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Export Reference (APA) 
Morgado, D., Laureano, Raul M. S. & Santos, N. (2024). Customer Lifetime Value-Based Predictive Techniques and Product Recommendation Systems. 5th International Conference on Quality Innovation and Sustainability.
Export Reference (IEEE) 
D. J. Morgado et al.,  "Customer Lifetime Value-Based Predictive Techniques and Product Recommendation Systems", in 5th Int. Conf. on Quality Innovation and Sustainability, Lisbon, 2024
Export BibTeX 
@misc{morgado2024_1770123069928,
	author = "Morgado, D. and Laureano, Raul M. S. and Santos, N.",
	title = "Customer Lifetime Value-Based Predictive Techniques and Product Recommendation Systems",
	year = "2024",
	howpublished = "Digital",
	url = "https://sites.google.com/view/icqis-2024"
}
Export RIS 
TY  - CPAPER
TI  - Customer Lifetime Value-Based Predictive Techniques and Product Recommendation Systems
T2  - 5th International Conference on Quality Innovation and Sustainability
AU  - Morgado, D.
AU  - Laureano, Raul M. S.
AU  - Santos, N.
PY  - 2024
CY  - Lisbon
UR  - https://sites.google.com/view/icqis-2024
AB  - In today's dynamic technological landscape, access to customer data has redefined traditional business paradigms. This shift requires companies to transition from product-centric to customer-centric models. This study delves into the fast-moving consumer goods (FMCG) retail sector, utilizing customer loyalty to precisely compute Customer Lifetime Value (CLV) through predictive methodologies based on decision trees. By integrating customer purchase and behavior analysis, this research establishes a framework for innovative product recommendation systems. Anticipating value fluctuations within a one-year hori-zon, this approach provides critical insights into customer behavior, empowering businesses to proactively manage marketing strategies and customer relation-ships, effectively mitigating potential revenue losses. The outcomes of this pre-dictive model promise a substantial impact on the FMCG retail sector, offering a blueprint for optimizing decisions on product recommendations. Furthermore, this study presents significant financial contributions, representing a substantial opportunity for revenue recovery by leveraging customer behavior insights and personalized product recommendation strategies.
ER  -