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.
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