Leveraging Machine Learning Algorithms for Comprehensive Marketing Analysis

This study evaluates the use of advanced marketing analytics techniques on consumer data set to inform and optimize decision-making in issues of customer segmentation, targeting, and campaign effectiveness. Using customer data with mixed variables that represent product preferences, purchase behaviors, demographic characteristics, and more, it applies the dataset of customers to the campaign. The study makes use of Artificial Neural Network models, Support Vector Machines, Random Forest, and XGBoost or Ensemble Boosting Machine Learning models that are non-linear for complex consumer behavior recognition. The previous part mentions the combination of Artificial Intelligence with human judgment as a way to solve the problems related to feelings and data management. The findings reveal that XGBoost improves the model accuracy by ensuring non-linear relationships among data variables which is facilitated by the ANN technique. Results revealed in this section that adaptive, data-driven strategies are now coming to digital consumer trends so the accuracy of marketing on dynamic market conditions will be increased.