Customer Lifetime Value Prediction: A Machine Learning Approach
Resumo
A previsão do Customer Lifetime Value (CLV) é de suma importância em diversos modelos de negócios. No entanto, esse cálculo varia de acordo com o contexto e o escopo do negócio. Este trabalho tem como objetivo realizar a previsão do CLV utilizando algoritmos de aprendizado de máquina e compará-lo com os principais modelos utilizados na literatura em 3 bases de dados diferentes. Durante o trabalho são descritas as escolhas e decisões tomadas para a construção dos modelos, sendo mostrado que os modelos de aprendizado de máquina apresentaram melhores resultados no cálculo do número esperado de transações, e obtiveram resultados muito semelhantes no modelo de cálculo do valor médio por transação.
Palavras-chave:
CLV, LTV, Aprendizado de Máquina
Referências
Aeron, H., Kumar, A., and Moorthy, J. (2012). Data mining framework for customer lifetime value-based segmentation. Journal of Database Marketing & Customer Strategy Management, 19(1):17–30.
Chan, C. C. H. (2008). Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer. Expert Systems with Applications, 34(4):2754–2762.
Desirena, G., Diaz, A., Desirena, J., Moreno, I., and Garcia, D. (2019). Maximizing customer lifetime value using stacked neural networks: An insurance industry application. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), pages 541–544.
Dwyer, F. R. (1997). Customer lifetime valuation to support marketing decision making. Journal of Direct Marketing, 11(4):6–13.
Ekinci, Y., Uray, N., and Ulengin, F. (2014). A customer lifetime value model for the banking industry: a guide to marketing actions. European Journal of Marketing, 48.
Fader, P. S., Hardie, B. G., and Lee, K. L. (2005a). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4):415–430.
Fader, P. S., Hardie, B. G. S., and Lee, K. L. (2005b). “counting your customers” the easy way: An alternative to the pareto/NBD model. Marketing Science, 24(2):275–284.
Hiziroglu, A. and Sengul, S. (2012). Investigating two customer lifetime value models from segmentation perspective. Procedia - Social and Behavioral Sciences, 62:766–774. World Conference on Business, Economics and Management (BEM-2012), May 4–6 2012, Antalya, Turkey.
Hughes, A. (1996). Boosting response with rfm. Marketing Tools, 5:4–7.
Kahreh, M. S., Tive, M., Babania, A., and Hesan, M. (2014). Analyzing the applications of customer lifetime value (CLV) based on benefit segmentation for the banking sector. Procedia - Social and Behavioral Sciences, 109:590–594.
Khajvand, M., Zolfaghar, K., Ashoori, S., and Alizadeh, S. (2011). Estimating customer lifetime value based on rfm analysis of customer purchase behavior: Case study. Procedia Computer Science, 3:57–63. World Conference on Information Technology.
Kotler, P. (1974). Marketing during periods of shortage. Journal of Marketing, 38(3):20–29.
Mammadzada, A., Alasgarov, E., and Mammadov, A. (2021). Application of bg / nbd and gamma-gamma models to predict customer lifetime value for financial institution. In 2021 IEEE 15th International Conference on Application of Information and Communication Technologies (AICT), pages 1–6.
Méndez-Suárez,M.andCrespo-Tejero,N.(2021). Whydobanksretainunprofitable customers? a customer lifetime value real options approach. Journal of Business Research, 122:621–626.
Reinartz, W. J. and Kumar, V. (2000). On the profitability of long-life customers in a non-contractual setting: An empirical investigation and implications for marketing. Journal of Marketing, 64(4):17–35.
Sargeant, A. (2003). Using donor lifetime value to inform fundraising strategy. Nonprofit Management and Leadership, 12:25 – 38.
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.
Singh, S. and Jain, D. (2013). Measuring customer lifetime value: Models and analysis. SSRN Electronic Journal.
Sun, Y., Liu, H., and Gao, Y. (2023). Research on customer lifetime value based on machine learning algorithms and customer relationship management analysis model. Heliyon, 9(2):e13384.
Temor Qismat, Y. F. (2020). Comparison of classical rfm models and machine learning models in clv prediction. Master’s thesis, Norwegian Business School BI Open, Oslo.
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.
Vanderveld, A., Pandey, A., Han, A., and Parekh, R. (2016). An engagement-based customer lifetime value system for e-commerce. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, page 293–302, New York, NY, USA. Association for Computing Machinery.
Chan, C. C. H. (2008). Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer. Expert Systems with Applications, 34(4):2754–2762.
Desirena, G., Diaz, A., Desirena, J., Moreno, I., and Garcia, D. (2019). Maximizing customer lifetime value using stacked neural networks: An insurance industry application. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), pages 541–544.
Dwyer, F. R. (1997). Customer lifetime valuation to support marketing decision making. Journal of Direct Marketing, 11(4):6–13.
Ekinci, Y., Uray, N., and Ulengin, F. (2014). A customer lifetime value model for the banking industry: a guide to marketing actions. European Journal of Marketing, 48.
Fader, P. S., Hardie, B. G., and Lee, K. L. (2005a). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4):415–430.
Fader, P. S., Hardie, B. G. S., and Lee, K. L. (2005b). “counting your customers” the easy way: An alternative to the pareto/NBD model. Marketing Science, 24(2):275–284.
Hiziroglu, A. and Sengul, S. (2012). Investigating two customer lifetime value models from segmentation perspective. Procedia - Social and Behavioral Sciences, 62:766–774. World Conference on Business, Economics and Management (BEM-2012), May 4–6 2012, Antalya, Turkey.
Hughes, A. (1996). Boosting response with rfm. Marketing Tools, 5:4–7.
Kahreh, M. S., Tive, M., Babania, A., and Hesan, M. (2014). Analyzing the applications of customer lifetime value (CLV) based on benefit segmentation for the banking sector. Procedia - Social and Behavioral Sciences, 109:590–594.
Khajvand, M., Zolfaghar, K., Ashoori, S., and Alizadeh, S. (2011). Estimating customer lifetime value based on rfm analysis of customer purchase behavior: Case study. Procedia Computer Science, 3:57–63. World Conference on Information Technology.
Kotler, P. (1974). Marketing during periods of shortage. Journal of Marketing, 38(3):20–29.
Mammadzada, A., Alasgarov, E., and Mammadov, A. (2021). Application of bg / nbd and gamma-gamma models to predict customer lifetime value for financial institution. In 2021 IEEE 15th International Conference on Application of Information and Communication Technologies (AICT), pages 1–6.
Méndez-Suárez,M.andCrespo-Tejero,N.(2021). Whydobanksretainunprofitable customers? a customer lifetime value real options approach. Journal of Business Research, 122:621–626.
Reinartz, W. J. and Kumar, V. (2000). On the profitability of long-life customers in a non-contractual setting: An empirical investigation and implications for marketing. Journal of Marketing, 64(4):17–35.
Sargeant, A. (2003). Using donor lifetime value to inform fundraising strategy. Nonprofit Management and Leadership, 12:25 – 38.
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.
Singh, S. and Jain, D. (2013). Measuring customer lifetime value: Models and analysis. SSRN Electronic Journal.
Sun, Y., Liu, H., and Gao, Y. (2023). Research on customer lifetime value based on machine learning algorithms and customer relationship management analysis model. Heliyon, 9(2):e13384.
Temor Qismat, Y. F. (2020). Comparison of classical rfm models and machine learning models in clv prediction. Master’s thesis, Norwegian Business School BI Open, Oslo.
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.
Vanderveld, A., Pandey, A., Han, A., and Parekh, R. (2016). An engagement-based customer lifetime value system for e-commerce. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, page 293–302, New York, NY, USA. Association for Computing Machinery.
Publicado
25/09/2023
Como Citar
RAMOS, João Marcos Alves Modesto; SILVA, Fabrício A..
Customer Lifetime Value Prediction: A Machine Learning Approach. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 486-500.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2023.234262.
