AI-Enabled Marketing Research: A Short Course

The course begins with comprehensive qualitative data analysis using Gemini’s advanced language processing capabilities. You’ll learn to systematically process interview transcripts, focus group discussions, open-ended survey responses, and social media content. We cover automated coding techniques, thematic analysis frameworks, and sentiment assessment approaches that maintain analytical rigor while dramatically reducing processing time.

A significant portion of the seminar focuses on AI-assisted survey design methodology using Gemini’s generative capabilities. You’ll master intelligent questionnaire development, response option optimization, question flow enhancement, and bias detection. We explore systematic approaches to creating survey instruments that leverage Gemini for cultural adaptation, readability assessment, and validity optimization across diverse market contexts.

The course provides detailed instruction on synthetic respondent generation as a methodological breakthrough for market research. You’ll learn to create AI-generated respondents that reflect specific demographic, psychographic, and behavioral profiles. We cover validation techniques, sample integration strategies, and ethical considerations for combining synthetic and authentic respondent data to enhance research scope and reduce costs.

Throughout the course, you’ll work with Google Colab notebooks that integrate Gemini’s API for scalable analysis workflows. We will address prompt engineering best practices, output validation protocols, and quality assurance techniques specific to marketing research applications.

Each day includes extensive practical exercises using Google Colab notebooks, real-world case studies from retail and B2B contexts, and interactive demonstrations of Gemini’s capabilities for marketing research applications.

You should have access to a computer with a reliable internet connection and a Google account.

You will receive detailed instructions before the course for:

• Setting up a Google Gemini account with API access capabilities.
• Accessing Google Colab (the free tier is sufficient for course exercises).
• Installing necessary Python libraries through Colab’s built-in package manager.

The course uses Google’s integrated ecosystem: Gemini for AI processing and Google Colab for Python-based workflows. All computing is cloud-based through Google’s platform, requiring no local software installation. Basic familiarity with spreadsheet applications (Google Sheets or Excel) is helpful for data organization.

No prior Python programming experience is required, though we will use Python code within Google Colab notebooks. All necessary code will be provided, and we’ll walk through each step systematically. The goal is to make advanced AI capabilities accessible to marketing research professionals regardless of technical background.

If you’d like to prepare, basic familiarity with marketing research concepts and survey methodology is helpful.

This course is targeted at marketing research professionals, consumer insights analysts, brand managers, and market researchers who want to leverage Google’s Gemini AI tools to enhance their research capabilities. If you are interested in scaling your qualitative analysis and improving survey design through AI, this course is for you.

The content assumes practical experience with traditional marketing research methods and an understanding of fundamental survey design principles, but no programming experience is required. We’ll provide all necessary technical instruction within the marketing research context.

Day 1: Qualitative data analysis using Gemini

• • Foundational concepts
    • Introduction to Gemini’s capabilities for text analysis
    • Prompt engineering fundamentals for marketing research applications
    • Setting up Gemini API access and Google Colab integration
  • Automated coding and thematic analysis
    • Processing interview transcripts and focus group discussions
    • Thematic analysis and pattern recognition at scale
    • Sentiment analysis and emotional tone assessment
  • Advanced applications
    • Social media content analysis and brand mention monitoring
    • Open-ended survey response processing and categorization
    • Cross-cultural content analysis and adaptation
  • Hands-on exercises
    • Coding real interview transcripts using Gemini
    • Building automated analysis workflows in Google Colab

Day 2: AI-assisted survey design

• • Intelligent questionnaire development
    • Leveraging Gemini for question generation and refinement
    • Response option optimization and scale development
    • Readability assessment and language simplification
  • Bias detection and mitigation
    • Identifying leading questions and response bias sources
    • Demographic inclusivity evaluation and improvement
    • Cross-cultural survey translation and validation
  • Hands-on exercises
    • Designing surveys for specific research objectives using Gemini
    • Testing and refining questionnaires through AI feedback
    • Creating multilingual survey instruments with cultural adaptation

Day 3: Synthetic respondents

• • Conceptual foundations
    • Understanding synthetic respondent methodology and applications
    • Ethical considerations and transparency requirements
    • Integration strategies with traditional sampling methods
  • Respondent profile development
    • Creating detailed demographic and psychographic profiles
    • Behavioral pattern modeling and consumer journey mapping
    • Market segment simulation and persona development
  • Validation and quality control
    • Statistical validation of synthetic versus authentic respondent patterns
    • Bias assessment in AI-generated responses
    • Sample integration protocols and weighting strategies
  • Implementation and scaling
    • Cost-benefit analysis of synthetic respondent integration
    • Organizational workflow integration and team training
    • Client communication and methodological explanation
  • Hands-on exercises
    • Generating synthetic respondents for specific market segments
    • Conducting simulated surveys with AI-generated participants
    • Building scalable synthetic respondent generation workflows