{
  "source": {
    "name": "Machine Relations Index",
    "creator": "Jaxon Parrott",
    "publisher": "Machine Relations Research",
    "attribution": "Machine Relations Index (MRI) by Jaxon Parrott, Machine Relations Research — machinerelations.ai",
    "canonicalUrl": "https://machinerelations.ai/index/categories/martech-advertising",
    "citation": "Machine Relations Index (MRI) by Jaxon Parrott, Machine Relations Research — machinerelations.ai. Martech & Advertising, observed Machine Relations Index category, 2026-05-10 to 2026-11-02. Release mri_score_v2.1+2026-11-02+f9aaeca0db15. https://machinerelations.ai/index/categories/martech-advertising. CC BY 4.0.",
    "license": {
      "id": "CC-BY-4.0",
      "url": "https://creativecommons.org/licenses/by/4.0/"
    },
    "guidance": [
      "Use live tool results, not remembered figures. Discover category keys and question shapes first; use source_role editorial_media for publications. Preserve returned rank semantics when filtering.",
      "Report the category, question shape, citation numerator and denominator, and source.release window. When overall.byEngine is present, each entry is that engine's cited runs over that engine's observed runs for the whole window: use it for whether an engine cites a domain, and do not invent a rate the field does not return. A byEngine rate is not a rate inside one category. An engine leaderboard is that whole-window rate; an engine-and-category leaderboard is the same rate limited to sources with a published segment in the category. Do not infer causality, guaranteed future citations or placement value, and do not read Google AI Mode or Google AI Overviews as a trend across 2026-09-25. Collecting segments and engines below the evidence floor withhold rates and ranks; an absent domain was not observed in this release, not proven never cited.",
      "Read the returned questionContext, questionSet and naming fields before interpreting a rank. Missing or null context means unavailable for that release, never zero. When present, read questionContext.readAs and limitations, including namesReviewedCoverage and sourceRoleBasis; a rank describes the sampled questions, not an entire market.",
      "naming splits each source's runs in a segment by whether the question contains a reviewed name for it. A source with namesReviewed false is unresolved on every run. notNamed means no reviewed name matched, not proof the question never refers to the source, and not unprompted: the question may name other brands, and a comparison of A and B can cite C. Compare sources only within one segment, whose runs they share; do not re-rank sources by a split. Different sources' named/notNamed buckets need not contain the same questions. A sourceRole whose sourceRoleBasis is name_pattern is a guess from the domain name, not verified.",
      "sourceRoleBasis says what a sourceRole rests on. reviewed is a reviewer's decision and domain_suffix a rule such as .gov. site_evidence is not a reviewer's decision: it is an automated classification of identity evidence cached for the domain (text read from its homepage, such as the title, description and schema.org types, or titles answer engines returned for its pages), accepted by the current role rules at confidence 0.72 or more. That evidence is not re-read for each release, and the API does not return its read date. A release withholds a site_evidence role, keeping the domain and its citations as uncategorized_source with sourceRoleBasis none, when none of the domain's hostnames (the domain, its www host and any host on it the homepage was read from) has a DNS address at build time. A domain DNS gives no clear answer for keeps its role, and a release whose DNS check finds no address for too many domains to trust withholds none. An address shows that the name exists, not that the site still publishes. namesReviewed concerns the names used to match questions, never whether a site is live or was checked.",
      "Keep every comparison on the same source.release.releaseId and artifactSha256. Re-read if the release changes between calls. REST release is a current-release assertion (409 on mismatch), not historical retrieval; MCP callers compare returned identities. Question texts and cited-page records are not public; aggregate context is not a raw-data replay.",
      "Attribute as source.citation verbatim. It begins with \"Machine Relations Index (MRI) by Jaxon Parrott, Machine Relations Research — machinerelations.ai\" and includes the finding, the release id and the canonical URL (CC BY 4.0). Follow the interpretation guidance returned with that release, including any field-specific limitations."
    ],
    "release": {
      "releaseId": "mri_score_v2.1+2026-11-02+f9aaeca0db15",
      "methodologyVersion": "mri_score_v2.1",
      "generated": "2026-11-02",
      "windowStart": "2026-05-10",
      "dataThrough": "2026-11-02",
      "artifactSha256": "4244052432d3dc3e8b8b5965caad8a11038a30993bdad0030b287a6793af49bb"
    }
  },
  "category": "martech-advertising",
  "label": "Martech & Advertising",
  "shapes": [
    {
      "questionShape": "best_x",
      "label": "Best tools",
      "status": "published",
      "collectingSince": null,
      "runsObserved": 135,
      "runDates": 8,
      "citedDomainObservations": 242,
      "url": "https://machinerelations.ai/index/categories/martech-advertising/best_x",
      "apiUrl": "https://machinerelations.ai/api/mri/v2/categories/martech-advertising/best_x"
    },
    {
      "questionShape": "how_choose",
      "label": "How buyers choose",
      "status": "published",
      "collectingSince": null,
      "runsObserved": 129,
      "runDates": 8,
      "citedDomainObservations": 224,
      "url": "https://machinerelations.ai/index/categories/martech-advertising/how_choose",
      "apiUrl": "https://machinerelations.ai/api/mri/v2/categories/martech-advertising/how_choose"
    },
    {
      "questionShape": "is_x_worth",
      "label": "Is it worth it",
      "status": "published",
      "collectingSince": null,
      "runsObserved": 131,
      "runDates": 8,
      "citedDomainObservations": 208,
      "url": "https://machinerelations.ai/index/categories/martech-advertising/is_x_worth",
      "apiUrl": "https://machinerelations.ai/api/mri/v2/categories/martech-advertising/is_x_worth"
    },
    {
      "questionShape": "news_topic",
      "label": "News-driven citations",
      "status": "published",
      "collectingSince": null,
      "runsObserved": 578,
      "runDates": 53,
      "citedDomainObservations": 1243,
      "url": "https://machinerelations.ai/index/categories/martech-advertising/news_topic",
      "apiUrl": "https://machinerelations.ai/api/mri/v2/categories/martech-advertising/news_topic"
    },
    {
      "questionShape": "problem_first",
      "label": "Problem-first research",
      "status": "published",
      "collectingSince": null,
      "runsObserved": 131,
      "runDates": 8,
      "citedDomainObservations": 256,
      "url": "https://machinerelations.ai/index/categories/martech-advertising/problem_first",
      "apiUrl": "https://machinerelations.ai/api/mri/v2/categories/martech-advertising/problem_first"
    },
    {
      "questionShape": "top_list",
      "label": "Top lists",
      "status": "published",
      "collectingSince": null,
      "runsObserved": 100,
      "runDates": 7,
      "citedDomainObservations": 204,
      "url": "https://machinerelations.ai/index/categories/martech-advertising/top_list",
      "apiUrl": "https://machinerelations.ai/api/mri/v2/categories/martech-advertising/top_list"
    },
    {
      "questionShape": "x_vs_y",
      "label": "Comparisons",
      "status": "published",
      "collectingSince": null,
      "runsObserved": 100,
      "runDates": 7,
      "citedDomainObservations": 162,
      "url": "https://machinerelations.ai/index/categories/martech-advertising/x_vs_y",
      "apiUrl": "https://machinerelations.ai/api/mri/v2/categories/martech-advertising/x_vs_y"
    }
  ]
}