Highlights
- The dataset: We analyzed over 230,000 AI source citations generated from prompts specific to the marketing automation industry. The AI responses behind them come from four AI models and were run in 12 countries over the course of Q2 2026.
- Reddit and TechRadar together make up 27% of ChatGPT's citations: That makes ChatGPT the most concentrated engine.
- 68% of Google AI Overview's citations are from commercial domains: Vendor domains, the highest commercial share of any model.
- ~88% of top sources differ between any two models: There is no single AI ranking to win in marketing automation software.
- 12% average shared top sources: Across the four models, the range runs from 5% to 33%.
- 46% local-domain citations for Microsoft Copilot: The most localized engine, versus 15% for Grok, the least.
Which sources should you target to get cited as a marketing automation brand?
Each model has its own anchor source.
| Model | Top cited domains (share of that model's citations) |
|---|---|
| Grok | reddit.com (4.0%), g2.com (3.8%), zapier.com (3.0%), salesforce.com (3.0%), youtube.com (2.6%) |
| Google AI Overview | youtube.com (8.1%), brevo.com (2.9%), zapier.com (2.7%), clientify.com (1.9%), salesforce.com (1.7%) |
| Microsoft Copilot | wmtips.com (6.9%), getresponse.com (4.8%), thecmo.com (4.0%), data.stateglobe.com (2.1%), logicielfrance.com (1.9%) |
| ChatGPT | reddit.com (18.2%), techradar.com (9.1%), rankyak.com (2.4%), spotsaas.com (2.3%), softwareinspect.com (1.6%) |
ChatGPT is the most concentrated: a striking 27% of its marketing automation citations go to just two domains, Reddit and TechRadar. Grok and Google AI Overview spread their citations far more thinly across vendor sites and YouTube. A marketing automation brand optimizing for one AI model is, on average, absent from ~88% of the domains the other models prefer.
How have AI source rankings changed over time in the marketing automation industry?
We split the ~110-day window at its midpoint (early May 2026) and re-ranked the most-cited domains in each half, ranking by each domain's share of citations within its half rather than raw counts.
The steepest fallers are G2, LinkedIn, EmailToolTester, InsiderOne, MarketBetter.ai, and Gartner. On the way up, TechRadar entered the second half at #2 overall almost entirely on the strength of ChatGPT; GetResponse climbed from #15 to #4 (+110% share), ActiveCampaign from #25 to #12 (+52%), and TheCMO from #24 to #11 (+47%). The story is a rise in community and tech-press media alongside a handful of vendor domains gaining ground in AI answers over the window.
What type of content do AI models cite for marketing automation software?
We classified every cited domain by category. Commercial content (vendor and software domains) dominates every model, but the mix beyond that splits the field cleanly.
Three behaviors stand out. Google AI Overview is the most vendor-centric engine, sending more than two-thirds of its citations straight to commercial marketing software sites, and it cites editorial content the least (6.4%). ChatGPT is the most editorial, with 22.9% of citations going to tech-press and review media, more than 3x Google AI Overview's editorial rate, and the lowest commercial share of the four. Microsoft Copilot is the only model that meaningfully cites reference material (13.4%, vs 0.5–6.7% elsewhere), pulling in encyclopedic and informational content the others skip. For a vendor, ChatGPT visibility runs through earned press coverage and community discussion; Google AI Overview visibility runs through your own domain and competitor comparison pages.
Do AI models cite local-language content for marketing automation software?
Across the non-English language markets (Spanish, German, Italian, Swedish, Dutch, French), local-country-domain citations make up about 26% of all citations, meaning roughly three-quarters of sources in non-English markets still sit on global or English-language domains. Local sourcing varies sharply by market.
France and the Netherlands pull in the most local content; the Spanish-speaking markets lean hardest on global domains, with Mexico bottoming out at under 14% local citations. Aggregated across Spain, Mexico, and Argentina, Spanish sits at just 19.8% local, the lowest of any language.
Which AI model relies most on local sources for marketing automation software?
Microsoft Copilot is by far the most localized engine, with 46.2% of its citations in non-English prompts on local domains, and it leads in Spanish and French; Google AI Overview leads German, Italian, Dutch, and Swedish. Grok is the most global, localizing only 15.2% of the time despite citing the most sources overall. ChatGPT and Google AI Overview sit in the middle.
Google AI Overview's Dutch localization rate (68%) is the highest single cell in the matrix, with Microsoft Copilot's Dutch (59%) and French (56%) close behind, but Google AI Overview's overall average is dragged down by a global-leaning Spanish result and no French coverage in scope. Microsoft Copilot is the most consistently local engine across the board. Grok is uniformly the least localized, its highest cell (French, 26%) is below Microsoft Copilot's lowest. A strong local-language presence is far more likely to be picked up by Microsoft Copilot than by Grok.
Localization here is measured by whether the cited domain uses the market's own country code (.de.fr.it.nl.se, and .es/.mx/.ar for Spanish), not by detecting the actual language of the page, so the figures are a directional lower bound on true local-language sourcing. Google AI Overview had no French citations in scope and is excluded from that cell.
Should marketing automation software brands optimize for each AI model separately?
The headline finding is divergence, so yes. We took each model's 20 most-cited domains and measured, for each pair, the share of domains the two lists hold in common out of all domains they mention between them. The average across all six model pairs is just 12.3%, which means about 88% of the domains one model relies on are absent from any given other model's top tier.
The overlap is lopsided. Grok and Google AI Overview share 33% of their top domains, both lean on the same vendor and aggregator sites (Salesforce, Zapier, Brevo, Gartner) plus YouTube and Reddit. But every pairing involving ChatGPT or Microsoft Copilot slides to 5–11%. ChatGPT, in particular, is an outlier: its top-20 is dominated by community and tech-press media (Reddit, TechRadar) and a long tail of localized comparison sites, sharing only two or three domains with any other engine.
| Model pair | Shared top sources | Shared domains |
|---|---|---|
| Grok ↔ Google AI Overview | 33.3% | 10 |
| Microsoft Copilot ↔ Google AI Overview | 11.1% | 4 |
| Grok ↔ Microsoft Copilot | 8.1% | 3 |
| Google AI Overview ↔ ChatGPT | 8.1% | 3 |
| Grok ↔ ChatGPT | 8.1% | 3 |
| Microsoft Copilot ↔ ChatGPT | 5.3% | 2 |
How many sources does each AI model cite per answer?
The four engines differ by roughly four-fold in how many sources they cite in a single answer.
Grok cites roughly 4.2x as many sources per response as Microsoft Copilot. Practically, Grok offers many more "slots" for a brand to appear in any given answer, while Microsoft Copilot's short citation lists make each slot far more competitive.
Context
This analysis draws from Temso's AI visibility monitoring platform, which tracks how brands appear in AI model responses across ChatGPT, Grok, Google AI Overview, and Microsoft Copilot. It covers how these four engines cite web sources when answering marketing automation software questions. The data spans 12 country markets and 7 languages over roughly 110 days (March 10 to June 29, 2026), comprising 22,572 AI responses and over 230,000 cited source links.
It measures what AI models cite, not the underlying quality of any marketing automation brand. Citation share is a visibility signal, not a quality judgment. The localization analysis uses a domain-extension proxy and is directional. These findings are observational, they describe correlations in citation behavior, not causes.
Methodology
How we measured this
We collected the source links cited by four AI engines as they answered a fixed set of marketing automation software prompts, repeated across 12 country markets and 7 languages over a 110-day window. Each cited link was attributed to its publishing domain, and each domain was classified by type using a global registry. Cross-model overlap was measured using Jaccard similarity on each model pair's top-20 most-cited domains, reported as the average across all six pairs. Content-type mix came from grouping cited domains by category. Temporal analysis split the observation period at its midpoint and compared each domain's share of citations in each half.
The analysis covers over 230,000 cited sources from 22,572 AI responses. Sample sizes exceeded recommended thresholds for every reported finding. Localization is measured by a country-code-domain proxy (the source records carry no detected content language), so those figures are directional and exclude English-language prompts, which sit overwhelmingly on generic domains. Model coverage varied across the observation window, so temporal movements are reported as relative ranks within each half.
Frequently asked questions
Do different AI models cite the same sources for marketing automation software questions?
No, barely. Any two models share only about 12% of their top-20 most-cited domains on average, so roughly 88% of the sources one engine relies on are absent from another's top tier. The only meaningful agreement is between Grok and Google AI Overview, which share 33%.
Which AI model should a marketing automation vendor optimize for?
It depends on the content you can produce. Google AI Overview rewards your own vendor and comparison pages (68% commercial citations). ChatGPT rewards earned tech-press coverage and community discussion (23% editorial, plus heavy Reddit citing). Because the models overlap so little, there is no single source strategy that wins everywhere.
Which model cites the most sources per answer?
Grok, by a wide margin, about 27 sources per response, versus roughly 10 for Google AI Overview, 8 for ChatGPT, and about 7 for Microsoft Copilot. That 4.2x spread means Grok answers offer many more openings for a brand to appear.
Which AI engine is best for non-English marketing automation markets?
Microsoft Copilot. It relies on local sources the most, citing local-country domains 46% of the time in non-English markets and leading across most languages. Grok is the least localized at 15%, defaulting to global and English-language sources.
Which marketing automation sources are gaining ground in AI answers?
Community and tech-press media, plus a few vendors. Reddit moved to the top spot and TechRadar entered the second half at #2, while GetResponse climbed from #15 to #4 and ActiveCampaign from #25 to #12. G2, LinkedIn, and Gartner slid down the ranking over the same window.
What types of sites get cited most for marketing automation software?
Commercial vendor sites lead across every model, from 37% (ChatGPT) up to 68% (Google AI Overview). Beyond that, ChatGPT favors editorial tech press and Reddit, Microsoft Copilot uniquely leans on reference material (13%), and user-generated content is a consistent 18–28% everywhere.
How reliable are these localization numbers?
They are directional. Localization is measured by whether a cited domain uses the local country code (.de.fr.nl, etc.), not by detecting the page's actual language. A local-language article on a .com domain is not counted, so the true local-language sourcing rate is somewhat higher than reported.
