PREDICTING LEAD CONVERSION RATE IN THE EDUCATION SECTOR WITH MACHINE LEARNING TECHNIQUES
DOI:
https://doi.org/10.47820/recima21.v2i9.712Keywords:
crisis caused by COVID-19 accelerated processes of changeAbstract
The crisis caused by COVID-19 accelerated processes of change in the global economy, leading to changes in companies' structures, business models and routines. Small and Medium Enterprises (SMEs) in particular have faced challenges of finding paths for the journey of digital transformation and adaptation in the industry 4.0 era, which makes them need support to integrate their transformations. The objective of the work is to predict the probability of conversion of leads using Machine Learning (ML) in order to improve the process of closing enrollment opportunities in SMEs in the education sector. The work is based on the Digital Transformation Model for SMEs (MTD_SMEs), specific approach in ML technology and Knowledge Discovery in Database (KDD). The methodology involves a three-step sequence of the KDD_AZ process. Data were collected from a university center in southern Brazil. Results indicate that the 8 attributes used are significant to predict lead conversion. The ML technique, Logistic Regression reached a raw accuracy of 100%, thus contributing to an increase in the conversion rate, saving time for teams and filtering “unlikely” leads, and also helps marketing improve its aim to bring qualified/hot leads.
Downloads
References
Almeida, T. D., Cavalcante, C. G. S., & Fettermann, D. C. (2017, setembro). Indústria 4.0: Tecnologias e nível de maturidade de suas aplicações. 11º Congresso Brasileiro de Inovação e Gestão de Desenvolvimento do Produto, São Paulo, Brasil.
Altinkaya, M,, & Zontul, M. (2013). Urban bus arrival time prediction: a review of computational models. International Journal of Recent Technology and Engineering, 2(4), 164-169.
Batista, M. R. S. (2019). A utilização de algoritmos de máquinas em problemas de classificação. 112p. Dissertação (Mestrado Profissional em Matemática, Estatística e Computação Aplicada à Indústria) - Universidade de São Paulo, Instituto de Ciências Matemáticas de Computação, São Paulo. Recuperado em 30 abril, 2021, de <http://www.teses.usp.br/teses/disponiveis/55/55137/tde-25032019-141126/en.php>.
Biagioni, J., Gerlich, T., Merrifield, T., & Eriksson, J. (2011). Easytracker: automatic transit tracking, mapping, and arrival time prediction using smartphones. Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (pp. 68-81). ACM
Breiman, L. (2001, October). Random Forests. (R. E. Schapire, Ed.) Machine Learning, 45, 5-32. doi:10.1023/A:1010933404324 [GS Search]
Bruce, P., & Bruce, A. (2017). Practical Statistics for Data Scientists. O’Reilly Media.
Calipinar, H., & Ulas, D. (2013). Model suggestion for SMEs economic and environmental sustainable development. In: Nwankwo, S.; Ndubisi, N. Oly (Ed.). Enterprise Development in SMEs and Entrepreneurial Firms (pp. 270-290). Business Science Reference, IGI Global.
Costa, O. S., & Gouveia, L. B. (2021). Industria 4.0: Uma Proposta de Modelo de Transformação Digital para as Pequenas e Médias Empresas. In: Martins, E. R. Engenharia de Produção Planejamento e Controle da Produção em Foco. (Vol. 1, cap. 9, pp. 117-133). Guarujá, SP: Cientifica Digital. doi: 10.37885/201102037.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17[3], 1-6.
Ghotra, B, McIntosh, S., & Hassan, A. E. (2015, May). Revisiting the impact of classification techniques on the performance of defect prediction models. Proceedings of the 37th International Conference On Software Engineering (pp. 789-800), Florence, Italy: IEEE Press.
Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. (3nd ed). Morgan Kaufmann Publishers, Burlington.
Kassambara, A. (2017). Machine Learning Essential: Practical guide in R. STHDA.
Heller, K. A., Williamson, S., & Ghahramani, Z. (2008). Statistical models for partial membership. Proceedings of the 25th International Conference on Machine Learning (pp. 392-399).
Kormáksson, M., Barbosa, L., Viera, M. R., & Zadrozny, B. Bus travel time predictions using additive models. (2014). Proceedings of the 14th International Conference On Data Mining (pp. 875-880), Shenzhen, China, IEEE.
Machado, V. P. (2011). Inteligência Artificial. Teresina: EDUFPI.
Menezes, V. V., Farrar, J. T., & Bower, A. S. (2018). Westward mountain-gap wind jets of the northern Red Sea as seen by QuikSCAT. Remote Sensing of Environment (pp. 677-699), 209. https://doi.org/10.1016/j.rse.2018.02.075
Mirković, V., Lukić, J., & Martin, V. (2019). Key characteristic of organizational structure that supports digital transformation. Strategic Management and Decision Support Systems in Strategic Management. Proceedings of the 24th International Scientific Symposium (pp. 255-261). Subotica, Republic of Serbia.
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
Monard, M. C., & Baranauskas, J. A. (2003). Conceitos de aprendizado de máquina. In: Rezende, S. O. Sistemas inteligentes: fundamentos e aplicações (pp. 89-94). Barueri, SP: Manole.
Neves, D. V., Dias, F. C. A., & Cordeiro, D. (2018) Uso de aprendizado supervisionado para análise de confiabilidade de dados de crowdsourcing sobre posicionamento de ônibus. Escola de Artes, Ciências e Humanidades Universidade de São Paulo.
NVIDIA. (2018, August 2). Blog: Supervised Vs. Unsupervised Learning. The Official NVIDIA Blog. Recovered in 27 may, 2021, from <https://blogs.nvidia.com/blog/2018/08/02/supervised-unsupervised-learning/>.
Oh, J., Hessel, M., Czarnecki, W. M., Xu, Z., Van Hasselt, H., Singh, S., & Silver, D. (2021). Discovering reinforcement learning algorithms.
Tan, M., & Shao, P. (2015). Prediction of student dropout in e-learning program through the use of machine learning method. International Journal of Emerging Technologies in Learning (IJET), 10[1], 11-17.
Ulas, D. (2019). Digital Transformation Process and SMEs. Procedia Computer Science, 158, 662-671. doi: 10.1016/j.procs.2019.09.101.
Wang, Y., Wang, F., Huang, J., Wang, X., & Liu, Z. (2009). Validation of artificial neural network techniques in the estimation of nitrogen concentration in rape using canopy hyperspectral reflectance data. Int. J. Remote Sens. 30, 4493-4505. doi: 10.1080/01431160802577998
Willing, P., & Johnson, S. (2004). Factors that influence students’ decision to dropout of online courses. Journal of Asynchronous Learning Network, Nashville, 8 [4], 105-118.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2021 RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218
This work is licensed under a Creative Commons Attribution 4.0 International License.
Os direitos autorais dos artigos/resenhas/TCCs publicados pertecem à revista RECIMA21, e seguem o padrão Creative Commons (CC BY 4.0), permitindo a cópia ou reprodução, desde que cite a fonte e respeite os direitos dos autores e contenham menção aos mesmos nos créditos. Toda e qualquer obra publicada na revista, seu conteúdo é de responsabilidade dos autores, cabendo a RECIMA21 apenas ser o veículo de divulgação, seguindo os padrões nacionais e internacionais de publicação.
Clique para ver detalhes 
