Predictive analytics is a technique that utilizes past data to predict forthcoming trends and actions. The process entails using statistical analysis, analytical queries, and machine learning algorithms to construct prediction models. Methods such as machine learning, regression models, and decision trees have made substantial contributions to the advancement of strategies for digital marketing. Machine learning leverages past data to foresee future output values, while regression models gauge the correlation between dependent and independent variables. Decision trees are used to divide consumers into segments and make predictions about their future behavior. This is done using a model that resembles a tree. These strategies assist marketers in predicting consumer behavior, optimizing campaigns, and enhancing customer engagement (Halton, 2024, pg1). Google Analytics helps design digital marketing strategies by analyzing user behavior, traffic sources, and content performance. Businesses may find marketing trends by studying user behavior, traffic sources, and content performance. This data may be used for predictive analytics to predict trends, optimize marketing, and target customers. Predictive analytics helps improve resource allocation, planning, and ROI by examining past sales data. Combining Google Analytics with predictive analytics improves digital marketing and sales (Rosario, 2023, pg1). Example: Google Analytics and predictive analytics help an online clothin