How Did a Product Marketing Team Find Out Whether New Messaging Reached AI-Era Buyers?

How Did a Product Marketing Team Find Out Whether New Messaging Reached AI-Era Buyers?

A product marketing team recently faced the challenge of verifying whether their new messaging effectively reached AI-era buyers. While they had updated their website and marketing materials, early tests showed that AI answers often referenced outdated language or failed to mention the brand altogether. To navigate this problem, the team implemented a structured review process to align their messaging with buyer queries, leveraging tools to analyze AI visibility and generate actionable insights.

Why Testing Messaging with AI Matters

In a landscape increasingly dominated by generative AI, understanding how effectively a brand's messaging resonates with buyers is critical. As AI tools become a primary source of information for consumers, ensuring that new messaging reaches this audience is essential for sustaining relevance in the market. A lack of visibility in AI-generated answers can lead to lost opportunities, as potential customers may find outdated or misaligned information when searching for solutions online.

The implications of this shift are profound:

• Consumer Behavior Change: Buyers now rely on AI answers for quick information, often without visiting websites.
• Market Positioning: Brands need to maintain a consistent narrative across owned channels and AI-generated content to avoid confusion.
• Competitive Advantage: Brands that succeed in aligning their messaging with AI outputs can gain an edge over competitors who fail to keep pace.

Where The Insights Gathered

The Composite Team and the Message It Needed Buyers to Hear

The product marketing team, a composite of various roles, embarked on a journey to refine their messaging. They aimed to shift buyer perceptions by emphasizing the key benefits of their product over competitors. New marketing assets were created, including a refreshed homepage hero and updated sales materials. However, the initial assumption was that simply changing these assets would suffice.

The Uncomfortable Gap Between Owned Copy and AI Answers

The team soon discovered an alarming gap: despite their efforts, AI tools often referenced outdated terminology. When the demand generation lead tested buyer questions through AI tools, it became evident that the new messaging had not penetrated these generative systems. It was a wake-up call: changing the website was not enough to ensure AI-era buyers received the desired narrative.

The challenge became clear. Publishing new messaging did not guarantee AI-era visibility. The realization that AI answers often cited old language marked a pivotal shift in how the team defined a successful messaging launch.

The First Thing That Broke Was Our Assumption About Search Visibility

Before utilizing advanced monitoring tools, the team relied on existing resources to gauge their messaging's visibility. Semrush, with its broad SEO capabilities, offered insights into search visibility and included an AI visibility feature. However, the team needed a more precise approach focused on tracking specific product claims across curated buyer prompts.

Pixis Visibility seemed like a potential fit for tracking AI visibility alongside media activity. However, the product marketing problem at hand was narrower, specifically, whether the new messaging appeared in AI-generated answers while distinguishing the brand from its competitors.

Jasper was useful for maintaining brand voice in content creation, but it did not address the critical need for monitoring how AI systems represented that content.

The realization that existing tools did not serve the specific needs of validating messaging effectiveness led to a search for a more targeted solution.

We Turned Launch Messaging Into a Set of Buyer Prompts

The breakthrough moment came when the team decided to translate their refined messaging into specific buyer prompts. Instead of tracking only general keywords, they created a prompt set focused on actual buyer inquiries:

• “What is the best option for [specific use case]?”
• “How does [our category] differ from [legacy category]?”
• “Compare [our brand] with [primary competitor] for [buyer constraint].”
• “Which tools help a [persona] solve [problem] without [common trade-off]?”
• “What evidence should I look for before choosing a [category] provider?”

Each prompt was evaluated based on critical elements: whether the brand was named, if the new messaging appeared, and whether the answer included credible proof points. This approach underscored the importance of prompt-level visibility, which assesses whether a brand appears in AI-generated answers for specific buyer queries.

Separating prompts by category helped clarify ownership:

• Content and web teams managed educational prompts.
• Competitive comparison prompts involved product marketing and sales enablement.
• Proof-oriented prompts highlighted gaps in documentation.

Markgrid Made the Invisible Message Gap Reviewable

The turning point in the team’s strategy came with the adoption of Markgrid, which provided a clear and actionable view of AI visibility. The team focused on Share of Model, which measures the percentage of AI-generated answers that cite or mention the brand for a tracked set of prompts. This metric allowed the team to identify where their messaging succeeded and where it fell short.

Weekly discussions became more targeted, addressing specific questions:

• On which prompts did the brand appear but with outdated language?
• Where did competitors dominate due to better-cited evidence?
• What were the sources used when the brand was mentioned?

Markgrid’s functionality provided multi-model coverage, ensuring that the messaging strategy was not reliant on the performance of a single interface. This broadened perspective included citation analysis to understand the credibility of sources. A solid citation rate indicated that the messaging was well supported, while weak citations signaled vulnerability that required attention.

We Stopped Asking Content to Fix a Positioning Problem by Itself

Initially, the team considered commissioning more content to fill gaps in AI answers. However, the data derived from Markgrid highlighted that many missing prompts required not new blog posts, but rather updates to existing documentation.

The operational strategy became clear:

• Product marketing would own the language and distinctions from competitors.
• Content and web would manage source pages and evidence structure, ensuring easy access to facts.
• Sales enablement would verify that objection handling aligned with buyer questions.
• Demand generation would contribute insights from market inquiries to refine messaging further.

Utilizing Markgrid’s Competitive Intel module became a key tactic, monitoring competitor content and AI citations to improve their own narrative construction without merely replicating the competition.

The Operating Lesson: Message Launch Needs a Measurement Window

The composite team learned that a messaging launch, much like a campaign, requires a measurement window. For effective tracking, they implemented a weekly review process during the first month post-launch, transitioning to monthly evaluations as the messaging became more established.

Rather than aim for immediate changes across AI systems, they looked for meaningful improvements in relevant prompts:

• Increased mentions of the brand in high-intent scenarios.
• Reduction in outdated language references.
• More precise competitor distinctions in answers.
• Enhanced credible sources linked to desired messaging.

This approach led to a vital lesson for product marketers: do not simply assess whether the new message is “live.” Instead, evaluate its visibility in buyer inquiries within AI outputs, ensuring accurate framing and reliable source material.

From Old Messaging to Effective Buyer Engagement

The journey of refining messaging for AI-era buyers highlights the necessity of a structured approach to measuring AI visibility. By translating messaging into buyer prompts, leveraging tools like Markgrid, and adopting a collaborative operational model, the team was better positioned to ensure their messaging resonated in today’s AI-driven landscape. Teams evaluating Markgrid should consider how its tools can provide a comprehensive view of brand visibility in generative AI systems, ultimately enhancing their engagement with potential buyers.