Inference and hyper-personalization at scale: the convergence of stochastic architectures and generative ai in maximizing customer lifetime value within financial ecosystems
Inference and hyper-personalization at scale: the convergence of stochastic architectures and generative ai in maximizing customer lifetime value within financial ecosystems
DOI:
https://doi.org/10.51473/rcmos.v1i2.2024.1881Keywords:
Hyper-personalization, GenAI, Causal Inference, Data Architecture, LTV.Abstract
This study presents an exhaustive analysis of the application of Causal Inference models and Generative Artificial Intelligence (GenAI) in behavioral segmentation within large-scale financial institutions. It investigates the transition from monolithic data architectures to Event-Driven Architectures, demonstrating how the reduction of informational latency directly impacts engagement and revenue metrics. The paper details the engineering behind a real-world case study that resulted in a 46% uplift in transactional message engagement and generated R$ 19 million in incremental revenue, proposing a new paradigm for Customer Lifetime Value (LTV) management. The research concludes that the orchestration of stochastic algorithms with real-time data governance is the determining vector for customer retention in the digital economy
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AGARWAL, AJAY; GANS, JOSHUA; GOLDFARB, AVI. Prediction machines: the simple economics of artificial intelligence. Boston: Harvard Business Review Press, 2018.
ANDERSON, CHRIS. The long tail: why the future of business is selling less of more. New York: Hyperion, 2006.
DEHGHANI, ZHAMAK. Data mesh: delivering data-driven value at scale. Sebastopol: O’Reilly Media, 2022.
FADER, PETER S.; HARDIE, BRUCE G. S. Probability models for customer-base analysis. Journal of Interactive Marketing, v. 23, n. 1, p. 61–69, 2009. DOI: https://doi.org/10.1016/j.intmar.2008.11.003
GOODFELLOW, IAN; BENGIO, YOSHUA; COURVILLE, AARON. Deep learning. Cambridge: MIT Press, 2016.
GUPTA, SUNIL; LEHMANN, DONALD R. Managing customers as investments: the strategic value of customers in the long run. Upper Saddle River: Wharton School Publishing, 2005.
HINTON, GEOFFREY. Deep learning—A technology with the potential to transform health care. JAMA, v. 320, n. 11, p. 1101–1102, 2018. DOI: https://doi.org/10.1001/jama.2018.11100
IMBENS, GUIDO W.; RUBIN, DONALD B. Causal inference for statistics, social, and biomedical sciences: an introduction. Cambridge: Cambridge University Press, 2015. DOI: https://doi.org/10.1017/CBO9781139025751
KLEPPMANN, MARTIN. Designing data-intensive applications: the big ideas behind reliable, scalable, and maintainable systems. Sebastopol: O’Reilly Media, 2017.
KOTLER, PHILIP; KARTAJAYA, HERMAWAN; SETIAWAN, IWAN. Marketing 4.0: moving from traditional to digital. Hoboken: Wiley, 2016.
NEWMAN, SAM. Building microservices: designing fine-grained systems. Sebastopol: O’Reilly Media, 2015.
OSTERWALDER, ALEXANDER; PIGNEUR, YVES. Business model generation: a handbook for visionaries, game changers, and challengers. Hoboken: Wiley, 2010.
PEARL, JUDEA. Causality: models, reasoning, and inference. 2. ed. Cambridge: Cambridge University Press, 2009. DOI: https://doi.org/10.1017/CBO9780511803161
PEARL, JUDEA; MACKENZIE, DANA. The book of why: the new science of cause and effect. New York: Basic Books, 2018.
PORTER, MICHAEL E.; HEPPELMANN, JAMES E. How smart, connected products are transforming competition. Harvard Business Review, nov. 2014.
RIES, ERIC. The lean startup: how today’s entrepreneurs use continuous innovation to create radically successful businesses. New York: Crown Business, 2011.
RUSSELL, STUART; NORVIG, PETER. Artificial intelligence: a modern approach. 4. ed. Harlow: Pearson, 2020.
SCHWAB, KLAUS. The fourth industrial revolution. Geneva: World Economic Forum, 2016.
THRUN, SEBASTIAN; PRATT, LORIEN (Org.). Learning to learn. Berlin: Springer, 1998. DOI: https://doi.org/10.1007/978-1-4615-5529-2
VASWANI, ASHISH et al. Attention is all you need. Advances in Neural Information Processing Systems, v. 30, 2017.
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Copyright (c) 2024 Willian Gouveia de Aguiar (Autor)
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