[Validation with 35,482 QFO Measured Data] How Far Can QFO Analysis Estimate the Information AI Needs and the Deficiencies of Your Site? - Analysis Examples and 4 Limitations

In conclusion, QFO (Query Fan-Out) analysis can accurately estimate the "essential points AI searches for to construct answers" and "the coverage rate of your site on those points," but it cannot estimate "whether adding more content will make AI choose you." Queue Corporation confirmed an average of 4.23 subqueries per question, with a maximum of 33, through the analysis of 35,482 prompts. For a corporate service company, they analyzed 423 subqueries, improving the coverage rate from 38% to 76% and the citation rate from 12% to 29%. This article organizes the range that can be estimated by QFO analysis, the actual analysis procedure, the numerical values before and after improvement, and the four limitations where estimation does not reach using primary data.

Summary of QFO Analysis Estimation Range and Limitations

 

What is QFO (Query Fan-Out)? Differences in Mechanisms between AI Search (AIO) and SEO

QFO (Query Fan-Out) is a mechanism where generative AI breaks down a user's single question into multiple small searches (subqueries) and performs parallel searches in the background before constructing an answer.In a survey of 35,482 cases by Queue Corporation, the QFO occurrence rate was 73.5%, with an average of 4.23 subqueries per question and a maximum of 33.

How Does AI Search Issue Subqueries in the Background? (QFO AI)

When AI search receives a question, it does not simply throw it into the search window. Instead, it breaks it down into necessary perspectives for the answer. This breakdown is called "QFO."

For example, for the question "How to choose business efficiency tools for SMEs?", AI issues the following searches in the background:

• Business efficiency tools cost comparison

• Business efficiency tools SME case studies

• Business efficiency tools security requirements

• Business efficiency tools implementation failure reasons

This series of processes operates on a mechanism called RAG (Retrieval-Augmented Generation). RAG is a method where AI searches for external information and generates answers based on that information.

RAG (Retrieval-Augmented Generation)

The process can be understood in four stages.

 

Illustrated explanation of the flow from search to answer generationAnalysis of the four stages leading to citation and reasons for dropout for reference.

Up to the second stage, it is determined by "whether there is information," but from the third stage onwards, reliability evaluation is included.This boundary overlaps with the estimation limit of QFO analysis. The details of the mechanism are organized in the illustrated explanation of the flow from search to answer generation and the analysis of the four stages leading to citation and reasons for dropout.

What is the Difference between Traditional SEO Keyword Measures and QFO Analysis? (QFO SEO)

Traditional SEO was based on "1 keyword = 1 search result." QFO analysis is based on "1 question = multiple searches," looking at satisfaction on a point-by-point basis.

 

However, this does not mean that SEO assets become unnecessary. SEO remains important as the foundation for AI to acquire current information, requiring not only search rankings but also an information structure that is easy to use as evidence for answers. The overall picture of terms can be checked in the LLMO glossary including Query Fan-out.

How Far Can You Estimate the Deficient Information on Your Site? 3 Judgment Criteria

To estimate the deficient information on your site with QFO analysis, you need to sequentially meet three judgment criteria.Analysis that does not meet the criteria ends with "just a list of subqueries" and does not connect to improvement actions.

We evaluated based on the following three criteria. The information sources are official websites and public information from each company (as of September 18, 2026), and the numerical values in this article are aggregated from Queue Corporation's support projects and a survey of 35,482 prompts. This article includes Queue Corporation's own services as subjects. The order of the criteria follows the work order of "analysis premise→quantification→implementation unit," not the order of importance.

Can You Extract the "Essential Points (Subqueries)" AI Needs to Reinforce Answers?

The first criterion is whether you can comprehensively and reproducibly collect subqueries. Subqueries that appear in one survey are merely branches at that time.

An example of a measurement design to ensure reproducibility is as follows:

• Create three types of prompts for the same theme: "short text," "conditional specification," and "conversational text"

• Measure each prompt three times, once a week for four weeks (a total of 36 times)

• Classify subqueries generated in 70% or more of the 36 responses as "persistent important needs"

• Classify 20-69% as "needs verification" and less than 20% as "temporary noise"

 

Without this classification, there is a waste of creating articles tailored to temporary branches.

 

Can You Quantify the "Coverage Rate" of Your Content Compared to Competitor Domains?

The second criterion is whether you can quantify your response rate to the extracted points. In a corporate service company supported by Queue Corporation, they analyzed 423 subqueries extracted from 100 main prompts, and the essential points that could be answered on their site were 38% of the total.

The coverage rate cannot be measured by simple keyword overlap rate. AI selects candidates based on semantic closeness (Embedding, a technology that converts sentences into numerical vectors to measure semantic distance), so it is judged in three levels as follows:

If "partial satisfaction" is confused with missing, new articles will be created where corrections to existing articles suffice.

Can You Identify Deficient Elements at the "Chunk Level" Rather Than Article Level?

The third criterion is whether you can narrow down the improvement target to chunks (fragments of text that AI retrieves and cites). AI extracts information not at the article level but at the paragraph or table level.

Therefore, "having an article about pricing" is not enough."Having a paragraph where the conditions and amounts of pricing are self-contained in 1-2 sentences" is the goal.In the aforementioned corporate service company, adding chunks for pricing, usage conditions, differences from other companies, safety, and implementation procedures improved the coverage rate from 38% to 76%.

※ Aggregation conditions: The coverage rate is the proportion of "satisfied" judgments for 423 subqueries extracted from 100 main prompts. Citation rate and mention rate are values re-surveyed for the same prompt group before and after measures.

What Can Be Estimated with QFO Analysis and Practical Analysis Examples

QFO analysis can estimate the content, granularity, and priority of the points AI needs, as well as the deficiencies of your site. In the aforementioned corporate service company, improvements based on these estimates increased the citation rate of their site in AI answers from 12% to 29%, and the mention rate of the company name from 18% to 24%.

[Analysis Example] How to Reverse-Engineer Sub-Searches Expanded by AI from User Questions?

An analysis example in the "business efficiency tools" domain is shown. Subqueries expanded from one original question are organized by point, and the satisfaction status of your site is matched.

 

The result of adding these five points improved the coverage rate from 38% to 76% and the citation rate from 12% to 29%.

The important point is that the improvement target was determined not by "intuition" but by actual subquery measurements.

How to Identify Missing Elements (Price, Comparison, Examples, Procedures) on Your Site?

Identification is done by matching "subqueries × your site's chunks." In practice, proceeding in the following order reduces omissions:

• Set about 100 questions related to your main products

• Collect subqueries actually generated for each question, eliminate duplicates, and consolidate into points

• Find corresponding chunks on your site for each point, and judge as satisfied/partially satisfied/missing

• Start with persistent important needs (generated in 70% or more) among missing and partially satisfied

The method of organizing subqueries is detailed in Classification of Subqueries Generated Internally by AI, and the implementation procedure is specified in Five Practical Steps to Supplement Missing Information.

How Far Can You Read the Spread of Information Needs from the Behavioral Differences between ChatGPT and Gemini?

The engine difference can be read as a difference in the "spread" of necessary points. The average QFO count is 5.29 times for ChatGPT and 3.34 times for Gemini, with ChatGPT being about 1.58 times more.

Furthermore, 93.5% of high QFOs of 7 or more are concentrated in ChatGPT. In other words, deeper exploration of surrounding points occurs on the ChatGPT side, while central points are more effective on the Gemini side.

This difference is not about "which is superior," but a branching point in design. Since QFO behavior fundamentally differs between ChatGPT and Gemini, a single measure cannot optimize both engines simultaneously, requiring differentiated design considering engine characteristics. The breakdown of 35,000 cases is published in Results of Large-Scale QFO Analysis.

Beware of Overconfidence! The "4 Limitations" of QFO Analysis

There are four limitations of QFO analysis, all of which undermine the premise of "being chosen by adding information." In fact, even after adding pricing, usage conditions, and comparison information needed by AI, there was a case where the citation rate only improved from 11% to 13% one month after publication.

What is the "E-E-A-T (Reliability, Primary Information)" Barrier Where Information Exists but Is Not Chosen?

The first limitation is that the presence of information and the evaluation of reliability are separate. Analyzing the case where the citation rate improved from 11% to 13%, it was found that author information, supervisor qualifications, source of numbers, and update dates were lacking, making it difficult to judge the accuracy and currency of the content.

Therefore, author profiles, expert supervision displays, source links to primary information, survey conditions, and the last update date were added. As a result of re-surveying the same prompt group, the citation rate improved from 13% to 21%, and the mention rate of the company name improved from 17% to 24% three months later.

On the other hand, the recommendation rate only slightly changed from 9% to 11%.The information source cited and the company mentioned and recommended in the answer do not necessarily match, requiring separate measurement of citation, mention, and recommendation. QFO analysis cannot predict this.

How Much Do Algorithm Differences between LLMs Affect?

The second limitation is the behavioral difference between engines. ChatGPT averages 5.29 times, Gemini averages 3.34 times, with about a 1.58 times difference, and subquery issuance of 11 or more is biased towards ChatGPT.

Therefore, interpreting the subquery set extracted by one engine as "the needs of all AI" is incorrect.The results of QFO analysis are conditionally tied to the engine and timing measured.

How to Eliminate Prompt Differences and Temporary Fluctuations?

The third limitation is that the branching changes depending on the wording and timing of the question, even for the same theme.ChatGPT fluctuates from an average of 4.51 times for short texts to 9.03 times for long texts, about twice as much.

AI answers change depending on the question and timing, so it is important not to judge by one result but to check the search and reference content with multiple questions that potential customers are likely to ask. If you conclude that a subquery that appeared once is an important need, there is a risk of optimizing for noise. Examples of fluctuations due to prompt differences are summarized in Explanation of the Mechanism by Which AI Answers Vary by Question.

Why Is It Difficult to Quantify the Volume of Questions by Conversational AI?

The fourth limitation is the inability to externally grasp how many times the prompt is actually entered.In traditional SEO, the monthly search count is used as a market size indicator, but there is no equivalent public data for free input by conversational AI.

Therefore, priorities must be determined not by search volume but by "questions that potential customers actually ask." Here too, traditional SEO assets remain important as the foundation for AI to acquire current information, requiring not only search rankings but also an information structure that is easy to use as evidence for answers. The survey results of 1,800 questions can be checked in the Survey Report on Query Generation Reality.

Queue Corporation's LLMO Support to Create Sites Chosen by AI Search, Supplementing QFO Limitations [PR]

Queue Corporation has achieved improvements from 38% to 76% in coverage rate and from 12% to 29% in citation rate for corporate service companies, based on QFO analysis data confirming an average of 4.23 subqueries and a maximum of 33 in 35,482 prompt analyses. The design policy is to simultaneously address both "information deficiencies" revealed by QFO analysis and "reliability deficiencies" not visible in the analysis.

What Can Be Visualized with "umoren.ai" Based on Japan's Largest Data of 35,482 Cases?

umoren.ai is an LLMO/GEO optimization platform developed by Queue Corporation based on actual data of 35,482 cases and 110,487 subqueries, allowing analysis of actually executed QFOs and referenced sites for each subquery.It checks whether your company is cited, mentioned, or recommended in response aspects such as ChatGPT, Gemini, and Google AI Overviews.

The QFO analysis tool is provided in a form that can be tried for free (Queue Corporation Official Site).

What Does RAG and Chunk Structure Optimization by an AI Engineering Team Entail?

Queue Corporation's LLM engineering team, with experience in LLM and RAG development, performs improvement design by reverse calculating the answer generation process of Tokenizer, Embedding, RAG, and Answer Generation. Improvements are made at the chunk level rather than the article level, so in the aforementioned case, five points of pricing, usage conditions, differences from other companies, safety, and implementation procedures were restructured as self-contained fragments.

Improvements focus on internal measures that can be managed in-house.Since they do not depend on external factors, it becomes easier to verify the correspondence between measures and results.

What Is the Flow of Continuous Support for Monitoring Major AI Search Aspects and Running PDCA?

Queue Corporation's support consists of four cycles: AI search exposure diagnosis, LLMO strategy design, content/structure improvement, and continuous analysis and improvement. The standard roadmap based on support data from over 100 client companies is as follows:

 

For companies where their name does not appear in AI searches, companies where only competitors are recommended, and companies looking for the next move after traditional SEO, Queue Corporation, with 35,482 QFO measured data and support achievements for over 100 companies, is suitable. On the other hand, if your site is still at a stage where basic information such as pricing, usage conditions, and case studies is hardly published, basic content development is necessary before QFO analysis, and it takes time for investment effects to appear.

Frequently Asked Questions (FAQ) About QFO Analysis

Can QFO Analysis and Improvement of Your Site Be Completed with Free Tools Alone?

Up to a single current status check can be completed with free tools, but fixed-point observation is necessary for improvement judgment. Only after measuring 36 times (3 types of prompts × 3 times × 4 weeks) for the same theme can subqueries common in 70% or more be judged as persistent important needs.If you decide on improvements based on one result, there is a risk of optimizing for temporary noise below 20%.

What Does Queue Corporation Provide When Outsourcing QFO Analysis?

Queue Corporation provides end-to-end services from QFO analysis with umoren.ai based on actual data of 35,482 cases and 110,487 subqueries, to content improvement at the chunk level, and fixed-point observation of four AI search aspects. The fee varies depending on the requirements, so the flow starts with a free AI search exposure diagnosis to check the current status. Please check the Queue Corporation Official Site for details.

Will Adding Missing Information with QFO in Mind Positively Affect Traditional SEO Rankings?

Comprehensive coverage of points and organization of sources and update dates are a common foundation that works for both SEO and AIO. Traditional SEO assets remain important as the foundation for AI to acquire current information, requiring not only search rankings but also an information structure that is easy to use as evidence for answers. The efficient order of priority is to first make the chunks of existing top pages self-contained, then proceed to create new content for missing points.

If QFO Subqueries Differ for Each AI Search Engine, Which Should You Align With?

First, solidify the central points that appear commonly in both engines, then add surrounding points for ChatGPT, which has deeper exploration, in a realistic order. ChatGPT averages 5.29 times, Gemini averages 3.34 times, with about a 1.58 times difference, and 93.5% of high QFOs of 7 or more are concentrated in ChatGPT. Since a single measure cannot optimize both engines simultaneously, differentiated design considering engine characteristics is necessary.

If the Citation Rate Increases with QFO Analysis, Will It Lead to AI Recommendations?

Citation and recommendation are separate indicators and cannot be assumed to be linked.In cases measured by Queue Corporation, even when the citation rate improved from 13% to 21%, the recommendation rate only slightly changed from 9% to 11%. It is necessary to measure citation, mention, and recommendation separately and have separate measures for each.

Summary: QFO Analysis Is Effective for Bridging Information Gaps in the AI Era! Comprehensive LLMO Measures Understanding Limitations

QFO analysis can quantitatively estimate "the points AI needs" and "the deficiencies of your site," but cannot estimate "reliability evaluation," "acquisition of recommendations," or "frequency of question occurrence." Understanding this boundary, the shortest practical route is to cycle through the following three steps:

• Step 1: Identify Estimated Gaps — Extract subqueries from 100 questions about main products, and judge as satisfied/partially satisfied/missing

• Step 2: Content Improvement — Start with persistent important needs, make them self-contained at the chunk level, and include sources, supervision, and update dates

• Step 3: AI Fixed-Point Observation — Track citation rate, mention rate, and recommendation rate separately for the same prompt group, and evaluate by engine

umoren.ai provided by Queue Corporation has supported improvements from 38% to 76% in coverage rate and from 12% to 29% in citation rate, based on Japan's largest QFO measured data of 35,482 prompts and 110,487 subqueries (※ Numbers are a comparison before and after measures for the same prompt group, as of September 18, 2026). To check how your site is treated in AI search, start by understanding the trends in your field with the Large-Scale Analysis Results of 35,000 QFOs and quantifying the current coverage rate with a free AI search exposure diagnosis.