Unlock Growth: Rethink Your Funnel with LLM Analytics
▼ Summary
– Customer journeys have shifted from open web platforms to closed AI environments, making direct observation impossible and creating funnel blindness for marketers.
– Marketing analytics now relies on two data streams: synthetic lab data (tracking chosen prompts) and observational field data (real user clickstream data).
– Lab data shows ideal system performance under test conditions but cannot predict real-world outcomes, conversions, or user behavior.
– Field data from clickstreams provides the only ground truth by capturing genuine user interactions, revealing real brand impact and friction points.
– Effective strategy requires managing the gap between lab data (what’s possible) and field data (what’s profitable), treating the customer journey as a dynamic feedback loop.
Understanding the shift in customer behavior is essential for any modern marketing strategy. For years, the focus was on navigating the complex “messy middle” of the consumer journey on the open web. Today, that journey has largely moved into closed AI environments like ChatGPT and Perplexity. This migration creates a significant challenge: funnel blindness, where traditional analytics can no longer directly observe how customers discover and evaluate brands. To overcome this, marketers must learn to reconstruct these journeys using data from Large Language Model (LLM) visibility tools, which rely on two distinct types of information.
The process of rebuilding the marketing funnel hinges on reconciling synthetic data with observational data. Synthetic data, often called lab data, comes from the specific prompts a brand chooses to test. Tools like Semrush’s AIO and Profound use this approach to map a brand’s potential presence within AI-generated answers. This method is excellent for benchmarking performance and comparing model outputs under controlled conditions. However, it has a major limitation: it only reflects an idealized, “best case” scenario. Lab data shows what is possible, not what is actually happening with real users in unpredictable environments. It lacks the context of genuine user habits and cannot predict real-world conversions or market shifts.
To compensate for the gaps in synthetic data, some vendors employ aggressive strategies like system-level saturation and user-level simulation. The first involves a brute-force analysis of millions of AI responses to understand a brand’s entire citation ecosystem. The second strategy injects thousands of synthetic personas, simulated users with different priorities, into testing environments. While these techniques can provide valuable structural insights and help stress-test systems, experts note they remain disconnected from authentic human behavior. They are useful for product development but cannot replicate the randomness of how real people interact with technology, especially as humans increasingly delegate online tasks to agentic AI.
The only way to validate what is truly effective is with field data, specifically clickstream data. This information records the actual digital footprints of users: the pages they view, the results they click, and the paths they follow. Companies like Similarweb and Datos gather this data through consented panels and browser extensions, providing a record of genuine user actions. The integrity of the underlying clickstream data is paramount for any LLM visibility tool to be trustworthy. Marketers should scrutinize the scale, quality, and cleaning processes of the data source. Weak panels with small samples can obscure emerging trends and minority behaviors.
Platforms built on robust clickstream data, such as those powered by Datos, offer a reliable ground truth. With tens of millions of anonymized users tracked globally, this data provides a real-time, actionable view of a brand’s impact, pinpointing moments of friction and success in a way synthetic methods cannot.
Ultimately, a successful strategy is forged in the gap between these two data streams. Lab data maps the territory of what’s possible, while field data validates what’s profitable. Managing the difference between them, calibrating the ideal scenario against evidence of what actually generates revenue, creates a dynamic intelligence feedback loop. The modern marketer’s task is no longer about analyzing a static funnel but continuously navigating this “messy middle” to connect potential with profit.
(Source: Search Engine Land)
