Aprendizagem de máquina para a identificação de clientes propensos à compra em Inbound marketing

Abstract

The most important point for a company should always be the customer and getting new customers is not always an easy strategy. Digital marketing techniques study how to attract new customers to businesses using digital platforms. By virtue of the popularization of these means, the strategies had to be shaped to the new possibilities. With just one click you can reach thousands of individuals, which means many new leads for the company. However, filtering out which of these individuals are really interested in the product or service offered by the company demands a lot of effort from the sales team. This overhead is detrimental in the sense that the company can lose revenue by not targeting the real opportunities. With the aim to minimize this problem, the present work offers a proposal whose objective is the automatic identification of the client achieved through digital marketing strategies. It is proposed the usage of Machine Learning techniques, in particular supervised classification algorithms, namely Decision Tree and Naive Bayes. It was used the Scikit-learn library available for the Python programming language. In addition, it was necessary to apply the SMOTE oversampling algorithm, due to the unbalance of the dataset. In addition, in order to optimize the classification, we used the techniques of attribute selection and model selection with hyperparameters adjustment. Finally, to evaluate the results, we used the confusion matrix, the precision and coverage metrics, and the accuracy and coverage curve. Due to the imbalance of the data, the precision metric did not report good indexes results, with averages of 5.5% of correctness. In addition, the coverage was around 83%. Even with such divergent results among the applied metrics, the present work reached its goal, identifying most of the real opportunities and reporting that using this approach, it would be possible to obtain a reduction of up to 85% in the effort applied by the sales team if they had to call for all the leads. As a consequence, the company may have a cost reduction with the resources applied to obtain new customers, allowing the sales team to find new customers with greater efficiency.