Abstract—According to the rapid advancements and fundamental transformations brought by generative Artificial Intelligence (AI), the marketing industry is experiencing significant changes. It offers advanced capabilities for content creation, customer engagement, and predictive analytics. However, the lack of domain-specific frameworks tailored for marketing obstructs the full utilization of these tools. This study introduces a new development matrix for generative AI applications in marketing, focusing on two key dimensions: personalization and clear data sources. By conducting a systematic literature review using the PRISMA methodology, this research collected insights from 142 publications and synthesized findings from 53 of them. The proposed matrix categorizes generative AI tools into four quadrants: Precise Personalization, Tailored Potential, Limited Insights, and Aimless Exploration. These categories highlight the importance of integrating personalized configurations with accurate and well-defined data sources to optimize marketing outcomes. The findings indicate that further development of generative AI for marketing can focus on personalization and clear data sources to align with the unique demands of marketing strategies. In the future, research will be encouraged to explore additional variables, such as localization and the influence of celebrity endorsements, to expand the applicability of this framework.

Keywords—marketing, generative Artificial Intelligence (AI), personalized marketing, data sources

Cite: Wei-Ting Huang and Chiang-Yu Cheng, "A New Development Matrix for Generative AI Applications in Marketing," Journal of Advanced Management Science, Vol. 13, No. 2, pp. 50-54, 2025.