Machine Learning Approaches to Customer Acquisition and Retention: A Framework for Predictive Lifecycle Management
Abstract
Firms have long understood that customers are assets whose value is created and captured across a relationship rather than realized in a single transaction, yet the management of that relationship has remained largely reactive, organized around campaigns and rules rather than around prediction. The maturation of machine learning, together with the abundance of behavioral and transactional data that firms now collect, makes it possible to manage the customer lifecycle predictively, anticipating which prospects will convert, which customers will defect, and which relationships will grow, and allocating resources accordingly. The promise is substantial, but realizing it requires more than the application of an algorithm to a dataset. It requires a coherent framework that connects the stages of the customer lifecycle to the predictive tasks that inform them, the data and features that fuel those tasks, and the organizational and deployment disciplines that convert a prediction into an action that creates value. This paper develops such a framework for predictive lifecycle management. It organizes the customer lifecycle into the acquisition, retention, and development stages, and it maps each stage to the families of machine learning models that serve it: propensity and look-alike models for acquisition, churn and survival models for retention, cross-sell and expansion models for development, and lifetime value models that span the lifecycle and govern the allocation of resources across it. The framework treats prediction not as an end in itself but as an input to a decision, and it therefore incorporates the uplift modeling that targets interventions where they change behavior, the experimentation that validates that they do, and the human and governance structures that keep the predictions accountable. The argument proceeds from the customer equity and relationship management traditions, through the supervised, unsupervised, and causal learning methods that the framework employs, to an implementation account grounded in the deployment of models as durable operational systems. The thesis is that predictive lifecycle management is most valuable not as a collection of isolated models but as an integrated system in which prediction, decision, and learning are joined across the entire arc of the customer relationship. The thesis is that predictive lifecycle management creates most value not as a collection of isolated models but as an integrated system in which prediction, decision, and learning are joined across the entire arc of the customer relationship. P-ISSN 2695-186X
Keywords
machine learning; customer acquisition; customer retention; churn prediction; customer lifetime value; uplift modeling; predictive analytics; customer lifecycle management; segmentation; customer rela
References
Adelanwa, A., Basnet, A., & Anene, U. N. (2023). Data driven digital transformation models for lifecycle performance management in infrastructure delivery. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2646-2662. https://doi.org/10.62225/2583049X.2023.3.6.5968 Adeyelu, O. O. (2018). A predictive compliance monitoring framework for detecting systemic safety risks through aviation consumer complaint data. Iconic Research and Engineering Journals, 1(8), 250-276. https://doi.org/10.64388/IREV1I8-1718478 Akin-Oluyomi, O. T., Atima, M. E., Akinleye, O. K., & Okoruwa, P. O. (2023). Predictive analytics approaches for improving demand forecasting accuracy in e-commerce procurement systems. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2173-2182. Aliliele, C., Mbonu, I. S., & Iwuanyanwu, U. (2023). Advances in predictive analytics models for student retention and institutional risk management systems. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2692-2711. https://doi.org/10.62225/2583049X.2023.3.6.5990 Ascarza, E. (2018). Retention futility: Targeting high-risk customers might be ineffective. Journal of Marketing Research, 55(1), 80-98. Ascarza, E., Neslin, S. A., Netzer, O., Anderson, Z., Fader, P. S., Gupta, S., et al. (2018). In pursuit of enhanced customer retention management: Review, key issues, and future directions. Customer Needs and Solutions, 5(1), 65-81. Atima, M. E. (2022). Artificial intelligence and the evolution of data driven marketing decision systems. International Journal of Multidisciplinary Research and Growth Evaluation, 3(4), 612-627. https://doi.org/10.54660/IJMRGE.2022.3.4.612-627 Basnet, A., Oghenemaiga, E., & Anene, U. N. (2021). Audience segmentation and forecasting models for enhancing targeted digital marketing effectiveness. Shodhshauryam, International Scientific Refereed Research Journal, 4(5), 279-305. Berry, L. L. (1995). Relationship marketing of services: Growing interest, emerging perspectives. Journal of the Academy of Marketing Science, 23(4), 236-245. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. Blattberg, R. C., & Deighton, J. (1996). Manage marketing by the customer equity test. Harvard Business Review, 74(4), 136-144. Blattberg, R. C., Kim, B. D., & Neslin, S. A. (2008). Database marketing: Analyzing and managing customers. Springer. Bolton, R. N. (1998). A dynamic model of the duration of the customer's relationship with a continuous service provider: The role of satisfaction. Marketing Science, 17(1), 45-65. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. 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, 785-794. 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. Dada, T., Isiekwu, C. P., & Oluwo, K. (2021). Designing CRM-based sales forecasting models for multichannel lending institutions. Iconic Research and Engineering Journals, 5(3), 451- 466. https://doi.org/10.64388/IREV5I3-1714352 P-ISSN 2695-186X Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business School Press. Devriendt, F., Moldovan, D., & Verbeke, W. (2018). A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: A stepping stone toward the development of prescriptive analytics. Big Data, 6(1), 13-41. Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. Gordini, N., & Veglio, V. (2017). Customers churn prediction and marketing retention strategies: An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce. Industrial Marketing Management, 62, 100-107. Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., Ravishanker, N., & Sriram, S. (2006). Modeling customer lifetime value. Journal of Service Research, 9(2), 139-155. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. Heskett, J. L., Jones, T. O., Loveman, G. W., Sasser, W. E., & Schlesinger, L. A. (1994). Putting the service-profit chain to work. Harvard Business Review, 72(2), 164-174. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. Springer. Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy online controlled experiments: A practical guide to A/B testing. Cambridge University Press. Komi, N. M. (2021a). Hybrid deep learning for wind power forecasting: From grid stability to market equity for small generators. International Journal of Multidisciplinary Research and Growth Evaluation, 2(6), 989-1014. https://doi.org/10.54660/IJMRGE.2021.2.6.989-1014 Komi, N. M. (2023). When accurate forecasts are not fair forecasts: Deep learning load prediction and equity across districts. Shodhshauryam, International Scientific Refereed Research Journal, 6(6), 476-546. https://doi.org/10.32628/SHISRRJ247102 Komi, N. M., & Adamolekun, A. (2021b). Interpretable machine learning for early failure prediction in distributed renewable energy assets. International Journal of Multidisciplinary Research and Growth Evaluation, 2(6), 1015-1038. https://doi.org/10.54660/IJMRGE.2021.2.6.1015-1038 Kumar, V., & Reinartz, W. (2018). Customer relationship management: Concept, strategy, and tools (3rd ed.). Springer. Kumar, V., & Shah, D. (2009). Expanding the role of marketing: From customer equity to market capitalization. Journal of Marketing, 73(6), 119-136. Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmanns, S. (2010). Undervalued or overvalued customers: Capturing total customer engagement value. Journal of Service Research, 13(3), 297-310. Ladapo, O. O., Dosunmu, A. A., Jooda, D., & Abolaji, T. O. (2022). Human-in-the-loop machine learning: A state of the art. Journal of Frontiers in Multidisciplinary Research, 3(1), 656- 669. https://doi.org/10.54660/.JFMR.2022.3.1.656-669 Lemmens, A., & Croux, C. (2006). Bagging and boosting classification trees to predict churn. Journal of Marketing Research, 43(2), 276-286. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. P-ISSN 2695-186X Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124-136. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765-4774. Mbonu, I. S., Aliliele, C., Iwuanyanwu, U., & Uzoka, E. (2021). A conceptual framework for risk based business intelligence architecture in financial technology platforms. International Journal of Multidisciplinary Research and Growth Evaluation, 2(6), 731-746. https://doi.org/10.54660/IJMRGE.2021.2.6.731-746 Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20-38. Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C. H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 43(2), 204-211. Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592-2602. Oyeleye, A. O., Eyetsemitan, R. A., Ambali, K. B., & Fadayomi, O. (2022). Reducing client onboarding cycle time in small professional services firms: A Lean Six Sigma process redesign framework. Gyanshauryam, International Scientific Refereed Research Journal, 5(2), 467-498. https://doi.org/10.32628/GISRRJ225357 Payne, A., & Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69(4), 167-176. Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O'Reilly Media. Reichheld, F. F. (2003). The one number you need to grow. Harvard Business Review, 81(12), 46- 54. Reichheld, F. F., & Sasser, W. E. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68(5), 105-111. Reinartz, W. J., & Kumar, V. (2000). On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing, 64(4), 17-35. Reinartz, W. J., & Kumar, V. (2003). The impact of customer relationship characteristics on profitable lifetime duration. Journal of Marketing, 67(1), 77-99. Reinartz, W. J., Thomas, J. S., & Kumar, V. (2005). Balancing acquisition and retention resources to maximize customer profitability. Journal of Marketing, 69(1), 63-79. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135-1144. Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109-127. Sanni, J. O., & Atima, M. E. (2021). Business intelligence dashboard frameworks resolving executive visibility gaps in strategic marketing governance. International Journal of Multidisciplinary Research and Growth Evaluation, 2(6), 633-646. https://doi.org/10.54660/IJMRGE.2021.2.6.633-646 P-ISSN 2695-186X Sanni, J. O., & Attah, A. (2023). Artificial intelligence assisted content optimization models resolving digital conversion performance bottlenecks issues. Gyanshauryam, International Scientific Refereed Research Journal, 6(6), 303-336. https://doi.org/10.32628/GISRRJ236641 Sanni, J. O., Ajiga, D., & Atima, M. E. (2020). Analytical models addressing measurement challenges of marketing return on investment in regulated services. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 636-648. https://doi.org/10.54660/IJMRGE.2020.1.5.636-648 Sanni, J. O., Atima, M. E., & Attah, A. (2022). Systematic review of attribution modeling methods resolving bias in multi-touch journeys. Shodhshauryam, International Scientific Refereed Research Journal, 5(1), 304-334. https://doi.org/10.32628/SHISRRJ247135 Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Counting your customers: Who are they and what will they do next? Management Science, 33(1), 1-24. Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553-572. Venkatesan, R., & Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy. Journal of Marketing, 68(4), 106-125. 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., where optimized weighted ensemble machine learning has been shown to improve model sustainability (Khoh et al., 2023) Verhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer engagement as a new perspective in customer management. Journal of Service Research, 13(3), 247-252. Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations (2nd ed.). Kluwer Academic Publishers. Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. Anene, U. N., & Clement, T. (2022). A resilient logistics framework for humanitarian supply chains: Integrating predictive analytics, IoT, and localized distribution. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(5), 398-424. https://doi.org/10.32628/IJSRCSEIT Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., & von Arx, S. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116. Delgado, M. M. (2023). Predictive customer lifetime value modeling: Improving customer engagement and business performance. Journal of Marketing Analytics, 11(3), 178-196. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236. Gupta, S., & Zeithaml, V. A. (2006). Customer metrics and their impact on financial performance. Marketing Science, 25(6), 718-739. Jain, M., Soni, G., Verma, D., Baraiya, R., & Ramtiyal, B. (2023). Selection of technology acceptance model for adoption of industry 4.0 technologies in agri-fresh supply chain. Sustainability, 15(6), 4821. https://doi.org/10.3390/su15064821 P-ISSN 2695-186X Kanchanapoom, T., & Chongwatpol, J. (2023). A novel approach to predicting customer lifetime value in B2B SaaS companies. Journal of Marketing Analytics, 11(2), 156-173. https://doi.org/10.1057/s41270-023-00234-6 Khoh, W. H., Pang, Y. H., Ooi, S. Y., Wang, L.-Y.-K., & Poh, Q. W. (2023). Predictive churn modeling for sustainable business in the telecommunication industry: Optimized weighted ensemble machine learning. Sustainability, 15(11), 8631. https://doi.org/10.3390/su15118631 Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of Marketing, 80(6), 36-68. Kumar, V., Petersen, J. A., & Leone, R. P. (2010). Driving profitability by encouraging customer referrals: Who, when, and how. Journal of Marketing, 74(5), 1-17. Lamrhari, S., El Ghazali, H., Oubrich, M., & El Faker, A. (2022). A social CRM analytic framework for improving customer retention, acquisition, and conversion. Technological Forecasting and Social Change, 174, 121275. https://doi.org/10.1016/j.techfore.2021.121275 Lappeman, J., Franco, M., Warner, V., & Sierra-Rubia, L. (2022). What social media sentiment tells us about why customers churn. Journal of Consumer Marketing, 39(5), 385-403. https://doi.org/10.1108/JCM-08-2021-4758 Lee, C. H., & Park, S. Y. (2023). Real-time customer analytics in e-commerce platforms: Architecture and implementation. IEEE Transactions on Engineering Management, 70(2), 234-247. Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2022). Challenges in deploying machine learning: A survey of case studies. ACM Computing Surveys, 55(6), 1-29. https://doi.org/10.1145/3533378 Rosario, A. M. F. T., & Dias, J. C. (2023). Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction. Decision Support Systems, 174, 114013. https://doi.org/10.1016/j.dss.2023.114013 Rust, R. T., & Verhoef, P. C. (2005). Optimizing the marketing interventions mix in intermediate- term CRM. Marketing Science, 24(3), 477-489. Verhoef, P. C., Kooge, E., & Walk, N. (2016). Creating value with big data analytics: Making smarter marketing decisions. Routledge. Wiesel, T., Skiera, B., & Villanueva, J. (2008). Customer equity: An integral part of financial reporting. Journal of Marketing, 72(2), 1-14. Wu, Z., Jing, L., Wu, B., & Jin, L. (2022). A PCA-AdaBoost model for e-commerce customer churn prediction. Annals of Operations Research, 318, 567-584. https://doi.org/10.1007/s10479-022-04526-5 Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46. Adelanwa, A., Basnet, A., & Anene, U. N. (2023). Performance intelligence models for optimization and outcome measurement in large-scale public services. Shodhshauryam, International Scientific Refereed Research Journal, 6(1), 319-354. https://doi.org/10.32628/SHISRRJ Basnet, A., Oghenemaiga, E., & Anene, U. N. (2023). Real time analytics and monitoring systems for livestream media performance using Google platforms. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(1), 883-908. https://doi.org/10.32628/IJSRCSEIT P-ISSN 2695-186X Dada, T., Isiekwu, C. P., & Oluwo, K. (2022). Advanced machine learning in customer lifecycle management for commercial banking. Iconic Research and Engineering Journals, 6(2), 345-362. https://doi.org/10.64388/IREV6I2-1714380 Adelanwa, A., Basnet, A., & Anene, U. N. (2023). Predictive analytics models for financial risk detection and fraud prevention in public systems. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2663-2679. https://doi.org/10.62225/2583049X.2023.3.6 Zhou, T., Li, H., & Liu, Y. (2022). Predicting customer lifetime value using machine learning techniques in e-commerce. Electronic Commerce Research and Applications, 55, 101185. https://doi.org/10.1016/j.elerap.2022.101185 Kumar, V., Zhang, X., & Luo, A. (2014). Modeling customer opt-in and opt-out in a permission- based marketing context. Journal of Marketing Research, 51(4), 403-419. Sizan, M. R. (2023). Predictive modeling approaches to the challenge of customer churn in e- commerce. Journal of Business and Management Studies, 5(4), 134-149. Patel, N., & Kumar, S. (2023). Deep learning applications in customer relationship management. Journal of Business Research, 163, 113981. https://doi.org/10.1016/j.jbusres.2023.113981 Sun, J., Chen, Z., & Zhang, Y. (2022). Customer lifetime value prediction using machine learning and temporal behavioral features. Expert Systems with Applications, 207, 117891. https://doi.org/10.1016/j.eswa.2022.117891 Atima, M. E., Sanni, J. O., & Attah, A. (2022). Predictive audience segmentation models resolving targeting inefficiencies in regulated professional service enterprises. Shodhshauryam, International Scientific Refereed Research Journal, 5(1), 271-303. https://doi.org/10.32628/SHISRRJ247134 Eyetsemitan, R. A., Ambali, K. B., Oyeleye, A. O., & Fadayomi, O. (2023a). Change management in small business digital transformation: A systematic review and lean change adoption framework. Gyanshauryam, International Scientific Refereed Research Journal, 6(6), 521- 550. https://doi.org/10.32628/GISRRJ236648 Eyetsemitan, R. A., Ambali, K. B., Oyeleye, A. O., & Fadayomi, O. (2023b). User acceptance testing in small business technology deployment: A structured validation framework for lean operational environments. International Journal of Multidisciplinary Research and Growth Evaluation, 4(6), 1512-1531. https://doi.org/10.54660/IJMRGE.2023.4.6.1512- 1531 Sanni, J. O., & Atima, M. E. (2021). Analytics driven go to market frameworks addressing compliance sustainability complexity. International Journal of Multidisciplinary Research and Growth Evaluation, 2(6), 647-660. Sanni, J. O., Ajiga, D., & Atima, M. E. (2020). Systematic review of product management strategies in mobile network rollouts across emerging markets. International Journal of Multidisciplinary Research and Growth Evaluation, 1(5), 661-673. https://doi.org/10.54660/IJMRGE.2020.1.5.661-673 Agu, M. U., Akomolafe, O., & Bello, A. (2023). A review of integrated data management systems for enhancing financial forecasting accuracy. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2335-2344. Filani, O. M., Nnabueze, S. B., Ike, P. N., & Wedraogo, L. (2022). Real-time risk assessment dashboards using machine learning in hospital supply chain management systems. International Journal of Multidisciplinary Evolutionary Research, 3(1), 65-76. https://doi.org/10.54660/IJMER.2022.3.1.65-76
More Articles from IIARD INTERNATIONAL JOURNAL OF ECONOMICS AND BUSINESS MANAGEMENT
E-Commerce Platform Utilization for Opportunity Recognition Among Business Education Postgraduate Students in Rivers State Universities
Author: Elechi, Ezinne, Wogboroma, Nyemaekile Ph.D, Godpower, Yiraodi Joel Ph.D
Developing Logistics Services for Cross-Border B2c E-Commerce in Vietnam
Author: Nguyen Thi Huyen Trang
Effect of Monetary and Fiscal Policy Measures on Economic Growth in Nigeria
Author: Jaja, Philomina Ejiroghene, Prof. C. G. Nwikina, Prof. C. R. Amadi, Prof. S. N. Amadi
The Role of Strategic Leadership in Mitigating Organizational Knowledge Loss During Digital Migration
Author: Ejalonibu Ojo Sakirulai
Effect of Socio-Economic Characteristics on the Poverty Status of Rural People in Plateau State, Nigeria
Author: Mankwat Gizo, Paul Dung Gadi, Ladi Mankwat Gizo