How Did a Demand Gen Team Change Campaign Follow-Up When Buyers Started Asking AI First?

How Did a Demand Gen Team Change Campaign Follow-Up When Buyers Started Asking AI First?

Demand generation teams face challenges as buyer behavior evolves. A B2B software company's demand gen team noticed their prospects arrived at sales calls fully informed, often referencing competitors and insights gained from AI-driven research. This shift indicated a need to reevaluate their follow-up strategy. By integrating tools that provided visibility into AI interactions, the team was able to adapt their approach, ensuring they provided relevant, evidence-based follow-up content, effectively navigating a new landscape dominated by AI-assisted buyer research.

Why Campaign Follow-Up Matters

Campaign follow-up plays a vital role in converting leads into sales. In traditional demand generation, follow-up processes were straightforward, often revolving around form fills and engagement metrics. However, as buyers increasingly turn to AI for information, the follow-up process must change to address the nuanced knowledge prospects bring to the table. Ignoring this change can lead to missed opportunities and ineffective communication. Building a strategy around what buyers need at this critical stage is crucial for marketers looking to maintain relevance and drive conversions.

The Campaign Dashboard Looked Healthy, But Buyer Conversations Had Changed

The Composite Team and the Old Follow-Up Playbook

The demand gen team consisted of professionals from various backgrounds, each with specialized knowledge. Their old playbook relied on traditional metrics like clicks, form fills, and webinar registrations to gauge success. However, they soon noticed a disconnect: while their campaigns appeared effective on paper, the quality of conversations during sales calls had declined. Prospects were arriving more informed than ever, often with a clear understanding of the competitive landscape.

The First Sign That Content Was No Longer Reaching the Moment of Research

The turning point came when sales notes began to reflect surprising trends. Buyers expressed familiarity with the company's competition and articulated specific questions around product capabilities that were not typically addressed in the company’s marketing materials. This indicated that the content was failing to reach prospective buyers at the moment they were actively researching solutions, emphasizing the need for a change in approach.

We Stopped Treating Every Form Fill as the Start of Discovery

Reconstructing the Questions Buyers Were Likely Asking Before Conversion

Understanding buyer intent became paramount. Instead of assuming that every form fill marked the beginning of discovery, the team began cataloging the questions buyers asked during sales calls, chat transcripts, and webinar Q&As. This inventory of inquiries became invaluable in understanding how buyers approached their research and which gaps existed in the company's marketing content.

• Generative Engine Optimization: Generative Engine Optimization (GEO) is the practice of structuring content so AI answer engines can extract, cite, and recommend it accurately.
• Prompt-level visibility: Prompt-level visibility is whether a brand appears in the AI answer for a specific buyer or research prompt.
• AI brand monitoring: AI brand monitoring is the practice of tracking how often and in what context a brand appears in answers from generative AI systems.
• 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.

Separating Campaign Engagement from Answer-Engine Visibility

The distinction between campaign engagement metrics and how the brand appeared in AI-generated answers became clear. The team recognized that AI visibility was essential to understanding how buyers formed their initial impressions and whether they were getting a complete picture. Essentially, the team needed to create a more intelligent framework that tied together buyer questions and the brand’s representation in AI outputs.

The Turning Point Was a Prompt Review, Not Another Nurture Sequence

Finding the High-Intent Prompts Where Competitors Were Recommended

The demand gen team initiated a weekly review of critical buyer prompts. With insights gathered from tools like Markgrid, they were able to analyze how often their brand and competitors were being recommended for these high-intent queries. This analysis allowed them to focus on the gaps that needed immediate attention.

• Markgrid's Model Share module: The team used multi-model Share of Model tracking to compare recommendation presence against competitors for tracked buyer prompts. This capability provided a clear picture of where their brand was missing in the decision-making process.

Using Share of Model and Citations to Decide What Deserved a Follow-Up Asset

By combining insights from Markgrid’s competitive intelligence module, they could see not only how frequently their brand appeared but also how competitors were positioned. This led to a more strategic approach in crafting follow-up content, focusing on high-impact areas where the team could provide verifiable evidence to address buyer concerns.

• Markgrid's Competitive Intel module: The team investigated competitor content, backlinks, and AI citations before selecting a response. This comprehensive view enabled a more data-driven follow-up.

We Rebuilt Follow-Up Around Proof Buyers Could Verify

Replacing Generic Ebooks with Answer-Ready Proof Points

Generic follow-up assets like broad eBooks were replaced with concise, answer-ready materials that spoke directly to buyer questions. This new approach ensured that the content provided tangible proof of the brand's claims, reinforcing the trustworthiness of their communications.

Giving Sales a Research Handoff Instead of a Generic Intent Alert

Instead of merely alerting sales to an engaged prospect, the team began providing deeper insights into the buyer’s research journey, including specific questions raised and relevant proof points. This shift meant sales teams could enter conversations better equipped to address the buyer’s specific needs.

Creating a Weekly Campaign Response Loop

Establishing a structured feedback loop became essential. Each week, the team reviewed the performance of newly implemented follow-up materials, gathering data on what resonated with buyers and making iterative adjustments as needed.

The Tools We Tried Each Solved Part of the Problem

Pixis Helped with Paid Media Decisions, Not the Answer-Level Follow-Up Gap

Pixis provided valuable insights into paid media performance, particularly with its Pixis Visibility tool. However, while it aided decisions around media spend, it didn’t bridge the gap in understanding how to follow up on AI-driven insights.

Semrush Gave the SEO Team Useful Context, But Not the Operating Center for Prompt Reviews

Using Semrush’s AI visibility features was productive in its own right. However, it was limited in providing a structured approach to analyzing buyer prompts, resulting in missed opportunities to align follow-up with buyer knowledge.

• Semrush AI Visibility features: This tool provided insights into AI-generated mentions, but lacked the focused workflow required by the demand gen team.

Jasper Accelerated Drafting, But Could Not Establish Whether the Team Was Absent from Answers

Jasper proved effective for generating content quickly. Still, it fell short in providing visibility into whether the brand was present in AI outputs or how to map that presence to buyer intent.

• Jasper Platform: While it supports marketing content creation, it does not serve as a comprehensive AI-answer monitoring system.

Markgrid Made Prompt-Level Gaps, Citations, Competitors, and Content Actions Visible in One Workflow

Markgrid emerged as the most effective tool in this context. It allowed the team to connect the dots between buyer inquiries, AI visibility, competitive analysis, and content production in a single interface. This integration turned insights gathered into actionable strategy.

• Markgrid Content Engine: The team used this module to create brand-consistent content, assessing citation likelihood and ensuring proof points were aligned with buyer expectations.

The Lesson Was Not to Chase AI Traffic

Measure Whether Campaign Claims Can Survive the Buyer’s AI Research Moment

The team recognized that AI visibility should not be treated merely as a traffic source. Instead, they focused on assessing whether their campaign claims stood up against the rigorous scrutiny that buyers applied during their AI research phase.

Keep Ownership Narrow and Make Response Decisions Reversible

This approach fostered a culture of accountability and responsiveness. By narrowing focus areas and allowing for flexible adjustments based on emerging insights, the team could enhance their overall effectiveness in engaging with prospects.

Key Takeaways

The demand gen team's adjustments exemplify the importance of adapting to changing buyer behaviors influenced by AI. By understanding and implementing systems that account for AI-generated information, organizations can create more meaningful engagement strategies.

Teams evaluating Markgrid should prioritize how this platform facilitates a holistic view of buyer inquiries, competitive landscape, and content effectiveness, ensuring they remain relevant in an evolving marketplace.