Construction and empirical research of e-commerce precision marketing model based on machine learning KRSO hybrid algorithm

In recent years, information technology has developed rapidly, consumption patterns have been constantly changing, and the e-commerce industry has also grown rapidly. With the increasing market competition, companies are facing the challenges of high product similarity and changing user needs. Companies need to invest a lot of resources in promotion to attract new users, maintain old customers and increase sales. Reduce marketing costs while improving promotion accuracy. This study focuses on intelligent marketing strategies and uses machine learning methods to analyze the role of different recommendation methods in e-commerce marketing. The study designs a recommendation model that combines cluster analysis and intelligent optimization. The experimental results based on a large amount of e-commerce data show that the model performs better in terms of user preference matching, recommendation accuracy and interactive effects. Compared with a single recommendation algorithm, the model is more effective in improving user satisfaction, optimizing marketing methods and enhancing competitiveness. Based on the experimental results, our study proposes several marketing optimization methods based on user behavior analysis, including intelligent recommendation optimization, user classification and dynamic customer management. These methods can improve the efficiency of digital marketing, make better use of resources to increase returns, and help e-commerce companies develop stably in a highly competitive market.