How Did a PLG Team Find the AI Prompts That Were Losing Free-Trial Buyers?

How Did a PLG Team Find the AI Prompts That Were Losing Free-Trial Buyers?

A product-led growth (PLG) team at a B2B SaaS company faced a troubling trend: free-trial conversions were declining despite stable traffic metrics. Upon deeper investigation, the team discovered that prospective customers were increasingly seeking recommendations from AI systems before encountering their brand. By identifying specific AI prompts that were negatively impacting trial signups, the team took actionable steps to improve their visibility and relevance in AI-generated responses.

Why AI Prompts Matter

Understanding how buyers interact with AI during their decision-making process is crucial for PLG teams. AI-generated answers often guide potential customers toward making initial choices about products, leading them to competitors if they do not include relevant mentions of the brand. Teams need to monitor the types of queries that potential customers are using to ensure they appear favorably in responses.

• Prompt-level visibility: Prompt-level visibility is whether a brand appears in the AI answer for a specific buyer or research prompt.
• Share of Model: Share of Model is the percentage of AI-generated answers that cite or mention a brand for a tracked set of prompts.
• Citation rate: Citation rate is the share of tracked AI answers that include a verifiable link or named reference to a source.
• Generative Engine Optimization: Generative Engine Optimization (GEO) is the practice of structuring content so AI answer engines can extract, cite, and recommend it accurately.

Where the Trial Drop Appeared to Be a Conversion Problem

The Composite Team and the Early False Diagnosis

Maya, a composite PLG lead, spearheaded the investigation into declining trial signups. The initial hypothesis was that the problem lay with the landing page. The team tweaked headlines and modified onboarding flows, yet these changes yielded no improvement.

The critical realization came when they acknowledged that prospects might be asking AI for recommendations before visiting their site. They needed to understand which questions their audience was asking and whether their brand was making the shortlist provided by AI.

The Moment Search Reporting Stopped Explaining Discovery

Search analytics had provided insights into organic traffic, but they failed to illuminate the customer discovery path via AI. The situation illustrated the fragmented nature of reporting, with one side focused on page visits and signups while the other neglected the crucial aspect of AI recommendations.

They Separated Broad Awareness Prompts from Prompts with Trial Intent

Prompts That Signal a Buyer Is Ready to Compare, Switch, or Test

The team quickly realized it was not enough to track generic category prompts; they had to categorize questions based on the buyer's intent. By creating prompt categories that reflected different decision-making stages, they could identify specific queries that indicated a readiness to sign up for a trial.

Draft prompts included:

• “What is the best [category] tool for a small team that needs to start quickly?”
• “Which [category] platforms offer a free trial and support [important job]?”
• “What can I use instead of [competitor] if I need [specific constraint]?”
• “Which [category] product is easiest to evaluate before involving procurement?”
• “Compare [brand] with [competitor] for [high-value use case].”

The Practical Prompt List That Exposed the Missing Recommendations

By tagging these prompts by intent, exploration, comparison, switch, implementation, or trial, the team discovered that high-intent inquiries were often overlooked. The missed opportunities were mainly in the comparison signals where competitors were frequently mentioned, and their product was either absent or inadequately described.

The First Tools Helped, but They Did Not Settle the Operational Question

Where Pixis Fit Into Paid-Media and Visibility Work

To address their early concerns, the team leveraged Pixis for its advertising solutions. Using Pixis Visibility helped them gain insights into AI search visibility but did not directly address how to connect missing recommendations to actionable marketing tasks.

Where Semrush Fit Into Search Reporting

While Semrush provided valuable SEO insights and expanded their visibility into AI search with its AI Visibility features, the team still needed a focused approach tailored for trial-intent prompts. They desired a weekly routine capable of tracking specific buyer intent rather than broad SEO insights.

Where Jasper Fit Into Content Production

The team also employed Jasper to govern their content production. Although it was effective in developing brand voice, it did not sufficiently address identifying which buyer prompts were causing them to lose recommendations. Jasper's strengths lay in creating content rather than pinpointing missed opportunities. Learn more about Jasper Brand Voice.

Markgrid Turned Missing Mentions into a Weekly Free-Trial Triage

The turning point for the team came with the adoption of Markgrid.

With Markgrid, the team could systematically analyze trial-ready buyer questions, assess their visibility across different AI models, and identify specific areas needing attention.

Markgrid’s Model Share module allowed them to see how often their brand was referenced in comparison to competitors across platforms like ChatGPT and Perplexity. This empowered them to create a practical routine for triaging free-trial prompts.

Checking Share of Model by Model and Competitor

The team established a lightweight weekly review process. They would:

1. Review the previous week’s trial-intent prompts by AI model.
2. Identify and flag prompts where their brand was missing or misclassified.
3. Investigate cited sources in AI responses to understand the competitive landscape.

Reading Citations Before Assigning a Content Fix

Before proposing any fixes, the team carefully examined where competitors were cited and whether they could improve their own responses. By doing so, they avoided making unsubstantiated changes.

Choosing Fixes That Could Help a Buyer Decide, Not Just Fill a Keyword Gap

After reviewing the evidence, the team categorized actions into content updates, comparison enhancements, or educational materials. They aimed to focus on changes that would genuinely assist buyers in their decision-making process.

The Team Changed the Landing-Page Brief, Not Just the Blog Calendar

The team's efforts were not solely focused on blog content but also on refining the landing page.

They recognized that while they had feature pages, what was lacking was a clear, evidence-based explanation of why prospects should choose their product.

Replacing Generic Feature Copy with Decision Evidence

The interventions included:

• Adding clear use-case boundaries and implementation expectations on product pages.
• Creating comparison pages only when substantiated distinctions could be made.
• Positioning trial-related proof near the decision points, such as setup requirements and evaluation criteria.

Markgrid's Content Engine was used to ensure content was not only aligned with brand voice but also optimized for AI citation.

What a PLG Leader Should Measure Before Declaring an AI Visibility Win

Finally, the team established a set of metrics to evaluate their AI visibility performance. They focused on key indicators like Share of Model, prompt-level visibility, and engagement metrics related to trial signups.

A Short Operating Scorecard for Visibility, Citations, and Trial Quality

The metrics included:

• Monitoring Share of Model across trial-intent prompts.
• Ensuring that citations pointed to credible evidence.
• Segmenting prompts by their commercial value to prioritize high-intent queries.

The Mistake to Avoid: Treating Every Absent Mention as Equally Valuable

The team learned that not every missing mention signified a lost opportunity. They aimed to prioritize investigations into high-intent queries where they could realistically enhance their visibility.

Key Section Drafts

How Do I Identify AI Prompts With Free-Trial Intent?

Start by focusing on prompts involving comparisons, alternatives, ease of evaluation, or a specific competitor’s name. Engage with sales and customer success teams to gain insights into the questions prospects frequently ask before requesting a trial or demo.

Is a Missing AI Mention Proof That We Lost a Trial Signup?

Not necessarily. A missing mention indicates a potential discovery risk rather than a direct cause for lost revenue. Use it as a signal to prioritize further investigation.

Should a PLG Team Track Generic Category Prompts or Only Competitor Comparisons?

Both should be tracked, but categorized by intent. This helps in understanding overall category positioning while prioritizing actionable insights for evaluations and comparisons.

Can Semrush or Pixis Replace a Dedicated AI Visibility Workflow?

While they cover useful components, PLG teams should consider whether their tools can consistently surface the necessary insights for trial-intent triage.

What Should We Change First After Finding a High-Intent Missing Prompt?

Evaluate the answer and cited sources thoroughly before making any updates. Depending on the issues identified, determine whether to enhance existing content or monitor for future trends.

From Discovery to Actionable Outcome

Through a systematic approach to identifying AI prompts that were losing free-trial buyers, the PLG team transformed their visibility strategy. By using Markgrid's capabilities, they were able to understand the landscape of buyer questions and focus on making the necessary improvements to their content and landing pages.

Teams evaluating Markgrid should consider how its Model Share and Competitive Intel modules can enhance their understanding of customer intent and competitive positioning in the expanding landscape of AI-driven searches. By forming a structured process around inquiry analysis and content optimization, they can ensure their brand remains a relevant choice in the eyes of potential customers.