stanislavnianko Secondary Research
Part of the discovery-phase skill pack ·
evidencegroup · readsdiscovery-context.md(runprofile-builderfirst if missing).
Pull from published sources when primary research is constrained or unavailable. Standard tool for pre-sale discovery (no time/budget for primary), regulated domains (interview restrictions), and analogous-domain framing.
Step 1 — Read discovery context
Read discovery-context.md (sections 1. Client → Domain, 2. Product / Initiative, 6. Constraints) and problem-canvas.md if it exists.
If discovery-context.md is missing, ask the BA inline: "(a) client domain / sector; (b) any regulatory constraints (GDPR / HIPAA / SOC2 / none)?" — tag the output [ASSUMED DOMAIN]. Never block; recommend profile-builder for high-stakes work.
Step 2 — Decide research scope
| Scope | Signal |
|---|---|
| Industry baseline | "We need to know what 'normal' looks like" |
| Best practice / state-of-the-art | "What have leading orgs done about this?" |
| Regulatory / compliance | "What's required vs forbidden?" |
| Analogous domain | "No data in our domain — can we borrow from <adjacent>?" |
| Sizing / TAM | "Is the opportunity even commercially worth it?" |
A good secondary-research run usually picks 2-3 of these.
Step 3 — Delegate to web-research skills if available
If deep-research / exa-search / market-research are installed:
"Use
deep-researchfor<scope>in<client domain>. Surface:<3-5 sub-questions>. Output cited."
Without those, fall back to: industry analyst reports (Gartner, Forrester, McKinsey, BCG public excerpts), regulatory body publications (FDA, GDPR-EU, FCA, etc.), academic search (Google Scholar, arXiv), vendor whitepapers (treat with skepticism), conference proceedings, public earnings call transcripts (surprisingly rich for sizing).
Step 4 — Capture findings with provenance
Per finding, capture:
- Claim — the specific assertion
- Source — URL, title, author, date
- Source quality — primary research / analyst report / vendor blog / forum / academic
- Recency — within 12 months / 1-3 years / older
- Implication for our hypothesis — supports / contradicts / orthogonal
Step 5 — Confidence and gap statement
End the doc with two short sections:
- What primary research would still be needed — even after secondary, what's a desk research can't tell us
- Confidence on each problem-canvas claim — high / medium / low, with reasoning
Step 6 — Analogous domain caveat
If using analogous domain (e.g., "no data on healthcare CRM, borrowing from financial-services CRM"): explicitly state where the analogy holds and where it breaks. Analogies smuggle wrong assumptions if not bounded.
Output
./discovery/secondary-research.md per ./template.md.
Append to _log.md: [secondary-research | YYYY-MM-DD] scopes: <list>; sources: <count>; supports: <N>; contradicts: <N>; orthogonal: <N>.
Anti-patterns
- Vendor blog as primary source. Treat as marketing, not data. Use only as "what they want you to think".
- Old reports. A 2020 SaaS benchmark is mostly irrelevant in 2026. Tag recency, prefer <18 months.
- Stacking weak sources to fake strength. Five vendor blogs ≠ one analyst report ≠ one peer-reviewed study. Don't conflate.
- No "what's missing" section. Without naming the gap, downstream synthesis assumes secondary is enough — usually wrong.