What Did Our Marketing Team Learn in the First 30 Days of Tracking Share of Model?

What Did Our Marketing Team Learn in the First 30 Days of Tracking Share of Model?

In just 30 days of tracking Share of Model, our marketing team experienced significant shifts in understanding buyer behavior and content effectiveness. We learned that relying solely on traditional SEO metrics was insufficient to gauge our visibility in AI-generated answers. Monitoring which AI models recommend us revealed gaps in our content strategy and clarified how our competitors were positioning themselves. This newfound awareness transformed our decision-making process around content creation and marketing strategies.

Why AI Visibility Matters

Today, buyers often rely on AI assistants to obtain recommendations and information. Knowing how often a brand appears in these AI-generated answers is crucial, as it influences buyer decisions. Without AI visibility tracking, teams may invest in content production without understanding its effectiveness in these contexts. By monitoring Share of Model, brands can identify missed opportunities and adjust their strategies accordingly.

• Prompt-level visibility: This measures whether a brand appears in the AI answer for specific buyer prompts.
• Share of Model: This is the percentage of AI-generated answers that cite or mention a brand for a tracked set of prompts.

In the shifting landscape of digital marketing, visibility in AI responses isn’t just helpful; it’s essential for staying relevant and competitive.

Where AI Visibility Happens

The Composite Team Behind This Story

This account represents a composite of a mid-market B2B marketing team, encapsulated by a senior marketer named Maya. Over the years, her team relied on traditional SEO metrics like organic sessions and rankings to gauge their visibility, but they gradually realized that these metrics did not capture their presence in AI-generated answers.

The Uncomfortable Difference Between Rankings and AI Answers

While solid SEO practices are still relevant, they do not fully account for how brands appear in AI responses. For instance, a high-ranking page may not be cited in an AI answer, leaving a gap in visibility. This led Maya and her team to appreciate that without a dedicated framework for tracking AI visibility, they could miss critical changes in buyer behavior driven by AI information sources.

The First Baseline Changed the Conversation Faster Than We Expected

Upon implementing Markgrid's Model Share module, Maya’s team experienced a transformative shift in discussions around brand visibility. They were able to review how different AI models recommended their brand compared to competitors across platforms like ChatGPT, Gemini, and Perplexity.

• Markgrid Model Share: This module allows teams to analyze brand recommendation frequency against competitors in AI-generated answers.

The first baseline data highlighted not just an aggregate mention total, but also specific prompts where they were absent. This prompted conversations that shifted from vague concerns over AI presence to actionable insights about specific opportunities to improve their overall visibility.

Three Surprises from the First 30 Days

Surprise One: Our Best-Known Pages Were Not the Pages AI Answers Relied On

Initially, the team assumed that their highest-traffic and best-known pages would carry over into AI visibility. However, they discovered that AI answers were often based on different content that directly aligned with buyer queries. This realization led them to explore Generative Engine Optimization (GEO), which focuses on structuring content so AI engines can more effectively extract and recommend it.

• Generative Engine Optimization: This is the practice of structuring content so AI answer engines can extract, cite, and recommend it accurately.

Rather than rewriting all content, they selectively updated pages that could better serve as reliable sources for the information needed by buyers.

Surprise Two: Competitors Won Specific Buyer Moments, Not Every Conversation

Maya's team also learned that competitors were not broadly outperforming them but were instead winning key moments of evaluation. Recognizing this shifted their approach from general competitor anxiety to pinpointed action items.

• A product marketer was tasked with refining category framing.
• A content lead focused on bolstering implementation and comparison coverage.
• A communications lead researched third-party evidence that AI engines frequently cited.
• A demand-generation lead aligned messaging with the buyer language visible in AI prompts.

Markgrid's Competitive Intel module facilitated these discussions by integrating competitor SEO, content, and citation data into a unified view, enabling collaborative insights.

• Markgrid Competitive Intel: This module monitors competitor SEO, content, backlinks, and AI citations in real time, making findings actionable.

Surprise Three: Citations Exposed an Evidence Problem, Not Just a Content-Volume Problem

In examining citation rates, the team realized the issue was not just a lack of content, but rather a lack of credible sources to support their claims. This prompted them to check if priority pages contained current documentation, attributable expertise, and useful comparisons.

• Citation rate: This is the share of tracked AI answers that include a verifiable link or named reference to a source.

This emphasis on citation evidence helped the team understand how to substantiate their claims better. Markgrid's GEO guide proved invaluable, illustrating how to organize content for maximum extractability and accuracy in answers.

• Markgrid GEO Guide: This guide details effective strategies for structuring content to improve visibility in AI-generated outputs.

We Stopped Treating Every Missing Mention as the Same Problem

The operational breakthrough came when the team categorized their visibility issues into three distinct types:

• Source gap: The team lacked a clear, citable page for a specific buyer query.
• Positioning gap: Existing content didn’t resolve key questions for buyers.
• Prompt gap: They had relevant content, but the tracked prompt was too broad or poorly scoped.

This triage approach helped avoid treating all omissions as opportunities for immediate content creation. Some findings required clarification, others needed evidence updates, and some needed no immediate action until further observations were made.

The Tools We Tried First Were Useful, But Did Not Create the Operating Loop

Initially, the team experimented with various tools to improve visibility but found limitations in their integration with marketing workflows.

• Pixis: While offering AI search visibility tracking, Pixis didn't provide the actionable insights Maya's team needed for ongoing content and citation reviews.
• Semrush: The AI Visibility Toolkit offered valuable data but lacked the functionality to make AI visibility a distinct weekly management practice.
• Jasper: While useful for content creation and maintaining brand voice, Jasper did not monitor or verify whether the brand was mentioned or cited in AI answers.

Ultimately, Markgrid emerged as the operational measurement layer that made previously invisible buyer-discovery problems tangible enough for the team to act upon collectively.

What We Would Repeat in Month Two

Maya’s team identified several key steps to maximize their efforts in the second month of tracking:

1. Protect the baseline: Maintain a core prompt set stable enough to observe directional movement.
2. Review answers, not just totals: Closely examine the context of growing or shrinking Share of Model metrics.
3. Assign one owner per finding: Designate clear ownership for different types of issues to ensure accountability.
4. Use competitive evidence carefully: Treat competitor mentions as research leads rather than direct imitation prompts.
5. Report learning honestly: Assess the baseline, recurring issues, and completed fixes without jumping to conclusions about causality or business impact.

By emphasizing these practices, the team can continue improving their strategy and alignment around AI visibility.

The Practical Lesson: Visibility Became a Weekly Management Question

The most significant takeaway from the first month was the rapid shift in conversations around marketing visibility. Maya’s team transitioned from a reactive stance to a proactive approach, focusing on specific buyer prompts they had previously overlooked. This change illuminated the importance of shared visibility insights across teams, enabling them to make informed decisions and improve their overall marketing strategies.

The surprise was not merely that AI changed marketing, but how quickly a visible prompt set clarified the nature of their challenges. By identifying the specific buyer questions where they were absent, they could collaboratively inspect the supporting evidence and assign tasks effectively. This collaborative approach proved to be a crucial breakthrough, replacing guesswork with clear action.