MktPraxis
Interactive Textbook Module Syllabus

Marketing Research in the Era of Big Data and AI

Why primary research design matters more than ever: big data tells you what happened, AI predicts what's next, but neither can tell you why or what questions to ask. How the discipline has transformed, the integration model, and what the modern researcher role looks like.

⏱️ ~42 min reading & interactives 📑 10 sections 🎓 Level: Core undergraduate 📐 Math: No math 📚 Courses: Marketing Research Methods, Marketing Analytics 🏷️ Topics: AI in research & analytics, Secondary data
Full Interactive Chapter (Account Required) The complete chapter reading, embedded live mini-widgets, inline Check-Your-Understanding questions, and progress tracking are available to enrolled students and verified instructors.
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Chapter Section Outline (10 Sections)

Topics and managerial frameworks covered in Marketing Research in the Era of Big Data and AI:

  1. 1. The Question That Haunts the Discipline
  2. 2. What Big Data Actually Changed (and What It Didn't)
  3. 3. How AI Is Reshaping (Not Replacing) Research Design
  4. 4. What Generative AI Costs, and How It Fails Confidently
  5. 5. The New Researcher — What the Job Actually Looks Like Now
  6. 6. The "Why Not Just Use AI?" Conversation
  7. 7. Real Examples — When Data Alone Wasn't Enough
  8. 8. The Integration Model — How It Actually Works Now
  9. 9. Case Application — MetroStyle Fashion Big Data and AI Integration
  10. 10. So What? — Your Career and This Discipline

Interactive Concept Mini-Widgets Embedded in This Chapter (3)

Students manipulate these interactive visual simulations directly inside the chapter reading before answering gated check-in questions:

Big Data or Primary Research?

Present a business question. Student decides whether it can be answered with existing big data / AI or requires designed primary research.

Synthetic-Respondent Stress-Test

A survey question + two distribution panels: "Synthetic respondents (AI)" vs "Real humans" (revealed after the student predicts). A market toggle (US non-Western) and a use-case toggle (early concept screen final go/no-go).

Teaches: synthetic respondents are a validated COMPLEMENT for early triage, but they compress variance and carry cultural bias -- complement, never replacement; always validate.

The Omniscience Trap

A product decision card + a slick "AI analysis" panel giving a confident, data-backed recommendation, and two buttons: "Ship it (trust the AI)" / "Design a study first. " Scenarios echo real flops (Google Glass, Fire Phone, Juicero).

Teaches: data volume isn't insight; a confident AI answer routinely misses the question that needed primary research and design judgment.

Paired Browser-Based Analytics Calculators (2)

This chapter connects directly to these full MktPraxis analytical apps (with pre-loaded business case scenarios, CSV upload, and reproducible Python export):

🤖 Gen AI Cost Estimator

Estimate what it actually costs to deploy LLMs in marketing — token pricing, conversation accumulation, model tier trade-offs, and ROI vs. human agents.

Pre-loaded Business Scenarios: Customer Support Chatbot · Personalized Email Subject Lines · Product Description Generator · Review Sentiment Analyzer · Marketing Analytics Assistant · Ad Copy Variant Generator

😊 Sentiment Lab

Analyze customer emotion using VADER polarity and the NRC-VAD lexicon. Score text on valence, arousal, and dominance, and map sentiments onto Russell's circumplex model.

Pre-loaded Business Scenarios: Reddit Posts: University Enrollment System Feedback · Influencer Swimwear Brand: Customer Review Analysis · Water Bottle Brand Competitive Intelligence