The landscape of digital advertising is undergoing a fundamental transformation as artificial intelligence shifts from a creative tool to a central driver of media strategy and execution. Jellyfish, a digital marketing agency under the Brandtech Group umbrella, has introduced a proprietary methodology known as "Share of Model" to redefine how brands measure their influence and allocate their advertising budgets. By moving beyond traditional search engine optimization and standard display metrics, the agency is utilizing Large Language Models (LLMs) such as OpenAI’s ChatGPT and Google’s Gemini as sophisticated discovery engines to inform high-level media buying decisions. This shift reflects a broader industry movement where AI assistants are no longer viewed merely as experimental interfaces but as critical components of the modern consumer’s path to purchase.

The Evolution of Brand Visibility: From Share of Voice to Share of Model

For decades, "Share of Voice" (SOV) served as the primary metric for determining a brand’s dominance within a specific market or medium. Traditionally, SOV was calculated by measuring a brand’s advertising spend or its presence in organic search results relative to its competitors. However, the rise of generative AI has complicated this metric. As consumers increasingly turn to AI chatbots to research products, seek recommendations, and compare services, the visibility of a brand within the training data and real-time outputs of these models has become a vital indicator of market health.

Jellyfish’s "Share of Model" product addresses this shift by quantifying how often and in what context an AI model mentions a brand compared to its competitors. The tool does not merely count mentions; it employs advanced natural language processing to analyze the underlying themes, sentiment, and cultural context of those mentions. This allows brands to understand not just if they are being talked about, but how they are being positioned by the algorithms that now guide millions of consumer decisions daily.

This methodology represents a pivot toward "Answer Engine Optimization" (AEO), a discipline that focuses on making brand information easily digestible and authoritative for AI models. By understanding the specific signals that lead an LLM to recommend one brand over another, Jellyfish can identify gaps in a brand’s digital presence and translate those insights into actionable targeting signals for automated advertising platforms.

Case Study: Project Management Institute and the 90-Day Transformation

The efficacy of the Share of Model approach was recently demonstrated through a comprehensive 90-day campaign for the Project Management Institute (PMI). As a global professional organization dedicated to project, program, and portfolio management, PMI operates in a highly competitive space where authority and trust are paramount. The goal of the campaign was to increase global awareness and drive enrollment for its certification programs by leveraging AI-driven insights.

According to data released by Jellyfish, the campaign, which concluded in early January, yielded extraordinary results that far exceeded industry benchmarks for traditional programmatic buying. By using Share of Model insights to refine their targeting strategy, PMI saw a 20% lift in total sales volume. Furthermore, the organization experienced a 45% increase in conversion rates, suggesting that the AI-derived signals were highly effective at identifying high-intent audiences.

The most significant metric reported was a 156% improvement in return on ad spend (ROAS). This indicates that the integration of AI model analysis allowed for much more efficient budget allocation, reducing wasted spend on low-performing segments and doubling down on the contexts where the brand held the most significant influence or faced the most critical competitive threats.

Integrating AI Insights with Google’s Performance Max

A critical component of the Jellyfish strategy is the seamless integration of LLM data into Google’s Performance Max (PMax) campaigns. PMax is an automated, goal-based campaign type that allows advertisers to access all of their Google Ads inventory from a single campaign, using Google’s own AI to optimize performance across Search, YouTube, Display, Discover, Gmail, and Maps.

While PMax is powerful, its effectiveness is often limited by the quality of the "signals" or data inputs provided by the advertiser. Jellyfish’s innovation lies in using Share of Model data as a primary signal. By feeding the AI’s own understanding of brand sentiment and competitive positioning back into the PMax algorithm, the agency creates a feedback loop. This ensures that the automated buying engine is prioritizing placements that align with the brand’s strengths as perceived by the AI ecosystem.

For example, if an analysis of Gemini and ChatGPT reveals that a brand is frequently associated with "sustainability" but lacks visibility in "technological innovation," the media strategy can be adjusted in real-time to bolster the brand’s presence in innovation-focused contexts. This level of granularity allows for a dynamic media strategy that evolves as quickly as the models themselves are updated.

The Role of The Brandtech Group and the Rise of AI Agents

Jellyfish’s move into AI-driven media buying is part of a larger strategic shift within its parent company, The Brandtech Group. Since acquiring Jellyfish in 2023, The Brandtech Group has positioned itself as a leader in the "marketing-as-a-service" space, heavily emphasizing the replacement of manual processes with automated AI agents.

The agency has recently deployed AI agents designed to handle tasks that were traditionally the domain of human media buyers, such as campaign setup, keyword optimization, and performance reporting. Jellyfish claims that these agents have slashed campaign launch times by as much as 65%. By automating the administrative and technical "heavy lifting," human strategists are freed to focus on high-level creative direction and the interpretation of complex data sets like the Share of Model metrics.

This transition is not without controversy in the advertising world, as it raises questions about the future of entry-level roles in media agencies. However, proponents argue that the sheer volume of data generated in the modern digital ecosystem makes human-only management impossible. The integration of AI agents is seen as a necessary evolution to maintain pace with the speed of real-time bidding and the complexity of multi-channel attribution.

Strategic Implications and the Future of Measurement

The success of the PMI campaign serves as a proof of concept for a new era of measurement in advertising. As third-party cookies continue to be phased out and privacy regulations like GDPR and CCPA limit the ability to track individual users, "model-based" measurement offers a viable alternative. Instead of tracking a specific person across the web, brands can track the "collective consciousness" of the internet as synthesized by LLMs.

There are several key implications for the broader industry:

1. The Convergence of SEO and Media Buying: Traditionally, organic search and paid media operated in silos. The Share of Model approach forces these departments to collaborate, as the brand’s organic "authority" within an AI model directly informs the paid targeting strategy.
2. The Importance of Data Quality: For a brand to be recommended by an LLM, its digital footprint—including website content, press releases, and reviews—must be clean, accurate, and extensive. This places a premium on high-quality content creation.
3. Real-Time Competitive Intelligence: Share of Model provides a near-instantaneous look at how competitors are being perceived. If a rival brand suddenly gains traction in a specific niche according to ChatGPT, an agency can adjust its PMax signals within hours to counter the move.
4. Bias and Model Transparency: One of the challenges facing this new methodology is the inherent "black box" nature of AI models. Agencies must be wary of biases within the models that might skew brand sentiment data, requiring a layer of human oversight to ensure the data is being interpreted correctly.

Conclusion

The results achieved by Jellyfish and the Project Management Institute signal a maturation of artificial intelligence in the marketing sector. No longer confined to the periphery of "cool" tech experiments, AI is now being integrated into the foundational mechanics of how products are sold and how brands are built.

As OpenAI continues to test its own ads manager and Google further integrates Gemini into its advertising suite, the competition for "Share of Model" will likely become the next great frontier in digital marketing. For brands like PMI, the early adoption of these tools has provided a significant competitive advantage, demonstrating that in an era of algorithmic discovery, the most successful marketers will be those who learn to speak the language of the models. The 156% improvement in ROAS is not just a statistic; it is a clear indication that the future of media strategy is autonomous, data-intensive, and deeply rooted in the intelligence of the machine.