Tracking AI-Driven Conversions and the Evolution of Local Search Visibility for Modern Brands

The emergence of generative AI as a primary discovery tool for consumers has fundamentally altered the digital marketing landscape, presenting both a significant measurement challenge and a new frontier for lead generation. Recent data from CallRail indicates that AI citation clicks currently account for approximately 1% to 2% of total call volume for its customer base. While this percentage may appear modest, it represents a substantial growth trajectory, effectively doubling since January. This shift signals that AI assistants—ranging from ChatGPT and Gemini to Perplexity and Grok—are moving from novelty tools to integral components of the customer decision-making journey.

This data was brought to light during the second session of SEJ Live on August 26, where Sean McCrohan, Vice President of Technology at CallRail, provided a granular look at how AI is influencing consumer behavior. Joined by Steve Wiideman, an industry-recognized consultant advising multi-location brands, the discussion underscored a critical disconnect between the actual influence of AI in the buying cycle and the limited visibility afforded by current reporting metrics.

A Shifting Chronology: From Early Adoption to Current Metrics

The integration of AI into local search did not happen overnight, but its acceleration has been rapid. Throughout the first half of 2026, brands began to notice erratic traffic patterns, often struggling to reconcile their Google Analytics 4 (GA4) data with actual business inquiries. According to McCrohan, internal metrics at CallRail revealed a 65% growth in AI-driven traffic over a six-month period earlier this year. However, as of late August, that growth has surged further, with both tracked citation clicks and self-reported attribution—where a customer explicitly names an AI tool as the source of their referral—rising by more than 100% since the start of the year.

This rapid, albeit quiet, rise suggests that while AI-driven traffic remains a small fraction of total web activity, it is scaling at a rate that traditional search engine optimization (SEO) models are not yet equipped to handle. For multi-location franchises, this creates a unique operational hurdle: the need to track leads that originate in an AI-powered chat interface but culminate in a phone call or a store visit.

The Measurement Gap: Why AI Activity Stays Hidden

One of the most pressing issues identified by industry experts is the "black box" nature of current AI ad networks. Unlike traditional search advertising systems—such as Google Ads, which offer robust, standardized tracking pixels and attribution modeling—the tools available for ChatGPT, Claude, and other large language models are in their infancy.

McCrohan highlighted that tracking AI activity from the initial prompt all the way to a closed sale is currently fraught with technical barriers. Many of the intermediary tools marketers rely on for search attribution are not yet supported by AI vendors. Furthermore, there is a noted reluctance from major AI developers, including OpenAI, to engage in transparent discussions regarding the tracking of ad performance or referral data. Consequently, many marketers are left with only two reliable signals: direct citation clicks, which function similarly to standard referral traffic, and anecdotal attribution provided by the customer during a call.

This difficulty is compounded by the behavior of AI crawlers. McCrohan noted that, unlike the standard Googlebot, many AI agents do not execute scripts on a webpage. This renders traditional, in-browser dynamic number swapping—a common tactic used to track calls from specific traffic sources—largely ineffective for AI-driven visits. To circumvent this, brands must implement server-side number swapping, a more complex technical requirement that ensures the phone number presented to an AI agent remains consistent with the data used for search rankings.

The 24-Hour Reality: AI Traffic Patterns

A notable finding from the SEJ Live discussion is the distinct temporal shift in how consumers engage with AI versus traditional web search. Historically, CallRail has observed that roughly 50% of website traffic occurs outside of standard 9-to-5 business hours. When filtering specifically for AI-generated search behavior, that figure rises to nearly two-thirds.

This indicates that AI assistants are increasingly being used as after-hours research tools. A user might begin their research in the early afternoon, return to the assistant late at night, and eventually make a phone call long after traditional staff have departed. This "always-on" nature of AI search makes the implementation of automated voice agents and 24-hour lead capture systems not just an advantage, but a necessity. Because AI responses frequently highlight a "top three" list of service providers rather than a single result, a missed call during these late-night research sessions often results in an immediate loss of business to a competitor who has better availability.

Strategic Implications for Multi-Location Brands

For brands with a large physical footprint, the rules of the game are changing. Steve Wiideman emphasizes that "governance beats ranking signals at scale." In the age of AI, the importance of consistent, accurate business data—often referred to as NAP (Name, Address, and Phone number) data—cannot be overstated. Because AI models aggregate data from across the web, conflicting information across various platforms can lead to a brand being excluded from an AI’s recommendation.

Wiideman advocates for a strategy focused on "semantic triples," which are specific, verifiable claims about a business. By creating a prompt library—a repository of 100 to 125 key claims that a business wants to be associated with—marketers can better steer the AI toward their brand. This is a departure from traditional keyword stuffing, focusing instead on establishing the entity as an authoritative source in the eyes of the AI.

Moreover, the phenomenon of "prompt drift"—where the same query yields different results when repeated—makes it difficult to rely on any single citation source. Consequently, businesses should avoid over-optimizing for a specific platform. Instead, they should maintain a broad digital presence, ensuring that their reputation is reflected across Yelp, Apple Maps, Bing, and niche review sites. Maintaining an average review score between 4.5 and 4.7 remains a critical benchmark, as the probability of AI recommendation drops significantly for businesses falling below this range.

The Path Forward: Integration and Adaptation

As the industry looks toward the future, it is clear that the transition to AI-driven search is not a total abandonment of past practices, but an evolution. The core principles of SEO—quality content, consistent business data, and strong customer service—remain the bedrock of success. However, the mechanism of delivery has shifted.

The next phase of this development will likely involve AI agents that not only provide information but proactively facilitate transactions. As McCrohan noted during the session, it is foreseeable that in the near future, an AI agent will verify a business’s contact information and directly initiate the call for the user, effectively bypassing the need for a landing page visit entirely.

While this may sound daunting, the experts maintain a measured outlook. The integration of call transcripts into CRM systems allows businesses to understand the specific language their customers use, which often differs from the professional jargon found on corporate websites. By aligning website content with these real-world customer inquiries, businesses can better bridge the gap between their offerings and the intent behind AI-driven searches.

Ultimately, the rise of AI in local search is an invitation for brands to tighten their operational control and embrace more sophisticated, data-driven approaches to lead attribution. As the technology continues to mature, the brands that win will be those that view AI not as a threat to their search rankings, but as a dynamic, 24-hour channel that requires both technical precision and a commitment to authentic, consistent brand identity. For those looking to delve deeper into these strategies, the full session from SEJ Live remains available, offering a comprehensive roadmap for navigating the complexities of AI-driven visibility.