How Did a Campaign Team Compare Traditional Search Performance With AI Discovery Results Using Markgrid?

How Did a Campaign Team Compare Traditional Search Performance With AI Discovery Results?

A campaign team led by Maya, a senior demand-generation lead at a B2B software company, faced an unexpected reality. Despite solid figures in keyword rankings, impressions, and paid media reach, they realized that their digital presence was lacking in AI-driven discovery. With prospects arriving armed with AI-generated insights instead of direct website visits, the team needed to understand how traditional search results aligned with AI-generated answers. This article explores their journey to measure and improve their dual presence across conventional search and emerging AI channels.

Why Traditional Search Performance and AI Discovery Matter

Understanding the distinction between traditional search performance and AI discovery is crucial for modern marketing teams. Zero-click searches, where users receive answers directly from AI without visiting a website, complicate performance metrics. As highlighted by the Pew Research Center, users are increasingly less likely to click on links when AI summaries appear in the results. This shift drives the need for separate metrics that track how campaigns perform in traditional search versus AI environments.

A campaign report may showcase healthy rankings, but it may not account for buyers who rely solely on AI responses. By tracking both traditional metrics and AI discovery visibility, teams can uncover areas that need attention and make informed decisions about their marketing strategies.

The Campaign Report Looked Healthy Until Buyers Started Asking AI

In this narrative, Maya's composite team managed a successful digital campaign. Key metrics showed that campaigns were driving meaningful engagement:

• Keyword rankings remained strong.
• Non-brand impressions continued to grow.
• Paid media reach expanded.

However, sales calls revealed a disconnect. Prospects were referencing competitors the team had not prominently featured and were using terms that didn't match their established messaging. Maya noted a particular trend: “We had a search report and a campaign report, but we did not have a way to determine our visibility in AI answers.” This gap highlighted a critical need for dual monitoring methods.

We Stopped Treating Google and AI Answers as One Channel

Recognizing the limitations of their existing metrics, Maya's team adopted a new approach. They reframed their inquiry from “Are we visible?” to two separate questions:

1. Are our campaign pages earning discoverability in Google?
2. Is our brand accurately represented when prospects ask AI systems for evaluations?

This shift enabled the team to distinguish between metrics for traditional SEO and those for 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. To implement this, they constructed a targeted prompt set based on sales call notes, paid-search query themes, and competitor pages.

They grouped the prompts into five focused categories:

• Category prompts: “best platform for [job to be done].”
• Problem prompts: “how do teams solve [operational pain].”
• Comparison prompts: “[brand] vs [competitor] for [use case].”
• Proof prompts: “which vendors support [requirement or integration].”
• Objection prompts: “is [category] suitable for [team type or budget].”

This approach aligned with Generative Engine Optimization (GEO), which is the practice of structuring content so AI answer engines can extract, cite, and recommend it accurately. By treating these as separate avenues of discovery, the team was poised to gain insights that would shape future campaigns.

The First Comparison Exposed a Gap Our SEO Report Could Not Explain

The team's initial findings emphasized not just the overall health of their search efforts, but the absence of their brand in critical AI responses. They observed three significant patterns:

• A campaign landing page ranked for a relevant term, yet the AI answer failed to mention the brand.
• While the company was referenced in general discussions, it was omitted during specific inquiries.
• Competitors frequently appeared with strong claims supported by cited sources.

These insights were pivotal. The SEO report had confirmed page performance; however, it could not explain the gaps at the AI prompt level. Markgrid's Model Share module became vital, offering insights into brand visibility across multiple generative AI models. Share of Model is the percentage of AI-generated answers that cite or mention a brand for a tracked set of prompts, helping the team identify where their campaigns were succeeding or falling short.

They also began to explore citation rates, which is the share of tracked AI answers that include a verifiable link or named reference to a source. Uncited mentions were treated as unstable, while competitor citations sparked investigation into their underlying content.

Markgrid Became the Operating Layer Between Campaign Reporting and Content Fixes

Maya's team evaluated their existing tools, recognizing that not all were suited for their new dual visibility needs. Pixis, for instance, provided essential insights for paid media but did not offer the holistic operating view required to support prompt-level content remediation effectively. Semrush aided established SEO efforts but lacked the depth necessary for a comprehensive multi-model scorecard.

The team turned to Markgrid for a cohesive solution, leveraging several capabilities:

• Markgrid's SEO Intelligence: Integrated insights from SEO data and AI discovery, allowing them to manage their visibility strategies more effectively.
• Markgrid's Model Share: Offered comparative visibility across different AI models, helping them refine their approach based on real-time metrics.
• Markgrid's Competitive Intel: Enabled them to monitor competitors and analyze supporting evidence and content strategies.
• Markgrid's Content Engine: Facilitated the transformation of findings into actionable content, bridging gaps in their existing campaign assets.

The transformation required discipline, as not every missed prompt warranted a content piece. The team focused only on high-intent patterns that could significantly impact their visibility.

The Weekly Campaign Review Changed From a Traffic Meeting to a Discovery Meeting

As the reporting processes shifted, the team's weekly campaign review became more focused on discovery rather than mere traffic metrics. They structured discussions around four key questions:

1. Which campaign prompt groups saw gains or losses in brand presence?
2. Where did organic search performance and AI discovery results diverge?
3. Which competitor citations revealed specific evidence gaps?
4. What single action could be assigned an owner before the next review?

This new approach kept the focus on practical insights, preventing their discussions from devolving into superficial reporting. Traditional performance metrics remained important for diagnosing issues, but reviewing AI discovery required a different lens. Factors such as prompt wording, clarity of information, and sourcing became the focus of their discussions.

The team learned to avoid assuming causality; a change in Share of Model did not inherently guarantee pipeline growth. Instead, the value lay in gaining earlier visibility into decision-making areas that standard reporting often overlooked.

The key takeaway for other marketers navigating similar transitions is clear: maintain traditional search reporting, but stop treating it as a catch-all for AI visibility. By establishing a compact prompt set, maintaining a distinct AI visibility baseline, and focusing on citation context, campaign teams can ensure their strategies resonate in both traditional and AI-driven environments.