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<p>Consumer preferences are notoriously unpredictable, which makes it difficult to coordinate E-commerce marketing efforts with the aims of the digital economy and frequently results in an unjust chasm. This paper presents a new approach to data analysis by modelling the digital economy and consumer preferences with quantitative equations, testing their linear relationship, and then utilizing a cascaded deep learning model to identify when there are misalignments. As a result of this education, the unfair component separating economic growth and consumer preference is the difference between non-linear and linear customer visit counts. The section transitions from the product’s marketing strategy to its substantial economic growth through unfair and preference dilations in the cascade. Constantly updated using real-world E-commerce data, the model suggests course corrections to make marketing more effective and customer happiness higher while reducing the observed unfair component.</p>

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Consumer preference analysis and marketing strategy research based on deep learning under the background of the digital economy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月21日

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  • HuiLing Chen,
  • Hang Xu

摘要

Consumer preferences are notoriously unpredictable, which makes it difficult to coordinate E-commerce marketing efforts with the aims of the digital economy and frequently results in an unjust chasm. This paper presents a new approach to data analysis by modelling the digital economy and consumer preferences with quantitative equations, testing their linear relationship, and then utilizing a cascaded deep learning model to identify when there are misalignments. As a result of this education, the unfair component separating economic growth and consumer preference is the difference between non-linear and linear customer visit counts. The section transitions from the product’s marketing strategy to its substantial economic growth through unfair and preference dilations in the cascade. Constantly updated using real-world E-commerce data, the model suggests course corrections to make marketing more effective and customer happiness higher while reducing the observed unfair component.