Mastering Organic Search in the AI Era

A more in-depth investigation is required when search data indicates inadequate content performance. Brian Piper, the University of Rochester’s content strategy director, explained at the Marketing Analytics & Data Science Conference how his team improved its SEO strategy to adapt to the AI era.

Seeing Organic Traffic Drop to Almost Zero

A University of Rochester article first received considerable attention from newsletters and social media before falling dramatically. While this met immediate awareness aims, the team desired long-term results. They devised a data-driven content strategy centered on search optimization. The strategy was effective, with organic search traffic increasing 540% between 2017-18 and 2022-23.

Increasing Rankings and Organic Search Traffic

SEO remains essential to marketing analytics, although increasingly unexpected. The University of Rochester’s data-driven approach showed many important strategies:

• Meta descriptions: Including specific program names increased click-through rates.
• Images: Incorporating text and logos into photos now improves search visibility.
• Social media: Optimizing graphic assets and profile keywords for search results.
• Voice search: Targeting featured snippets to become the only voice search result.
• Community platforms: Participating on sites like Reddit where younger generations seek, leveraging expertise to deliver value without direct selling.

The emphasis is on continuous data analysis, modification implementation, and results measurement to boost organic traffic.

Optimizing Content for AI-generated Results

Modern SEO must consider AI platforms in addition to traditional search. The University of Rochester team examines the presentation of their content in ChatGPT and Gemini to determine how to boost visibility. For example, their piece “Why Did the United States Enter World War I?” was written to appeal to students interested in the subject. While ChatGPT cited but did not highlight the article, Google’s AI did. The team is now analyzing AI citations to optimize content for both traditional search and AI platforms.

Using AI to Help with SEO Analytics

Brian emphasized that AI may improve marketing analytics beyond simply optimizing content for search engines. He addressed Google Search Console’s limitations, which include a lack of clarity in displaying page URLs and keywords, by creating a comprehensive dashboard using Looker Studio. This program collects search data for their NewsCenter articles, allowing him to know which keywords rank for certain URLs.

Brian then exported these numbers to ChatGPT, where he used the RACE methodology to develop 20 content ideas specifically designed to interest potential students. This method is intended to increase the University of Rochester’s knowledge and improve search ranks.

Experimenting with AI and AI SEO

Marketers and analysts should investigate and experiment with AI engines, as Brian did. Make data-driven recommendations, test them, and tweak them as needed. Keep in mind that successful strategies may shift as search engines evolve.

Source- contentmarketinginstitute