GEO / local SEO / AI search / generative engine optimization

HubSpot AEO: Becoming #1 CRM in AI search

Recorded on Jun 2, 2026

More and more B2B buyers no longer start their research on classic Google search, but on AI-powered answer engines. They compare products in ChatGPT, use Perplexity, or read Google's AI Overview—often without a single click to a website. For marketing teams, the visible part of the customer journey shifts: visibility is created in generated answers, not only in organic rankings.

HubSpot recognized early that potential customers were moving from traditional search engines to so-called answer engines like ChatGPT, Gemini, and Perplexity. At the same time, the company lacked a reliable system to measure AI visibility and verify whether targeted AEO measures (Answer Engine Optimization) were actually working. In June 2025, the HubSpot marketing team therefore began working with XFunnel, a specialized AEO tool for measuring and optimizing presence across multiple AI platforms.

Building an AEO measurement architecture

The central question was: Does HubSpot appear when an answer engine is asked about solutions to problems the products address? To answer this systematically, the team modeled the buyer journey along typical prompts and answer paths in AI interfaces. Prompt tracking bridges the gap between classic keyword logic and the fragmented world of generative answers.

Product-led AEO and container structure

HubSpot set up dedicated XFunnel containers for each product line. The measurement architecture included an overarching brand container for HubSpot overall and eight dedicated product containers for CRM, Marketing Hub, Sales Hub, Service Hub, Content Hub, Commerce Hub, Data Hub, and Breeze. Within each container, there were also feature-specific views, such as "Email Automation" in Marketing Hub. This structure allowed sub-teams to run experiments and optimize product-specific AEO performance while the marketing team maintained an overall view of the strategy.

Four core KPIs for answer engines

After defining relevant prompts, HubSpot focused on four metrics that together provide a more complete picture of AI presence than isolated ranking metrics:

Answer Engine Visibility (%): How often HubSpot appears in target prompts in answers.
Answer Engine Share of Voice (%): Share of mentions compared to competitors for the same prompts.
Answer Engine Citations: Frequency with which HubSpot pages are cited as sources in AI answers.
Answer Engine Citation Share (%): Share of citations to HubSpot URLs relative to competitors.

These metrics separate pure brand mentions from the qualitatively more valuable citation of own content—a crucial difference because citations enable direct traffic and influence on the generated answer. Brand awareness alone is not enough in answer engines if own pages are not referenced as trusted sources.

Three-pillar strategy for AEO

From the data analysis, HubSpot derived that successful AEO rests on two foundations: meaningful, structured content on the own website and a strong external presence in sources from which answer engines draw information. Based on this, a strategy with three pillars emerged: on-site content optimization, off-site amplification, and community engagement with forum growth.

Pillar 1: On-site content optimization

Initial AI visibility scores were solid, but citation scores remained weak: answer engines rarely referenced HubSpot pages. The growth team identified hyper-personalized AI answers as the cause—generic product pages were not enough to answer purchase and industry questions. The central user question was: "Will HubSpot work for my business?" Personalization in AEO therefore means providing very specific answers to industry- and use-case-specific questions.

Industry-specific content and structured data

HubSpot scaled industry solutions pages with an AI-powered content system that generated from case studies and was human-reviewed. Breadcrumb and FAQ schema made the pages machine-readable and helped answer engines reliably extract relevant sections. Result: 92 percent of pages were cited by answer engines, AI visibility increased by 49 percent. In addition, the team published software comparison articles for target industries, such as "The 5 best CRMs for construction businesses." Citations for these posts increased by 642 percent, overall mentions by 58 percent—a signal that comparison formats are particularly often used as sources in AI answers.

FAQ glossary for top-of-funnel terms

In the "Problem Exploration" phase, HubSpot was often missing from answers. The team launched an FAQ glossary for terms like "What is marketing automation?" or "How does lead scoring work?" Each page provides a definition, related questions, and links to HubSpot features. Definitions are popular sources for AI answers because they are concise and fact-oriented. Citation share for related prompts increased by 60 percent, brand visibility in awareness prompts by 35 percentage points when the glossary was cited.

Optimizing product pages for AEO

In parallel, product pages were specifically overhauled for answer engines: clearer value propositions, more precise feature descriptions, and structured data so AI systems can reliably extract and cite product information. Where general marketing language previously dominated, HubSpot focused on explicit answers to typical purchase and evaluation questions.

Pillars 2 and 3: Off-site and community

In addition to on-site measures, HubSpot invested in off-site amplification and community engagement. Answer engines draw information not only from the own domain, but also from external reference sources, forums, and review platforms. A broad, consistent presence in these environments increases the likelihood of being mentioned or cited in generated answers.

The case study thus shows a repeatable GEO workflow: measure, identify gaps in citations, produce highly specific content, set technical signals for machine-readable answers, and strategically expand external visibility. For SEO and GEO leaders, HubSpot's approach provides a practical blueprint: prompt-based monitoring, product-driven measurement containers, clear KPIs for visibility and citation, and content formats that specifically serve AI answers. Those who want to stay ahead in generative search surfaces must steer visibility not only in SERPs, but directly in the answers of AI platforms.

Kim Ishikawa (KI)

AI-supported processing of GEO, AI search and generative engine optimization. The model was specifically trained on content about ChatGPT search, Perplexity, AI overviews and local visibility in AI answers; it has processed a large amount of content on entity optimization, structured data and brand presence in generative systems. The editorial team classifies GEO strategies and connects classic SEO with new AI search channels.