Digital Marketing Intelligent Strategy Based on Big Data Algorithms

With the help of a large amount of user data, enterprises can gain more specific and personalized insights and develop more accurate marketing strategies. Big data algorithms bring new perspectives and methods for understanding and predicting user behavior. This study explores the optimization of digital marketing under the big data algorithm. Digital marketing strategy refers to the process of matching products, services, and enterprises with target customers under the condition of digital media. First, this study uses random forest (RF) algorithm as the main data analysis tool to preprocess the collected data. Then, using the user database and Internet data to validate the model, comprehensive indicators such as customer acquisition cost (CAC), user conversion rate, and customer lifetime value (CLV) are used to evaluate the performance of RF. At the same time, combined with multi-dimensional features of user behavior data such as purchase history, browsing habits, and social interaction, this study analyzes the relationship between these features and marketing strategy formulation. The research shows that the user purchase conversion rate of RF digital marketing strategy is still higher than 85 %, so the digital marketing strategy of big data algorithm can effectively reduce CAC and increase CLV. By mining massive user data and market data, this study provides a reference for the development of enterprises' marketing strategies and marketing mode innovation.