Mapeando perfis de consumidores de E-Commerce com RFM e K-Means
Resumo
Compreender os consumidores é fundamental para a tomada de decisão assertiva em um ecossistema integrado e competitivo. Contudo, obter suas características e trabalhá-las de forma a personalizar serviços e alocar recursos eficientemente é um desafio não trivial. Este artigo analisa dados de um negócio de e-commerce com o objetivo de segmentar seus consumidores a partir do método RFM (Recency, Frequency, Monetary) com o algoritmo K-Means, visando a interpretação desses grupos para ações acionáveis e a exploração do processo de segmentação. Como resultado, quatro segmentos com comportamentos distintos foram identificados, fornecendo insights estratégicos sobre o consumo do varejo no ambiente digital.
Palavras-chave:
RFM, K-Means, E-Commerce, Consumidores, Clustering
Referências
Almeida, J., Silva, J., & Cavalcante, E. (2020). Extração, Integração e Importação de Dados Heterogêneos em Cidades Inteligentes: Um Mapeamento Sistemático. In Anais do IV Workshop de Computação Urbana (COURB), 57-70.
Anitha, P., & Patil, M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University – Computer and Information Sciences, 33(5), 1785-1792.
Amutha, R., & Khan, A. (2023). Customer Segmentation using Machine Learning Techniques. Tujin Jishu/Journal of Propulsion Technology, 44(3).
Blum, L., Elgendi, M., & Menon, C. (2022). Impact of Box-Cox Transformation on Machine-Learning Algorithms. Frontiers in Artificial Intelligence, 5, 877569.
Bruce, P., & Bruce, A. (2019). Estatística Prática para Cientistas de Dados: 50 Conceitos Essenciais. Alta Books.
Christy, A., Umamakeswari, A., & Priyatharsini, L. (2021). RFM ranking – An effective approach to customer segmentation. Journal of King Saud University – Computer and Information Sciences, 33(1), 1251-1257.
Cruz, W. (2021). Crescimento do e-commerce no Brasil: desenvolvimento, serviços logísticos e o impulso da pandemia de Covid-19. GeoTextos, 17(1), 67-88.
Gupta, S., Kushwaga, P., & Badhera, U. (2023). Identification of benefits, challenges, and pathways in E-commerce industries: An integrated two-phase decision-making model. Sustainable Operations and Computers, 4, 200-218.
Ho, T., Nguyen, S., & Nguyen, H. (2023). An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry. Business Systems Research Journal, 14, 26-53.
Hughes, A. (1994). Strategic Database Marketing. Probus Press.
Kodinariya, M., & Makwana, P. (2013). Review on Determining Number of Cluster in K-Means Clustering. International Journal of Advance Research in Computer Science and Management Studies, 1, 90-95.
Lewaa, I. (2024). Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis. Journal of Data Science and Intelligent Systems, 2(1), 29-36.
Li, X., Sun, Y., & Chen, X. (2022). Saline-Sodic Soil EC Retrieval Based on Box-Cox Transformation and Machine Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1692-1700.
Ma, X., Li, J., & Guo, Z. (2024). Role of big data and technological advancements in monitoring and development of smart cities. Heliyon, 10(15), e34821.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. University of California Press, 1(1), 281-297.
Marutho, D., Handaka, S., & Wijaya, E. (2018). The Determination of Cluster Number at K-Mean Using Elbow Method and Purity Evaluation on Headline News. In Anais do Seminário Internacional de Aplicação de Tecnologia para Informação e Comunicação de 2018 (ISemantic), 533-538.
Pan, C., Bai, X., & Li, F. (2021). How Business Intelligence Enables E-commerce: Breaking the Traditional E-commerce Mode and Driving the Transformation of Digital Economy. In Anais da II Conferência de E-Commerce e Tecnologia da Internet de 2021 (ECIT), 26-30.
Tang, Z., Jiao, Y., & Yuan, M. (2024). RFM user value tags and XGBoost algorithm for analyzing electricity customer demand data. Systems and Soft Computing, 6, 200098.
Wei, J., Lin, S., & Wu, H. (2024). A review of the application of the RFM model. African Journal of Business Management, 4(19), 4199-4206.
Yoshida, M., Santos, M., & Freire, F. (2024). Modelo RFM aplicado à melhoria de vendas em indústrias usando clusterização e método AHP-gaussiano. In Anais do XII Simpósio de Engenharia de Produção.
Anitha, P., & Patil, M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University – Computer and Information Sciences, 33(5), 1785-1792.
Amutha, R., & Khan, A. (2023). Customer Segmentation using Machine Learning Techniques. Tujin Jishu/Journal of Propulsion Technology, 44(3).
Blum, L., Elgendi, M., & Menon, C. (2022). Impact of Box-Cox Transformation on Machine-Learning Algorithms. Frontiers in Artificial Intelligence, 5, 877569.
Bruce, P., & Bruce, A. (2019). Estatística Prática para Cientistas de Dados: 50 Conceitos Essenciais. Alta Books.
Christy, A., Umamakeswari, A., & Priyatharsini, L. (2021). RFM ranking – An effective approach to customer segmentation. Journal of King Saud University – Computer and Information Sciences, 33(1), 1251-1257.
Cruz, W. (2021). Crescimento do e-commerce no Brasil: desenvolvimento, serviços logísticos e o impulso da pandemia de Covid-19. GeoTextos, 17(1), 67-88.
Gupta, S., Kushwaga, P., & Badhera, U. (2023). Identification of benefits, challenges, and pathways in E-commerce industries: An integrated two-phase decision-making model. Sustainable Operations and Computers, 4, 200-218.
Ho, T., Nguyen, S., & Nguyen, H. (2023). An Extended RFM Model for Customer Behaviour and Demographic Analysis in Retail Industry. Business Systems Research Journal, 14, 26-53.
Hughes, A. (1994). Strategic Database Marketing. Probus Press.
Kodinariya, M., & Makwana, P. (2013). Review on Determining Number of Cluster in K-Means Clustering. International Journal of Advance Research in Computer Science and Management Studies, 1, 90-95.
Lewaa, I. (2024). Customer Segmentation Using Machine Learning Model: An Application of RFM Analysis. Journal of Data Science and Intelligent Systems, 2(1), 29-36.
Li, X., Sun, Y., & Chen, X. (2022). Saline-Sodic Soil EC Retrieval Based on Box-Cox Transformation and Machine Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1692-1700.
Ma, X., Li, J., & Guo, Z. (2024). Role of big data and technological advancements in monitoring and development of smart cities. Heliyon, 10(15), e34821.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. University of California Press, 1(1), 281-297.
Marutho, D., Handaka, S., & Wijaya, E. (2018). The Determination of Cluster Number at K-Mean Using Elbow Method and Purity Evaluation on Headline News. In Anais do Seminário Internacional de Aplicação de Tecnologia para Informação e Comunicação de 2018 (ISemantic), 533-538.
Pan, C., Bai, X., & Li, F. (2021). How Business Intelligence Enables E-commerce: Breaking the Traditional E-commerce Mode and Driving the Transformation of Digital Economy. In Anais da II Conferência de E-Commerce e Tecnologia da Internet de 2021 (ECIT), 26-30.
Tang, Z., Jiao, Y., & Yuan, M. (2024). RFM user value tags and XGBoost algorithm for analyzing electricity customer demand data. Systems and Soft Computing, 6, 200098.
Wei, J., Lin, S., & Wu, H. (2024). A review of the application of the RFM model. African Journal of Business Management, 4(19), 4199-4206.
Yoshida, M., Santos, M., & Freire, F. (2024). Modelo RFM aplicado à melhoria de vendas em indústrias usando clusterização e método AHP-gaussiano. In Anais do XII Simpósio de Engenharia de Produção.
Publicado
19/05/2025
Como Citar
KUTZ, Ewerthon J.; SENEFONTE, Helen C. M..
Mapeando perfis de consumidores de E-Commerce com RFM e K-Means. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 9. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 99-112.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2025.8768.
