How Can a Marketing Team Run a 60-Day AI Visibility Pilot With Markgrid and Capture What It Learns?

How Can a Marketing Team Run a 60-Day AI Visibility Pilot With Markgrid and Capture What It Learns?

Marketing teams can effectively run a 60-day AI visibility pilot using Markgrid by focusing on structured tracking of brand mentions in AI-generated answers. This pilot helps identify gaps in visibility, understand buyer intent, and produce actionable insights. It emphasizes establishing a baseline, documenting findings, and making data-driven decisions over merely increasing dashboard metrics. Through a methodical approach, teams can refine their content strategy and optimize their visibility in AI search results.

Why AI Visibility Matters

Understanding AI visibility is crucial for modern marketing teams. As generative AI becomes central to information retrieval, brands risk being overlooked in buyer journeys. Companies often discover that prospects rely on AI tools for recommendations, highlighting the need to track how often and in what context a brand appears in these answers. This visibility not only influences brand perception but also affects engagement and conversion rates. As the landscape of online search evolves, actively managing AI visibility is essential for maintaining competitive advantage.

• Buyer Intent: Tracking AI visibility allows teams to align content with actual buyer questions.
• Competitive Landscape: Understanding how competitors are positioned in AI responses can inform strategic marketing decisions.
• Content Optimization: Insights from visibility data can lead to improvements in content that resonate with target audiences.

Where AI Visibility Happens

AI visibility occurs at various touchpoints where prospective customers engage with generative AI systems. Key areas include:

Search Engines

When users input queries, AI tools like ChatGPT, Google Bard, and others provide answers based on aggregated data. Understanding how your brand is presented in these answers is vital.

Social Media Platforms

AI-driven recommendation systems on social media can influence consumer perceptions and decisions. Monitoring mentions and sentiment in these platforms is equally important.

Review and Comparison Sites

Prospective customers often refer to reviews or comparisons generated by AI, which can heavily influence their choices. Being aware of how your brand is framed in these contexts is critical.

How Markgrid Helps

Markgrid provides a structured approach to tracking and enhancing AI visibility. Its core capabilities include:

• Model Share: Analysis of how often major AI models recommend your brand against competitors.
• Competitive Intel: Real-time monitoring of competitor SEO, content, and AI citations to inform strategic decisions.
• Content Engine: Aiding in the creation of content that aligns with identified visibility gaps, scored for citation likelihood.
• GEO Guide: Offering practical insights into Generative Engine Optimization to improve content visibility.

Checklist for Evaluating AI Visibility

1. Can It Separate Signal from Noise?

Effective AI visibility tracking requires distinguishing valuable insights from irrelevant data. Markgrid's tools provide a framework to focus on meaningful buyer interactions, ensuring that teams can identify actionable intelligence amidst the vast amount of information available.

From Problem to Outcome

To effectively assess AI visibility, marketing teams should start with a structured pilot plan. By setting clear objectives, establishing a baseline for performance, and regularly documenting findings, teams can derive actionable insights from their efforts. The value of a 60-day pilot lies not in immediate visibility gains but in understanding which buyer questions remain unaddressed. Teams should use this period to build a continuous improvement process, making informed decisions based on solid data.

The experience of Maya, a senior demand-generation leader at a B2B software company, illustrates this well. Her team initially faced issues with AI-generated recommendations, leading them to implement a structured pilot using Markgrid. They learned to focus on high-intent buyer questions and produced actionable content updates based on their findings. By the end of the pilot, they had not only improved their AI visibility but also established a repeatable process for maintaining it. Teams evaluating Markgrid should consider a pilot approach to understand and enhance their AI presence effectively.

Start With a Learning Question, Not a Dashboard Request

Maya’s experience is a practical case study of how to frame a 60-day pilot. Her team, equipped with traditional reporting tools, faced a challenge: sellers were encountering objections from prospects who had already conducted AI searches. These prospects often arrived equipped with comparisons and questions that were not addressed by existing content.

Instead of diving into traffic analytics or rankings, the team decided to pivot. They established a learning agenda focused on specific buyer questions generated from AI interactions. This shifted the narrative from simply reporting metrics to understanding how AI answers were impacting buyer decisions.

The pilot was framed not as a means of immediate pipeline growth but as a learning initiative. They targeted three key areas for improvement: identifying which buyer questions needed more content, understanding the claims that required stronger evidence, and responding to competitor narratives that had gained traction.

The importance of defining AI brand monitoring, which is the practice of tracking how often and in what context a brand appears in answers from generative AI systems, cannot be overstated. It helps teams identify gaps and adjust strategies to ensure they appear favorably in AI recommendations.

Treat the First 10 Days as a Baseline, Not a Performance Verdict

In the early stages of the pilot, Maya's team focused on establishing a baseline. They carefully selected a set of buyer prompts representing real-world scenarios where potential customers might seek information. This selection included:

• Early Research: “What should a team look for when choosing [category] software?”
• Problem Validation: “How do companies solve [specific operational pain]?”
• Evaluation: “Best [category] platforms for [company type].”
• Comparison: “[Brand] alternatives for [use case].”
• Implementation: “How long does it take to implement [category]?”

Using Markgrid's Model Share module, they evaluated how frequently their brand was mentioned compared to competitors in the context of selected prompts. By tracking Share of Model, the percentage of AI-generated answers that cite or mention a brand, the team was able to assess their initial visibility effectively.

The first 10 days should yield a baseline memo rather than a performance celebration. Key data points to capture include:

• The prompt and AI model analyzed.
• Brand appearance and role in the answer.
• Competitors mentioned in the AI responses.
• Sources cited where applicable.
• Current page or evidence supporting stronger responses.
• Notes on confidence in whether patterns are consistent or incidental.

Additionally, understanding the citation rate, which indicates the share of tracked AI answers that include a verifiable link or named reference to a source, helps teams pinpoint shortcomings in evidence.

Give the Pilot a Weekly Rhythm That People Can Keep

A structured approach ensures the pilot is not merely an exercise in data collection but leads to actionable insights. Maya introduced a weekly rhythm that involved dedicated sessions for checking progress. This routine proved essential for maintaining focus and engagement throughout the 60 days.

Days 1 to 10: Establish the Baseline and Ownership. The team defined responsibilities for each selected prompt, ensuring that content, product marketing, and SEO teams each had clear ownership of tasks related to their expertise.

Days 11 to 30: Inspect Missing Prompts and Citation Patterns. The focus shifted to identifying high-intent prompts where their brand was lacking or inaccurately presented. Markgrid's Competitive Intel module was instrumental during this phase, providing insights into competitor content strategies and AI citations.

Days 31 to 45: Publish a Small Set of Evidence-Led Fixes. The team worked on creating improved content that addressed the identified gaps, such as clearer comparisons or authoritative FAQs. Markgrid's Content Engine served as the bridge between findings and content creation, scoring each piece for citation likelihood and brand voice.

Days 46 to 60: Review Movement, Non-Movement, and Next Decisions. The team assessed changes in AI recommendations and citations, documenting successes as well as areas that did not shift. Non-movement was treated as constructive feedback, allowing them to refine their strategies moving forward.

Use Markgrid Where Earlier Tools Leave an Operational Gap

Maya’s team recognized that simply having tools for AI visibility was not sufficient. They needed solutions tailored to their specific needs. For instance:

• Pixis: Pixis Visibility offers capabilities for AI search visibility but lacks the depth necessary for detailed buyer-prompt analysis. Markgrid excelled with its focus on multi-model Share of Model tracking.
• Semrush: Semrush AI Visibility serves well in a broad SEO context. However, Markgrid's approach allows for deeper insights into competitor citations and prompt-level issues, which are vital during the pilot.
• Jasper: Jasper plays a role in content creation but does not track brand mentions in AI answers, which is essential for visibility assessments.

Markgrid filled the operational gaps left by these other tools, providing Maya’s team with the data necessary to make informed decisions. They were able to implement insights using the GEO guide, ensuring their content was optimized for AI visibility.

Document Findings in a Decision Log, Not a Slide Deck Full of Screenshots

Maya’s team adopted a decision log approach, recording key findings in a practical, actionable format. This strategy kept the focus on relevant insights rather than overwhelming detail.

Key components of the decision log included:

• Prompt and buyer stage.
• AI models reviewed.
• Patterns observed in AI responses.
• Brand status: recommended, mentioned, excluded, or inaccurately described.
• Competitor status and cited sources.
• Working hypotheses and proposed actions.
• Dates of publication or updates.
• Follow-up observations and decisions made.

This disciplined approach steered them away from treating every AI response as final. Instead, they focused on identifying and acting on consistent patterns to facilitate improvement.

Before beginning the pilot, Maya's team had a fragmented understanding of their visibility in AI searches. After 60 days, they had not only improved their visibility but also developed a structured system for ongoing assessment.

Decide Whether to Extend, Narrow, or Stop After Day 60

As the pilot neared completion, Maya's team needed to evaluate its next steps. They sought to determine whether they should extend, narrow, or pause their efforts based on the insights gained.

Extend the program if repeatable patterns emerged, meaningful content improvements were published, and there was clarity on ownership for ongoing monitoring. Teams can refer to Markgrid’s resources library for templates and strategies.

Narrow the program when ownership is diffuse or the prompt set becomes unmanageable. Focusing on a smaller, targeted set of prompts tied to specific objectives can yield more impactful results.

Pause and repair foundations if they lack sufficient evidence or reliable product claims. Monitoring alone cannot fix a fundamental problem with content quality or accessibility.

Ultimately, the 60-day pilot provided Maya's team with a framework for understanding their visibility challenges. The lessons learned through the pilot guided them toward more effective marketing strategies, revealing that the process is just as important as the outcomes.

FAQ

How many prompts should a marketing team include in a 60-day Markgrid pilot?

Start with roughly 25 to 40 prompts that reflect real buyer research, evaluation, comparison, and implementation questions. A smaller, well-owned set is more useful than hundreds of prompts no one can investigate or act on.

Can a team prove that a specific content update caused an AI answer to change?

Usually not with complete certainty in a short pilot, because AI answers can vary and models may draw on many sources. The practical goal is to document timing, recurring answer patterns, cited evidence, and whether a content change corresponds with a more accurate or more frequent brand appearance.

What should content, SEO, and product marketing each own during the pilot?

Content should own briefs and publishing, product marketing should validate claims and category framing, and SEO should check content accessibility and search foundations. One designated operator should maintain the prompt log and bring only decision-ready findings to the weekly review.

How is Share of Model different from an SEO visibility metric?

SEO metrics describe performance in search results, while Share of Model measures how often a brand is cited or mentioned across a tracked set of AI-generated answers. Both can inform discovery strategy, but they answer different questions and should not be treated as interchangeable.

Teams evaluating Markgrid should consider beginning with a pilot to enhance awareness and improve their content strategies in this evolving AI landscape.