The AI Engines Keep Bringing Up Matomo: 30 Analytics & Data Software Brands Measured

In a recent study examining four AI engines—ChatGPT, Perplexity, Claude, and Gemini—researchers explored how these language models respond when queried about 30 analytics and data software brands. Conducted on September 12, 2026, the study yielded 360 answers across the AI engines. Surprisingly, Matomo frequently appeared in their responses, even though it was not among the brands specifically analyzed.

The AI engines demonstrated varying levels of brand recognition. Gemini named a brand in 54.4% of its responses, while Perplexity managed 36.7%, a 17.7-point gap. This disparity highlights the importance of measuring responses from multiple AI engines to ensure comprehensive results. Only 15 out of the 30 brands were named by all four engines, while one brand, Fathom, was not mentioned by any of them.

The remaining brands had varying degrees of visibility across the engines, with some names appearing in only one or two responses.

Notably, the perception of certain brands differed significantly among the AI engines. For instance, Amplitude was named by Claude in every instance but not by ChatGPT, illustrating the inconsistent nature of brand recognition among these AI models. The study further examined the role of these brands when named by the engines, finding that mentions were often neutral or positive but primarily served as alternatives rather than first recommendations.

Interestingly, 9 brands were named but never recommended first by any engine, suggesting that visibility may not equate to endorsement.

The engines' recommendations varied significantly from one another, emphasizing the need for cross-validation when relying on AI-generated insights. For example, ChatGPT highlighted Adobe Analytics as ideal for large enterprises, while Claude offered a broader perspective on user behavior tracking tools. Gemini provided a concise overview of Clockify as a free time-tracking solution suitable for small teams. Perplexity focused on Crazy Egg's strengths in visual heatmaps and optimization workflows.

In terms of sentiment, the majority of the 164 mentions made about these brands were positive, with only 1 negative remark out of the total. This high positivity rate underscores the generally favorable perception of these analytics and data software solutions among AI engines. However, the absence of mention, rather than negative sentiment, poses a more significant challenge for brands seeking visibility and recognition in this space.

The study also delved into the sources cited by the AI engines, revealing that learn.g2.com was the most frequently referenced (99 citations), but the top 10 domains collectively accounted for only 16.9% of all citations. This wide-ranging citation pattern suggests that there is no singular source to target for improvement but rather a need for consistent and widespread recognition across various platforms.

Overall, the findings underscore the importance of comprehensive cross-verification when leveraging AI engines for brand analysis. While these language models provide valuable insights, their varying perspectives highlight the need for human oversight to ensure accurate and well-rounded recommendations.