Abstract
The variation in user behavior in digital marketing significantly influences the conversion rate, making the challenge of positively impacting conversion performance a critical concern for businesses. This paper explores how digital marketing conversions are formed and how conversion rates can be improved by considering the characteristics of user behavior, user behavior paths, and user value segmentation. Based on this framework, the study proposes and validates a systematic approach that enables data-driven targeting and personalized intervention strategies. Case data is presented to conduct predictive analysis and evaluate effectiveness. The results demonstrate that conversion strategies based on user behavior segmentation can substantially enhance conversion performance, with the analysis predictions closely aligning with actual outcomes. By integrating user behavior insights into the decision-making process, this research provides valuable insights for enterprises aiming to achieve precision in digital marketing and improve conversion decision-making.
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How to cite this paper
Research on Digital Marketing Conversion Improvement Strategies and Predictive Analysis Based on User Behavior Segmentation
How to cite this paper: Niming Wang. (2026) Research on Digital Marketing Conversion Improvement Strategies and Predictive Analysis Based on User Behavior Segmentation. Journal of Humanities, Arts and Social Science, 10(7), 780-784.
DOI: http://dx.doi.org/10.26855/jhass.2026.07.006