Methodology

The Recommendation Share Model

How ranked, cited and mentioned events across eight engines are weighted into a single share-of-answer metric, and how to read its confidence interval.

CiteRank AI Research·8 February 2026·12 min readMethod note

Method note. Method note. This entry documents how CiteRank measures. It reports no findings; any numbers shown are worked examples.

MethodologyRecommendation ShareWeighting

Key takeaways

—Recommendation Share is share of naming within a defined competitive set, not a popularity score.
—Defining the competitive set is a strategic decision that changes the number materially.
—Movement below the confidence interval is not movement.

The definition

Of all brand-naming events across your prompt corpus, in a given window, on a given engine set, what share belongs to you? That is Recommendation Share. It is deliberately relative — it moves when competitors move, which is the point.

Defining the competitive set

Include too few competitors and the metric flatters you. Include everything the engines ever name and it becomes noise. The workable rule is to include every brand the engines name above a frequency floor across the corpus, whether or not you consider them a competitor — the engine's opinion is the one being measured.

Weighting

Ranked events carry the most weight, cited next, mentioned least. Weights are fixed and published so scores remain comparable across windows; tuning weights to make a number look better destroys the series.

Confidence and honest reporting

Because sampling is repeated and engines are stochastic, every reported share carries an interval. A two-point move inside a five-point interval is not a result. Report the interval alongside the number, always.

Evidence basis

How this entry was produced

Method note. This entry documents how CiteRank measures. It reports no findings; any numbers shown are worked examples.

Research type: Method note — documentation of measurement approach
Classification: Method note
Data collected: Published 2026-02-08. No data collection: this entry documents method rather than reporting a study.
Prompt sample size: Not applicable — no corpus was sampled for this entry.
Replays per prompt: Not applicable — no prompts were replayed for this entry.
Engines covered: Method applies to all eight supported engines: ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, Microsoft Copilot, Grok, Meta AI.
Engine versions: Not stated. Engine vendors do not expose a stable build identifier for every model, so a version cannot be claimed accurately.
Methodology: Written by the CiteRank research team from the production measurement pipeline. Internal editorial review only; no external or academic peer review.

Limitations

This entry reports no findings. Any number shown is a worked example chosen for clarity, not a measurement.
AI engines are non-deterministic: an identical prompt can return a different answer on replay, so any figure derived from them is an estimate, never a fixed value.
Engine vendors change retrieval and ranking behaviour without notice, so anything stated here describes the stated window only.

Methodology, limitations and disclosure

Every CiteRank study states who produced it, what it measured and where it stops being reliable. The full scoring model is documented on the methodology page.

Author: CiteRank AI Research
Author role: CiteRank AI Research team — measurement, prompt-corpus design and scoring
Review: Internal editorial review by the CiteRank AI Research team. No external or academic peer review was conducted.
Published: 8 Feb 2026
Last updated: 8 Feb 2026
AI engines: Not engine-specific
Model versions: Specific model build identifiers are not disclosed by every vendor and are therefore not claimed here.
Industry scope: Cross-industry
Geographic scope: Global
Language scope: English
Sample size: Not disclosed for this entry
Conflicts of interest: CiteRank AI publishes this research and sells an AI visibility platform. No third party funded, commissioned or reviewed this entry.
Data availability: Underlying raw data is not published. Method and scoring are documented on the Methodology page.

Limitations

AI engines are non-deterministic: an identical prompt can return a different answer on replay, so every figure is a sampled estimate rather than a fixed value.
Engine vendors change retrieval and ranking behaviour without notice. Findings describe the sampling window stated above, not a permanent state.
Results describe the prompt corpus that was designed for this study. A different corpus for the same brand can produce a materially different picture.
This entry does not disclose a sample size, so its figures should not be treated as statistically representative.

Corrections and revisions

No corrections have been issued for this entry since publication on 8 Feb 2026. If a figure or claim here is wrong, write to research@citerank.in. Substantive corrections are published inline with the date they were made, and the original wording is retained in the note.

Suggested citation

CiteRank AI Research (2026). The Recommendation Share Model. CiteRank AI. https://www.citerank.in/research/recommendation-share-model

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