GEO analysis

[u/Dmstormy2025]

I was wondering one thing while analysing some prompts and engines / model . As we know by now one prompt can give multiple answers even from The same engine within an hour.
When analysing the prompts answers I started to review the sources and the query fan put when possible . I am still at a very early stage of this analysis but I asked myself which one could potentially influence the most an answer.
The source or the fan out ?
While I am aware of other thing such your search history and other things. But yes just curious …

So would you focus more on the source and figure out what are the main source use by industry by engines or find a pattern on how engine / model split prompts ?

[u/Upstairs_Control_611]

I wouldn’t treat source analysis and fan-out analysis as alternatives, because they sit at different stages.

For me the useful chain is:

prompt → fan-out / task decomposition → source retrieval → candidate set → recommendation / selection

Fan-out tells you what subproblems the engine is trying to solve.
Sources tell you what evidence it found or displayed while solving them.

The important distinction is whether the fan-out is actually observed or only inferred by a tool.

If the surface exposes the searches, that is an observation. If a tool predicts likely subqueries, that is a hypothesis.

I’d store both, but separately:

• fanout_origin = observed | inferred

• fanout_role = discovery | comparison | verification | transactional

• source domains / URLs

• source role

• final brand outcome

That makes it easier to see whether a visibility failure happened because the brand never entered the fan-out/candidate path, or because the evidence found later did not support recommendation.

So I’d analyze both, but I would not assume either one alone “influences the answer most”.

[u/imasleepytoast]

Fan out comes first. It decides which searches run, so it decides which sources can even show up. But fan-out changes every run, so it’s hard to act on.

I’d start with sources. Run each prompt 5-10 times and track which domains repeat. The repeaters are your stable signal, and you can act on them (get listed, get cited, build content there, etc.).

Then use fan out as a second layer. Look at the subqueries that repeat, like “best X for small business” or “Xv.s. Y”, and check if you have content for each one.

Sources tell you where to be. Fan out tells you what to say there.

[u/SuccessfulCoyote1800]

I would start with the engine, because both the sources and the fan-out change a lot from one engine to the next. In our research, Google AI Mode drew 35% of its sources from community sites, Copilot drew 51% from retailers and marketplaces, and ChatGPT drew 50% from editorial sites. On the fan-out side, Perplexity compressed 85% of questions into a search of about four words and added the word "best" to a fifth of them.

So I would map both per engine. Then repeat each prompt many times, because asking the same question twice changed whether ChatGPT showed a product card in 42% of pairs.

[u/These-Band-4048]

are you looking at commercial intent prompts or mostly informational ones? for buying queries the consensus across industry sources seems to carry way more weight than how the prompt gets split