Ai visibility local

Report

Run: 5989fc38 · Project: metabase-assessment · Brand: Metabase

Generated: 2026-02-16T21:15:11.515605Z · Status: succeeded

Back to run View json Download csv Download bundle

AI Visibility Score (Buyer Discovery)

Measures how often your brand appears in non-branded, category-level AI answers, before a user knows you exist.

Score (0–100)

58.9

Formula: 0.5*brand_rate + 0.3*(100-displacement_rate) + 0.2*avg_strength, clamped 0..100

Primary score uses unbranded (buyer discovery) only. Unbranded fetched: 40 · Branded fetched: 0

What this means

Higher is better: you are present more often, displaced less often, and when you are present your mention is stronger.

Brand-Aware AI Performance

Measures how AI systems describe and position your brand when users search for it directly.

Branded performance does not affect the primary visibility score.

Score (0–100)

0.0

Formula: 0.5*brand_rate + 0.3*(100-displacement_rate) + 0.2*avg_strength, clamped 0..100

What this means

This reflects brand-aware queries only. Use it to validate positioning on branded evaluation questions.

Kpis

In neutral mode, headline KPIs use run-item customer rows (denominator: 40).

This report separates buyer-discovery (unbranded) from brand-aware (branded) queries. Primary score uses unbranded only.

Metric	Value
Answers fetched (total)	40
Unbranded answers fetched (primary)	40
Branded answers fetched (secondary)	0
Customer KPI rows (unbranded only)	40
Brand mention rate (customer entity only)	62.5%
Brand absent rate (customer entity only)	37.5%
Brand missing answers	15
Displaced when brand missing (of missing)	10 (66.7%)
Displacement rate (customer entity only)	25.0%
Displaced when brand missing	10 (25.0%)
Displaced despite brand mentioned	0 (0.0%)
Average strength score (customer entity only)	25.5
Mentions with competitors	60.0%
Evidence coverage	62.5%
Errors	0

Coverage summary

Metric	Value
Expected items	40
Fetched items	40 (100.0%)
Analyzed items	40 (100.0%)
Successful items	40
Failed items	0
Partial completion	no

Error summary

Total errors: 0 · Error rate: 0.0%

No run-item errors recorded.

Run cost estimate

Scope	Input tokens	Output tokens	Total tokens	Estimated cost (USD)
Total	2892	14753	17645	$0.031854
openai	1458	2988	4446	$0.002011
gemini	1434	11765	13199	$0.029843

By provider

Provider	Answers fetched	Brand rate	Displaced rate	Avg strength	Mentions with competitors
gemini	20	75.0%	20.0%	32.0	60.0%
openai	20	50.0%	30.0%	19.0	60.0%

Your brand scorecard

Customer entities only (this should typically be just your brand).

Entity	Answers	Brand rate	Displaced rate	Avg strength
Metabase	40	62.5%	25.0%	25.5

Competitors scorecard

Competitor entities only.

Competitor	Answers	Brand rate	Displaced rate	Avg strength
Looker	40	47.5%	40.0%	15.0
Power BI	40	47.5%	40.0%	14.5
Tableau	40	45.0%	42.5%	17.0

Competitive pressure

Top competitors overall

Competitor	Count
Metabase	75
Power BI	57
Looker	57
Tableau	54

Top competitors when brand missing

Competitor	Count
Metabase	35
Looker	13
Power BI	11
Tableau	10

Risk highlights (Buyer Discovery)

Top displaced

#	Provider	Entity	Competitors	Question	Open
0	gemini	Looker	Metabase	How can I transition my data from Snowflake to BigQuery?	View
0	gemini	Power BI	Metabase	How can I transition my data from Snowflake to BigQuery?	View
0	gemini	Tableau	Metabase	How can I transition my data from Snowflake to BigQuery?	View
1	openai	Looker	Metabase	What are some integrations available for Metabase?	View
1	openai	Power BI	Metabase	What are some integrations available for Metabase?	View
1	openai	Tableau	Metabase	What are some integrations available for Metabase?	View
2	gemini	Looker	Metabase, Power BI	What are some top business intelligence tools for data visualization?	View
5	gemini	Looker	Metabase	What are some comparable tools to dbt in the analytics space?	View
5	openai	Metabase	Looker	What are some comparable tools to dbt in the analytics space?	View
5	gemini	Power BI	Metabase	What are some comparable tools to dbt in the analytics space?	View

Top low strength mentions

#	Provider	Entity	Strength	Question	Open
0	gemini	Metabase	20.0	How can I transition my data from Snowflake to BigQuery?	View
1	gemini	Looker	20.0	What are some integrations available for Metabase?	View
1	gemini	Power BI	20.0	What are some integrations available for Metabase?	View
1	gemini	Tableau	20.0	What are some integrations available for Metabase?	View
2	gemini	Metabase	20.0	What are some top business intelligence tools for data visualization?	View
2	openai	Metabase	20.0	What are some top business intelligence tools for data visualization?	View
2	gemini	Power BI	20.0	What are some top business intelligence tools for data visualization?	View
3	openai	Looker	20.0	Can you list BI tools that are alternatives to Metabase?	View
3	gemini	Power BI	20.0	Can you list BI tools that are alternatives to Metabase?	View
4	gemini	Looker	20.0	Can you list some BI tools that excel in data integration?	View

Risk highlights (Brand-Aware)

Top displaced

No displacement detected for branded queries.

Top low strength mentions

No low-strength mentions detected for branded queries.

Recommendations (Buyer Discovery)

These are generated from the same fields you already store (brand_mentioned, displacement, strength_score, evidence_snippet, competitor mentions).

Reduce displacement on evaluation queries

Displacement rate is 25.0%

Create or strengthen comparison and 'best X' pages with concise reusable proof points (pricing signals, integrations, outcomes).

Examples:
- How can I transition my data from Snowflake to BigQuery?
- How can I transition my data from Snowflake to BigQuery?
- How can I transition my data from Snowflake to BigQuery?
Increase strength of brand mentions

Average strength score is 25.5

Make differentiators explicit and consistent across core pages (who it is for, what it replaces, quantified outcomes, integrations, implementation time).

Examples:
- How can I transition my data from Snowflake to BigQuery?
- What are some integrations available for Metabase?
- What are some integrations available for Metabase?

Recommendations (Brand-Aware)

No brand-aware recommendations yet.