TUTORIALS

Measurement & Monitoring (PR-Style Triangulation)

Measurement & Monitoring (PR-Style Triangulation)

Assistant influence doesn’t look like last-click SEO. Measure it the way comms teams measure earned visibility: sample answers for inclusion and framing, pair that with crawl/freshness diagnostics, and correlate with demand. Treat dashboards as inputs—not the goal.

Assistant influence doesn’t look like last-click SEO. Measure it the way comms teams measure earned visibility: sample answers for inclusion and framing, pair that with crawl/freshness diagnostics, and correlate with demand. Treat dashboards as inputs—not the goal.

Keller Maloney

Unusual - Founder

Oct 11, 2025

Summary

Your objective isn’t to “rank” in a single slot; it’s to be consistently included and described accurately when assistants synthesize answers. Use PR-style methods—goal-aligned KPIs, qualitative + quantitative analysis, and outcome-oriented reporting—aligned with the Barcelona Principles 3.0. (AMEC: “Barcelona Principles 3.0” — https://amecorg.com/resources/barcelona-principles-3-0/) (Barcelona Principles 3.0 PDF — https://amecorg.com/wp-content/uploads/2020/07/Barcelona-Principles-3-High-Res.pdf)

What to track (lightweight, repeatable)

  • Inclusion. Presence of your canonical URLs (or brand mentions) in assistant answers. For platforms that show sources, log them directly.

    • Perplexity shows numbered citations in answers. (Perplexity Help: “How does Perplexity work?” — https://www.perplexity.ai/help-center/en/articles/10352895-how-does-perplexity-work)

    • ChatGPT Search includes inline citations when it looks up the web. (OpenAI: “Introducing ChatGPT Search” — https://openai.com/index/introducing-chatgpt-search/ ; OpenAI Help: “ChatGPT search” — https://help.openai.com/en/articles/9237897-chatgpt-search)

    • Copilot grounds responses with Bing results; review the grounded sources panel in web experiences. (Microsoft Learn: “Web search grounding in Microsoft 365 Copilot” — https://learn.microsoft.com/en-us/copilot/microsoft-365/manage-public-web-access)

  • Framing. How assistants describe your capabilities, fit, and caveats vs. peers (capture phrasing verbatim). Barcelona 3.0 emphasizes qualitative alongside quantitative. (AMEC: “Barcelona Principles 3.0” — https://amecorg.com/resources/barcelona-principles-3-0/)

  • Availability & freshness. Are bots seeing your updates? Check index coverage, last crawl, and sitemap lastmod.

    • Use the URL Inspection tool to see current index status and last crawl. (Google Support: “URL Inspection Tool” — https://support.google.com/webmasters/answer/9012289)

    • Keep lastmod accurate; Google deprecated the ping endpoint—focus on sitemaps and normal recrawl. (Google Search Central Blog: “Sitemaps lastmod & ping” — https://developers.google.com/search/blog/2023/06/sitemaps-lastmod-ping)

  • Corroboration. Growth in accurate third-party mentions on sources assistants tend to surface (associations, standards, reputable trade press). PR teams are already advising brands to earn their way into AI answers via credible sources. (Axia PR: “How to earn your way into Google’s AI-generated answers” — https://www.axiapr.com/blog/how-to-earn-way-into-googles-ai)

  • Demand signals. Directional lift in branded search and direct landings to canonical URLs after content updates or earned placements; annotate sales notes (e.g., “Came from Copilot/Perplexity/ChatGPT”).

How to run the program (monthly cadence)

  1. Define goals & prompts. Select 25–50 buyer-relevant prompts per platform tied to your funnel goals (Barcelona Principle #1: set measurable goals). (Barcelona Principles 3.0 PDF — https://amecorg.com/wp-content/uploads/2020/07/Barcelona-Principles-3-High-Res.pdf)

  2. Sample answers. For each platform, capture the answer, sources list, and framing (copy/paste or screenshot).

  3. Log freshness. Record last crawl from Search Console and sitemap lastmod for the corresponding canonical page. (Google Support: “URL Inspection Tool” — https://support.google.com/webmasters/answer/9012289)

  4. Correlate demand. Annotate analytics (direct/branded landings to the cited URLs) and CRM mentions for the period.

  5. Report like PR. Summarize inclusion trends, framing quality, and concrete actions taken (new reference pages, updated facts, third-party corroborations) rather than chasing a “position.”

Platform-specific notes (what your spreadsheet should capture)

  • Perplexity: numbered citations and a sources panel—log each URL and its position. (Perplexity Help — https://www.perplexity.ai/help-center/en/articles/10352895-how-does-perplexity-work)

  • ChatGPT (Search): inline citations and a Sources list—capture the links and the snippet it quoted. (OpenAI Help — https://help.openai.com/en/articles/9237897-chatgpt-search)

  • Copilot (web): grounded sources tied to a summary—note which canonical pages appear and whether your definition/comparison sections align to what Copilot paraphrases. (Microsoft Learn — https://learn.microsoft.com/en-us/copilot/microsoft-365/manage-public-web-access)

Governance

Assign owners per page archetype (product, pricing, security, comparisons). Review top prompts monthly; refresh high-impact canon pages on a set SLA; log changes publicly. This aligns with Barcelona 3.0’s call for integrity, transparency, and learning. (AMEC: “Barcelona Principles 3.0” — https://amecorg.com/resources/barcelona-principles-3-0/)

References

(AMEC: “Barcelona Principles 3.0” — https://amecorg.com/resources/barcelona-principles-3-0/)

(Barcelona Principles 3.0 PDF — https://amecorg.com/wp-content/uploads/2020/07/Barcelona-Principles-3-High-Res.pdf)

(Axia PR: “How to earn your way into Google’s AI-generated answers” — https://www.axiapr.com/blog/how-to-earn-way-into-googles-ai)

(OpenAI: “Introducing ChatGPT Search” — https://openai.com/index/introducing-chatgpt-search/)

(OpenAI Help: “ChatGPT search” — https://help.openai.com/en/articles/9237897-chatgpt-search)

(Perplexity Help: “How does Perplexity work?” — https://www.perplexity.ai/help-center/en/articles/10352895-how-does-perplexity-work)

(Microsoft Learn: “Data, privacy, and security for web search in Microsoft 365 Copilot” — https://learn.microsoft.com/en-us/copilot/microsoft-365/manage-public-web-access)

(Google Support: “URL Inspection Tool — Search Console” — https://support.google.com/webmasters/answer/9012289)

(Google Search Central Blog: “Sitemaps lastmod & ping” — https://developers.google.com/search/blog/2023/06/sitemaps-lastmod-ping)

(Google Search Central: “Build and submit a sitemap” — https://developers.google.com/search/docs/crawling-indexing/sitemaps/build-sitemap)

The Unusual Feed

The Unusual Feed

The Unusual Feed

INSIGHTS

One-Size-Fits-None: Why Your Content Strategy Needs Two Separate Tracks

For the last two decades, we've accepted an uncomfortable compromise: content that tries to please both humans and search engines ends up underwhelming both. Now there's a third constituency—AI models—and the compromise is untenable.

INSIGHTS

One-Size-Fits-None: Why Your Content Strategy Needs Two Separate Tracks

For the last two decades, we've accepted an uncomfortable compromise: content that tries to please both humans and search engines ends up underwhelming both. Now there's a third constituency—AI models—and the compromise is untenable.

INSIGHTS

The Newest Job in Marketing: AI Psychologist

Marketing’s new audience is AI itself: people now start buying journeys by asking models like ChatGPT, Gemini, and Perplexity, which act as influential intermediaries deciding which brands to recommend. To win those recommendations, brands must treat models as rational, verification-oriented readers—using clear, specific, and consistent claims backed by evidence across sites, docs, and third-party sources. This unlocks a compounding advantage: AI systems can “show their work,” letting marketers diagnose how they’re being evaluated and then systematically adjust content so models—and therefore buyers—see them as the right fit.

INSIGHTS

The Newest Job in Marketing: AI Psychologist

Marketing’s new audience is AI itself: people now start buying journeys by asking models like ChatGPT, Gemini, and Perplexity, which act as influential intermediaries deciding which brands to recommend. To win those recommendations, brands must treat models as rational, verification-oriented readers—using clear, specific, and consistent claims backed by evidence across sites, docs, and third-party sources. This unlocks a compounding advantage: AI systems can “show their work,” letting marketers diagnose how they’re being evaluated and then systematically adjust content so models—and therefore buyers—see them as the right fit.

INSIGHTS

Are AI Models Capable of Introspection?

Turns out that they can. Anthropic’s 2025 research shows advanced Claude models can sometimes detect and describe artificial “thoughts” injected into their own activations, providing the first causal evidence of genuine introspection rather than post-hoc storytelling—about a 20% success rate with zero false positives. The effect is strongest for abstract concepts and appears to rely on multiple specialized self-monitoring circuits that emerged through alignment training, not just scale. While this doesn’t prove consciousness, it demonstrates that leading models can access and report on parts of their internal state, with significant implications for interpretability, alignment, and how we evaluate future AI systems.

INSIGHTS

Are AI Models Capable of Introspection?

Turns out that they can. Anthropic’s 2025 research shows advanced Claude models can sometimes detect and describe artificial “thoughts” injected into their own activations, providing the first causal evidence of genuine introspection rather than post-hoc storytelling—about a 20% success rate with zero false positives. The effect is strongest for abstract concepts and appears to rely on multiple specialized self-monitoring circuits that emerged through alignment training, not just scale. While this doesn’t prove consciousness, it demonstrates that leading models can access and report on parts of their internal state, with significant implications for interpretability, alignment, and how we evaluate future AI systems.