Overall Recommendation
Overall Recommendation is friction AI’s top-level metric for AI-driven discovery. It summarizes how strongly AI assistants recommend your brand across tracked prompts through three key drivers: Visibility, Sentiment, and Purchase Intent.Definition
Overall Recommendation is a composite score that summarizes how strongly AI assistants recommend your brand in purchase-intent and consideration queries, relative to your competitors, based on tracked prompts over time.In plain terms: Overall Recommendation shows how likely AI is to recommend you when customers ask what to buy.
What It’s Made Of
The Overall Recommendation score is built from three components that together capture both presence (are you in the answer?) and preference (are you the recommended choice?).Visibility
How often your brand is mentioned in AI responses
Sentiment
How positively your brand is described when mentioned
Purchase Intent
How often your brand is suggested as an option to buy or choose
Why These Components
These three dimensions cover the full spectrum of what makes a recommendation compelling:- Visibility ensures you’re part of the conversation
- Sentiment ensures you’re portrayed positively
- Purchase Intent measures direct recommendation strength
How It’s Calculated
The Overall Recommendation score is computed as a weighted combination of the three components above. Weighting is optimized to reflect recommendation outcomes, not raw mentions.
Scope and Granularity
Your Overall Recommendation score can be viewed at multiple levels:- By Platform
- By Category
- By Time Range
- vs Competitors
See your score across each AI platform:
- ChatGPT (OpenAI)
- Claude (Anthropic)
- Gemini (Google)
- Google AI Overviews
- Perplexity
How to Interpret Changes
When Your Score Goes Up
You appear in more answers
You appear in more answers
AI is mentioning your brand more frequently across prompts.
You're recommended more directly
You're recommended more directly
When you appear, AI is positioning you as a choice to consider or buy.
Your framing becomes more positive
Your framing becomes more positive
The language AI uses about your brand is more favorable.
When Your Score Goes Down
Competitors are recommended more often
Competitors are recommended more often
Your share of recommendations is decreasing relative to others.
Your sentiment shifts negative or neutral
Your sentiment shifts negative or neutral
AI’s portrayal of your brand is less favorable than before.
You drop out of high-intent prompts
You drop out of high-intent prompts
You’re appearing less in queries where purchase decisions happen.
The query mix changes
The query mix changes
New prompts were added or removed, changing the sample.
Edge Cases and Limitations
LLM outputs can be variable
LLM outputs can be variable
AI responses aren’t deterministic. friction AI reduces noise by running prompts repeatedly and aggregating results over time.
Scores reflect observed outputs
Scores reflect observed outputs
Your Overall Recommendation score reflects what AI actually says today, not guaranteed future answers. Model updates can shift results.
External factors matter
External factors matter
Results can shift due to AI model updates, changes in browsing/citation behavior, or seasonal query intent.
Examples
Example A: High Visibility, Low Recommendation
Situation: Your Visibility score is 75, but your Purchase Intent score is 40. What it means: AI mentions you often, but rarely suggests you as the “best” option or recommends you directly. Fix focus:- Product comparisons and “best for” content
- Review aggregation and social proof
- Clear use-case positioning
Example B: Low Visibility, High Sentiment
Situation: Your Visibility score is 35, but your Sentiment score is 85. What it means: When AI does mention you, it’s very positive. But you’re absent from many prompts entirely. Fix focus:- Content coverage and entity optimization
- Citation building and distribution
- Expanding presence in new query categories
FAQ
Is Overall Recommendation the same as share of voice?
Is Overall Recommendation the same as share of voice?
Why does my score differ by platform?
Why does my score differ by platform?
Each AI platform (ChatGPT, Claude, Gemini, etc.) uses different models, training data, and citation behaviors. It’s normal to perform better on some platforms than others.
How often does the score update?
How often does the score update?
Scores update based on your analysis schedule. System prompts run automatically, and scores reflect the most recent data from tracked prompts.
Can I compare my score against competitors?
Can I compare my score against competitors?
Yes. friction AI tracks the same prompts for your competitors, so you can see exactly how your Overall Recommendation score compares to theirs.