When ChatGPT and Perplexity disagree, score each assistant before you average them. A blended citation rate can read 30% while the engine your buyer actually opens names you on zero queries. The site's panel definition is already fixed: twenty category buying questions, and six names is a 30% citation rate. Brands with no GEO work typically sit at 0–10%. What counts as good, and why a single run is an anecdote, is in what is a good AI citation rate. This post is the split inside that number. Worked example, our arithmetic, not a client measurement: ChatGPT names you on 12 of 20 queries (60%) and Perplexity names you on 0 of 20. Pool the cells and you get 12 of 40 = 30%. Average the two rates and you also get 30%, because both panels are the same length. Either blend hides a total miss. Do not ship the blend as the KPI.
The blend that hides a zero
A citation rate is a fraction. The numerator is queries where an assistant names you. The denominator is the panel. Collapse two assistants into one fraction and a zero can sit inside a number that looks like a program is working. 30% is the rate the citation-rate post calls a category win when it is real on the queries that matter. 30% produced by 12 hits and 20 misses is a different fact. One engine is carrying the whole score. The other is invisible.
The coincidence is the trap. (60% + 0%) / 2 = 30%, and 12/40 = 30%. Those match only because each engine was asked the same twenty questions. If the panels had different lengths, the unweighted average of the rates and the pooled-cell rate would diverge. Write the denominator down. A slide that says "citation rate: 30%" without the engine column is not a measurement.
What to put on the slide instead
Report one row per engine, then the pool if you still want a single line. The single line is a summary. It is not the decision.
| Engine | Named | Panel | Rate |
|---|---|---|---|
| ChatGPT | 12 | 20 | 60% |
| Perplexity | 0 | 20 | 0% |
| Pooled | 12 | 40 | 30% |
Those three rows are the worked example above. Replace the 12 and the 0 with your own counts. Keep the shape. If a third assistant — Google AI Overviews, or Claude — is on the panel, give it its own row. Do not fold it into the 40 until you have looked at its rate alone. The 30-minute visibility check already records an engine column, a named column, and a who-else column. Use those columns. Do not sum them before you read them.
Score each engine before you average
Run the same verbatim questions in each assistant. A fresh session, not an account that already knows your company — that mistake is in the 30-minute runbook. Then score three things per cell, not one:
- Named. Your company appears in the answer.
- Cited. Your URL is a source, which is a different event from being named.
- Who else. The competitors named, in order.
A cell where Perplexity cites a competitor's proof page and never says your name is a miss, even if ChatGPT names you in the first sentence. A cell where both name you and neither links you is a mention, not a source. Track them separately. Averaging "named" across engines answers a different question from "are we a source on the engine the buyer uses."
How a company becomes the source an assistant quotes is the GEO field guide. This post only stops the measurement error that makes that work look further along than it is. Answers also move. The citation-rate post already requires a monthly trend, because one run is noise. A split that is still there next month — Perplexity at zero, ChatGPT carrying the pool — is the finding.
What a split usually means
ChatGPT and Perplexity do not share one index. The citation-rate post already notes the practical result: a brand routinely shows up in one assistant and stays invisible in another, because each assistant retrieves differently. A split is the ordinary case. It is not proof that the brand is broken, and it is not proof that the zero engine is "wrong."
A retrieval miss is not an exclusion
If you are absent everywhere — empty HTML, two prices, two legal names — you are excluded, and the blend is not the diagnosis. That drop list is what gets a company excluded from AI answers. Fix the conflict before you tune copy for one assistant. If you are named on ChatGPT and absent on Perplexity for the same buyer question, you have a coverage gap on one retriever. The page may already be citable. The other engine has not picked it up, or it is quoting someone who answered the question more directly.
The page they can quote
A slogan does not travel across indexes. A page with one outcome and one set of figures does. The published D2C support proof is that shape, and it is not a citation-rate result: 64% of tickets resolved with no human (3,149 a month), 94.2% intent accuracy, 91.7% response accuracy, 99.1% policy compliance, first response from 4.2 hours to 6 minutes, $217,200 saved in year one against $92,000 of Year 1 investment. Those sentences can be lifted. If a second URL on your site states a different resolution rate, both engines have a reason to skip you. Agreement between your own pages is a precondition. It does not guarantee both assistants will name you this month.
When a named competitor shows up on the engine where you are absent, use why competitors show up in ChatGPT for that engine's answer, not for the blended rate. The five asymmetries — they answer the question, they publish a fact, someone else vouched, the entity is unclear, you tested a prompt nobody types — apply per assistant. A competitor who wins only on Perplexity is not a sitewide failure.
Which engine to fix first
Fix the assistant your buyers open. That rule is an operator assumption, not a measured share of your pipeline. Ask sales which tool prospects mention. If they paste Perplexity answers into deals and your Perplexity row is zero, the 60% ChatGPT rate is not the work. If they live in ChatGPT, a Perplexity zero is still real and still second.
Do not "fix" a disagreement by prompting a logged-in account until your name appears. That measures your own session. The 30-minute check uses a logged-out window for that reason. Do not add your brand to the query. Branded prompts measure recognition. And do not brief a content program against the pooled 30% while one row is zero — you will publish for the engine that already names you and call the average an improvement. Dropping the zero engine to raise the rate is the same cheat. Shrinking the denominator is not a gain.
After the per-engine baseline is honest, the program that keeps pages and measurement current is Ascent: a 12-month program covering brand, website, content, SEO, and GEO, billed monthly with no upfront fee. The monthly number is not a catalog sticker. What GEO costs, and what it does not, is a separate post. This one does not reprice it.
What to do this week
1. Keep the twenty category questions fixed. Same wording in every assistant. No brand name in the prompt. The definition of the rate lives in the citation-rate post. 2. Add a row per engine before you calculate a pool. If ChatGPT is 12/20 and Perplexity is 0/20, write both. The 30% pool is the summary, and in this example it is our arithmetic. 3. Read who-else on the zero row. If the same competitor is named on the queries that pay, that engine is the backlog. The 30-minute spreadsheet already has the column. 4. Check exclusions only if every engine misses you. Crawler, contradictory figures, and a split entity are in the exclusion post. A one-engine miss is not that list. 5. Run the free [AI Citation Audit](/tools/geo-citation-audit/). It scores category buying questions across the major assistants and shows where you are named and who is named instead. Read the report by engine. Do not average it into a trophy.
ChatGPT and Perplexity disagree because they retrieve different pages. A pooled 30% can be a 60% on one assistant and a 0% on the other. Score the rows. Fix the engine your buyers use. Start with the free AI Citation Audit.
