Integrating GenAI interactions in marketing studies: A methodological guide

Reflecting the ubiquitous presence of generative AI (GenAI), marketing researchers have begun integrating it as an interaction partner in research studies; however, technical barriers and methodological gaps limit its broader adoption. This paper provides a comprehensive guide for implementing GenAI interactions in marketing studies, particularly within the Qualtrics platform. First, a literature review categorizes diverse GenAI applications—from scaling qualitative methods to creating novel experimental paradigms—distinguishing between GenAI as research method versus research object to help researchers identify untapped opportunities. Second, the paper presents a decision framework for when GenAI offers value over traditional methods and explains the principal architecture (frontend, API, backend) that enables researchers to connect any GenAI model to their studies. Crucially, it demonstrates how AI coding assistants can translate natural language instructions into functional code, eliminating programming barriers for non-technical researchers. Third, step-by-step tutorials establish emerging best practices through four implementations spanning text and image modalities: search assistants, automated interviewers, co-creation tools, and hyper-personalized messaging. These implementations serve as adaptable templates empowering researchers to create custom solutions for their unique questions. By mastering AI coding assistants rather than copying specific code, researchers gain methodological independence in a rapidly evolving landscape. This work democratizes GenAI integration and enables previously impossible experimental paradigms.