Research on Precision Marketing and Smart Tourism Service Optimization of Online Marketing Driven E-commerce Platform Based on Big Data and Machine Learning

E-commerce platform occupies an important position in the modern economy, how to improve the precision marketing effect of the platform through online marketing has become the key to the development of enterprises. At the same time, the development of smart tourism services also puts forward higher requirements for personalized recommendations and precise marketing. The widespread application of big data technology and machine learning methods provides new opportunities, making it possible to optimize platform marketing and services through data-driven strategies. In this paper, we collect and process data from e-commerce users to construct an e-commerce user profile model. The model is used to accurately categorize users, and NSE precision marketing strategies are developed and implemented. Decision trees, random forests, support vector machines, and LightGBM algorithms are used to predict users’ purchasing behavior and interest preferences. Meanwhile, the smart tourism service model was used to generate a list of Top-K attractions as a recommendation list for users to provide personalized tourism services. The empirical analysis results show that the online marketing strategy based on these techniques can effectively improve the user conversion rate and increase the overall revenue of the platform, and the number of orders on the e-commerce platform after the use of the platform increased from 138 to 245, which is an increase of 77.62%. Furthermore, the level of personalization of smart tourism services has been significantly enhanced. After structural equation analysis, it can be seen that the standardized coefficients of the influence of smart tourism marketing experience on behavioral intention and perceived value are 0.136 and 0.193 respectively while the P-value is less than 0.05, which indicates that the smart tourism marketing experience has a positive influence on tourists’ behavioral intention and perceived value. The study shows that the combination of big data and machine learning provides a strong technical support for the optimization of e-commerce platforms and tourism services, and can help enterprises to achieve the precise marketing objectives and the enhancement of user service experience in the changing market environment.