Measurement Methodology

1. How data is captured

One report may mix several capture methods. Their reproducibility differs, so they are never merged into a single trend line.

2. Four metric layers

Looking only at the first two layers is self-deception: being mentioned is not being trusted, and being trusted is not conversion.

3. Engines currently covered

The list reflects the platform's actual integrations, not marketing copy, grouped by capture channel — engines we sample automatically, engines reached through a third-party interface, manual-entry channels, and registered-but-not-yet-integrated ones are each listed as they are. Engine ecosystems differ by market; markets without local-language sampling are explicitly marked "not covered" rather than filled with English data.

15 of them are auto-sampleable (14 via official APIs + 1 via server-side UI sampling; 19 engines registered in total). Every registered entry is listed below so you can reconcile them one by one.

4. Known limits of this method

• No ranking on small samples

  When the source matrix has fewer than 10 samples we show a notice instead of a ranking - a top-source list from a handful of samples is noise, and acting on it wastes budget.

• Capture methods are never merged

  Official API, third-party, UI simulation and manual entry differ in reproducibility and are never merged into one trend line. Averaging them produces a blended metric that does not exist - and looks perfectly normal.

• Brand-named questions are pooled separately

  When the question itself contains the brand (e.g. "how is XX"), AI almost always repeats it. Those samples are excluded from the mention-rate numerator and denominator to prevent false positives.

• Uncovered markets are not filled in

  A market without local-language sampling is shown as "not covered" rather than filled with English samples, which would systematically overstate your visibility there.

• Egress region affects results

  Reaching the same engine from a domestic versus an overseas egress can return different retrieval results (regional ranking). Samples from the two egresses are not merged; reports label them separately.

• Trends beat single points

  A single sample fluctuates by nature: in field measurements, re-running the same prompts on the same day yields a source-overlap of only 0.32–0.43 — a single run carries almost no signal. Judge by trends over consecutive weeks, and note that a rise or fall across a capture-method switch may come from the switch, not from brand performance.

• Engine-side sampling parameters are beyond our control

  We cannot control the engines' internal sampling parameters: peer-reviewed research measured 9–28% decision changes across repeated runs even at temperature 0. Any single snapshot should therefore be read as a distribution — which is exactly why this report uses repeated sampling and confidence intervals.

• Numbers are not directly comparable across engines (or across app/web)

  Engines differ in retrieval sources and training corpora (research measured systematic gaps of 30+ percentage points in brand mention rates between Chinese and international engines), and the App and web surfaces of the same platform use different source pools. Compare engines by trend only, never as a cross-engine ranking; this report labels each engine separately.

• Content quality scores do not predict citation probability

  A large-scale study of Chinese-language citations found that a page's "composite quality score" is not a leading predictor of any citation outcome. This platform's article GEO score is a content-engineering aid; it is not a promise that raising the score raises citation probability.

• Individual AI answers fluctuate by nature (this is not a decline)

  The AI re-runs retrieval and cross-validation each time: the same question can yield different answers and different cited sources tomorrow — external research measured roughly 65% of cited sources changing between adjacent days. So "we were mentioned last week but not this week" is usually mechanical volatility; judge the real trend from overall rates across many sampling rounds (this platform uses multi-round sampling and confidence intervals).

• Transactional and knowledge questions follow different citation rules

  For transactional questions (local services, e-commerce, product selection), some engines answer directly from their own ecosystem — merchant databases, product cards, map listings — without citing external pages. For knowledge-oriented questions (industry expertise, B2B proposals, brand reputation), external sources remain the main basis. Citation counts therefore cannot be compared across question types: fewer citations on one type may simply mean that scenario runs in a closed ecosystem, not that your content is weak. This platform reports by question type and does not merge across types.

• Search ranking is not the same as being cited by AI

  Large-scale research shows about 88.46% of ChatGPT citation URLs come through its search retrieval path (you must be indexed before you can be cited), yet for Google AI Overview only about 37.9% of citations come from the top 10 organic results — the rest come from positions 11-100 and beyond 100 (Ahrefs, March 2026, analysis of 4 million URLs; the same firm reported about 76% in 2025, more than doubling within about a year, so such ratios must be read with their timestamp). Being ranked #1 is thus neither necessary nor sufficient for citation. This platform counts only citations that actually appear in AI answers and does not use search ranking as a proxy.

5. What these numbers cannot tell you

• API results are not what consumers see (the API and the consumer product can differ in strategy)
• Not appearing this time is not the same as not existing (we may simply not have sampled it yet)
• Being cited by AI is not conversion (landing pages, pricing and sales sit between the mention and the order)
• A rising trend is not proof it was our doing (competitor moves and engine updates also change results)
• Figures are not comparable across markets (the same "visibility rate" means different things, so read them with their definition)

6. What we do not do

Stating what we refuse to do is more useful than listing everything we can do - every item below has a matching constraint in the product, not just a promise.

• We never store your login passwords

  We do not store, request or hold them. Where publishing would require keeping a password, we would rather disable the entire channel (Reddit runs through your own desktop client instead) than lower this bar. Revocable OAuth tokens are not the same thing - revocation always stays in your hands.

• No "where to say what" playbooks for UGC platforms

  On community platforms we only monitor and correct your own assets. A checklist feature is functionally a scaled deployment manual, and that does not change because a human performs each step.

• No guaranteed rankings, no guaranteed citations

  AI answers are probabilistic and generated fresh each time; there is no position that can be locked. Any claim of guaranteed first place or guaranteed citation contradicts the technical facts and advertising rules. The report quality gate automatically checks for such absolute claims.

• No implication that adding structured data gets you cited

  Google explicitly states no special schema is needed. Structured data describes facts already on the page; citation follows from content being crawlable and trustworthy, not from a tag. We also never declare things we do not know (such as stock availability).

• Technical facts and compliance conclusions are never AI-generated

  Fitment parameters, certification numbers and regulatory applicability are things you act on, so they come only from your source data or a verifiable official source; AI only packages and translates. Market taboos require a source, a reviewer and a review date before they can be entered - an LLM cannot fabricate that combination.

• No compliance rules without provenance

  Every market taboo and regulatory date must trace to a concrete source with a named reviewer. A rule without provenance is never used to block anything - we would rather block too little than stop your legitimate content with an unsourced rule.