Optimizing Marketing Strategies Through Customer Segmentation and Visual Analytics

Abstract

This paper looks into integrating customer segmentation based on AI, predictive analytics, and real-time visual analytics to improve marketing decision-making. The research is to leverage deep learning-based clustering and LSTM predictive model to enhance targeted marketing strategies as well as optimize customer engagement and conversion rates. An Autoencoder-based segmentation part was used in combination with supervised learning models (Random Forest, GBM, LSTM) and interactive visual dashboards. The datasets applied in the study are real-world consumer datasets, upon which the model is evaluated based on ARI, Silhouette Score, RMSE, and R² metrics. The segmentation accuracy of the Autoencoder is better than that of K-Means and GMM: an ARI of 0.91. LSTM model proved to have R² of 0.95, which provided significant improvement in the predictive marketing efficiency. This increased response rate by 48% and quadrupled acquisition cost savings by 37%. This validates AI-driven frameworks as superior to all manual segmentation and forecasting methods. With this, the study also shows how AI can be used to optimize marketing efficiency, scalability, precision, and adaptability. This is something that future research needs to consider to further enhance personalized marketing.