AI market research: a step-by-step workflow guide

Why workflow matters more than tools

A well-designed workflow keeps AI outputs traceable, comparable across quarters, and resistant to hallucination. Without one, every research pass becomes a bespoke prompt exercise that no one else can reproduce. The five-stage workflow below is opinionated but tool-agnostic — plug in the models and crawlers you already have.

The five-stage workflow

1. 1. Frame the decision. Write down the single decision the research must inform (enter a segment, price a product, prioritise a feature). If you cannot state it in one sentence, you are not ready to research.
2. 2. Collect structured sources. Pull first- party signals (analytics, CRM, support tickets), competitor surfaces (websites, SERP, reviews), and market data (industry reports, filings). Store each source as markdown so the model can read structure, not just prose.
3. 3. Run structured analysis. Feed sources into the model with an explicit JSON schema per analysis type — segments, jobs-to-be- done, willingness-to-pay, feature gaps. Require citations for every quantitative claim.
4. 4. Synthesise insights. Ask the model to list what is common across sources, what conflicts, and what only one source claims. The conflicts are where the real insight lives.
5. 5. Version and re-run. Save the prompt, inputs, and outputs. Re-run every quarter and diff the results — that diff is your actual market signal.

Data collection: what to gather

• First-party — analytics funnels, CRM deal notes, support tickets, churn interviews.
• Competitor — pricing pages, feature lists, changelogs, G2/Trustpilot reviews, job postings.
• Market — SERP volumes, trade reports, regulatory filings, industry associations.
• Voice-of-customer — recorded sales calls, NPS verbatims, community threads.

Prompts you can copy

You are given {N} customer interview transcripts.
Return JSON with an array "segments". Each item:
- name (short)
- job_to_be_done (one sentence, quoted where possible)
- top_three_pains
- willingness_to_pay_signal
- evidence_ids (list of transcript IDs)
If a field lacks evidence, return "Unknown".

Given the JSON outputs from segment, competitor, and
market analyses, produce three sections:
- consensus (claims supported by 2+ sources)
- conflicts (claims that disagree, with the disagreement)
- weak_signals (single-source claims worth watching)
Each bullet must cite the source IDs it comes from.

Common pitfalls

• Skipping the framing step and asking the model to "do market research".
• Using the model's training data as a source instead of fresh crawls.
• No versioning — you cannot see change if you cannot diff.

Running this workflow in QuickAnalyzes

The QuickAnalyzes workbench maps to each stage: company analysis handles first-party framing, the market-context module pulls competitor and market data, and the analysis engine runs the structured prompts with citations. Every run is stored, so quarter-over- quarter diffs are one click away. See our competitor analysis guide for a deeper dive on stage two.