Desenvolvimento de um processo estruturado para a modelagem de previsão de churn em modelos de negócio de recorrência

Abstract

The recurring purchase business model, such as subscription clubs, has grown significantly in recent years. Since acquiring new customers is more costly than retaining existing ones, customer loss (churn) negatively impacts business competitiveness, making the ability to predict it a strategic advantage. The variety of available methods and the lack of a standardized model evaluation process pose both academic and practical challenges for the sector. In this context, this study structured a procedure for developing churn prediction models, consisting of three stages: data preparation, model application, and model evaluation. First, the data is standardized and balanced, comparison metrics are defined, and lastly a stability analysis is conducted. To validate the method, its application was performed in a real case of a book subscription club. Seven predictive variables were selected to train three models: logistic regression, a combination of the kmeans algorithm with logistic regression, and a multilayer perceptron (MLP) neural network. Logistic regression was identified as the best-performing method, achieving 71,1% of accuracy and 44,6% of precision. These results are superior to the actual model used by the company, that has 71,8% of accuracy and 12,1% of precision. Overall, the structured procedure proved effective in model selection and decision-making for churn prediction development in a standardized and robust way. Keywords: