About AgentRank
Search has compressed into a single answer. Shoppers no longer scroll a results page — an LLM hands them a recommendation. The brand named in that answer wins discovery, consideration and the click. The ones omitted lose share quietly, model by model, market by market.
AgentRank is the measurement layer for that new surface. It is a continuous audit of how leading AI models talk about your category: where your brand stands, which retailers and content ecosystems each model leans on, and what to do next.
Navigating the Dashboard
The left sidebar is organised into four working areas. Home takes you back to Worlds. Everything else groups by access tier and analytical depth.
- —Leaderboard — brand ranking with WTD SOV, SOV, Visibility and Share of #1 Rank
- —AI Intel — platform-by-platform competitive read
- —Cross Category — compare brand strength across segments
- —Markets — country-level opportunity map
- —Flash — Market / Manufacturer / AI Platform flash reports
- —Portfolio — house-of-brands rollup
- —AI Influences — citation sources and information ecosystems
- —Consumer Search Topics — query intent clustering
- —AI Driver Rankings — what drives each platform's recommendation
- —Product Signal Intelligence — SKU-level signal capture
- —Shows on every world, but data is Baby category only at this time
- —Innovation Intelligence
- —Consumer Intelligence
- —Social Intelligence
- —Review Intelligence
- —Click any item to open an enquiry — our team responds within 48h
- —User Guide (this page)
- —Coverage — markets, categories and platforms tracked
- —Whitepapers — methodology and POVs
- —AI Rank Services — packaged engagements
Modules at a Glance
At the brand level, three analytical lenses sit underneath the navigation — they answer different questions and together give a full picture of AI Share of Voice.
- —AI Share of Voice across every tracked brand
- —Model consensus leader — who AI platforms agree on as #1
- —Active erosion alerts — brands slipping out of top-5
- —Opportunity radar — auto-generated next moves
- —Parent-company AISOV rollup across all owned brands
- —Portfolio drill-down — segment strength, market split
- —Cross-platform coverage side-by-side
- —Best-rank watch by model at a glance
- —Most influential retailers cited per market & category
- —Information ecosystems — social, video, forums ranked
- —Per-platform phrase bank & deep dive
- —Non-negotiables — what to fix when all models agree
Reading the Leaderboard
The Leaderboard is the fastest way to see who is winning AI mindshare in the world you've selected. Each row is a brand; the default columns are the four metrics that matter most.
| Metric | What it measures |
|---|---|
| WTD SOV | Weighted Share of Voice — position-weighted score share across all platforms and segments in scope. |
| SOV | Raw Share of Voice — share of mentions, unweighted by rank position. |
| Visibility | Coverage breadth — % of platform × segment combinations where the brand appears at all. |
| Share of #1 Rank | % of platform × segment combinations where this brand is the #1 recommendation. |
Read them together: a brand can have high Visibility but low Share of #1 Rank — it's everywhere but never first. That's the classic challenger pattern.
Deep Dives — Baby demo only
The sidebar label carries a small "Baby only" badge to remind you. Performance views are unaffected — those remain live across every world you have access to.
Expansion — by enquiry
The Expansion modules — Innovation, Consumer, Social and Review Intelligence — sit outside the open platform. Clicking any of them opens an enquiry dialog; submit it and our team will be in touch within 48 hours to set up a tailored walkthrough.
What Is the Dataset
AI assistants are becoming a discovery channel. Brands that appear first in AI responses gain preferential exposure at the moment of intent — with no ability to buy their way in through advertising.
How to Read a Row
A single row carries five layers of context — when it was captured, what was asked, where, by which AI, and what came back.
| Layer | Example |
|---|---|
| Snapshot | SNAP-2026-03-M01 · March 2026 · Final |
| Segment | Beauty & Personal Care → Skin Care → Facial Care: Cleansing |
| Geography | United States · AMER · Developed · English |
| AI platform | ChatGPT (GPT-5) · General AI |
| Recommendation | CeraVe · Rank 1 · Score 35 · L'Oréal · Mainstream · Leader |
Field Reference
The dataset contains 37 fields across five logical groups.
Scoring & Methodology
Scores are position-weighted, not raw counts — they reflect the relative visibility premium of each rank position.
Example: CeraVe scores 35 (ChatGPT R1) + 25 (Claude R2) + 20 (Gemini R3) = 80 / 175 = 45.7% AISOV in US Facial Cleansing.
Common Filters & Use Cases
| Use case | Filter / method |
|---|---|
| Focus on one brand across all markets | Filter Brand ID = [ID] (more stable than name) |
| Compare platforms in one country | Filter Country, pivot Platform × Rank |
| Identify Rank 1 sweeps | Filter Rank = 1, count unique Brand by Segment |
| Track one segment across countries | Filter Segment_Code, group by Country + Platform |
| Isolate premium brands only | Filter Price Tier = Premium |
| Exclude WIP data | Filter Data_Status = Final |
| European markets only | Filter Greater Region = EMEA |
| Score share by manufacturer | Group by Manufacturer, sum Score / total segment pool |
Glossary
Questions about this dataset? Contact your ConsensysAI account team.