Usage-and-Fit Engine Prioritization Rule
Focus optimization on engines with material usage or exceptional audience fit
- Difficulty
- Starter
- Time to result
- ~days to results
- Steps
- 5
- Confidence
- 93%
This rule allocates AEO effort using two filters: total usage and category-specific audience fit. The default is to focus on the engines responsible for most relevant activity rather than treating every LLM as equally important. In the episode’s market context, that means prioritizing ChatGPT and the Gemini/Google AI ecosystem. A smaller engine enters the plan only when the target audience uses it disproportionately, as engineers may use Claude more than the general population. The output is a short, defensible platform list that concentrates measurement, content work, and citation outreach where they can matter. Because engine adoption changes quickly, teams periodically rerun the comparison instead of turning the current ranking into a permanent rule.
Origin
Extracted from Marketing Against The Grain
Core principles
- 01Usage should determine default platform priority
- 02Audience concentration can override the default
- 03Small teams should not optimize equally for every engine
- 04Platform priorities must be revisited as adoption changes
How to run it
- 1
Map relevant engines
List the answer engines and AI search experiences that could influence the target audience.
Pro tip Group connected experiences, such as Gemini, AI Mode, and AI Overviews, when assessing the broader ecosystem.
Watch out Do not mistake media attention for meaningful audience usage.
- 2
Rank by material usage
Estimate which engines account for most relevant user activity and place those at the top of the optimization plan.
Pro tip Use relative order and magnitude when exact traffic estimates are unavailable.
Watch out Equal allocation across every engine wastes scarce effort.
- 3
Apply the audience-fit exception
Check whether a smaller engine has unusually high adoption within the company’s category or profession.
Pro tip Use customer interviews, analytics, and sales conversations to validate platform preferences.
Watch out Anecdotal enthusiasm from a few users is not sufficient evidence of concentration.
- 4
Set the active platform list
Select the smallest set of engines that captures major usage and any proven audience-specific exception.
Pro tip Tie every selected engine to a measurement and optimization owner.
Watch out Do not add a platform without specifying why it deserves resources.
- 5
Reassess periodically
Repeat the usage-and-fit comparison as engine adoption, partnerships, and audience behavior evolve.
Pro tip Review quarterly in fast-moving markets.
Watch out Current platform leadership may not persist.
In the wild
A general B2B company focuses on ChatGPT and Gemini. A developer-infrastructure company discovers that a significant share of its customers use Claude for technical research, so it adds Claude tracking and optimization despite Claude’s smaller overall usage.
→ Each company concentrates effort on the engines most likely to influence its actual buyers.
Common mistakes
Optimizing every engine equally
This ignores large differences in usage and dilutes the work available for the most consequential platforms.
Ignoring audience-specific behavior
Overall market share can hide an engine that is unusually influential within a specialist category.
Is it for you?
Best for
Teams choosing which LLMs and AI search products to monitor and optimize first.
Not ideal for
Organizations with abundant resources and a strategic need to maintain comprehensive coverage across every engine.
From the transcript
“My general recommendation is only spend time where there's heavy usage.”
“I wouldn't spend any time on the other LLMs unless you're in a category where people are specifically using that LLM.”
“Like engineers tend to use cloud more. So if you're an engineering company, that's different.”
From the episode
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