A guide for B2B marketers who want to learn how to track AI content and SEO performance.
B2B marketers are trying to measure content performance in a landscape shaped as much by LLMs as traditional search. Unfortunately, the dashboards that worked three years ago no longer match how buyers gather information. You now need a single view that blends SEO signals, AI-influenced behaviour, and the performance of AI-assisted content. Without that, teams will struggle to understand what is creating visibility and what is quietly losing ground.
When we build these analytics environments for global tech companies, Data Studio becomes the control center. It connects GSC, GA4, SEMrush, and the new AI data that marketing teams are starting to capture. It creates an integrated view that supports editorial decisions, content investments, and revenue planning. For example, a CMO can now look at a single page and understand how content performs across channels and how AI-assisted assets have influenced the pipeline.
“The problem most B2B teams face is that the underlying buyer journey has changed faster than the reporting stack that supports it,” explains John Wilkes, Co-Founder of Somebody Digital. People still search, but they also consult ChatGPT, generate their own comparisons, and use AI for early problem discovery. The signals are fragmented across platforms that were never designed to be read together, and when teams try to piece everything together manually, they lose the patterns that matter. One of the biggest lessons we’ve taken from building Somebody Digital is that analytics structure always shapes strategy. When the dashboard is fragmented, the content plan follows the same path.
Across our enterprise clients that operate in multiple regions, the same pattern shows up. Teams feel the pressure to scale content, experiment with AI, and expand their presence, but they lack the visibility needed to prove where the work is driving ROI. They often rely on exports, spreadsheets, and static reports. It slows decisions and hides early signals that could give them an advantage. “We see teams publishing huge volumes of content without knowing how LLMs interpret it,” confirms John. AI search behaviour becomes visible only if you know how to instrument the data.
Building an AI era content analytics dashboard starts with the sources that B2B teams already know: GSC for search signals, GA4 for behaviour and conversions, and SEMrush for keyword and competitive patterns. The difference now is the AI layer. Teams add custom parameters for AI-assisted pages, tagged campaigns distributed through ChatGPT or Gemini, and events that capture AI-influenced engagement. Once everything flows into Data Studio, the dashboard evolves into a single environment that shows how people find, compare, and interact with content.
The most useful view combines organic search performance, LLM-influenced discovery, and the performance of AI-assisted assets. “Traditional SEO metrics still matter, but the relationship between AI-generated engagement and organic behaviour reveals the real opportunities,” adds John from Somebody Digital. When we run programs with enterprise clients, we often see AI-assisted content surfacing intent types weeks before GSC catches the pattern. It becomes a leading indicator for topics worth investing in.
Interpreting the data becomes a strategic exercise rather than a reporting task. Teams look at what pages attract the strongest LLM-influenced visits and what topics gain authority when supported by AI-enhanced formats. They study where human-edited content lifts engagement and how long tail opportunities evolve when AI queries shift. This becomes even more valuable across multilingual programs. Operating in 16 languages gives us a unique view here. AI-assisted workflows help scale production, but human quality control drives the final lift in performance.
The insights feed directly into content workflows. AI supports ideation, drafting, refinement, and testing, but it does so inside a measurement framework that keeps decisions grounded. The Test What Matters Framework is often the backbone. It helps teams compare AI-generated assets with human-optimised versions in a way that is structured and repeatable.
B2B marketers are entering a phase where AI content performance, LLM visibility, and SEO have to be managed together. A unified analytics layer lets teams see these signals early and build strategies that compound over time. It gives leaders clarity on where to invest, what to sunset, and how to evolve the content engine for a future shaped by new discovery patterns. It is the type of foundation that lets teams scale with confidence rather than guesswork.
GSC, GA4, SEMrush, and AI tagging parameters.
You track it through tagged links, AI engagement parameters, and custom events.
Through structured testing using dashboard views that isolate performance attributes.
It improves workflow and coverage when supported by strong human editing and clear measurement.
Engagement depth, assisted conversions, LLM-impacted visits, and authority growth over time.
