Decision query

A decision query is a question a user asks while they are in the middle of choosing — “how do I choose,” “which one is right for us,” “is it worth doing” — and the answer directly determines the shortlist and its order.

The classic three-way split of search intent — navigational, informational and transactional — comes from Andrei Broder’s 2002 taxonomy. “Commercial investigation” is a category marketers added later to describe the comparison and selection stage before a purchase. Decision queries map roughly onto that category.

Examples

• “How do I choose a GEO vendor?”
• “Which AI training provider is best for a manufacturing team?”
• “Is it worth hiring an agency for AI search, or should we do it in-house?”
• “What should I look for in an AI agent implementation partner?”

None of these names a brand, but all of them invite the AI to name some.

Why they carry extra weight in GEO

Traditional search returns a list of links and leaves the comparison to the user. An AI answer hands over a shortlist and the reasons for it. The first one or two items get far more attention than the rest.

That is why, on these questions, first recommendation rate and answer position mean more than plain mention rate. Being on the list is necessary; being near the top is what changes outcomes.

How content should respond

What works is offering a decision framework, not self-praise:

• which dimensions to look at when choosing;
• which kinds of solution fit which conditions;
• when you should not do it at all.

When a model assembles an answer to “how do I choose,” it needs criteria it can restate. A passage that only lists your own strengths is hard to extract and hard to treat as a neutral source. A page that honestly says “if you only have a few dozen pages and one market, you probably don’t need outside help” is more useful to a reader, and more quotable for a model.

Comparison pages are a natural format for this — see the compare section, for example GEO agency vs. in-house.

What a good answer page looks like

A page built for a decision query usually opens with a one-sentence verdict (“for most teams under a certain size, X is the better fit”), then lays out the criteria in a short list or table, then covers the exceptions. It names the trade-offs openly, including the ones that don’t favor you. That structure follows answer-first writing, and it gives a model a ready-made, neutral-sounding framework to restate.

How it divides the work with other content

Judgments like “how to choose,” “how long until it works” and “is it worth it” contain opinions and change as the industry changes. They belong in longer insights articles that can be updated or retired, such as how to choose a GEO vendor. The wiki only handles definitions.

For measurement, keep decision questions separate from category keyword questions and long-tail questions in your question set. They behave differently, and mixing them hides real changes.