Prompt
I need reporting on branded and unbranded AI queries
Latest observation
I can help with that. To report on branded vs. unbranded AI queries, you usually want to separate queries into:
- Branded: includes your company/product/website name or close variants
- Unbranded: all other relevant category/intent queries without brand terms
Recommended reporting structure
1) Core KPIs
Track these separately for branded and unbranded:
- Impressions / mentions
- Clicks / visits / referrals
- CTR
- Leads / conversions
- Conversion rate
- Share of voice / share of answers if you’re tracking AI surfaces
- Sentiment / answer position if available
2) Segmentation fields
Useful dimensions:
- Query text
- Brand flag: branded / unbranded
- AI source: ChatGPT, Perplexity, Gemini, Copilot, etc.
- Topic/category
- Funnel stage: awareness / consideration / conversion
- Date range
- Geography / language
- Device, if available
3) Example report layout
| Metric | Branded AI Queries | Unbranded AI Queries | % Change MoM |
|---|---|---|---|
| Queries/mentions | |||
| Clicks | |||
| CTR | |||
| Leads | |||
| Conversion rate |
How to classify queries
A practical approach:
- Build a brand keyword list: brand name, product names, common misspellings, abbreviations, domain, executives if relevant
- Mark a query as branded if it contains any of those terms
- Keep a review bucket for ambiguous cases
If you want a more robust setup
You can also classify:
- Direct branded: exact brand/product mention
- Indirect branded: “best [brand] alternative”, “[brand] pricing”, “[brand] reviews”
- Competitor-branded
- Pure unbranded
Common data sources
Depending on your stack:
- AI referral logs / server logs
- Analytics platform referrers
- Search console for AI-overlap queries
- Prompt/query exports from AI tools
- Brand monitoring / digital PR tools
Useful insights to include
- Branded AI queries usually indicate existing demand
- Unbranded AI queries indicate top-of-funnel discovery
- Compare which AI platforms drive more branded intent vs. discovery intent
- Identify unbranded topics that are converting but underrepresented in content
If you want, I can also help you build:
- a dashboard template,
- a query classification ruleset, or
- a SQL/Excel model for branded vs. unbranded AI query reporting.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
Want this interpreted for your brand?
Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.