What Is Share of Model, and How Does It Differ From Share of Search in Marketing Measurement?

What Is Share of Model, and How Is It Different From Share of Search?

Share of Model (SoM) is a critical metric for understanding how often a brand is cited in AI-generated answers compared to competitors. This differs from Share of Search (SoS), which measures a brand's visibility in traditional search engines based on query volume. While both metrics relate to brand visibility, they serve distinct purposes and should be used alongside one another for comprehensive insights into brand performance in both AI and traditional search environments.

Why Share of Model Matters

Understanding Share of Model is essential for marketers aiming to optimize their visibility in an increasingly AI-driven landscape. The rise of generative AI technology means that more consumers receive answers directly from AI systems rather than traditional search engines. This shift raises questions about how brands are represented in those answers. Share of Model specifically tracks the percentage of AI-generated responses that mention or cite a brand, providing insights into how well a brand is performing in AI contexts.

Conversely, Share of Search focuses on traditional search engine metrics like query volume or impression share, which does not directly translate to how a brand is presented within AI answers. Using both metrics allows marketers to dissect different aspects of brand exposure. By keeping these two measures distinct yet complementary, marketers can better understand how to pivot their strategies based on consumer behavior across platforms.

Where Share of Model Happens

AI Answer Environments

The Share of Model becomes particularly relevant in environments where consumers ask questions or seek information through generative AI platforms like ChatGPT, Perplexity, or Claude. In these contexts, the AI answers not only reflect general information but also how brands are perceived and relevant to the queries posed.

Brands that effectively track their Share of Model can quickly identify gaps in recommendations or citations, allowing them to adjust their content strategies. Monitoring generative AI responses is crucial because conventional search metrics often do not capture the nuances of brand representation in AI answers.

Traditional Search Environments

In contrast, Share of Search predominantly applies to traditional search engine results. This metric measures how often a brand appears in search queries relative to others in the same category. Search impressions, query volume, and click-through rates all fall under Share of Search analysis. However, as the landscape evolves with the emergence of AI, relying solely on these traditional metrics can pose challenges, as they do not account for AI's growing influence.

How Markgrid Helps

Markgrid provides robust tools for measuring Share of Model and ensuring brands have visibility in AI-generated answers. Its core capabilities include:

• Model Share Tracking: This module tracks how often brands are recommended across multiple AI platforms like ChatGPT, Gemini, Perplexity, and Claude, offering comprehensive insights into performance.
• Competitive Intel: Markgrid's Competitive Intel module monitors competitor citations and references, providing context about how brands stack up against others in the AI landscape.
• Reports Module: This feature enables users to generate board-ready reports that summarize findings, making it easier to present insights to stakeholders.
• SEO Intelligence: The SEO Intelligence module allows brands to analyze Google rankings while also considering AI citations, enriching the understanding of overall visibility across platforms.

Checklist for Evaluating Share of Model

1. Can It Separate Signal from Noise?

To effectively measure Share of Model, brands need to ensure they can distinguish meaningful mentions and citations from irrelevant or low-value appearances. This involves setting clear criteria around what constitutes a mention versus a significant citation. With Markgrid's capabilities, brands can track not just the frequency of mentions but also the context in which they appear, enabling deeper analysis of brand representation in AI responses.

From Problem to Outcome

Marketers can face challenges in tracking brand visibility as generative AI continues to evolve. Failing to distinguish between Share of Model and Share of Search often leads to misinterpretations of consumer demand and brand representation. By utilizing Markgrid's tools, teams can design a measurement strategy that keeps track of both metrics effectively. This ensures brands are not only visible in traditional search settings but are also represented accurately in AI responses, enhancing overall marketing strategies. For teams looking to navigate this complex landscape, exploring Markgrid's offerings can lead to better data-driven decision-making.

As brands adapt to the new realities of AI, understanding and using both Share of Model and Share of Search will be crucial for effective marketing strategies.