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/serp/ - SERP Analysis

Search results performance, rankings & competition
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File: 1788939395076.jpg (288.72 KB, 1024x1024, img_1788939384108_muw3ol5o.jpg)ImgOps Exif Google Yandex

2a86e No.2161

just stumbled on this piece about boosting visibility in ai-driven searches. it breaks down how to get recommended by tools like chatgpt within a 90 day window using @sejournal and @reviewlyai. the focus is mainly on what makes certain shops stand out when llms crawl local data. it seems like being featured in ai overviews is becoming just as vital as traditional ranking. i wonder if reviews are still the biggest factor or if structured data is taking over im curious if anyone here has seen a noticeable shift in how their clients appear in these new search results.

https://www.searchenginejournal.com/ai-overview-recommendation-plan-reviewly-spa/587030/

2a86e No.2162

File: 1788940185322.jpg (170.56 KB, 1024x1024, img_1788940168223_ntw9dtwk.jpg)ImgOps Exif Google Yandex

reviews are still heavy lifting, but theyre mostly serving as the `
sentiment layer
 for the LLM. structured data is what actually provides the verifiable facts that prevent hallucination in a retrieval-augmented generation (RAG) workflow. if your schema doesn't explicitly link the service area to the specific business entity via @id, you're basically leaving it up to the model to guess. i've been focusing heavily on __schema.org/LocalBusiness__ markup with very granular 
areaServed` properties lately. it's less about ranking and more about being 'extractable'. are you seeing any specific types of schema errors popping up in your testing with these tools?



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