Begin by updating your web analytics configurations to recognize traffic from known AI sources:
- Google Analytics 4 (GA4) Filters: Create segments or custom reports for referrals containing keywords like “ai,” “openai,” “bard,” “copilot,” etc. One GA4 technique is to use a regex filter (as suggested by analytics experts) to capture any session with domains such as .ai, .openai, or copilot in the referrer. This will surface visits from AI chat tools that disclose themselves. As mentioned, Bing’s Copilot shows as copilot.microsoft.com, and ChatGPT’s browser plugin traffic may appear with search.chatgpt in the referrer. Perplexity AI and Claude (Anthropic) also have distinct referrer patterns when users click through. By tracking these, you can quantify how much traffic AI responses are driving to your site.
- Server Log Monitoring: In addition to GA4, consider analyzing server logs for known AI crawler user agents. For instance, OpenAI’s GPTBot is used to crawl sites for training data; monitoring its activity on your site tells you if your content is being ingested for future ChatGPT knowledge. Likewise, other engines might have crawlers (Google’s standard Googlebot handles SGE indexing for now, but Google has an AdsBot and possibly a “Google-Extended” crawler for AI training). Ensure your logs capture user-agent strings and use scripts to flag ones related to AI.
- Search Console & Bing Webmaster Tools: As of now, Google Search Console does not provide separate metrics for SGE impressions versus classic search. Google has stated that clicks and impressions from AI overviews are counted in Search Console, but not labeled separately. Keep an eye on total impressions and click-through rate – a sudden change when SGE rolled out could indicate impact (for example, impressions might go up while CTR goes down due to the AI answer absorbing clicks). Bing Webmaster Tools, on the other hand, might start showing data if Bing’s AI integration drives impressions; check if they have beta features for “AI interactions” data.
- Third-Party SGE Tracking Tools: Because official analytics are limited, several SEO tool providers have introduced SGE tracking solutions. Tools like SGE Tracker, SE Ranking’s AI Overview Tracker, or ROAST’s SGE Ranking Report can monitor which URLs get cited in Google’s generative answers for your target queries. These tools essentially run queries and scrape the AI snapshot to identify sources mentioned. Setting up such tools with a list of your important keywords can provide a “share of voice” metric – e.g. what percentage of the time your site appears as a source in AI results versus competitors.
- AI Search Visibility Graders: Another emerging category is AI visibility auditing. For example, HubSpot’s AI Search Grader is a free tool that runs industry-related queries and assesses how often your brand appears, providing a score for your share of voice in ChatGPT, Perplexity, and Gemini (Google). It also gauges brand sentiment in AI results. Using such a tool during your initial setup can give you a baseline “GEO score” to improve upon.
By combining GA4 data, log analysis, search engine consoles, and specialized tracking tools, you’ll establish a baseline of your current visibility in generative engines. This framework lets you attribute value to being cited even when you don’t get clicks, which is essential for making the business case for GEO work. For example, if a key informational query no longer drives traffic to you because the AI fully answers it, you can still show that your content was featured as a source, thus reaching the user in a brand exposure sense (an impression without a click). Define metrics like “AI impressions,” “AI referral traffic,” and “AI citation share” and report on them alongside traditional SEO metrics.