LTVHub: Uma Ferramenta Modular para Cálculo do Customer Lifetime Value com Suporte a Múltiplos Modelos
Resumo
O Customer Lifetime Value (CLV) é uma métrica essencial para identificar clientes mais valiosos, possibilitando estimativas abrangentes de lucro. Além disto, permite que a empresa forneça um serviços de acordo com as expectativas de seus clientes, melhorando a qualidade do relacionamento entre consumidor e o negócio. Apesar de suas vantagens, o CLV não é uma métrica utilizada em larga escala, e a aplicação de um contexto ao outro apresenta uma série de dificuldades, principalmente em relação aos dados utilizados. O objetivo deste trabalho é fornecer uma ferramenta que permita a estimativa do CLV para diferentes usuários de maneira visualmente intuitiva, modular, extensível e flexível, fornecendo uma ótima estimativa do CLV esperado.
Palavras-chave:
LTV, Machine Learning, CLV
Referências
Berger, P. D. and Nasr, N. I. (1998). Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing, 12(1):17–30.
Fader, P. S., Hardie, B. G., and Lee, K. L. (2005). “counting your customers” the easy way: An alternative to the pareto/nbd model. Marketing science, 24(2):275–284.
Fader, P. S. and Hardie, B. G. S. (2013). The gamma-gamma model of monetary value. Marketing Science Institute Working Paper, 2:1–9.
Jain, D. and Singh, S. S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of Interactive Marketing, 16(2):34–46.
Popa, A.-L., Sasu, D. V., and Tarcza, T. M. (2021). Investigating the importance of customer lifetime value in modern marketing – a literature review. Annals of the Faculty of Economics, 30(2):410–416.
Qismat, T. and Feng, Y. (2020). Comparison of classical rfm models and machine learning models in clv prediction. Master thesis, BI Norwegian Business School, Oslo, Norway. GRA 19703, Master of Science.
Ramos, J. and Silva, F. (2024). A solution for predicting the customer lifetime value of different market segments. In Anais do XII Symposium on Knowledge Discovery, Mining and Learning, pages 81–88, Porto Alegre, RS, Brasil. SBC.
Schmittlein, D. C., Morrison, D. G., and Colombo, R. (1987). Counting your customers: Who are they and what will they do next? Management Science, 33(1):1–24.
Ullah, A., Mohmand, M. I., Hussain, H., Johar, S., Khan, I., Ahmad, S., Mahmoud, H. A., and Huda, S. (2023). Customer analysis using machine learning-based classification algorithms for effective segmentation using recency, frequency, monetary, and time. Sensors, 23(6):3180.
Venkatesan, R. and Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy. Journal of Marketing, 68(4):106–125.
Fader, P. S., Hardie, B. G., and Lee, K. L. (2005). “counting your customers” the easy way: An alternative to the pareto/nbd model. Marketing science, 24(2):275–284.
Fader, P. S. and Hardie, B. G. S. (2013). The gamma-gamma model of monetary value. Marketing Science Institute Working Paper, 2:1–9.
Jain, D. and Singh, S. S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of Interactive Marketing, 16(2):34–46.
Popa, A.-L., Sasu, D. V., and Tarcza, T. M. (2021). Investigating the importance of customer lifetime value in modern marketing – a literature review. Annals of the Faculty of Economics, 30(2):410–416.
Qismat, T. and Feng, Y. (2020). Comparison of classical rfm models and machine learning models in clv prediction. Master thesis, BI Norwegian Business School, Oslo, Norway. GRA 19703, Master of Science.
Ramos, J. and Silva, F. (2024). A solution for predicting the customer lifetime value of different market segments. In Anais do XII Symposium on Knowledge Discovery, Mining and Learning, pages 81–88, Porto Alegre, RS, Brasil. SBC.
Schmittlein, D. C., Morrison, D. G., and Colombo, R. (1987). Counting your customers: Who are they and what will they do next? Management Science, 33(1):1–24.
Ullah, A., Mohmand, M. I., Hussain, H., Johar, S., Khan, I., Ahmad, S., Mahmoud, H. A., and Huda, S. (2023). Customer analysis using machine learning-based classification algorithms for effective segmentation using recency, frequency, monetary, and time. Sensors, 23(6):3180.
Venkatesan, R. and Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy. Journal of Marketing, 68(4):106–125.
Publicado
29/09/2025
Como Citar
DE PINHO BRAGA, Maria Eduarda; M. RAMOS, João M. A.; SILVA, Fabrício A.; R. AYLON, Linnyer B..
LTVHub: Uma Ferramenta Modular para Cálculo do Customer Lifetime Value com Suporte a Múltiplos Modelos. In: DEMONSTRAÇÕES E APLICAÇÕES - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 40. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 112-117.
DOI: https://doi.org/10.5753/sbbd_estendido.2025.247672.
