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04 Read · 7 min

Reading presence without over-reading it

Presence, share of voice, and how a brand can be mentioned everywhere and recommended nowhere.

Three numbers that are not the same

Presence is whether you appeared at all. Share of voice is how much of the named competitive set was you. Recommendation is whether the engine actually endorsed you rather than merely listing you.

These decouple constantly. A brand can appear in nine of ten answers and be recommended in none, because it keeps being named in the roll-call of alternatives while a competitor is named as the choice. On a presence metric that looks like a win. Commercially it is a loss, and a loss you will not notice unless you look at recommendation separately.

Aggregate scores hide the thing you need

A single visibility score is useful for tracking direction over time and nearly useless for deciding what to do. The decision information lives in the per-prompt breakdown: which specific questions you lose, and to whom.

Sort by the prompts where you are absent and read those responses first. That set is your actual work queue. A prompt where you are absent and a well-corroborated competitor is recommended is a different problem from one where the engine gave a generic answer naming nobody — the first is a competitive gap, the second is an unclaimed space, and they need opposite responses.

Where this lives in SolCrys

  • The workspace dashboard carries the aggregate view — presence, share of voice, movement.
  • The per-prompt view breaks this down by question, which is where you should spend your reading time.
  • Recommendation is scored separately from presence, per response, so a brand that is present but never endorsed shows up as exactly that.

Lab: Build your work queue

Needs a free SolCrys account (no credit card). No quota consumed.

  1. List the prompts where you were absent.
  2. For each, note whether a competitor was recommended or the answer named nobody in particular.
  3. Separate the list into competitive gaps and unclaimed spaces. You will treat them differently in Module 5.

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Check yourself

  • Can you name a prompt where you are present but not recommended, and explain why that is worse than it looks?
  • What is the difference in required response between a competitive gap and an unclaimed space?
  • Why is an aggregate score a poor basis for deciding what to do next?

Go deeper

This lesson is the spine. These guides are the depth.

Measurement

AI Share of Recommendation

AI Share of Recommendation measures how often answer engines recommend a brand, not just whether they mention it. Learn how to track and improve it.

Measurement

AI Recommendation Score

AI can name your brand and recommend a rival in the next sentence. The Recommendation Score grades every AI answer 0-100 on how favorably it positions you across ChatGPT, Gemini, Perplexity, Google AI Overviews and Claude, plots you against every competitor, and shows the verbatim line behind every point.

Citation & Source Influence

Cited but Not Recommended: Why AI Citations Are the Wrong KPI

Getting cited as a source and getting recommended as the pick are two different AI-search outcomes, and they are decoupling. Why most teams track the wrong one, what a 100-query study and our own 5-engine data show, and the KPI to measure instead.

Continue

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Turn AI answer gaps into governed marketing execution.

Start free with a ChatGPT visibility read, then add multi-engine tracking, Corporate Context governance, and the action-to-result loop when you are ready.

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