{"id":33,"date":"2026-10-03T03:56:18","date_gmt":"2026-10-03T01:56:18","guid":{"rendered":"https:\/\/markaigen.com\/research\/benchmarks\/"},"modified":"2026-10-08T05:27:31","modified_gmt":"2026-10-08T03:27:31","slug":"benchmarks","status":"publish","type":"page","link":"https:\/\/markaigen.com\/nl\/research\/benchmarks\/","title":{"rendered":"AI Marketing Benchmarks"},"content":{"rendered":"<div id=\"mkb-app\" class=\"mkb-app\">\n<section class=\"hero\">\n  <div class=\"hero-glow\"><\/div>\n  <svg class=\"hero-ribbon\" viewbox=\"0 0 760 440\" aria-hidden=\"true\">\n    <defs><lineargradient id=\"mkb-creative\" x1=\"0\" x2=\"1\"><stop offset=\"0\" stop-color=\"#075EF5\"><\/stop><stop offset=\".34\" stop-color=\"#7643F8\"><\/stop><stop offset=\".68\" stop-color=\"#EA4CCC\"><\/stop><stop offset=\"1\" stop-color=\"#FA755F\"><\/stop><\/lineargradient><lineargradient id=\"mkb-ai\" x1=\"0\" x2=\"1\"><stop offset=\"0\" stop-color=\"#075EF5\"><\/stop><stop offset=\".52\" stop-color=\"#09C4FB\"><\/stop><stop offset=\"1\" stop-color=\"#00E6A5\"><\/stop><\/lineargradient><\/defs>\n    <path d=\"M-40 295 C135 140 275 360 445 220 S700 95 835 180\" stroke=\"url(#mkb-creative)\" stroke-width=\"54\"><\/path>\n    <path d=\"M-60 345 C110 195 280 410 450 270 S700 150 850 225\" stroke=\"url(#mkb-ai)\" stroke-width=\"29\"><\/path>\n  <\/svg>\n  <div class=\"wrap hero-inner\">\n    <div>\n      <div class=\"crumb\"><a href=\"\/nl\/\">Home<\/a><span>\u203a<\/span><a href=\"\/nl\/research\/\">Research<\/a><span>\u203a<\/span><span>Benchmarks<\/span><\/div>\n      <div class=\"eyebrow\" style=\"color:var(--cyan)\"><span class=\"spark\"><\/span> Markaigen Benchmark Intelligence<\/div>\n      <h1>See where your <span class=\"accent\">AI marketing<\/span> actually stands.<\/h1>\n      <p class=\"hero-copy\">Explore AI adoption, maturity, governance, agentic readiness, AI-search readiness and measurement readiness across modelled Netherlands and Belgium cohorts. Assess your organisation and find practical next steps.<\/p>\n      <form class=\"hero-search\" id=\"mkb-benchmarkSearch\" role=\"search\" action=\"\"><label class=\"sr-only\" for=\"mkb-searchInput\">Find a benchmark<\/label><input id=\"mkb-searchInput\" type=\"search\" autocomplete=\"off\" placeholder=\"Find a benchmark: governance, GEO, ROI, agents\u2026\"><button type=\"submit\">Find benchmark<\/button><input type=\"hidden\" name=\"trp-form-language\" value=\"nl\"\/><\/form>\n      <p id=\"mkb-searchStatus\" class=\"sr-only\" role=\"status\"><\/p><div class=\"hero-chips\"><button class=\"hero-chip\" type=\"button\" data-search=\"AI search readiness\">AI search readiness<\/button><button class=\"hero-chip\" type=\"button\" data-search=\"Governance\">Governance<\/button><button class=\"hero-chip\" type=\"button\" data-search=\"Agentic AI\">Agentic AI<\/button><button class=\"hero-chip\" type=\"button\" data-search=\"ROI\">ROI<\/button><\/div>\n      <div class=\"hero-actions\"><a class=\"btn primary\" href=\"#mkb-assessment\">Compare your organization \u2192<\/a><a class=\"btn dark\" href=\"#mkb-explorer\">Explore benchmark data<\/a><\/div>\n    <\/div>\n    <aside class=\"hero-demo\" aria-label=\"Modelled AI Marketing Benchmark Index\">\n      <div class=\"demo-label\"><span>AI Marketing Benchmark Index<\/span><span class=\"badge\"><span class=\"badge-dot\" style=\"background:var(--cyan)\"><\/span>Evidence + model v0.6<\/span><\/div>\n      <div class=\"score-ring\"><svg viewbox=\"0 0 200 200\" aria-hidden=\"true\"><circle class=\"track\" cx=\"100\" cy=\"100\" r=\"90\"><\/circle><circle class=\"value\" cx=\"100\" cy=\"100\" r=\"90\"><\/circle><\/svg><div class=\"score-number\"><strong>50<\/strong><span>\/ 100 modelled median<\/span><\/div><\/div>\n      <div class=\"mini-dims\"><div class=\"mini-dim\"><span>Evidence cut-off<\/span><strong>8 Oct 2026<\/strong><\/div><div class=\"mini-dim\"><span>Modelled middle 50%<\/span><strong>39\u201361<\/strong><\/div><div class=\"mini-dim\"><span>Synthetic records<\/span><strong>100,000<\/strong><\/div><div class=\"mini-dim\"><span>Status<\/span><strong>Not a survey<\/strong><\/div><\/div>\n      <p class=\"demo-note\"><b>Modelled estimate \u2014 not directly observed.<\/b> This index translates official and independent evidence into a reproducible synthetic distribution. The 100,000 records are simulated organisations, not respondents or real companies.<\/p>\n    <\/aside>\n  <\/div>\n<\/section>\n\n<section class=\"section-sm context\" aria-labelledby=\"mkb-context-title\">\n  <div class=\"wrap\">\n    <div class=\"context-head\"><div><div class=\"eyebrow\">Evidence benchmark v0.5<\/div><h2 id=\"mkb-context-title\">Start with what is directly observed.<\/h2><\/div><p>Official statistics and independent marketing research remain separate from Markaigen's modelled indices. Population definitions are shown because these figures are not interchangeable.<\/p><\/div>\n    <div class=\"context-grid\">\n      <article class=\"context-card reveal\"><strong>33.2%<\/strong><h3>Netherlands enterprise AI adoption<\/h3><p>Enterprises with 10+ persons employed using at least one AI technology in 2025. Eurostat flags the Netherlands series as a break in 2025.<\/p><a href=\"https:\/\/ec.europa.eu\/eurostat\/web\/products-statistical-reports\/w\/ks-01-26-009\" target=\"_blank\" rel=\"noopener\">Eurostat \u00b7 official observed \u2197<\/a><\/article>\n      <article class=\"context-card reveal\"><strong>34.5%<\/strong><h3>Belgium enterprise AI adoption<\/h3><p>Enterprises using at least one AI technology in 2025; large enterprises reached 76.4% in the harmonised Eurostat size table.<\/p><a href=\"https:\/\/statbel.fgov.be\/en\/news\/artificial-intelligence-gaining-ground-belgian-enterprises\" target=\"_blank\" rel=\"noopener\">Statbel \/ Eurostat \u00b7 official observed \u2197<\/a><\/article>\n      <article class=\"context-card reveal\"><strong>70%<\/strong><h3>Dutch marketers using AI<\/h3><p>DDMO 2026 primary research sample: n=436 Dutch marketing professionals recruited through the GfK panel.<\/p><a href=\"https:\/\/ddma.nl\/kennisbank\/ddmo-2026-ai-evolves-from-a-marketing-tool-into-an-organisational-challenge\/\" target=\"_blank\" rel=\"noopener\">DDMA \u00b7 independent observed \u2197<\/a><\/article>\n      <article class=\"context-card reveal\"><strong>66%<\/strong><h3>AI policy in marketing organisations<\/h3><p>DDMA reports 66% with AI policy and 56% with data governance in the same 2026 primary research sample.<\/p><a href=\"https:\/\/ddma.nl\/kennisbank\/ddmo-2026-ai-evolves-from-a-marketing-tool-into-an-organisational-challenge\/\" target=\"_blank\" rel=\"noopener\">DDMA \u00b7 independent observed \u2197<\/a><\/article>\n      <article class=\"context-card reveal\"><strong>15.3%<\/strong><h3>Enterprise marketing budget allocated to AI<\/h3><p>Average in Gartner's n=401 2026 CMO Spend Survey; the vast majority of respondents represented organisations above US$1bn revenue.<\/p><a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2026-05-11-gartner-2026-cmo-spend-survey-finds-cmos-allocate-15-point-3-percent-of-marketing-budgets-to-ai-but-only-30-percent-are-ready-to-scale-ai-capabilities\" target=\"_blank\" rel=\"noopener\">Gartner \u00b7 enterprise context \u2197<\/a><\/article>\n      <article class=\"context-card reveal\"><strong>30%<\/strong><h3>Mature AI readiness among surveyed CMOs<\/h3><p>Gartner's enterprise-heavy 2026 CMO sample. Markaigen uses this only as a soft large-enterprise model constraint, not a NL\/BE prevalence claim.<\/p><a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2026-05-11-gartner-2026-cmo-spend-survey-finds-cmos-allocate-15-point-3-percent-of-marketing-budgets-to-ai-but-only-30-percent-are-ready-to-scale-ai-capabilities\" target=\"_blank\" rel=\"noopener\">Gartner \u00b7 independent observed \u2197<\/a><\/article>\n    <\/div>\n  <\/div>\n<\/section>\n\n<section class=\"section\" id=\"mkb-explorer\" aria-labelledby=\"mkb-explorer-title\">\n  <div class=\"wrap\">\n    <div class=\"section-head\"><div><div class=\"eyebrow\"><span class=\"spark\"><\/span> Benchmark explorer<\/div><h2 id=\"mkb-explorer-title\">Compare the metrics that matter.<\/h2><\/div><p>Choose a modelled cohort and inspect the median, quartiles, evidence anchors and provenance. These indices are evidence-constrained estimates designed to bridge public evidence to the future proprietary Markaigen Wave 1 dataset.<\/p><\/div>\n    <div class=\"demo-banner\"><span class=\"spark\" style=\"color:#B56A00\"><\/span><div><b>Evidence &amp; Modelled Edition v0.6.<\/b> Observed source facts are labelled as evidence. Explorer distributions are <b>Modelled estimates \u2014 not directly observed<\/b>, generated from 100,000 reproducible synthetic organisations calibrated to documented sources. Synthetic n is never a survey sample size.<\/div><\/div>\n    <div class=\"filters\" aria-label=\"Benchmark cohort filters\">\n      <div class=\"field\"><label for=\"mkb-market\">Market<\/label><select id=\"mkb-market\"><option>Netherlands<\/option><option>Belgium<\/option><option>Netherlands + Belgium<\/option><\/select><\/div>\n      <div class=\"field\"><label for=\"mkb-industry\">Industry<\/label><select id=\"mkb-industry\"><option>All industries<\/option><option>Information &amp; communication<\/option><option>Professional services<\/option><option>Wholesale &amp; retail<\/option><option>Manufacturing<\/option><option>Administrative &amp; support<\/option><option>Real estate<\/option><option>Accommodation &amp; food<\/option><option>Construction<\/option><\/select><\/div>\n      <div class=\"field\"><label for=\"mkb-size\">Company size<\/label><select id=\"mkb-size\"><option>All sizes (10+)<\/option><option>Small 10\u201349<\/option><option>Medium 50\u2013249<\/option><option>Large 250+<\/option><\/select><\/div>\n      <div class=\"field\"><label for=\"mkb-model\">Business model<\/label><select id=\"mkb-model\" disabled><option>Available after Wave 1 survey<\/option><\/select><\/div>\n      <div class=\"field\"><label for=\"mkb-year\">Release<\/label><select id=\"mkb-year\" disabled><option>v0.6 \u00b7 evidence through 8 Oct 2026<\/option><\/select><\/div>\n      <button class=\"reset\" id=\"mkb-resetFilters\" type=\"button\">Reset<\/button>\n    <\/div>\n    <noscript><p>The interactive explorer requires JavaScript. You can still read the evidence below and download the model summary.<\/p><\/noscript><div class=\"bench-grid\" id=\"mkb-benchGrid\"><\/div>\n    <div class=\"explorer-detail\">\n      <section class=\"chart-card\" aria-labelledby=\"mkb-chartTitle\"><div class=\"chart-head\"><h3 id=\"mkb-chartTitle\">AI Adoption distribution<\/h3><span id=\"mkb-cohortLabel\">Netherlands \u00b7 All industries \u00b7 All sizes<\/span><\/div><p class=\"chart-sub\" id=\"mkb-chartSub\">Evidence-constrained score distribution for the selected synthetic cohort.<\/p><div class=\"histogram\" id=\"mkb-histogram\" role=\"img\" aria-label=\"Modelled benchmark distribution chart\"><\/div><div class=\"hist-labels\"><span>Low<\/span><span>Score 0\u2013100<\/span><span>High<\/span><\/div><div class=\"marker-row\" id=\"mkb-markerRow\"><\/div><div class=\"chart-source\"><span id=\"mkb-chartSource\">Source: Markaigen evidence-constrained model v0.6<\/span><span>Confidence tier: <b id=\"mkb-chartTier\">Modelled<\/b><\/span><\/div><\/section>\n      <aside class=\"detail-card\"><div class=\"eyebrow\" style=\"margin-bottom:10px\">Selected benchmark<\/div><h3 id=\"mkb-detailTitle\">AI Adoption Benchmark<\/h3><p id=\"mkb-detailCopy\"><\/p><ul class=\"detail-stats\" id=\"mkb-detailStats\"><\/ul><div class=\"action-box\"><span>From benchmark to action<\/span><strong id=\"mkb-actionTitle\"><\/strong><a id=\"mkb-actionLink\" href=\"#mkb-\">Open the related Markaigen resource \u2192<\/a><\/div><\/aside>\n    <\/div>\n  <\/div>\n<\/section>\n\n<section class=\"section catalogue\" id=\"mkb-catalogue\" aria-labelledby=\"mkb-catalogue-title\">\n  <div class=\"wrap\">\n    <div class=\"section-head\"><div><div class=\"eyebrow\"><span class=\"spark\"><\/span> Benchmark roadmap<\/div><h2 id=\"mkb-catalogue-title\">Explore the benchmark roadmap.<\/h2><\/div><p>Six modelled indices are available in the explorer today. The remaining areas describe planned research, not published findings.<\/p><\/div>\n    <div class=\"phase-tabs\" role=\"group\" aria-label=\"Benchmark roadmap filter\"><button class=\"phase-tab active\" type=\"button\" data-phase=\"all\">All 31<\/button><button class=\"phase-tab\" type=\"button\" data-phase=\"p1\">Phase 1<\/button><button class=\"phase-tab\" type=\"button\" data-phase=\"p2\">Phase 2<\/button><button class=\"phase-tab\" type=\"button\" data-phase=\"p3\">Phase 3<\/button><\/div>\n    <div class=\"catalogue-grid\" id=\"mkb-catalogueGrid\"><\/div>\n  <\/div>\n<\/section>\n\n<section class=\"section assessment\" id=\"mkb-assessment\" aria-labelledby=\"mkb-assessment-title\">\n  <div class=\"wrap\">\n    <div class=\"section-head\"><div><div class=\"eyebrow\"><span class=\"spark\"><\/span> Compare your organization<\/div><h2 id=\"mkb-assessment-title\">Build your AI Marketing Benchmark Index.<\/h2><\/div><p>Eight dimensions turn the benchmark from passive research into a diagnostic. The versioned weighting is transparent and versioned; change it only through a documented methodology release.<\/p><\/div>\n    <div class=\"assessment-shell\" id=\"mkb-assessmentShell\">\n      <section class=\"assess-panel\"><div class=\"assess-step\" id=\"mkb-assessStep\">Question 1 of 8<\/div><div class=\"assess-progress\"><i id=\"mkb-assessProgress\"><\/i><\/div><div id=\"mkb-questionArea\" aria-live=\"polite\"><\/div><\/section>\n      <aside class=\"assess-side\"><div class=\"eyebrow\" style=\"color:var(--cyan)\">Index methodology<\/div><h3>Eight dimensions. One transparent score.<\/h3><p>The initial versioned weighting follows the benchmark model defined for Markaigen. This is a product methodology proposal, not an industry standard.<\/p><ul class=\"weight-list\"><li><span>AI Adoption<\/span><b>15%<\/b><\/li><li><span>Strategy &amp; Maturity<\/span><b>15%<\/b><\/li><li><span>Data Readiness<\/span><b>15%<\/b><\/li><li><span>Governance &amp; Trust<\/span><b>15%<\/b><\/li><li><span>AI Skills<\/span><b>10%<\/b><\/li><li><span>Agents &amp; Automation<\/span><b>10%<\/b><\/li><li><span>AI Search &amp; GEO<\/span><b>10%<\/b><\/li><li><span>Measurement &amp; ROI<\/span><b>10%<\/b><\/li><\/ul><\/aside>\n    <\/div>\n    <section class=\"result\" id=\"mkb-assessmentResult\" aria-live=\"polite\"><div class=\"result-grid\"><div class=\"result-score\"><span>Your self-assessment index<\/span><strong id=\"mkb-resultScore\">0<\/strong><h3 id=\"mkb-resultLevel\">Exploring<\/h3><span id=\"mkb-resultPercentile\">Modelled percentile<\/span><\/div><div class=\"radar-wrap\"><div class=\"chart-head\"><h3>Your dimension profile<\/h3><span>0\u2013100 by dimension<\/span><\/div><svg id=\"mkb-radar\" viewbox=\"0 0 600 420\" role=\"img\" aria-label=\"Your AI marketing benchmark profile\"><\/svg><\/div><\/div><div class=\"result-summary\"><div class=\"result-box\"><h4>Strongest dimensions<\/h4><ul id=\"mkb-strengths\"><\/ul><\/div><div class=\"result-box\"><h4>Priority gaps<\/h4><ul id=\"mkb-gaps\"><\/ul><\/div><\/div><div class=\"recommendations\" id=\"mkb-recommendations\"><\/div><div class=\"result-actions\"><button class=\"btn secondary\" type=\"button\" id=\"mkb-downloadResult\">Download result JSON<\/button><button class=\"btn secondary\" type=\"button\" id=\"mkb-saveResult\">Save to my workspace<\/button><button class=\"btn primary\" type=\"button\" id=\"mkb-printResult\">Print \/ save result<\/button><button class=\"btn secondary\" type=\"button\" id=\"mkb-restartAssessment\">Start again<\/button><\/div><p id=\"mkb-saveStatus\" role=\"status\"><\/p><div id=\"mkb-savePanel\"><\/div><p style=\"font-size:13px;color:var(--n600);margin-top:14px\">The percentile is modelled against the balanced NL\/BE synthetic v0.6 cohort. It is not a survey percentile and will be replaced by proprietary Wave 1 distributions.<\/p><\/section>\n  <\/div>\n<\/section>\n\n<section class=\"section method\" id=\"mkb-methodology\" aria-labelledby=\"mkb-method-title\">\n  <div class=\"wrap\">\n    <div class=\"section-head\"><div><div class=\"eyebrow\"><span class=\"spark\"><\/span> Methodology &amp; data<\/div><h2 id=\"mkb-method-title\">Understand the evidence behind each score.<\/h2><\/div><p>This release combines published external evidence with a reproducible model. Surveys, opt-in tool data, crawler panels and connected performance data are planned research layers; they are not inputs collected by this release.<\/p><\/div>\n    <div class=\"layers\"><article class=\"layer reveal\"><div class=\"num\">A<\/div><h3>Observed evidence v0.5<\/h3><p>Eurostat, CBS, Statbel, DDMA, Gartner and OECD statistics stored with population definitions, dates, limitations and source IDs.<\/p><\/article><article class=\"layer reveal\"><div class=\"num\">B<\/div><h3>Modelled benchmark v0.6<\/h3><p>Evidence-constrained synthetic distributions. Every output is labelled modelled and carries a source trail; 100,000 synthetic records are not respondents.<\/p><\/article><article class=\"layer reveal\"><div class=\"num\">01<\/div><h3>Planned: Markaigen surveys<\/h3><p>Annual and quarterly research for adoption, skills, governance, budgets, organization and perceived value.<\/p><\/article><article class=\"layer reveal\"><div class=\"num\">02<\/div><h3>Planned: opt-in tool data<\/h3><p>Aggregated opt-in scores from relevant Markaigen tools: GEO readiness, technical SEO, ROI, agent risk and more.<\/p><\/article><article class=\"layer reveal\"><div class=\"num\">03<\/div><h3>Planned: crawler panel<\/h3><p>Recurring public-web datasets measuring structured data, AI crawler access, performance, disclosure and visibility.<\/p><\/article><article class=\"layer reveal\"><div class=\"num\">04<\/div><h3>Planned: performance data<\/h3><p>Opt-in aggregated data from GA4, Search Console, CRM and ad platforms for referral, conversion and revenue benchmarks.<\/p><\/article><\/div>\n    <div class=\"method-grid\"><article class=\"method-card\"><h3>Every published benchmark must disclose<\/h3><ul><li>Sample size and respondent \/ domain definition<\/li><li>Collection period and last-updated date<\/li><li>Market, industry and company-size cohort<\/li><li>Data source and collection method<\/li><li>Median, P25\/P75 and distribution \u2014 not average alone<\/li><li>Year-over-year methodology consistency<\/li><li>Confidence tier and known limitations<\/li><li>Benchmark version number<\/li><li>Privacy \/ aggregation threshold<\/li><li>Any sponsor involvement<\/li><\/ul><\/article><article class=\"method-card\"><h3>Research integrity rules<\/h3><div class=\"method-note\"><strong>No fabricated proprietary benchmarks.<\/strong><br>v0.5 contains observed external evidence. v0.6 contains explicitly modelled estimates. Neither is described as a Markaigen survey or proprietary respondent dataset.<\/div><ul><li>Never sell an organic ranking or benchmark outcome.<\/li><li>Keep sponsors separate from methodology and scoring.<\/li><li>Publish corrections and version changes.<\/li><li>Use a minimum aggregation threshold before exposing cohort statistics.<\/li><li>Separate observed data, survey responses, external sources and modelled estimates.<\/li><li>Do not publish individual company results without explicit consent.<\/li><\/ul><\/article><\/div>\n  <\/div>\n<\/section>\n\n<section class=\"section\" aria-labelledby=\"mkb-external-title\"><div class=\"wrap\"><div class=\"section-head\"><div><div class=\"eyebrow\">Sources &amp; provenance<\/div><h2 id=\"mkb-external-title\">Every number has a professional source trail.<\/h2><\/div><p>The release package includes a machine-readable source registry and evidence master. Markaigen preserves population definitions instead of averaging incompatible studies.<\/p><\/div>\n<div class=\"external-grid\">\n<article class=\"external-card\"><div class=\"source\">S01 \u00b7 EUROSTAT \u00b7 OFFICIAL<\/div><h3>Harmonised enterprise AI statistics.<\/h3><p>2025 country, size-class and industry adoption. Dataset codes <b>isoc_eb_ai<\/b> and <b>isoc_eb_ain2<\/b>; report DOI 10.2785\/9221093. Netherlands 2025 carries a break-in-series flag.<\/p><a href=\"https:\/\/ec.europa.eu\/eurostat\/web\/products-statistical-reports\/w\/ks-01-26-009\" target=\"_blank\" rel=\"noopener\">Open Eurostat source \u2197<\/a><\/article>\n<article class=\"external-card\"><div class=\"source\">S02 \u00b7 CBS \u00b7 OFFICIAL<\/div><h3>Dutch micro-enterprise AI evidence.<\/h3><p>2025 micro AI use 13.8% with a published 95% interval of 12.8\u201314.8; additional purpose, acquisition and barrier context.<\/p><a href=\"https:\/\/www.cbs.nl\/nl-nl\/longread\/rapportages\/2026\/gebruik-van-ai-technologie-door-nederlandse-microbedrijven?onepage=true\" target=\"_blank\" rel=\"noopener\">Open CBS source \u2197<\/a><\/article>\n<article class=\"external-card\"><div class=\"source\">S03 \u00b7 STATBEL \u00b7 OFFICIAL<\/div><h3>Belgian AI, data and cloud context.<\/h3><p>Official Belgian enterprise release reporting 34.5% AI adoption in 2025 plus selected data-analysis and cloud indicators. Statbel data are CC BY 4.0.<\/p><a href=\"https:\/\/statbel.fgov.be\/en\/news\/artificial-intelligence-gaining-ground-belgian-enterprises\" target=\"_blank\" rel=\"noopener\">Open Statbel source \u2197<\/a><\/article>\n<article class=\"external-card\"><div class=\"source\">S04 \u00b7 DDMA DDMO 2026 \u00b7 n=436<\/div><h3>Marketing-specific Netherlands evidence.<\/h3><p>GfK-panel primary sample of Dutch marketing professionals: AI use, policy, capabilities, data governance and management ownership.<\/p><a href=\"https:\/\/ddma.nl\/kennisbank\/ddmo-2026-ai-evolves-from-a-marketing-tool-into-an-organisational-challenge\/\" target=\"_blank\" rel=\"noopener\">Open DDMA source \u2197<\/a><\/article>\n<article class=\"external-card\"><div class=\"source\">S05\/S06 \u00b7 GARTNER \u00b7 n=401 \/ n=402<\/div><h3>Enterprise marketing spend, readiness and automation.<\/h3><p>Used as enterprise context and soft model constraints only. The CMO Spend Survey is heavily weighted toward very large organisations.<\/p><a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2026-05-11-gartner-2026-cmo-spend-survey-finds-cmos-allocate-15-point-3-percent-of-marketing-budgets-to-ai-but-only-30-percent-are-ready-to-scale-ai-capabilities\" target=\"_blank\" rel=\"noopener\">Open Gartner source \u2197<\/a><\/article>\n<article class=\"external-card\"><div class=\"source\">S07 \u00b7 OECD \u00b7 COMPARATIVE CONTEXT<\/div><h3>Cross-country firm AI adoption.<\/h3><p>2025 comparative adoption and firm-size\/industry gaps used as a cross-check, not as a substitute for harmonised Eurostat NL\/BE data.<\/p><a href=\"https:\/\/www.oecd.org\/en\/about\/news\/announcements\/2026\/01\/ai-use-by-individuals-surges-across-the-oecd-as-adoption-by-firms-continues-to-expand.html\" target=\"_blank\" rel=\"noopener\">Open OECD source \u2197<\/a><\/article>\n<\/div>\n<div class=\"method-grid\" style=\"margin-top:24px\"><article class=\"method-card\"><h3>Download the audit trail<\/h3><ul><li><a href=\"https:\/\/markaigen.com\/nl\/wp-content\/plugins\/markaigen-benchmark\/assets\/source-registry.csv\/\" download=\"\">Source registry CSV<\/a><\/li><li><a href=\"https:\/\/markaigen.com\/nl\/wp-content\/plugins\/markaigen-benchmark\/assets\/evidence-master.csv\/\" download=\"\">Evidence master CSV<\/a><\/li><li><a href=\"https:\/\/markaigen.com\/nl\/wp-content\/plugins\/markaigen-benchmark\/assets\/SOURCES_AND_PROVENANCE.md\/\" download=\"\">Professional source register<\/a><\/li><li><a href=\"https:\/\/markaigen.com\/nl\/wp-content\/plugins\/markaigen-benchmark\/assets\/SOURCE_CITATION_GUIDE.md\/\" download=\"\">Source citation guide<\/a><\/li><li><a href=\"https:\/\/markaigen.com\/nl\/wp-content\/plugins\/markaigen-benchmark\/assets\/ACQUISITION_MANIFEST.md\/\" download=\"\">Data acquisition manifest<\/a><\/li><li><a href=\"https:\/\/markaigen.com\/nl\/wp-content\/plugins\/markaigen-benchmark\/assets\/METHODOLOGY_v0.5_EVIDENCE.md\/\" download=\"\">Methodology v0.5 \u2014 Evidence<\/a><\/li><li><a href=\"https:\/\/markaigen.com\/nl\/wp-content\/plugins\/markaigen-benchmark\/assets\/METHODOLOGY_v0.6_MODELLED.md\/\" download=\"\">Methodology v0.6 \u2014 Modelled<\/a><\/li><li><a href=\"https:\/\/markaigen.com\/nl\/wp-content\/plugins\/markaigen-benchmark\/assets\/benchmark-modelled-summary-v0.6.json\/\" download=\"\">Model summary JSON<\/a><\/li><\/ul><\/article><article class=\"method-card\"><h3>Interpretation rule<\/h3><p><b>Observed<\/b> means the source directly measured or surveyed the published statistic. <b>Modelled<\/b> means Markaigen calculated an estimate from documented evidence. A model can be useful, but it must never be described as a respondent result.<\/p><div class=\"method-note\"><strong>Evidence cut-off:<\/strong> 8 October 2026<br><strong>Model version:<\/strong> 0.6<br><strong>Simulation seed:<\/strong> 20261008<\/div><\/article><\/div>\n<\/div><\/section>\n\n<section class=\"section cta\" aria-labelledby=\"mkb-join-title\"><div class=\"wrap cta-grid\"><div><div class=\"eyebrow\" style=\"color:var(--cyan)\"><span class=\"spark\"><\/span> Research panel<\/div><h2 id=\"mkb-join-title\">Help build the benchmark marketers can actually use.<\/h2><p>Register your interest in future Markaigen benchmark research. We store your email, role, market and consent privately on Markaigen, solely to contact you about research participation. This form does not submit your assessment answers.<\/p><div class=\"hero-actions\"><a class=\"btn dark\" href=\"\/nl\/partner-advertise\/\">Discuss a research partnership<\/a><a class=\"btn dark\" href=\"#mkb-methodology\">Review methodology<\/a><\/div><\/div><form class=\"join-form\" id=\"mkb-joinForm\" action=\"\"><div class=\"mkb-honey\" aria-hidden=\"true\"><label>Leave blank<input name=\"company_site\" tabindex=\"-1\" autocomplete=\"off\"><\/label><\/div><label for=\"mkb-joinEmail\">Work email<\/label><input id=\"mkb-joinEmail\" name=\"email\" maxlength=\"190\" autocomplete=\"email\" type=\"email\" required placeholder=\"name@company.com\"><label for=\"mkb-joinRole\">Role<\/label><select id=\"mkb-joinRole\" name=\"role\" required><option value=\"\">Choose role<\/option><option>Marketing leader<\/option><option>Marketing \/ SEO specialist<\/option><option>Agency \/ consultant<\/option><option>Data \/ analytics<\/option><option>Other<\/option><\/select><label for=\"mkb-joinMarket\">Market<\/label><select id=\"mkb-joinMarket\" name=\"market\" required><option>Netherlands<\/option><option>Belgium<\/option><option>Europe<\/option><option>Other<\/option><\/select><label class=\"check\"><input type=\"checkbox\" name=\"consent\" value=\"1\" required><span>I agree that Markaigen may store these details and contact me about benchmark research. <a href=\"\/nl\/privacy-policy\/\">Privacy policy<\/a>. Requests are retained for up to 12 months; contact info@markaigen.com to withdraw or request deletion.<\/span><\/label><button class=\"btn ai\" type=\"submit\">Join the research panel \u2192<\/button><div class=\"join-success\" id=\"mkb-joinSuccess\" role=\"status\" aria-live=\"polite\"><\/div><input type=\"hidden\" name=\"trp-form-language\" value=\"nl\"\/><\/form><\/div><\/section>\n\n<section class=\"section-sm\" aria-labelledby=\"mkb-faq-title\"><div class=\"wrap faq\"><div class=\"eyebrow\">FAQ<\/div><h2 id=\"mkb-faq-title\" style=\"font-size:28px;line-height:1.2;margin-bottom:18px\">Benchmark methodology questions<\/h2><details><summary>Which values are real observations and which are modelled?<\/summary><p>The evidence cards are direct observations from named sources. Explorer scores and percentiles are evidence-constrained modelled estimates. The page labels both classes explicitly and the downloadable evidence master preserves the underlying source definitions.<\/p><\/details><details><summary>Why use median and quartiles instead of one average?<\/summary><p>Marketing organizations vary widely by size, maturity and sector. Median plus P25\/P75 makes the distribution visible and reduces the risk that a small number of extreme values distort the benchmark.<\/p><\/details><details><summary>How should Markaigen protect privacy?<\/summary><p>Use opt-in collection where required, aggregate before publishing, enforce minimum cohort sizes, avoid exposing organization-level results without consent, and document retention and deletion practices.<\/p><\/details><details><summary>Can sponsors influence the benchmark?<\/summary><p>They should not influence sample selection, scoring, methodology or results. Sponsorship can fund the research, but the relationship should be disclosed and separated from editorial and analytical control.<\/p><\/details><details><summary>When should Dataset structured data be added?<\/summary><p>Dataset structured data may be used for the observed evidence register once the downloadable dataset and methodology remain stable. The synthetic v0.6 population must still be described as modelled\/synthetic, not as collected respondent data.<\/p><\/details><\/div><\/section>\n<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":285210167,"featured_media":0,"parent":2534,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"ai4marketing-home","meta":{"_mkg_title_en":"","_mkg_title_nl":"","_mkg_description_en":"","_mkg_description_nl":"","_mkg_primary_intent":"","_wpcom_ai_launchpad_about_page":false,"_wpcom_ai_launchpad_gallery_page":false,"_wpcom_ai_launchpad_contact_page":false,"_wpcom_ai_launchpad_events_page":false,"_wpcom_ai_launchpad_video_page":false,"_wpcom_ai_launchpad_portfolio_piece":false,"footnotes":""},"categories":[],"class_list":["post-33","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI Marketing Benchmarks - Markaigen<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/markaigen.com\/nl\/research\/benchmarks\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Marketing Benchmarks - 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