What are the methods to distinguish between GEO and SEO traffic? How to avoid double counting?

When it is necessary to distinguish between GEO and SEO traffic and avoid double counting, this can be achieved through traffic source characteristics, user behavior paths, and content reach logic. GEO traffic originates from generative AI (such as large model answers, smart assistant references), while SEO traffic comes from clicks on traditional search engine results pages. The core difference between the two lies in the reach scenario and user path. **Differentiation Methods**: - Traffic Source Characteristics: SEO traffic usually contains clear search terms (e.g., "product price") and can be tracked through search engine backends (e.g., Google Search Console); GEO traffic mostly lacks traditional search terms and is often accompanied by AI dialogue context, which needs to be identified through API interfaces or AI platform data (e.g., model reference records). - User Behavior Path: The SEO user path is mostly "search → click landing page" with clear page jumps; GEO users may directly obtain AI-generated information summaries without traditional landing page visits, which needs to be judged through session interaction data (e.g., conversation records). **Avoiding Double Counting**: - Set Differentiated Tracking Parameters: Add exclusive UTM parameters to GEO traffic (e.g., "utm_source=ai_model") to distinguish it from SEO's "utm_source=search_engine". - Analyze Behavioral Differences: GEO traffic usually has a short stay time (direct information acquisition), while SEO traffic has page browsing depth. Cross-validation can be performed through metrics such as bounce rate and page stay time. It is recommended to prioritize configuring UTM parameters and cross-validate with user behavior data, while regularly auditing the traffic attribution model to ensure independent statistics of GEO and SEO traffic data.