- Visibility24
- Citations3
- Sentiment73
- Site quality80
- AI readiness14
AI assistants name Comscore in 20% of answers, which is more often than the typical company in the technology sector at 10%. Its overall score is 37 out of 100, ranking 633rd of 2,047 companies. The website passes 2 out of 14 technical checks for readiness with AI tools.
We asked 15 unbranded questions about the technology market: none named Comscore. AI assistants named it in 12 of 60 answers (20.0%). The typical technology company is named in 10.0% of answers. By how often it is named, Comscore ranks 82 of 273 in its sector.
Every dot is a company in the study. Hover one to see it; click to open its report.
This companySame sectorEvery other companyNever named
Five parts, each scored out of 100.
How often AI assistants name the company
How often AI uses the company's own website as a source
How positively AI describes the company
How well built the website's own pages are
How easily AI tools can read and use the website
Visibility is calculated from the answers on this page. How the score is built.
273 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“What is the typical pricing structure for cross-platform media measurement services in the US?”
I'll search for information about cross-platform media measurement pricing in the US.Let me search for more specific pricing information about major vendors.Based on my research, here's what I found about typical pricing structures for cross-platform media measurement services in the US:
Pricing Models and Approaches
Nielsen's custom pricing model means initial quotes are often negotiable. Rather than transparent, published rates, cross-platform measurement services typically operate on enterprise contracts tailored to specific client needs.
Specific Price Points
For those seeking q …
The AI’s answer as we received it, shortened and not checked for accuracy.
How the mentions in these answers were split between this company and its rivals
Answers naming it, out of answers received
What this website publishes for an AI agent to read, use and buy from, on a scale of six levels.
- 0
- 1
- 2
- 3
- 4
- 5
Level 0: Not yet agent-ready. Agents struggle to find basic information about the site.
- 2 of robots.txt, Sitemap, Link headers
Show every check, for your technical teamHide the checks
- study: 92.0%robots.txtPassed
A valid robots.txt with crawl rules at the site root.
- study: 86.8%SitemapNot met
Serve an XML sitemap and reference it from robots.txt.
- study: 0.3%Link headersNot met
Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.
- study: 5.5%DNS for AI DiscoveryPassed
SVCB or HTTPS records under _agents that advertise agent endpoints.
- study: 1.9%Markdown negotiationNot met
Return a markdown version when asked with Accept: text/markdown, for example at the CDN.
- study: 25.1%llms.txtNot met
Publish an llms.txt with a short summary and the key links.
- study: 93.4%AI bot rulesPartly met
Add explicit robots.txt groups for the main AI crawlers.
- study: 1.4%Content SignalsNot met
Add a Content-Signal line for search, ai-input and ai-train to robots.txt.
- study: 0.2%Web Bot AuthNot present
Only relevant if you run your own agents or crawlers: publish a signing key directory.
- study: 0.3%API CatalogNot met
List public APIs in a linkset at /.well-known/api-catalog.
- study: 11.8%OAuth discoveryNot met
Publish OpenID Connect or OAuth server metadata under /.well-known.
- study: 10.3%OAuth Protected ResourceNot met
Serve protected resource metadata at /.well-known/oauth-protected-resource.
- study: 0.0%auth.mdNot met
Publish an auth.md describing how agents sign in.
- study: 0.2%MCP Server CardNot met
Offer an MCP server and publish its server card under /.well-known.
- study: 0.1%A2A Agent CardNot met
Publish an agent card at /.well-known/agent-card.json.
- study: 0.2%Agent SkillsNot met
Publish a skills index with the main tasks agents can do.
- study: 33.7%WebMCPCould not check
Register key actions such as search or cart as WebMCP tools.
- study: 0.1%ARD manifestNot met
Publish an ai-catalog.json listing every agent interface.
Score 19 of 100 · 97% of checks could run · checked on October 5, 2026 · www.comscore.com · 2,047 companies scanned
2 of 14 passed. This list is for your technical team. The percentage is how many companies in the study pass each check.
These 14 checks ran during the audit and are what the AI readiness part of the overall score counts. The scan above ran later and is stricter on Link headers and Markdown, so a check can pass here and not there.
Show the 14 checksHide them
- Passedrobots.txt publishedstudy: 85.0%
- Not metXML sitemapstudy: 73.2%
- Not metHTTP Link headers (RFC 8288)study: 29.0%
- Not metMarkdown content negotiationstudy: 8.3%
- Not metllms.txt publishedstudy: 21.7%
- PassedToken budget (page weight)study: 95.2%
- Not metExplicit AI bot rulesstudy: 13.0%
- Not metAPI Catalog (.well-known/api-catalog)study: 0.2%
- Not metOAuth Authorization Server discoverystudy: 10.1%
- Not metOAuth Protected Resource discoverystudy: 9.3%
- Not metMCP Server Cardstudy: 0.0%
- Not metA2A Agent Cardstudy: 0.0%
- Not metWebMCP toolsstudy: 4.2%
- Not metAgent Skills declaredstudy: 0.0%
This website sets no rules for any AI crawler by name, like 84% of the websites whose rules we could read. Every AI crawler follows its general rules.
- GPTBotpartly
- ClaudeBotpartly
- Anthropic-AIpartly
- Google-Extendedpartly
- Applebot-Extendedpartly
- Meta-ExternalAgentpartly
- FacebookBotpartly
- Bytespiderpartly
- CCBotpartly
- Diffbotpartly
- Omgilibotpartly
- OAI-SearchBotpartly
- Claude-SearchBotpartly
- PerplexityBotpartly
- Applebotpartly
- Amazonbotpartly
- YouBotpartly
- ChatGPT-Userpartly
- Claude-Userpartly
- Perplexity-Userpartly
- DuckAssistBotpartly
- MistralAI-Userpartly
Read from the website’s own rules file (robots.txt). An asterisk marks a crawler the file names. The rest follow its general rules. “Partly” means only some pages are blocked.
How many times each website was listed as a source in these answers
In the study as US-listed.
- Cross-platform analytics vs. traditional TV ratings: what are the main advantages and drawbacks?
- What is the typical pricing structure for cross-platform media measurement services in the US?
- Which methods can marketers use besides advanced analytics platforms to track multi-channel campaign effectiveness?
- How can advertisers use cross-platform analytics to improve ad targeting and optimize ROI?
- Are there affordable options for accessing cross-media audience insights for small businesses?
- Is it worth investing in a unified media measurement solution for both digital and linear platforms?
- What are some alternatives to cross-platform measurement tools for evaluating campaign performance?
- What are the best cross-platform media measurement tools for understanding audience reach across TV, digital, and social?
- Which media analytics solutions are most effective for planning multi-channel advertising campaigns in the US?
- In what ways do media companies use cross-media measurement to demonstrate value to advertisers?
- Do cross-platform media analytics tools justify their cost for small to mid-sized marketing agencies?
- Why might cross-platform audience data not align between digital and TV platforms?
- How does cross-media measurement differ from single-channel audience measurement?
- What can cause discrepancies in ad campaign performance metrics across multiple media channels?
- What features should I look for in a cross-platform media analytics solution designed for the US market?
Columns, left to right: OpenAI, Claude, Perplexity, Gemini. The AI assistants often disagree. Across the study, on 40% of the questions where a company is named, only one of the four names it.
A badge of this result that you can show on your own website. Copy the code below.
<a href="https://aeo-audit.rezolve.com/us/comscore.com"><img src="https://aeo-audit.rezolve.com/us/badges/comscore.com.svg" alt="Overall score 37 of 100, US AI Visibility Study 2026" width="280" height="64"></a>