The rapid advancement of intelligent systems has reshaped how businesses approach complex challenges, particularly in dynamic sectors like e-commerce. This study utilizes a data-driven framework to optimize marketing resource allocation for a leading e-commerce platform in Turkey. Machine Learning techniques are integrated into a Segmentation-Targeting-Positioning strategy to improve customer segmentation, targeting accuracy, and positioning effectiveness. The process begins with the analysis of a business-provided dataset, followed by the design of a star schema for data structuring. Numerical variables are standardized, and categorical data is encoded using an Artificial Neural Network. The dataset is then compressed with an autoencoder before K-means clustering is applied to identify distinct customer segments. These methods identify distinct customer segments based on behavioral and demographic attributes, while Recency, Frequency, Monetary analysis prioritizes high-value groups. The framework’s performance is evaluated using Key Performance Indicators, demonstrating its potential to improve marketing efficiency and competitiveness in the e-commerce industry.