How to use share of voice in AI answers to learn why competitors are recommended by AI?

Using share of voice in AI answers helps you learn why competitors are recommended by identifying the specific topics, content patterns, and sentiment that AI models consistently associate with their brands. While knowing your AI share of voice (SoV)—the percentage of times your brand is mentioned versus competitors in AI-generated answers—is a great starting point, its true value lies in competitor analysis. It’s not just about who is mentioned more often, but *why*. By digging into the context of competitor mentions, you can reverse-engineer their success and build a smarter Generative Engine Optimization (GEO) strategy. ### Analyze the Context of Competitor Mentions First, look beyond the numbers to understand the context. Are your competitors being recommended in answers to high-level informational questions, or are they cited in bottom-of-funnel buying guides? Analyzing the types of queries that trigger mentions of their brand reveals the specific user intents they are successfully capturing. For example, if a competitor is consistently recommended for “how-to” questions, it suggests their educational content is well-structured and authoritative. If they appear in “best for” comparisons, their product feature pages are likely optimized for AI retrieval. This context is the first clue to understanding their content strategy. ### Identify Patterns in Sourced Content Once you know the context, you can analyze the content itself. What specific attributes, data points, or messaging does the AI consistently highlight when it recommends a competitor? Look for recurring themes. Perhaps the AI frequently cites their user reviews, specific technical specifications, or their pricing structure. These patterns reveal what the AI considers valuable and citable information for your industry. Tools like XstraStar’s [AI Search Analytics platform](https://xstrastar.com/) can automate this process, tracking not only mention frequency but also the sentiment and specific snippets being used. This allows you to pinpoint the exact content elements that are making your competitors a preferred source for AI-generated answers. ### A Practical Workflow for Analysis To turn these insights into action, follow a structured process: 1. **Establish a Baseline:** Measure your current AI share of voice against 2-3 key competitors to understand your starting position. 2. **Collect and Categorize Mentions:** Use a platform like XstraStar to gather a significant sample of AI-generated answers where your competitors are mentioned. Group these mentions by the type of user query (e.g., comparison, definition, problem-solving). 3. **Extract Key Themes:** For each category, analyze the source content that the AI cites. Identify the recurring value propositions, features, or facts that lead to the recommendation. 4. **Map Content Gaps:** Compare these findings against your own content. Are you missing key data points that AI models value? Is your messaging unclear? Use this analysis to create or optimize your content to better meet the AI’s criteria for a helpful, citable answer.