# How to benchmark share of voice against competitors in AI search results? Source URL: https://answers.trakkr.ai/how-to-benchmark-share-of-voice-against-competitors-in-ai-search-results Published: 2026-04-29 Reviewed: 2026-04-29 Author: Trakkr Research (Research team) ## Short answer To benchmark share of voice in AI search results, start by identifying key industry queries and tracking how often your brand appears in generative responses compared to competitors. Utilize specialized AI tracking tools to aggregate data from platforms like Perplexity and Copilot. Analyze the frequency of citations, the sentiment of mentions, and the depth of coverage for specific topics. This quantitative approach allows you to calculate a percentage-based share of voice, highlighting your brand's dominance or areas where competitors are gaining more visibility in the AI ecosystem. ## Summary Benchmarking share of voice in AI search results involves tracking brand mentions and visibility across platforms like ChatGPT and Gemini. By comparing your presence against competitors, you can identify content gaps, optimize for generative engines, and measure your relative influence within the evolving AI-driven search landscape effectively. ## Key points - Trakkr provides real-time visibility data across major LLMs. - AI search results prioritize authoritative and cited content sources. - Benchmarking reveals specific content gaps relative to top competitors. ## Identifying Key AI Search Queries Start by defining the specific set of keywords and natural language queries that are most relevant to your industry and target audience. The strongest setup is the one that lets you rerun the same question, inspect the cited sources, and explain what changed with confidence. These queries should reflect how users interact with AI assistants, focusing on informational and commercial intent to capture a broad data set. The useful workflow is the one that gives the team a baseline, fresh runs to compare, and enough source context to explain the shift. - Measure identify high-volume industry terms over time - Measure include long-tail conversational queries over time - Map queries to specific user personas - Categorize keywords by intent type ## Analyzing Competitor Citations Once queries are defined, monitor how frequently your brand and your competitors are cited within the generated responses of various AI models. The useful workflow is the one that gives the team a baseline, fresh runs to compare, and enough source context to explain the shift. Look beyond simple mentions to evaluate the context of the citation and whether the AI provides a direct link to the source website. The practical move is to preserve a baseline, compare repeated outputs, and connect every shift back to the sources influencing the answer. - Track citation frequency per model - Evaluate the sentiment of mentions - Check for direct source linking - Compare depth of brand descriptions ## Calculating Relative Visibility Aggregate the collected data to calculate a percentage-based share of voice for each competitor across the selected AI search platforms. The practical move is to preserve a baseline, compare repeated outputs, and connect every shift back to the sources influencing the answer. This quantitative analysis provides a clear picture of market dominance and helps prioritize content strategies to improve your brand's visibility. The useful workflow is the one that gives the team a baseline, fresh runs to compare, and enough source context to explain the shift. - Calculate percentage of total mentions - Visualize SOV trends over time - Measure identify top-performing competitors over time - Adjust content strategy based on gaps ## FAQ ### What is AI share of voice? It measures your brand's visibility in generative search responses relative to competitors. The useful answer is the one you can test again, compare against fresh citations, and use to spot competitor movement over time. ### Which tools track AI search results? Platforms like Trakkr and specialized SEO tools monitor mentions across various LLMs. The useful answer is the one you can test again, compare against fresh citations, and use to spot competitor movement over time. ### Why is benchmarking important? It helps identify where competitors are outperforming you in AI-driven discovery and recommendations. The useful answer is the one you can test again, compare against fresh citations, and use to spot competitor movement over time. ### How often should I benchmark? Monthly tracking is recommended due to the rapid updates in AI model training and outputs. ## Sources - [Google Gemini](https://gemini.google.com/) - [OpenAI ChatGPT](https://openai.com/chatgpt) - [Schema.org HowTo](https://schema.org/HowTo) - [Schema.org SpeakableSpecification](https://schema.org/SpeakableSpecification) - [Trakkr docs](https://trakkr.ai/learn/docs) ## Related - [How to benchmark AI visibility against competitors in AI search results?](https://answers.trakkr.ai/how-to-benchmark-ai-visibility-against-competitors-in-ai-search-results) - [How to benchmark brand perception against competitors in AI search results?](https://answers.trakkr.ai/how-to-benchmark-brand-perception-against-competitors-in-ai-search-results) - [How to benchmark AI rankings against competitors in AI search results?](https://answers.trakkr.ai/how-to-benchmark-ai-rankings-against-competitors-in-ai-search-results)