Consumer TechRAG-StandardEmerging Standard

Using LLMs for Market Research

This is about using tools like ChatGPT as a very fast junior market researcher: you ask it questions about consumers, brands, or markets, and it drafts insights, survey ideas, and segment descriptions instead of a human doing everything from scratch.

9.0
Quality
Score

Executive Brief

Business Problem Solved

Traditional market research is slow and expensive, and many teams cannot run full surveys or qualitative studies for every decision. LLMs offer a way to quickly generate hypotheses, draft questionnaires, summarize existing data, and simulate consumer perspectives to speed up the research cycle and reduce cost per insight.

Value Drivers

Speed of insight generation versus traditional consumer research cyclesLower cost for early-stage or exploratory research compared with full surveys and focus groupsAbility to test more ideas and concepts because the marginal cost of an LLM query is near zeroImproved productivity of research teams via automation of drafting, synthesis, and summarization tasksPotential risk reduction by using LLMs to pre-test ideas before committing to full-scale studies

Technical Analysis

Model Strategy

Frontier Wrapper (GPT-4)

Data Strategy

Vector Search

Implementation Complexity

Medium (Integration logic)

Scalability Bottleneck

Context Window Cost

Technology Stack

LLM(Low)Vector DB(Low)

Market Signal

Adoption Stage

Early Majority

Differentiation Factor

Focus on applying general-purpose LLMs specifically to the workflows of market and consumer research (survey design, concept testing, qualitative synthesis) rather than generic enterprise chat-with-data use cases.

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Source

https://www.msi.org/working-paper/using-llms-for-market-research-2/