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The panel recorded 15 more citation-positive observations at its second wave, but that was not a consistent gain across platforms. Google AI Mode fell from 29 citations to 11 while Perplexity rose from 5 to 35. The overall rate increased from 11.86% to 16.95%, yet its prompt-paired 95% interval included zero. The result is best read as a redistribution in one measured panel—not proof that AI visibility reliably improved or that editorial work caused the change.
What the 300-run panel measured
Bertrand Morel’s 2026 report for Edikka describes a repeated panel using 25 fixed, non-branded French prompts about measuring visibility in AI-generated answers. Each prompt was run three times in four environments: ChatGPT, Claude, Perplexity, and Google AI Mode. That made 300 planned runs per wave. Five runs at each wave were invalid for documented reasons, leaving 295 valid observations at J0 and 295 at J+30. One extra calibration row appears in the later archive but is outside the panel.
The prompt corpus, wording, coding rules, planned run count, and comparison method were held fixed. Recorded fields included brand mentions, linked citations, cited URLs, visible-source totals, technical validity, and conditions needed to interpret each answer. This is one brand, one French-language corpus, and two measurement dates; it is not a market-share estimate or a ranking of AI platforms. Morel’s report
Why the total concealed the platform reversal
Across all valid observations, citations increased from 35 of 295 at J0 (11.86%) to 50 of 295 at J+30 (16.95%), a reported rise of 5.08 percentage points. But the platform rows moved in opposite directions:
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| Platform | J0 citations | J+30 citations | J0 mentions | J+30 mentions |
|---|---|---|---|---|
| ChatGPT | 0/75 (0%) | 2/71 (2.82%) | 0 | 2 |
| Claude | 1/74 (1.35%) | 2/75 (2.67%) | 1 | 2 |
| Perplexity | 5/74 (6.76%) | 35/74 (47.30%) | 6 | 3 |
| Google AI Mode | 29/72 (40.28%) | 11/75 (14.67%) | 25 | 11 |
Google AI Mode supplied 29 of the 35 citation-positive observations at J0. At J+30, Perplexity supplied 35 of 50. A dashboard showing only the pooled total would miss that the largest source of citations changed platforms.
Mentions, citations, source share, and coverage tell different stories
A mention means the answer named Edikka. A citation means the interface exposed an Edikka URL as a source. Neither establishes that someone visited the site or took a useful action afterward; visits require attributable referral data, and business outcomes require separate evidence.
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- Mentions: fell from 32/295 observations (10.85%) at J0 to 18/295 (6.10%) at J+30.
- Visible-source share: Edikka URLs accounted for 36 of 1,752 visible sources (2.05%) at J0 and 51 of 1,974 (2.58%) at J+30.
- Prompt coverage with a mention: declined from 14 of 25 prompts (56%) to 7 of 25 (28%).
- Prompts with a citation: changed from 14 of 25 (56%) to 15 of 25 (60%).
The distinction is especially clear in Perplexity at J+30: it cited an Edikka URL 35 times but named the brand only three times. Counting either outcome as simply “visibility” would erase a meaningful difference.
What the uncertainty allows you to conclude
Morel reports a prompt-paired 95% interval of −1.45 to +11.82 percentage points for the citation-rate change. Because that interval includes zero, the observed increase does not establish a reliable overall improvement. The report used 10,000 bootstrap replications clustered by prompt, rather than treating repeated runs of the same prompt as wholly independent strategic topics.
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The platform redistribution is plain in the recorded counts. Its cause is not. The panel does not show that editorial changes produced the shift, nor does one brand’s French prompt set establish what customers see across markets, prompts, or access surfaces. Morel also reports that mention coverage across prompts fell from 14/25 to 7/25, with an interval of −52 to −4 percentage points.
How to report an AI visibility panel responsibly
- Show each platform beside the aggregate. A total can be arithmetically correct while masking opposite movements among the environments people use.
- Keep outcome definitions separate. Report mentions, citations, visible-source share, and prompt coverage as distinct measures; do not imply they equal referrals or business results.
- Publish numerators, denominators, and invalid-run handling. State planned and valid runs, and do not convert invalid observations into successes or absences.
- Analyze at the prompt level. Repeated runs of one prompt are not equivalent to independent topics; account for prompt clustering when estimating uncertainty.
- Preserve dates, conditions, coding rules, and evidence. Without those, later readings cannot be interpreted as a comparable time series.
What the report’s quality check can and cannot verify
In a separate AI-assisted documentary check, 30 J+30 rows were examined. Twenty-nine could be fully recoded; one archived answer was empty. Citation presence agreed across all 30 rows. Mention presence agreed for 27 of the 29 comparable rows, while Morel reported Cohen’s κ of 0 because positive mentions were rare and there was no positive-positive agreement in that sample. This was not an independent second human review.
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The public package contains structured observations, methodology, limitations, a manifest, and checksums, but not the raw answer text or screenshots. That restricts independent checking of the underlying answers; the reported quality check should not be mistaken for full independent validation.
How to apply the result to a monitoring dashboard
If you are evaluating an AI visibility measurement approach, check whether it provides platform-level rows, prompt-level reporting, clear definitions for mentions and citations, run denominators, transparent invalid-run handling, prompt-aware uncertainty, and dated archives of conditions and evidence. These are useful evaluation criteria, not a tested comparison of commercial tools.
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For follow-up measurement, Morel proposes examining prompt-platform pairs that repeatedly changed, verifying whether cited URLs are the intended pages, comparing source fidelity as well as presence, keeping a small weekly sentinel panel, replaying the frozen corpus monthly, and connecting citations to referrals or outcomes only when attribution exists. Together, those steps help distinguish a shifting count from a change that matters to readers or customers.
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