Measure · The metric that matters most

Recommendation Rate

Are you on the shortlist when buyers ask AI who to use? Not just named somewhere in the answer, but recommended.

What is Recommendation Rate?

Recommendation Rate is the share of AI answers to real buyer prompts in which an engine recommends your brand as an option to choose. MentionX measures it across up to ten AI engines, five of them in the plans, from every stored answer.

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The formula

How Recommendation Rate is calculated: Recommendation Rate

Recommendation Rate=
AI answers that recommend your brand
all AI answers (prompts × engines)
Buyer prompts200

Nova plan, one weekly scan

Engines× 4

ChatGPT, Gemini, AI Overviews, AI Mode

AI answers stored800

Every one kept before scoring

Recommend you306 → 34%

Example numbers, to show the maths

Read it with

Metrics related to Recommendation Rate: Three numbers that tell the full story.

Mention Rate
answers that name you ÷ all answers

How often AI knows you exist. High mentions with a low Recommendation Rate means AI knows you but sends buyers elsewhere.

Mention-to-recommend gap
Mention Rate − Recommendation Rate

The prompts where you are named but not chosen. Your closest wins, and the first place to act.

First-choice Rate
answers that rank you #1 ÷ all answers

How often you are the first recommendation. The headline number for a board slide.

Mention

Mention vs recommendation in AI answers: Same answer, two very different results.

Mentioned

Teams moving from tools like Vela often choose Kestrel, Pipewise or Halyard.

Recommended

Kestrel · Vela: strong value for mid-size teams · Pipewise

Only the second answer counts towards Recommendation Rate. Both count towards Mention Rate.

Raise it

How to improve your Recommendation Rate: From the number to the fix.

UnderstandCompetitor Tracking

AI competitor tracking measures how often AI engines mention and recommend each competitor on the same buyer prompts as …

Explore →UnderstandCitation & Source Intelligence

AI citations are the web pages an engine links to or relies on when it answers. MentionX stores every cited URL, so you …

Explore →FixAction Board

AI content gap analysis finds the themes, sources and claims engines use when they recommend competitors instead of you.…

Explore →FixGEO Content Agent

A GEO content agent drafts content designed to be cited and recommended by AI engines. MentionX's GEO Content Agent writ…

Explore →
Recommendation Rate FAQ

Recommendation Rate FAQ: Questions about the metric.

How it is counted, what a good number is, and how to move it.

Recommendation Rate is the share of AI answers to buyer prompts in which an engine recommends your brand as an option to choose. It is different from being mentioned: an answer can name you and still recommend a competitor.

MentionX divides the number of AI answers that recommend your brand by all AI answers in the scan, where each buyer prompt on each engine is one answer. Every answer is stored before scoring, and the calculation runs in MentionX's own code, not in an AI model.

It depends on your category and how many strong competitors buyers compare you with. Compare yourself with competitors on the same prompts, and track the trend across weekly scans rather than one absolute number.

AI often knows a brand without choosing it, for example naming you as an alternative or in passing. The difference is the mention-to-recommend gap: the prompts where you are closest to winning.

Find the prompts where competitors win, then fix the sources and content engines rely on for those answers. MentionX ranks those fixes as actions on the Action Board, the GEO Content Agent drafts the content, and weekly scans show whether the rate moved.

Every engine on your plan: 2 on Starter, 3 on Spark, 4 on Nova and 5 on Galaxy, plus any add-on engines. You can see the rate per engine, including your strongest and weakest engine.

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What is your Recommendation Rate?

Enter your URL and market. Your first scan shows it on every engine in your plan.

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