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In today's digital era, advertising has evolved from traditional broadcast-style promotional methods to more personalized and precise marketing strategies (Kim et al., 2022). The driving force behind this transformation is the rise of personalized advertising, representing a novel approach to deeply understanding consumer needs, behaviors, and preferences. Against this backdrop, personalized advertising research has become a prominent topic in the field of marketing, as businesses increasingly recognize the effectiveness of attracting and retaining target audiences by meeting individualized needs. Personalized advertising research aims to construct more intelligent and personalized advertising communication systems by leveraging advanced technologies such as big data (L. Li & Zhang, 2021; Zhu, 2021), artificial intelligence (Ford et al., 2023), and deep learning (X. Liu, 2023). This research has made significant progress, providing businesses with powerful tools to gain a deeper understanding of consumers and create more personalized experiences in advertising communication.
Central to this research is the precise generation of personalized advertising content. Researchers focus on developing systems capable of generating advertising content (Lopes & Casais, 2022) based on the unique characteristics of each user, achieved through the analysis of users’ historical behaviors (Noor et al., 2022), preferences, and social media activities (Jacobson & Harrison, 2022). This personalized generation not only encompasses textual information, but also includes multimedia elements such as images (Ramesh et al., 2022), videos (Singer et al., 2022), and audio (Kreuk et al., 2022), thereby enhancing the perceptibility and engagement of advertisements. With the flourishing development of social media and e-commerce platforms, consumers leave increasingly extensive digital footprints, offering richer data sources for personalized advertising. Consequently, researchers are committed to building more intelligent algorithms to accurately analyze and predict consumer behaviors, providing a more reliable foundation for the precise dissemination of personalized advertising (Chen et al., 2022; Feng & Chen, 2022).
As a successful case of intelligent advertising content generation technology, IBM Watson Advertising represents the application of IBM Watson artificial intelligence technology in the digital advertising domain, aiming to provide more intelligent and personalized advertising solutions. Leveraging cognitive computing and artificial intelligence, IBM Watson Advertising possesses the capability to comprehend, learn, and adapt to user behavior patterns. Through in-depth analysis of user behaviors and preferences, the system can generate more personalized and engaging advertising content. Additionally, IBM Watson Advertising integrates big data analytics, extracting insights from vast datasets to assist advertisers in gaining a better understanding of their target audience and adjusting advertising strategies. The platform's real-time optimization allows advertisers to obtain real-time advertising performance data during campaigns and dynamically optimize based on this information to enhance the effectiveness of advertisement placements. Finally, while ensuring advertising personalization, IBM Watson Advertising places a strong emphasis on user privacy protection, ensuring compliance with relevant privacy regulations and policies throughout the advertising delivery process.