Role of Predictive Analytics in Marketing: Analyzing Consumer Preferences and Campaign Performance

Predictive analytics is investigated in this paper as a marketing tool improving consumer involvement and campaign success. It uses artificial intelligence methods and regression models to evaluate consumer preferences, project buying behavior, and increase marketing effectiveness. Income, online interaction, and past acquisitions define main factors influencing purchase value. Artificial intelligence methods including neural networks and decision trees help to enable behavioral segmentation and customized marketing campaigns. Predictive analytics, according to case studies, increases success in retail campaigns, e-commerce, and sentiment-driven advertising targeting among other industries. Priority one should be ethical issues including marketing openness and GDPR compliance. Advances in explainable artificial intelligence, federated learning, and real-time predictive analytics will raise the ethical and marketing predictive model efficacy and implementation standards. Predictive analytics is absolutely essential for data-driven strategies since it helps to improve customer experience, maximize resource allocation, and raise marketing return on investment.