antonio, N., de Almeida, A. M., Nunes, L., Batista, F., & Ribeiro, R. (2018). Hotel online reviews: Creating a multi-source aggregated index. International Journal of Contemporary Hospitality Management, 30(12), 3574–3591. https://doi.org/10.1108/IJCHM-05-2017-0302
   Web of Science ®Google Scholar
• Arrese, Á. (2016). From Gratis to Paywalls: A brief history of a retro-innovation in the press’s business. Journalism Studies, 17(8), 1051–1067. https://doi.org/10.1080/1461670X.2015.1027788
   Web of Science ®Google Scholar
• Attfield, S., Kazai, G., & Lalmas, M. (2011). Towards a science of user engagement. Position paper. WSDM Workshop on User Modelling for Web Applications. http://www.dcs.gla.ac.uk/~mounia/Papers/engagement.pdf
   Google Scholar
• Ballew, B. (2009). Elsevier’s Scopus® Database. Journal of Electronic Resources in Medical Libraries, 6(3), 245–252.
   Google Scholar
• Burez, J., & Van den Poel, D. (2007). CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications, 32(2), 277–288. https://doi.org/10.1016/j.eswa.2005.11.037
   Web of Science ®Google Scholar
• Campbell, C. (2018, August). Engaged readers don’t churn: Retention lessons for digital subscriptions. WAN-IFRA Reports.
   Google Scholar
• Cardoso, G., Paisana, M., & Pinto-Martinho, A. (2020). Digital news report 2020 - PORTUGAL data. OberCom Reuters.
   Google Scholar
• Chakraborty, A., Ghosh, S., Ganguly, N., & Gummadi, K. P. (2017). Optimizing the recency-relevancy trade-off in online news recommendations. 26th International World Wide Web Conference, WWW 2017 (Vol. i. pp. 837–846). https://doi.org/10.1145/3038912.3052656
   Google Scholar
• Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Rudiger, W. (2000). Crisp-Dm 1.0. CRISP-DM Consortium, 3, 76.
   Google Scholar
• Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
   Web of Science ®Google Scholar
• Chen, T., & Guestrin, C. (2016). Xgboost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785
   Google Scholar
• Coussement, K., & De Bock, K. W. (2013). Customer churn prediction in the online gambling industry: The bene ficial effect of ensemble learning. Journal of Business Research, 66(9), 1629–1636. https://doi.org/10.1016/j.jbusres.2012.12.008
   Web of Science ®Google Scholar
• Coussement, K., Lessmann, S., & 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, 27–36. https://doi.org/10.1016/j.dss.2016.11.007
   Web of Science ®Google Scholar
• Coussement, K., & Van den Poel, D. (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications, 34(1), 313–327. https://doi.org/10.1016/j.eswa.2006.09.038
   Web of Science ®Google Scholar
• Coussement, K., & Van den Poel, D. (2009). Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers. Expert Systems with Applications, 36(3 PART 2), 6127–6134. https://doi.org/10.1016/j.eswa.2008.07.021
   Web of Science ®Google Scholar
• Davoudi, H. (2018). User Acquisition and engagement in digital News Media.
   Google Scholar
• Davoudi, H., Aijun, A., & Edall, G. (2019). Content-based dwell time engagement prediction model for news articles. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Vol. 2).
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Ekholm, P. (2019). Machine learning challenge winning solutions. https://medium.com/mittmedia/we-looked-into-why-our-subscribers-churned-with-machine-learning-d1ac1adf8009
   Google Scholar
• Farquad, M. A. H., Ravi, V., & Raju, S. B. (2014). Churn prediction using comprehensible support vector machine: An analytical CRM application. Applied Soft Computing Journal, 19, 31–40. https://doi.org/10.1016/j.asoc.2014.01.031
   Web of Science ®Google Scholar
• Felps, P. (2022). Piano shares how publishers can improve retention in a post-pandemic world. https://www.inma.org/blogs/conference/post.cfm/piano-shares-how-publishers-can-improve-retention-in-a-post-pandemic-world
   Google Scholar
• Fernandes, E., Moro, S., & Cortez, P. (2023). Data science, machine learning and big data in digital journalism: A survey of state-of-the-art, challenges and opportunities. Expert Systems with Applications, 221, 119795. https://doi.org/10.1016/j.eswa.2023.119795
   Web of Science ®Google Scholar
• Fernandes, E., Moro, S., & Cortez, P. (2024). A data-driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods. International Journal of Consumer Studies, 48(2), e13030.
   Web of Science ®Google Scholar
• Financial Times. (n.d.). Lessons on retention from the FT (and beyond). https://www.ftstrategies.com/en-gb/insights/lessons-on-retention-from-the-ft-and-beyond/
   Google Scholar
• Fitzpatrick, N. (2022). No News is Not Good News: The Implications of News Fatigue and News Avoidance in a Pandemic World. Athens Journal of Mass Media and Communications, 8(3), 145–160. https://doi.org/10.30958/ajmmc.8-3-1
   Google Scholar
• Fletcher, R., & Nielsen, R. K. (2017). Paying for online news: A comparative analysis of six countries. Digital Journalism, 5(9), 1173–1191. https://doi.org/10.1080/21670811.2016.1246373
   Web of Science ®Google Scholar
• Ganesh, J., Arnold, M. J., & 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), 65–87. https://doi.org/10.1509/jmkg.64.3.65.18028
   Web of Science ®Google Scholar
• Global Change Data Lab. (2021). Our World Data. https://ourworldindata.org/
   Google Scholar
• Guaglione, S. (2021). Publishers rethink their value to stave off subscription fatigue among new paying readers. https://digiday.com/media/publishers-rethink-their-value-to-stave-off-subscription-fatigue-among-new-paying-readers/
   Google Scholar
• Gunnarsson, C. L., Walker, M. M., Walatka, V., & Swann, K. (2007). Lessons learned: A case study using data mining in the newspaper industry. Journal of Database Marketing & Customer Strategy Management, 14(4), 271–280. https://doi.org/10.1057/palgrave.dbm.3250058
   Google Scholar
• Haggins, J., & Felps, P. (2022). Nurturing media subscribers is key to controlling churn. https://www.inma.org/blogs/conference/post.cfm/nurturing-media-subscribers-is-key-to-controlling-churn
   Google Scholar
• Hanzelik, P. P., Kummer, A., & Abonyi, J. (2022). Edge-computing and machine-learning-based framework for software sensor development. Sensors, 22(11), 4268. https://doi.org/10.3390/s22114268
   PubMed Web of Science ®Google Scholar
• Jayaswal, P., Prasad, B. R., Tomar, D., & Agarwal, S. (2016). An ensemble approach for efficient churn prediction in telecom industry. International Journal of Database Theory and Application, 9(8), 211–232. https://doi.org/10.14257/ijdta.2016.9.8.21
   Google Scholar
• Kartajaya, H., Kotler, P., & Hooi, D. H. (2019). Marketing 4.0: moving from traditional to digital. World Scientific Book Chapters (pp. 99–123).
   Google Scholar
• Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30.
   Google Scholar
• Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M., & Abbasi, U. (2014). Improved churn prediction in telecommunication industry using data mining techniques. Applied Soft Computing Journal, 24, 994–1012. https://doi.org/10.1016/j.asoc.2014.08.041
   Web of Science ®Google Scholar
• Kotler, P., Lane Keller, K., Brady, M., Goodman, M., & Hansen, T. (2016). Marketing Management (3rd ed.). Pearson Education Ltd.
   Google Scholar
• Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
   Google Scholar
• McKinney, W. (2010). Data structures for statistical computing in python. In S. van der Walt & J. Millman (Eds.). Proceedings of the 9th Python in Science Conference (pp. 56–61). https://doi.org/10.25080/Majora-92bf1922-00a.
   Google Scholar
• Newman, N., Fletcher, R., Kalogeropoulos, A., & Rasmus Kleis, N. (2019). Digital News Report. Digital News Report.
   Google Scholar
• Newman, N., Fletcher, R., Robertson, C. T., Eddy, K., & Nielsen, R. K. (2022). Reuters institute digital news report 2022.
   Google Scholar
• Newman, N., Fletcher, R., Schulz, A., Andi, S., Robertson, C., & Nielsen, R. (2022). Digital news report 2021 (10th ed.). In Reuters Institute. https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2021
   Google Scholar
• Newman, N., Fletcher, R., Schulz, A., Andı, S., Robertson, C. T., & Nielsen, R. K. (2021). Reuters institute digital news report 2021.
   Google Scholar
• Newman, N., Richard Fletcher, W., Schulz, A., Andı, S., & Kleis Nielsen, R. (2020). Reuters institute digital news report 2020. 112.
   Google Scholar
• Nitzan, I., & Libai, B. (2011). Social effects on customer retention. Journal of Marketing, 75(6), 24–38. https://doi.org/10.1509/jm.10.0209
   Web of Science ®Google Scholar
• Pattabhiramaiah, A., Sriram, S., & Manchanda, P. (2019). Paywalls: Monetizing Online Content. SSRN Electronic Journal, 83(2), 19–36. https://doi.org/10.1177/0022242918815163
   Google Scholar
• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12, 2825–2830.
   Web of Science ®Google Scholar
• Peña, V. C., Malthouse, E. C., & Mersey, R. D. (2023). Churning off the news: An analysis of newspaper subscriber churn across digital devices. Newspaper Research Journal, 44(2), 190–205. https://doi.org/10.1177/07395329231167374
   Google Scholar
• Pyle, D. (1999). Data preparation for data mining. In Diane D. Cerra (Ed.), Applied Artificial Intelligence (1st ed. Vol. 17, 5–6). Morgan Kaufmann Publishers Inc.
   Google Scholar
• Ribeiro, H., Barbosa, B., Moreira, A. C., & Rodrigues, R. (2022). Churn in services – a bibliometric review. Cuadernos de Gestion, 22(2), 97–121. https://doi.org/10.5295/cdg.211509hr
   Web of Science ®Google Scholar
• Rußell, R., Berger, B., Stich, L., Hess, T., & Spann, M. (2020). Monetizing Online Content: Digital Paywall Design and Configuration. Business & Information Systems Engineering, 62(3), 253–260. https://doi.org/10.1007/s12599-020-00632-5
   Web of Science ®Google Scholar
• Southern, L. (2019). How Swedish newspaper Dagens Nyheter halved subscriber churn in 2 years. https://digiday.com/media/how-swedish-newspaper-dagens-nyheter-halved-churn-to-8-in-2-years/
   Google Scholar
• SparkBeyond Team. (2021). Reducing churn for a European media company. https://www.sparkbeyond.com/case-study/reducing-churn-for-a-european-media-company
   Google Scholar
• Suárez, E. (2019). How to build a successful subscription news business: Lessons from Britain and Spain. February, 61–67.
   Google Scholar
• Taylor, R. (1990). Interpretation of the correlation coefficient: a basic review. Journal of diagnostic medical sonography, 6.1, 35–39.
   Web of Science ®Google Scholar
• Tsai, C.-F., & Lu, Y.-H. (2010). Data mining techniques in customer churn prediction. Recent Patents on Computer Science, 3(1), 28–32.
   Google Scholar
• Tsai, C., & Lu, Y. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547–12553. https://doi.org/10.1016/j.eswa.2009.05.032
   Web of Science ®Google Scholar
• Uner, M. M., Guven, F., & Cavusgil, S. T. (2020). Churn and loyalty behavior of Turkish digital natives: Empirical insights and managerial implications. Telecommunications Policy, 44(4), 101901. https://doi.org/10.1016/j.telpol.2019.101901
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Veseling, B. (2018). The road to 1 million subscribers: FT helps users to build ‘habit’. https://wan-ifra.org/2018/04/the-road-to-1-million-subscribers-ft-helps-users-to-build-habit/
   Google Scholar
• Xiong, Y., Wu, R., Zhao, S., Tao, J., Shen, X., Lyu, T., Fan, C., &Cui, P., (2023). A data-driven decision support framework for player churn analysis in online Games. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach CA USA.
   Google Scholar
• Yuan, S., Bai, S., Song, M., & Zhou, Z. (2017). Customer churn prediction in the online new media platform: a case study on juzi entertainment. 2017 International Conference on Platform Technology and Service, PlatCon 2017 - Proceedings (pp. 0–4). https://doi.org/10.1109/PlatCon.2017.7883698
   Google Scholar