We are no longer living in an age where organic search would suffice for generating traffic for your business. Instead, we now rely on AI assistants that can recommend content that outweighs search engine results in quality.
The next time you open an application like ChatGPT or Perplexity for researching a topic relevant to your business and wonder, “What should I do to get my content featured here?”, the answer is pretty simple. It is all about enhancing the visibility of your brand and content in the LLM environment.
But this is, of course, easier said than done.
Let us start by understanding the importance of content visibility in the current age of AI. We will then look at the new metrics that can help you measure your visibility on AI tools and applications:
Understanding Content Visibility in the Age of AI
AI and Large Language Models (LLMs) are the driving forces behind most modern business operations and strategies. If you are wondering how it works: think of LLMs as powerful librarians whose reach is measured by analyzing billions of interactions.
Over 70% of Gen Z opt to ask their queries and questions to AI systems instead of search engines. So, companies don’t have a choice but to boost the visibility of their content on AI applications. The presence of your blog article or how-to guide on AI results may not be immediately visible in your analytics dashboard or report. However, its hidden impact is undeniable and helps you maximize your content visibility.
Your most useful and relevant content pieces are most likely to be mentioned in response to prompts on AI tools. Improve the quality by analyzing the usefulness of your content, using tools to convert ChatGPT to human writing, and integrating relevant insights and data into your content assets.
Now, let us understand the key new metrics that showcase the performance of content in terms of AI visibility.
The New Metrics for AI Visibility
When improving the visibility of your content on AI applications and tools, here are some of the new metrics you should be calculating:
AI Content Footprint Tracking
Tracking the footprint of your brand or content in AI responses is one of the first things you must measure. Known as AI Brand Footprint, this measure tells you how prominent or frequent your brand mentions are in AI-generated responses.
For instance, if your SEO agency (say, ABC Agency) has been trying to maximize presence in AI applications. The AI content or brand footprint will measure the number of times your brand has been mentioned as “A study by ABC Agency…” or “ABC Agency advises that…” in the response.
You can manipulate this using server log analysis and analytics to manage traffic and referrals generated through content in AI tools. You can calculate the total number of AI impressions your brand or content asset has gotten using the following formula:
AI Impressions = AI-Sourced Traffic/Estimated Click-Through Rate
where,
- AI-sourced traffic is the actual traffic generated by AI features, and
- Click-Through Rate is the percentage of users who clicked on a link compared to the total times it was shown in the results.
Many consider this formula to be slightly inaccurate now and see AI impressions as the number of times your content is displayed within AI-generated responses or features.
However, when calculating the impact of AI impressions on your website traffic or performance, using your branded content strategy and mentions, using the formula above can actually reveal a lot about your AI visibility.
Retrieval Frequency by LLM
LLMs are the content pickers visiting websites and choosing fresh and informative content to integrate within AI-generated responses. Brands wanting to maximize their content performance using AI tools and applications must track the LLMs that visit their website.
Use log file analysis tools like Screaming Frog Log Analyser and Splunk to track visits by “ClaudeBot” or other similar user agents for other AI tools. Consider setting up regular reporting processes to track the sections of your website that are accessed the most and the frequency at which LLMs retrieve your data.
Content Attribution in AI-generated Responses
Mentions and content snippets are great ways to get your brand noticed in AI responses. However, having LLM citations is just as effective. You can ensure that your brand or content is cited by AI tools using the following methods:
- Utilize AI monitoring tools such as Peec AI or Ahrefs’ Brand Radar to track mentions, citations, and even opportunities that you have missed. These are AI responses where your competitors might have gotten a citation instead.
- Search for your product and brand regularly within LLM environments. Try rephrased queries to catch attribution in different kinds of contexts.
- Analyze AI-focused content and increase visibility for them, using a distinct UTM or tracking code. Using that, you can easily track every referral to the asset that triggered it.
AI Visibility: How to Track When LLMs Use Your Content?
We have now looked at the new metrics to consider when measuring AI visibility of your content. However, let us look at the step-by-step process to follow when tracking whether and how LLMs use your content:
Step 1: Identify AI-Sourced Traffic in Analytics
Assuming that you already use Google Analytics 4, add a custom channel group named “AI Agents” on your dashboard for easy tracking. The primary referrers that you must track using these groups include “gemini.google.com”, “chat.openai.com“, or similar domains.
If you notice a spike in your website traffic, due to referrals from these sources, you are halfway there. Drill down further and figure out the exact pages or articles generating all this attention and traffic to your website.
Step 2: Analyze Log Files for Bot Activity
Download your raw server logs and run scripts so you can easily differentiate LLM bots like GPTBot and ClaudeBot from the rest of your referrers. While you can do it manually, you can also use software like Screaming Frog Log File Analyser to filter and identify specific bot traffic.
By conducting a comprehensive log file analysis, you can track the frequency of bot crawlers on your website, paths, and any anomalies in the process. For instance, you can easily find answers to questions like: “Does GPTBot hit your blog posts more often after you changed the content strategy?” or “Does PerplexityBot visit your website more often compared to ClaudeBot?”
Step 3: Sample AI Output Systematically
Compiling a set of AI prompt samples will help you analyze your brand presence and key content assets regularly. To fully understand how each brand-related prompt works, run them through various AI tools and note the output. Doing so will help you identify and increase instances of any brand citations.
By conducting regular tests, you can soon see emerging patterns across LLMs for all your AI prompts and outputs. You can also assess how your competitors are performing on the tools by tracking their brand mentions and citations.
Step 4: Leverage Monitoring Platforms
To accurately track and measure various AI metrics and performance, you can either take a manual approach or use a robust monitoring platform. Most of them offer comprehensive and interactive dashboards that can help you track the real-time performance of your content on AI platforms.
When utilized to their fullest potential, these monitoring tools and platforms can also help benchmark your content performance against your competitors’. By tracking parameters such as the most cited pages, AI mention gaps, and more, you can identify what your competitors are getting right and what you can improve on.
Step 5: Reverse-Engineer Any Missed Opportunities
Use AI audits to identify prompts your brand should be mentioned for, but your competitor is instead, and note it down as a missed opportunity. In such cases, start by auditing your competitors’ content for recency, authority, and structure. After that, you can effectively identify the areas that they are getting right to help them get those citations. You can then consider refactoring or upgrading your content so that you address these prompts directly.
Making the Case for New Metrics to Measure AI Visibility
Regardless of your knowledge of AI, you may be aware that its online behavior is different from that of humans. The conventional analytics and metrics basically measure human interaction and activity, but new metrics track your interactions with LLMs. These “ghost” interactions may not exactly show up on dashboards. However, these are highly effective in generating the kind of results you are aiming for.
You can address this gap between traditional and new AI-focused metrics by adding AI-related parameters to your periodic performance reports.
Moreover, you can also expect the following benefits by implementing AI visibility metrics:
- When AI tools cite your brand consistently, you can accumulate more algorithmic trust and accelerate relationship building.
- Using retrieval logs and mention audits, you can unlock powerful insights about content that is performing well on AI.
- Gaining AI visibility early can grant a distinct competitive advantage and moats that will help you stay ahead of competitors.
Concluding Remarks
The new metrics for AI visibility can not only help identify useful insights but also help shape your content performance on AI tools and platforms. Double down on your log analysis, platform analytics, and use monitoring tools strategically to position your brand for optimal performance in an AI-first era.
Ensure that your content is citation-ready, and help your brand gain prominence on AI platforms — one prompt at a time. However, it is pivotal that you act immediately, so that you can successfully outpace the industry laggards and competitors for years.
