Machine Learning Applications for Consumer Behavior Prediction

Abstract

AbstractCustomers are growing increasingly interested in the degree of service that businesses can provide. Because the services provided by different providers are not significantly different, there is greater competition among firms to maintain and improve the quality of their services. This research study investigates how machine learning can help businesses learn more about their clients and provide them with more personalized services. It also acts as a pattern on how firms can improve the effectiveness of their marketing efforts to acquire new clients and establish long-term connections with existing customers.In marketing, the prediction of consumer behavior is frequently used to schematize service offerings and develop targeted marketing programs. We have implemented six different machine-learning algorithms to improve further our ability to forecast consumer behavior. We have presented six machine learning models to improve performance, including Random Forest, Gradient Boosting, Logistic Regression, LightGBM, XgBoost, and Decision Tree, to achieve better results. Gradient Boosting, according to this study’s findings, is the best overall model for performance and efficiency.KeywordsConsumer behaviorMachine learningPrediction