How to Perform a Content Gap Analysis with ChatGPT

Why this matters
In 2025, AI-assisted research accelerates “what to write next,” but generative suggestions must be validated against real search/user data and published in formats that AI systems can safely cite. This workflow pairs ChatGPT (for synthesis and brainstorming) with Perplexity Deep Research (for source-rich scans) and Google’s first-party data in Search Console—which now counts AI Overviews/AI Mode clicks in “Web.” You’ll move from ideas → evidence → prioritised briefs → answer-first pages you can actually measure.

1) Ground rules (so your results are defensible)

Start from first-party truth. Export queries/pages from Search Console → Performance (Web) to identify where you already appear and where impressions outpace clicks (an early sign of opportunity).
Use ChatGPT for structured ideation—not as a fact oracle. Treat LLMs as assistants that draft hypotheses and outlines you’ll verify with primary sources. Google’s policy cautions against scaled content abuse that lacks human value.
Cite primary sources. For any factual claims, link vendor docs, laws, or standards. This improves AI citation likelihood and protects users.
Design for answer engines. Publish with answer-first anatomy, explicit thresholds, and tables; add JSON-LD that matches visible text; and improve Core Web Vitals (INP/LCP/CLS).

2) Export and label your baseline (10–20 minutes)

In Search Console, export Queries and Pages for the last 90 days (or longer if seasonality applies) in the Web report.
Create columns for Intent (informational, comparison, troubleshooting), Impressions, Clicks, CTR, Avg Position, and a “Complex?” flag (multi-step queries, often candidates for AIO).
Highlight high-impression question-shaped queries with low CTR as “Gap Candidates.”

Why “Web”? Because Google includes clicks and impressions from AI Overviews / AI Mode in this report. There is no separate AIO tab.

3) Ask ChatGPT to generate “conversation maps,” not keyword dumps

Feed ChatGPT a subset of candidate queries plus any voice-of-customer notes (constraints like budget, compliance, team size). Ask it to propose a conversation map—branches by constraints/outcomes—and to propose an answer-first page outline for each branch:

“Given these queries and constraints, propose 5–8 decision questions users actually ask, with an answer-first outline (verdict + if/then thresholds + comparison criteria + edge cases). Include what primary sources we’d need to cite.”

Run 2–3 variants (different prompts) and consolidate overlapping ideas manually; keep only those that match your product and can be verified with primary sources.

4) Validate with Perplexity Deep Research (30–40 minutes)

For each shortlisted question, run Perplexity Deep Research to see:

Which sources are repeatedly cited,
What claims (numbers, thresholds) are consistent across sources,
Where the gaps are (missing edge cases, outdated versions).

Export or copy the citations into your brief and mark canonical sources to cite in your article. Perplexity notes that Deep Research performs dozens of searches and reads hundreds of sources before synthesizing—use that breadth to triangulate facts.

(Optional check): If Deep Research struggles on a topic, note that in your brief; it could signal a chance to publish the first high-quality explainer.

5) Prioritise with a simple scoring model

For each candidate page, score (a) Impact, (b) Effort, (c) Evidence availability, and (d) AIO likelihood (is the question multi-step/complex?). Weight by your business goals. Publish in sprints (e.g., 5 pages per week for 3 weeks).

6) Turn each brief into an answer-first, citable page (the template)

Title / H1 mirrors the natural query (“How to choose ___ for a ___ team when ___ applies”).
Executive answer (3–5 sentences) with scope/assumptions.
Decision thresholds using if/then statements.
Comparison table with criteria (e.g., SSO, offline support, latency, TCO).
Edge cases & exclusions (who shouldn’t use it).
Primary references (official docs).
Author bio + updated-on badge and mini change log.

This mirrors how AIO presents a snapshot + links and what Perplexity likes to cite.

7) Ask ChatGPT to stress-test each draft

Prompt ideas:

“List 10 edge cases where our recommendation could fail.”
“Translate our thresholds into a decision tree and point out ambiguous branches.”
“Given these vendor docs [paste], identify any version conflicts or deprecated features we risk mis-stating.”

Then you check these suggestions against the primary sources you already collected.

8) Add machine-legible signals (no gimmicks)

JSON-LD: Organization + page-relevant types (FAQ only for genuine Q&As; Product on PDPs). Must match visible text—don’t invent content for markup.
Crawl basics: sitemaps, canonicals, internal links.
Core Web Vitals: Prioritize INP improvements (reduce long tasks, streamline event handlers), and optimize LCP/CLS.

9) Publish and measure (2–4 weeks)

AIO presence & citations. For each target query/market/device, log whether AIO appears and which sources it links. Treat results as directional (AIO varies).

Search Console (Web). Track clicks/CTR for the newly published pages and annotate publication dates—Google includes AIO/AI Mode clicks/impressions here.

Perplexity citations. Re-run the same queries (standard + Deep Research) 1–2 weeks post-publication to see if your URLs appear among citations.

10) Worked example (abbreviated)

Scenario: SaaS backup vendor; users search “best backup for mixed Mac/Windows laptops + shared drives.”
Gap signals: High impressions, low CTR; question is multi-step (sizing, offline %, retention law) → AIO-likely.
ChatGPT conversation map surfaces branches: offline staff %, regulatory retention years, image vs. file backup, network constraints.
Perplexity Deep Research reveals consistent claims around immutability/WORM for long retention and the trade-off between image and file-level backups; identifies up-to-date vendor docs to cite.
Page built with thresholds (“If offline users >20%, prefer agents with bandwidth controls…”), table (criteria columns), and references.
Measurement shows AIO appears intermittently for the main query; after publication, GSC Web clicks to the page rise as AIO links rotate sources.

11) Common pitfalls (and fixes)

Pitfall: Treating ChatGPT output as fact.
Fix: Always verify with primary sources; cite them.
Pitfall: Publishing thin listicles.
Fix: Use answer-first design with thresholds, tables, and edge cases.
Pitfall: Ignoring performance.
Fix: Improve INP/LCP/CLS; Google recommends good Core Web Vitals and INP is now the interactivity metric.
Pitfall: Chasing “AIO schema.”
Fix: None exists; use supported structured data that matches visible text.
Pitfall: Mass-producing AI text.
Fix: Follow Google’s guidance on generative content—avoid scaled content abuse, add unique value, and review with humans.

12) Checklist (copy/paste)

Export GSC Web queries/pages; mark gap candidates.
Use ChatGPT to propose conversation maps and answer-first outlines.
Use Perplexity Deep Research to collect canonical sources and consistent claims.
Draft pages with verdict → thresholds → tables → edge cases, citing primary sources.
Add JSON-LD that matches visible text; fix crawl basics.
Improve INP/LCP/CLS on these pages.
Track AIO presence/citations and GSC Web clicks/CTR; iterate.

Bottom line

A content gap analysis with ChatGPT works when it’s anchored to real data (Search Console), stress-tested with source-rich research (Perplexity), and published in a format that AI systems can quote and link confidently. Do that—then measure it the way Google and the engines make possible today.

References

Google. Search Console Performance: What are impressions, position, and clicks?
Google. AI features and your website; Find information in faster & easier ways with AI Overviews.
Google. Using generative AI content on your website (policy).
OpenAI. Introducing ChatGPT Atlas.
Perplexity. Introducing Deep Research.
Google/Industry. AI Mode & AIO in Search Console totals.
web.dev. INP is officially a Core Web Vital.