AI-Powered Marketing Analytics for Predicting Consumer Purchase Behavior

Abstract

This research examines machine learning systems that use predictive patterns from consumers to develop electronic marketing strategies. Using regression analysis along with classification algorithms as parts of predictive modeling techniques makes purchasing prediction outcomes more accurate when based on historical consumer data sets. Through logistic and multivariate regression models users can generate forecasts about future purchase numbers and values yet Random Forest and gradient-boosting classification algorithms identify groups of consumers based on projected buying activities. The evaluation of time-series data using the RNN and LSTM neural networks of deep learning frameworks provides businesses with tools to forecast sustainable consumer behavior patterns. Multiple systems obtain behavioral patterns linked to digital communication platforms through training procedures using extensive databases combining population statistics and transaction records. Prediction model effectiveness stems from determining profitable customers together with maximizing marketing plan achievement. Real-time targeted campaigns enable automation of prediction systems as businesses provide personalized marketing deals with recommendations through automated processes to their consumers. Precision-targeting solutions through this strategy let businesses connect directly with customers to enhance conversion rates which produces strong market competition in digital markets today.