Topic clusters aren't new. What changed is who reads them first. Before an AI answer is written, the search engine may split the question into several smaller searches. Google calls this query fan-out, "issuing multiple related searches across subtopics and data sources" (AI features and your website, last updated December 10, 2025). A cluster plan starts from those same subtopics.
That makes it tempting to build a page for every one of those fan-out searches. Adam Tanguay's guide to topic clusters on Search Engine Land (October 5, 2026) answers that in one line: "So I focus on depth: covering recurring subtopics instead of building a page for every fan-out." I work the same way. Google goes further. Its guide to AI search calls it spam to build a separate page for every fan-out search in order to manipulate rankings, as I cover in search intent when AI does the searching. This post shows how I read my own data to decide which questions get a page, which get a section on an existing page, and how to judge each page once it's live.
What is a topic cluster now?
A topic cluster is a hub page and the pages around it, each answering one question people ask about the topic, linked in the order they ask them. Building one comes down to deciding which questions deserve their own page and which belong together. I give each question one home, either its own page or a section of a page that already draws those searches, which keeps my own pages from competing for the same searches. Two phrasings of the same question share a home; a different task gets its own. The hub links those pages in the order people ask. And I build a page only when someone is already searching for it.
Fan-out is also smaller than it sounds. Ahrefs studied 66.6 million ChatGPT answers from August and September 2026: "Only 14.4% of the 66.6 million responses required a web search", and when one did, ChatGPT "ran about 1.6 fan-out queries on average" (Ahrefs, October 12, 2026; a vendor's own study of free, logged-out answers). That's a handful of searches per answer, so covering the subtopics that keep coming back matters more than covering every possible one.
Which questions deserve their own page?
The ones with no home yet. Before I build anything, I check which page already draws the searches.
On The Quack Report, my Oregon Ducks site, I almost built a separate depth chart page. Those searches were already landing on the roster page, so I kept one page instead of splitting it in two. The Quack Report case study tells the rest.
To find the questions with no home, I read two reports. Google Search Console shows the words people actually search with. The new Topics view in Bing Webmaster Tools' AI Performance report lists Opportunity Queries, the searches "where other sites appear and yours does not" (Bing Webmaster Blog, October 13, 2026). Bing adds its own caution: "Treat the numbers as directional signals for planning, not as a precise ledger."
On WireRef, my electrical reference site, AI citations are spread across many pages rather than concentrated on one: the most cited page holds 17% of Bing's page export, and the top ten hold 43%. The table at the end of this post shows where the rest land, folder by folder.
Does every page in a cluster do the same job?
No. Tanguay sorts a cluster's pages into three kinds: answer-only pages, where the AI answer is enough and few people click; pages people still click through to; and questions people would rather take to a community like Reddit. I ran the pages of an independently owned fan reference through that sort. I leave out the project's name and page addresses, but the numbers are exact. The citations come from Bing and the clicks from Google, so read each column on its own, never one against the other.
| Page | Bing AI citations | Google clicks | What the data says |
|---|---|---|---|
| Lead release answer | 11.1K | 14.5K | Still clicked |
| Another installment FAQ | 7.8K | 68 | Cited, rarely clicked |
| Earlier-installment FAQ | 2.6K | 1.9K | Still clicked |
| Listening guide | 1.7K | 27 | Cited, rarely clicked |
| Series hub | 1.4K | 0 | Cited, rarely clicked |
| Earlier-installment page | 422 | 66 | Neither yet |
| Sequence guide | 199 | 80 | Neither yet |
Bing Webmaster Tools, AI Performance page export, and Google Search Console page rows, both Apr 13 to Oct 11, 2026. The label is my rule over each column on its own: "Still clicked" is 1K or more Google clicks; "Cited, rarely clicked" is 1K or more citations with under 100 clicks; anything else reads "Neither yet". 8 more pages in the cluster had under 100 of each.
The FAQ for another installment is the strongest answer-only candidate: Bing's AI cited it 7.8K times, and Google sent it 68 clicks over the same days. The two numbers come from different engines, so they don't show that an AI answer satisfied anyone; they mark a page to inspect. The lead release answer sits at the other end, with 14.5K Google clicks.
If those pages do different jobs, they need different measures. A page AI answers quote needs the answer in its first lines, in words a model can lift, and I measure it by citations. Tanguay says to expect those pages to lose clicks. A page people still click needs a reason to stay and a clear way onward, toward using what they learned, comparing options or making a decision.
What about the questions people take to Reddit?
Tanguay's third kind is the question people want answered by other people. From Apr 13 to Oct 11, 2026, 21 different searches that showed a page of the fan reference had "reddit" in them, 427 impressions in all, and 79% of those impressions were in the cluster about the main series. The most common one added "reddit" to a question about a release date.
Those people want other fans, not a reference page, and Tanguay's advice fits: "The job there is to show up as a person in the thread." I read those threads as research rather than as something to rank for. They show what fans still ask each other, and some of those may be questions the cluster hasn't answered yet. I treat them as leads to check against my own pages, not as proof of a gap.
Where does creativity come in?
Data shows where the gap is. It doesn't show what the page should be. That part starts with the person on the other end of the search: where they are, how much time they have, and what they're holding.
On Title Town PDX, my Portland women's sports site, the gap was the game-day questions the official team sites skip, like the bag policy at each stadium. No keyword list hands you that page. You find it by picturing a fan in the parking lot with the wrong bag. The Title Town PDX case study shows what those guides became.
Before I build it, I write a short page brief. It keeps what the data shows separate from what I imagined about the fan, and it sets what the page answers first and where it sends people next.
How does this work on enterprise sites?
The same way, at a bigger size. At my full-time job I work on SEO and AI visibility for enterprise brands across many industries, and the method doesn't change with the logo. I split demand into the few themes people actually search and give each theme its own pages and its own measures. I keep searches that include the brand's name apart from the ones that don't in every report, because the searches without it are where new buyers find you. Those questions get the articles, each linked to its neighbors and to the page where the decision happens, and the questions nobody answers well get the content only that brand can write.
The size changes one thing: I work from bulk exports with far more rows than the reporting screens show. AI helps me sort them into themes, which would take weeks by hand, and I decide which questions deserve their own page. The industry pages show how the method shifts from one industry to the next.
What can't this data tell you?
- Two engines, two columns. The citations are Bing's and the clicks are Google's, so neither column is a ratio of the other.
- The labels are mine. "Cited, rarely clicked" is a line I drew over two columns, not something either engine measures.
- Bing's page export is capped. It lists at most 300 pages. The fan reference fits under that; WireRef's export is at the cap, so it may leave out more cited pages.
- A citation is not a visit. It means an answer used the page, not that anyone clicked through.
- Section links count toward their page. Search Console lists a link to one section of a page (a URL ending in #something) as a separate row; the clicks here add those rows back to the page itself.
First published October 17, 2026.