The integration of Generative AI (gen AI) is rapidly growing in businesses (Chui et al., 2023). With many organizations already incorporating gen AI in various functions (see Ipsos Knowledge Panel Survey, 2023; Mittani et al., 2022) AI has shifted from a technical subject to a strategic business concern, with a notable percentage of C-suite executives and boards now engaging with gen AI tools. Additionally, a considerable number of companies plan to increase their AI investment due to gen AI advancements. According to WARC’s Marketer’s Toolkit (2023) 58% of respondents identify themselves as “cautiously progressive” towards gen AI, actively engaging in trials with large language model chatbots for strategic insights and exploring text-to-image AI applications to enhance creativity. Alongside this potential and growth, there are abundant challenges in managing gen AI-related risks, particularly around inaccuracy, with many organizations still developing strategies for mitigation.
In the ever-evolving landscape of advertising research, artificial intelligence (AI) could stand as a transformative force, reshaping how data is analyzed, insights are gleaned, and strategies are formed. “The ARF Handbook for Using AI in Advertising Research” is a comprehensive guide designed to navigate the complexities and potentials of AI in this dynamic field. This handbook aims to demystify AI and its myriad applications in advertising research, offering a clear path for professionals to integrate these technologies into their work effectively.
From the foundational concepts in Chapter 1:The Evolution of AI and Chapter 2: Basics of AI and Machine Learning to the comprehensive literature review focusing on practical applications highlighted in Chapter 3: Integrating AI into Marketing and Advertising Research, this handbook provides a thorough grounding in AI’s principles and practices. The heart of the handbook lies in Chapter 4: ARF Case Studies – AI Implementations in Advertising Research, presenting real-world scenarios ranging from survey design to synthetic research, responding to the existing lacuna in AI utilization in advertising research and demonstrating AI’s potentially profound impact on the industry. These case studies compare the relative performance of three of the most widely used LLMs: ChatGPT 4, Bard and Claude AI. They are presented here as concise versions with links provided for readers who wish to dive more deeply into the intricacies of any of the case studies. Chapter 5 delves into issues of Ethics and Transparency in AI-Driven Advertising Research, underscoring the need for practitioners to uphold the highest standards of privacy, fairness, and clarity. Finally, looking ahead, Chapter 6: The Future of AI in Advertising Research explores emerging trends, challenges, and opportunities, equipping readers to stay ahead in a rapidly changing domain.
This handbook is not just a repository of knowledge but a call to action – to embrace the AI revolution in advertising research and continue learning and adapting in this exciting journey. We thus open the possibility for ARF members to be active contributors by creating this handbook in the form of a WIKI. Your experiences, insights, and knowledge are invaluable in enriching this resource. Add your perspective and be a part of this research by submitting your comments below. If you have a case study or other example to submit for consideration, please email it to tadams@thearf.org.