Blog: Stop Writing One Kind of Content for Three Types of Searches

Informational content is seeing a major shift in the value it brings to a brand, particularly if your traffic reporting only tracks aggregate sessions. Informational queries now trigger AI Overviews roughly 36% to 39% of the time, compared with just 5% to 8% for commercial and transactional queries. This means the pressure most teams are feeling is concentrated almost entirely in one category rather than spread evenly across the content library. 

The fix isn’t abandoning content production, nor is it producing less of it. It’s setting a different target for each of the three search types, informational, commercial, and transactional, and building an SEO and AI search strategy around that distinction instead of writing the same “rank for the keyword” post regardless of what the person searching actually intends to do next.

Informational queries: the target is the mention, not the click.

The mechanism behind this shift is worth understanding before reacting to it. 

Roughly 99.2% of keywords that trigger AI Overviews carry informational intent, which is the exact category that “how-to” guides, definitional explainers, and general reference posts have always occupied. That concentration is a direct extension of how AI Overviews and generative search have reshaped search behavior over the past year, and it explains why this category is absorbing nearly all of the visible traffic decline while other categories remain comparatively stable.

The appropriate response is to change what the content is built to do. Content written to answer a simple question no longer earns a click for answering it, because a model can synthesize that answer directly on the results page. What it can still earn is a citation or a mention in AI results.

Original research, named data points, benchmark studies, and canonical explainers grounded in real audience research rather than generalized assumptions about what a topic’s audience wants to know help a brand differentiate themselves for inclusion in AI recommendations. Brands cited in an AI Overview still see approximately 35% more organic clicks than brands without a citation. AI-referred traffic also converts up to 23x higher than traditional search traffic. Capturing the citation and being mentioned in the answer are meaningful outcomes to optimize for in this category, rather than the ranking position.

Commercial queries: the target is the decision, not the definition.

Commercial, comparison-style searches occupy a genuine middle ground, and they deserve a different treatment than either of the other two categories. AI Overviews are expanding quickly here, up 71% over a recent six-month period, and comparison-format queries specifically trigger them in over 95% of cases, a trend consistent with the broader shift in where Google and OpenAI actually stand heading into 2026. At the same time, the intent behind these searches is different from a purely informational one. This is the search behavior that defines most B2C purchase research, where consumers weigh several options before committing, particularly for longer research cycles like finance, travel, or software, and generally still want to click through and verify claims directly rather than accept a summarized answer at face value.

The content that performs best in this category isn’t a generic “top 10” list that a model can compress into two sentences without losing anything of value. It’s comparison content grounded in direct testing, firsthand experience, or proprietary data, detailed enough that it becomes source material for the AI answer while still giving a research-stage buyer a reason to click through and confirm the details themselves. This is the one category where writing to be cited and writing to be clicked aren’t competing goals, and a single well-built asset can accomplish both simultaneously.

Transactional and navigational queries: the target is still the click.

This category is the clearest exception to the broader trend, and it’s worth stating plainly rather than treating it as an afterthought. AI Overviews barely touch transactional intent, appearing only 5 to 8% of the time overall, and e-commerce queries specifically sit closer to 3 to 4%. The reason is straightforward: a summarized answer cannot complete a purchase, book an appointment, or add an item to a cart, so someone ready to take that action still has to land on a page to follow through, which is exactly the behavior ecommerce SEO programs are built to capture.

Product pages, category pages, service pages, and local pages should continue to be optimized for conversion using the same fundamentals that have applied for years, an area where website optimization and conversion-focused design still do most of the work. This is not the part of the content strategy that needs to be reconsidered or deprioritized in response to AI Overviews, and treating it as though it does risks pulling attention away from the pages still generating the most direct commercial value.

Informational content isn’t ending because content stopped mattering. It’s ending because clicking stopped being the only reward for that one particular type of content. What that means in practice is that a content strategy now has to run three distinct targets simultaneously rather than applying one blanket approach across the entire library: mention-worthy work for informational searches, decision-worthy work for commercial searches, and conversion-worthy work for transactional ones. 

The brands adapting well to this shift are producing the right kind of content for each type of search, rather than continuing to treat every search as though it were the same request for the same outcome.