The Shift from Tracking Clicks to Tracking Conversations
For decades, search engine optimization has lived and died by the click. We tracked rankings, monitored click-through rates (CTR), and obsessed over the data inside Google Search Console. But the landscape of digital discovery has undergone a seismic shift. Today, AI search influence is no longer a theoretical concept—it is actively showing up in sales calls and CRM notes before it ever appears in a traditional SEO report.
A recent lead for a major agency stated, “Found you via Grok, actually.” This single comment highlights a massive disconnect in the industry. The agency wasn’t actively trying to rank in Grok, Elon Musk’s AI on X. They weren’t using specialized AI prompt tracking tools to monitor it. Yet, the AI was already acting as a brand ambassador, influencing how a high-value buyer discovered and evaluated their services.
This disconnect is at the heart of the modern SEO’s dilemma. Clients and stakeholders want visibility in ChatGPT, Perplexity, Gemini, and Google AI Overviews, yet they are hesitant to invest in a channel that doesn’t show up cleanly in traditional attribution models. To bridge this gap, a series of controlled experiments were conducted across diverse digital assets—including agency sites, personal brands, e-commerce stores, and test domains—to understand how AI search actually moves the needle on commercial outcomes.
Experiment 1: The Self-Promotional “Best Of” Strategy
One of the most debated tactics in the evolving world of Generative Engine Optimization (GEO) is the use of “best of” listicles published on a brand’s own website. The logic is simple: create a list of the top providers in your niche, place your own brand at the number one spot, and wait for Large Language Models (LLMs) to scrape and summarize that data.
While this might seem like a transparent marketing ploy to a human reader, LLMs function differently. They prioritize patterns and common citations. A significant study by Ahrefs recently analyzed ChatGPT responses across hundreds of “best” style queries and found that list-based posts were the most frequently cited page type. Crucial factors for these citations included the format of the list and the freshness of the content.
To test this, a list titled “Best SEO Agencies in Sydney” was published on a personal brand website, LawrenceHitches.com. The author’s own site was included in the ranking. Within a mere two weeks, the site began appearing across various AI search tools for related queries. The speed of this movement was particularly noteworthy; traditional organic rankings in Google rarely fluctuate that quickly for competitive commercial terms. This experiment proved that LLMs are currently susceptible to surface-level influence from structured, recently updated list content.
Experiment 2: Testing the Credibility of Fake Entities
Critics of the first experiment might argue that the results were skewed by the existing authority of the personal brand or its associations with established industry names. To eliminate this variable, a second experiment was launched using a fake business. A basic landscaping website was built solely for the purpose of SEO and AI testing, with no prior reputation or digital footprint.
The team published a similar “Best Landscapers in Melbourne” list on this brand-new domain. Mirroring the results of the first test, the site appeared in AI search responses within two weeks. This confirmed a critical reality: if a brand-new, unverified test site can surface in AI results this quickly, then “visibility” in an AI prompt is not necessarily a proxy for “trust” or “authority.”
This creates a significant conflict for modern brands. On one hand, data suggests that “Best X” pages attract AI citations. On the other hand, listing yourself as the top provider on your own website can damage buyer trust if the bias is too obvious. Industry leaders have noted that while founders may clamor for the “secret sauce” to appear in ChatGPT, a strategy built purely on self-promotion without third-party validation is unlikely to be sustainable in the long term as AI models become more sophisticated at identifying brand bias.
The Attribution Crisis: Why Prompt Tracking is Often Misleading
As brands scramble to understand their AI visibility, a new market of prompt tracking tools has emerged. However, relying on these tools as a primary success metric is dangerous. Research comparing tracking APIs with actual scraped user experiences has shown that brand overlap can be as low as 24%. This means that 75% of the time, the data an API provides might not match what a real user sees in their specific ChatGPT or Gemini session.
Because AI responses are non-deterministic and highly personalized based on user history and context, a screenshot of a brand mention is a “vanity metric” in its purest form. Instead of asking, “Did we show up in the prompt?”, sophisticated marketers are now asking, “Did this change how the buyer behaved?” This requires shifting focus toward qualitative signals from the sales floor, such as:
- Are leads mentioning specific AI tools during the initial discovery call?
- Is the sales team spending less time on basic education and more on specific solution tailoring?
- Has the overall speed of the buying cycle increased?
- Is there a noticeable softening in price resistance from leads coming through these channels?
Experiment 3: E-commerce, Digital PR, and the Messy Middle
Kadi, an e-commerce brand specializing in luggage, served as the third experimental subject. This test aimed to see if high-authority Digital PR and off-site mentions could drive AI visibility more effectively than on-site technical SEO. The team executed a series of creative data campaigns, including travel studies on “over-touristed destinations” and “airport cybersecurity guides,” as well as product placements in “best suitcase” round-ups.
The results were telling. While the digital PR efforts led to authority growth and temporary keyword spikes, they weren’t a “silver bullet.” The real insight came during a Black Friday sale. A customer found Kadi through a ChatGPT query regarding “kids carry-on” luggage. The buyer journey was complex: they used AI to find the brand, but then visited the site to check shipping policies, browsed the full product range, and debated color options before finally converting.
Surprisingly, the final attribution in the analytics platform pointed to Instagram. Without the customer specifically mentioning ChatGPT, the AI’s role in the decision-making process would have been entirely invisible. This reinforces the idea that AI search often functions in the “messy middle” of the consumer journey—the space between initial awareness and final purchase where buyers are trying to reduce risk and narrow down their choices.
Experiment 4: Agency Growth and the Compression of Consideration
The final experiment involved a comprehensive rebrand and migration of the StudioHawk website. Unlike the quick “listicle” tests, this was a long-term project focused on rebuilding service pages, enhancing social proof, and optimizing user experience. By 2025, the results were undeniable. SEO became the agency’s most efficient channel, driving 65% of inbound leads and nearly 60% of new revenue.
The most striking data point, however, was the difference in sales velocity. Inbound leads sourced from traditional SEO had an average closing time of 29 days. Leads that specifically referenced AI search tools closed in roughly 18 days. This 11-day gap represents a massive increase in efficiency.
The AI-influenced leads were “pre-sold.” Because the AI had already synthesized reviews, case studies, and third-party mentions, the buyers arrived with a higher level of confidence. They skipped the “education” phase and moved straight to “fit and execution.” In just six months, these AI-influenced conversations contributed over $100,000 in closed revenue. This proves that while AI search might not replace discovery, it significantly compresses the consideration phase of the buyer journey.
The New Reality: AI Compresses Consideration
The fundamental takeaway from these four experiments is that AI search is changing the *velocity* of buying decisions rather than just the *location* of search queries. We have moved from a traditional funnel to a “New Consideration Era.” In this era, the website is no longer the sole carrier of the brand’s message. AI summaries and third-party entity associations do the heavy lifting before the user ever clicks a link.
Consideration is the stage where buyers attempt to mitigate risk. They ask the AI: “Who is the most reliable?”, “How does Company A compare to Company B?”, and “Is this product worth the price?” When a brand has strong entity consistency across the web, the AI provides a confident recommendation. This pre-validation means that by the time a user reaches a website, the “messy middle” of their decision-making process is already largely resolved.
Strategic Recommendations for Brands in the AI Era
Based on the findings of these experiments, brands should pivot their SEO and content strategies toward the following four pillars:
1. Measure Outcomes, Not Just Mentions
While it is tempting to track every time your brand appears in a Gemini overview, these metrics are volatile. Focus on “Sales Velocity” and “Lead Quality.” If your sales cycle is shortening and your leads are more informed, your AI visibility strategy is working, regardless of what the prompt tracking tools say.
2. Prioritize Clarity Over Creativity
LLMs are designed to process information, not interpret abstract metaphors. To perform well in AI search, your content must be explicitly clear about what you do, who you serve, and what problems you solve. Avoid vague marketing speak and focus on being a “clear entity” that the AI can easily categorize.
3. Address Comparison and Risk Directly
Since AI is used to compress the consideration phase, your content should proactively answer the questions buyers ask when they are ready to buy. Create content that compares your solutions to competitors (fairly), explains your pricing structure, and provides deep-dive case studies that mitigate the perceived risk of a purchase.
4. Ensure Entity Consistency
AI models look for a “consensus” across the web. If your website says one thing, but your Google reviews, LinkedIn profile, and third-party news mentions say another, the AI will perceive a lack of authority. Consistency across all digital touchpoints is the new “backlink profile.” When your brand’s story is the same everywhere, the AI can recommend you with higher confidence.
Conclusion: The Future of Search is Evidence-Based
The results across these diverse experiments point to a singular conclusion: AI search is not a replacement for traditional SEO, but a high-speed filter for it. It amplifies the strengths of well-positioned brands and exposes the weaknesses of those with inconsistent digital presence. AI doesn’t just help people find information; it helps them make decisions.
For brands willing to look beyond simple attribution and embrace the “messy middle” of the buyer journey, the opportunities are immense. By focusing on entity authority and compressing the consideration phase, companies can turn AI search into their most powerful sales tool, driving not just more traffic, but faster revenue and higher-value customer relationships.