New Marketing Models in the Context of Digital Marketing

Abstract

Traditional marketing models have core problems such as insufficient customer insights and poor marketing efficiency. In the face of these challenges, this paper widely collects user behavior data from multiple channels such as social media, search engines, and e-commerce platforms, and conducts detailed cleaning and organization. On this basis, the Apriori algorithm is used to mine user data and build detailed user portraits. Based on the constructed user portraits, accurate advertising positioning and delivery are achieved. By matching user portraits with advertising content, ads can be accurately pushed to target audiences to increase advertising exposure and click-through rates. At the same time, the K-means clustering algorithm is used to optimize advertising creativity and content to make them more in line with user interests and needs, further increasing the attractiveness of advertising. During the advertising process, interacting with users and use the comment, like, share and other functions of social media platforms to collect user feedback and suggestions. The new marketing model has achieved results in increasing the click-through rate of advertisements, with the click-through rate increase ratio ranging from 10.5% to 34.6%. In terms of customer conversion rate, the customer conversion rate of the new marketing model is higher than that of the traditional model, showing its strong conversion potential and market adaptability. The maximum monthly sales of the new marketing model reaches $64,700, which is higher than the $35,000 of the traditional marketing model, indicating that the new marketing model can more effectively stimulate market potential and achieve higher sales performance. This paper provides a new path to adapt to the digital age, improve marketing efficiency and effectiveness, and help companies maintain their leading position in the fierce market competition and achieve sustainable development.