Customer Intent Analysis & Question Library
Understand what customers actually ask AI, and connect brand visibility to business outcomes
If you have keyword lists but don't know what customers actually type into AI. Or if you've built question sets but have no rationale for why those questions. This workflow systematically builds a question library based on customer journeys.
What you'll get
Step-by-step guide
- Define 2-3 key segments
- Create 1 journey × question type mapping table
Translate Customer Journeys into AI Question Formats
| Journey Stage | How Customers Ask AI | Marketing Goal |
|---|---|---|
| Awareness | "What is indemnity insurance?", "Why do I need it?" | Secure definition sentence citation |
| Consideration | "Indemnity vs private insurance difference", "Recommend criteria" | Brand appearance in comparisons |
| Decision | "Is [Brand] good?", "Pre-signup cautions" | Occupy trust/selection stage |
| Usage | "What documents for claims?", "Refund if I cancel?" | Fastest citation rate growth |
The 'Decision' stage is dominated by communities/comparison platforms, so neutral checklists get cited better than brand promotional documents. The 'Usage' stage moves citation rates quickly because they're procedural questions — just having the right structure works.
- Design 40 questions based on segment × journey
- Separate into 4 sets (A:learning/definition, B:comparison/recommendation, C:procedure/caution, D:brand/reputation)
- Select top 20 by Opportunity score from GEO Discover
Set Composition
| Set | Question Type | Purpose |
|---|---|---|
| A — Learning/Definition | "What is ~?", "Features of ~?" | Secure definition sentences/citation fragments |
| B — Comparison/Recommendation | "A vs B", "What fits ~?" | Track selection share |
| C — Procedure/Caution | "How to file?", "Cases where it fails?" | Fast citation improvement (priority 1) |
| D — Brand/Reputation | "[Brand] reviews", "[Brand] cautions" | Brand perception management |
- Extract pattern keywords from GSC and convert to question sentences
- Create search-based question priority list
- Establish conversion contribution landing type hypotheses
- Decide priority target platform per segment
- Create document structure checklist per platform
AI Platform Usage Patterns by Segment
- •Office workers/general users — ChatGPT-centric, 'summarize/recommend'
- •Professionals/researchers — Long comparisons/reviews on Claude
- •Students/info seekers — Source verification on Perplexity
- •Enterprise decision-makers — Risk, regulations, source citation important
- Check changes in positive/neutral/negative ratios
- Analyze which words AI associates with your brand
- Detect outdated information not reflecting regulatory changes
- Derive brand perception risk questions Top 10, list FAQs/updates needing revision
If negative descriptions appear, reinforcing the 'exceptions/caveats' section of your reference hub is more effective than writing rebuttal content.
- Filter 'SEO-strong/AI-weak' areas in opportunity analysis tab
- Prioritize content around procedure/caution questions
- Tag question sets (#comparison #procedure #decision-stage) — track trends by segment
Common questions
10 per set, about 40 total is sufficient to start. Continue reinforcing with GEO Discover afterward.
You can substitute with GSC search queries and GEO Discover auto-generated questions. Actual search data is the most accurate starting point.
