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Methodology

helloranked measures how visible brands are in the answers of major AI assistants. The methodology is public by design; numbers you can't interrogate are numbers you shouldn't trust.

How we measure

Each industry has a fixed, versioned panel of ~50 questions phrased the way real people ask AI assistants ("What CRM should a fast-growing startup use?"), each with paraphrase variants. Panels are immutable once frozen: any change creates a new version and is annotated on trend charts. Every week, each prompt variant is sent to each measured model 3 times (LLM answers are non-deterministic; single samples produce noise).

Measured models

From answers to scores

Each answer is analyzed by a two-stage extraction pipeline (alias matching, then an LLM judge that confirms brand identity, disambiguates common words like "monday" or "On", and records rank, sentiment, and whether the brand was explicitly recommended). Per brand and model we compute:

• Mention rate — share of answers mentioning the brand (weight 45%)
• Position score — how early the brand appears when mentioned, mean of 1/rank (weight 25%)
• Recommendation rate — share of answers explicitly recommending it (weight 20%)
• Citation rate — share of search-mode answers citing the brand's own site (weight 10%; ships with search-enabled measurement, currently 0)

The per-model score is the weighted blend, 0–100. The Global Visibility Score weights each model's score by its assistant's approximate consumer market share:

Market-share weights (v2)

Statistical confidence

LLM answers are non-deterministic samples, so every score carries sampling error. We quantify it with a percentile bootstrap: each model's responses are resampled with replacement 1,000 times, the score recomputed each time, and the middle 95% of outcomes reported as the confidence interval (the ± you see next to scores, and the shaded band on trend charts). Week-over-week movement is only ever displayed when the bootstrap confidence interval of the change excludes zero — everything else is reported as stable. If a rank moved but you don't see a movement arrow, that movement was indistinguishable from sampling noise.

Search-enabled modes & citations

Perplexity answers are always web-search-backed, and for a pilot set of industries (CRM, VPN, credit cards) we additionally measure ChatGPT, Claude, and Gemini with their web-search tools enabled. For these answers we record the pages the assistant cited. A brand's citation rate — the share of search-backed answers citing the brand's own domain — feeds the 10% citation component of that mode's score. The three "+ web search" variants are reported separately and are not part of the Global Visibility Score (their market-share split from the base assistants isn't measurable yet); Perplexity, being inherently search-backed, remains in the global blend. Week-over-week movement remains mention-based. Cited-domain panels on brand and industry pages come from this data.

Known limitations (roadmap)

• Search-mode measurement covers a 3-industry pilot; remaining industries are raw-model only for now.
• Cross-industry score comparisons are weaker than within-industry ones: each industry has its own prompt panel.
• Suite products are assigned to one primary industry (e.g. HubSpot lives in CRM, not Email Marketing).
• Market-share weights above are marked with their sources; placeholder entries are replaced as sourced data lands.

Questions about the methodology? It's versioned in the open — every raw answer is stored and re-analyzable.