> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mentionlab.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Fan-outs

> See the follow-up questions AI platforms suggest after answering your queries

When an AI platform answers a question, it often proposes what to ask next. Those suggestions are
**fan-outs**, and they're a direct read on how the model thinks your category branches — which makes
them a ready-made list of queries worth tracking.

Open **Intelligence → Fan-outs**.

## Headline numbers

| Card                 | What it counts                                                                                     |
| -------------------- | -------------------------------------------------------------------------------------------------- |
| **Total Fan-Outs**   | Every fan-out entry in scope. One response can produce several, so this exceeds the response count |
| **Unique Fan-Outs**  | Distinct fan-out texts. The same suggestion across ten responses counts once                       |
| **Avg per Response** | Fan-outs per response, among responses that produced at least one                                  |

<Warning>
  Every rate and average on this page divides by **responses that produced at least one fan-out** —
  never by all responses in the period. That's why **Avg per Response** can never drop below 1.0,
  and why adding models that don't emit fan-outs won't move it.
</Warning>

<Note>
  Uniqueness is an exact-text match. `Best CRM 2026` and `best CRM 2026` count as two distinct
  fan-outs.
</Note>

## Top Fan-Outs

A word cloud of the most frequent follow-up queries, with **Unigrams** / **Bigrams** / **Trigrams**
toggles.

<Info>
  This is a cloud of *terms extracted from* the top 50 fan-out texts, weighted by how often each text
  occurred — not a cloud of the fan-out queries themselves. Stop words and very short tokens are
  dropped. Bigrams and trigrams are usually the readable views.
</Info>

## Fan-Outs by Query

Which of your queries generate follow-ups, and how many:

| Column         | Meaning                                      |
| -------------- | -------------------------------------------- |
| **Query**      | The tracked query                            |
| **Total**      | Total fan-out entries for this query         |
| **Unique**     | Distinct fan-out texts (deduplicated)        |
| **Avg / exec** | Average fan-outs per response for this query |

Only queries that produced at least one fan-out appear. Sort by any numeric column.

Expand a row to see that query's individual fan-outs, each with its occurrence count and rate. The
header tells you the denominator explicitly — "(*n* responses with fan-outs)".

The ⋮ menu exports the current page along with every fan-out belonging to those queries.

## Turning fan-outs into queries

The practical loop:

1. Read the bigram/trigram cloud for themes you don't currently track.
2. Expand your highest-volume queries and skim the actual follow-up texts.
3. Add the good ones as new queries in **Project settings → Queries**.

A fan-out that appears in most responses is a question the model considers unavoidable in your
category. If you have no visibility on it, that's a gap worth closing.

<Note>
  **View as** has no effect here — fan-outs belong to a response, not to a brand, so the page is
  never re-scoped to another entity.
</Note>

## Exporting

**Project settings → Export data → Fan-Outs By Query** produces two CSVs: per-query aggregates, and
one row per unique fan-out per query with its occurrence count and rate.