A Study on Personalized Digital Marketing Content Creation Based on Consumer Psychoanalysis

Abstract At present, personalized recommendation technology is widely used in digital marketing. In this paper, on the basis of the existing personalized recommendation algorithm based on commodity characteristics, from the perspective of consumer psychology, we propose a multiple attitude recommendation algorithm under the apparent awareness of the customer. In this recommendation algorithm, the user’s recent and historical interest weights are added, and personalized digital marketing content recommendations are made based on consumer psychology. The MT algorithm designed in this paper has a higher recommendation accuracy when compared to other recommendation algorithms. A questionnaire survey is conducted to examine the influence of marketing content on consumers’ purchase intentions on shopping websites using the personalized recommendation system designed in this paper. The correlation analysis results indicate that the variables and the willingness to buy have a positive correlation at a significance level of 0.01. The final regression equation: willingness to buy = 0.065+0.126*information orchestration+0.113*pop-up ads+0.109*social channel recommendation+0.158*web system recommendation+0.152*user trust, which indicates that the variable of web system recommendation has the greatest effect on willingness to buy.