Prompt

LLM visibility dashboard for marketing team

Technology / Seo aeo tools1 observationLast seen Sep 24, 2026

Latest observation

Sep 24, 2026OpenAI APIWeb search: off

Here’s a practical LLM visibility dashboard concept for a marketing team — focused on answering: “How often do AI models mention our brand, products, and content, and in what context?”

1) Dashboard goals

Track how your brand appears in:

  • ChatGPT / Claude / Gemini / Perplexity / Copilot style answers
  • AI-generated product recommendations
  • AI summaries of category topics
  • Competitive comparisons
  • Content citations and backlinks

2) Core dashboard sections

A. Executive summary

Top-line KPIs:

  • Brand mention rate: % of prompts where your brand is mentioned
  • Share of voice in AI answers: your mentions vs competitors
  • Citation rate: how often your site/content is cited
  • Sentiment / context score: positive, neutral, negative mentions
  • Ranking presence: whether you appear in “best tools/products” lists
  • Category coverage: number of target topics where you show up

B. Prompt and topic performance

Break down by:

  • Topic / keyword cluster
  • Intent type:
    • Informational
    • Commercial
    • Comparison
    • Transactional
  • Model / platform
  • Geography / language
  • Device or user segment if applicable

Metrics:

  • Mentions per topic
  • Citation frequency
  • Competitor inclusion
  • Prompt variation coverage

C. Competitive visibility

Compare your brand against competitors:

  • Mention share
  • Citation share
  • Recommendation share
  • Average position in AI-generated lists
  • “Best for” category appearances
  • Brand sentiment compared with competitors

D. Content influence

Show which content assets are being used by LLMs:

  • Top cited pages
  • Top cited blog posts
  • Documentation / help center pages
  • Pages generating the most AI mentions
  • Freshness of cited content
  • Gaps: important pages not being cited

E. AI answer quality / accuracy

Useful for brand protection:

  • Hallucination rate
  • Incorrect brand attributes
  • Outdated product info
  • Pricing inaccuracies
  • Compliance-risk mentions
  • Unsupported competitor comparisons

F. Trend monitoring

Track over time:

  • Mentions by week/month
  • Citation growth
  • Model changes after content updates
  • Impact of launches / PR / SEO campaigns
  • Seasonal topic spikes

3) Recommended visuals

Use:

  • Line chart: mention rate over time
  • Stacked bar: share of voice by model
  • Heatmap: topic × model visibility
  • Table: prompts with exact AI answers and citations
  • Treemap: top cited content assets
  • Radar chart: your brand vs competitors across attributes
  • Alert cards: sudden drops, misinformation, competitor gains

4) Example KPI definitions

  • LLM Mention Rate = prompts where brand appears / total prompts tested
  • AI Citation Rate = responses citing your domain / total responses
  • Share of Voice = your mentions / all brand mentions in sampled answers
  • Recommendation Rate = times you’re recommended / times relevant category prompts are asked
  • Hallucination Rate = incorrect claims / total branded mentions

5) Alerts to include

Set alerts for:

  • Brand disappears from top answers
  • Competitor overtakes you in a key category
  • Negative sentiment increases
  • AI gives incorrect pricing/features
  • New cited pages emerge
  • Important content stops being referenced

6) Data sources

Possible inputs:

  • Prompt testing across major LLMs
  • SEO tools / search console
  • Website analytics
  • Backlink/citation monitoring
  • PR mentions
  • Product docs / knowledge base
  • Manual QA samples
  • API-based LLM query runs

7) Suggested dashboard layout

Header

  • Date range
  • Model filter
  • Market/language filter
  • Topic filter

Row 1: Executive KPIs

  • Mention rate
  • Citation rate
  • Share of voice
  • Recommendation rate
  • Hallucination rate

Row 2: Trends

  • Time series of mentions
  • Time series of citations
  • Time series of competitor share

Row 3: Topic visibility

  • Heatmap by topic and model
  • Top prompts where you appear / don’t appear

Row 4: Competitive view

  • Competitor comparison table
  • Best/worst categories

Row 5: Content influence

  • Top cited pages
  • Missing pages / opportunities

Row 6: Alerts and examples

  • Recent answer examples
  • Misinformation flags
  • Drops / anomalies

8) Nice-to-have features

  • Prompt library with reusable test prompts
  • Versioning for monthly benchmark runs
  • Exportable reports for leadership
  • Slack/email alerts
  • “Fix recommendation” panel for SEO/content actions
  • Annotation field for campaign launches or product changes

9) If you want a simple version first

Start with just 5 metrics:

  1. Brand mention rate
  2. Citation rate
  3. Share of voice
  4. Competitor comparison
  5. Incorrect claim count

And 3 charts:

  • Trend line
  • Topic heatmap
  • Top cited pages table

If you want, I can also turn this into:

  1. a wireframe,
  2. a Notion/Sheet template, or
  3. a BI dashboard spec for Looker/Tableau/Power BI.

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.