Generative AI for Personalised Marketing Campaign Design and Content Automation
K. Krunal Yadav
Government Degree College (Arts and Commerce), Adilabad, Telangana, India.
Gajjala Lilly Rani
Avanthi’s Scientific Technological and Research Academy, Hyderabad, Telangana, India.
Ankatwar Gajanan
*
Government Degree College (Arts and Commerce), Adilabad, Telangana, India.
Narote Preetham
Telangana Tribal Welfare Residential Degree College (Boys), Boath @ Adilabad, Telangana, India.
Alurwad Tripat Venkatreddy
Government Degree College, Nirmal, Telangana, India.
*Author to whom correspondence should be addressed.
Abstract
The transition from mass communication to data-driven digital engagement has increased the importance of artificial intelligence in marketing practice and organisational decision-making. This study examines the role of Generative AI in personalised marketing campaign design and content automation. It aims to assess how Generative AI supports personalised communication, evaluate AI-based content automation techniques, analyse the benefits and limitations of AI-generated campaigns, and propose a framework for AI-driven personalised marketing. The study adopts a quantitative secondary research design and synthesises evidence from peer-reviewed literature and authoritative industry reports published between 2021 and 2026. Sources were retrieved from Scopus, Web of Science, Google Scholar, and major consulting reports, and were analysed through thematic, comparative, and trend analysis. The findings indicate that Generative AI can support personalised marketing by using behavioural, demographic, and engagement data to generate customer-specific recommendations, advertisements, emails, and multi-channel communication. The analysis also shows that content automation can reduce the time, labour, and cost associated with content production while improving consistency and supporting real-time campaign optimisation. The proposed framework comprises five layers: data collection, AI processing, content generation, campaign delivery, and analytics. It is supported by performance indicators such as a Personalisation Score, customer engagement rate, and campaign conversion rate. However, the study also identifies important limitations, including data privacy requirements, regulatory compliance, algorithmic bias, fairness concerns, and risks associated with inaccurate or misleading AI-generated outputs. The study concludes that Generative AI can strengthen marketing effectiveness when it is implemented with transparency, governance, continuous monitoring, and appropriate human oversight.
Keywords: Generative AI, personalised marketing, content automation, AI-driven campaigns, customer engagement, marketing analytics, campaign optimisation, customer relationship management, ethical AI, digital marketing