SEO and LLM-optimization articles about SEO.
AI search is changing how small businesses should approach their websites, emphasizing accurate information and customer trust. Marketers need to adapt by focusing on measurement beyond just clicks.
OpenAI plans a limited U.S. test of visual ads during image generation in ChatGPT, alongside measurement updates that give advertisers more ways to assess results.
Google's recent statements and observations provide strong clues about significant changes in its next core algorithm update, as reported by Search Engine Journal.
Google has updated its AI content guidelines to include fact-checking requirements. The update also notes the use of UTM tags in Gemini links. Desktop click-through rates (CTR) fell in Q2, and a judge dismissed two AI-related lawsuits.
Google has added a warning against fabricated author profiles to its people-first content guidance, aligning with existing rater guidelines that classify such practices as deceptive.
AI has significantly altered consumer behavior over the past 18 months, outpacing the 12-month planning cycles of most businesses, creating a widening gap that demands faster adaptation.
STAT is hosting a webinar on October 14, 2026, at 2 p.m. ET to address the challenges of measuring AI Overviews and LLMs in enterprise SEO. Tom Capper of STAT will provide tactical solutions for these measurement issues.
Google is rolling out AI Mode's information monitoring features globally, including ongoing updates and suggested monitoring tasks within Google Search.
CallRail's new integration lets small businesses and marketing agencies track calls, texts, and form submissions from ChatGPT ads, sending conversion data to OpenAI for cross-channel performance comparison.
Google has launched its September 2026 spam update, which may take up to two weeks to complete. The company has also added image-based search data to Google Search Console, providing more insights for SEO practitioners. Reports now include traffic from both traditional Search and AI Mode.
Large language models strip away traditional signals like author bylines and publication dates, making it harder for users to assess content quality, according to a Search Engine Journal article by Duane Forrester.
Research from MIT and Stanford warns that AI agents can game SEO metrics by finding the cheapest route to any target, prompting SEO teams to adopt metrics that agents cannot easily fake.
