Richard Kirkendall

SEO agency we're working with has an interesting theory: ultra-granular collections based on search intent. Example: Instead of just one collection called 'Mens Shirts' with filters, they want 'Mens Blue Shirts Size L' as its own collection. Following this approach, they're adding 150+ collections total. The logic: Collections rank better than product pages in Shopify, and exact-match URLs capture specific buyer intent. Potential wins: - Capturing high-intent searches (size + color + product type) - Collection authority vs individual products - More indexed pages targeting long-tail keywords The alternative would be one strong collection with robust filtering, but that assumes shoppers use filters (many don't). Anyone tried this granular approach? Genuinely curious about real-world results.

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SEO Insight: I'm beginning to believe schema influences ChatGPT citations. For this eComm prompt set, MANY of the most cited sites use Product schema: At Nectiv, I've been having a lot of fun playing around with the AI Tracker that we've built. We're tracking a few different larger prompt sets for different industries including software and ecommerce queries. It's been cool to analyze the types of sites that get cited and different trends in ChatGPT. One thing that really stood out to me when analyzing our eCommerce prompt set. In the AI Tracker, we're analyzing 500+ prompts that are pulled from Amazon data. The goal is to give us a high level view of what influences results in the commerce space. When analyzing this prompt set, I was surprised at some of the most commonly sites domains. Many of these were editorial affiliate sites that were topically unrelated to commerce. Some of these included sites like NY Mag (3.0%), People (2.3%), FindThisBest (1.9%), Esquire (0.5%). When analyzing those site, one common thread is that the vast majority markup their editorial articles are marked up with Product structured data. Many of them use Product schema directly, while others embed it as a child element under a parent schema (NewsArticle). But it's there more oftentimes than not. FindThisBest is particular interesting. This is a site that has been completely banished from Google. Since the Helpful Content Updates have started rolling out, this site has gone to basically zero in Google search. However, it finds itself in the top 10 results for this set of commerce queries. Obviously, there could be many different factors going on, but it's very interesting to me that the editorial sites using Product schema, tend to get included in ChatGPT citations. This is making me thing that structured data elements might have a bigger role in ChatGPT citations after all. Next week, we're also hosting a webinar that goes over this and other insights we've found from tracking ChatGPT. I'll reference that in the comments below.

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Get your wallet's out, SEO's 💸 For years, SEOs and rank trackers have relied on &num=100 to pull the top 100 search results in one go. Now it’s gone (or at best only working about 50% of the time, according to Barry Schwartz) Rank tracking tools like SEMrush and Ahrefs now have to pull 10 pages of results to get the same data they used to get in one. If this change sticks, it could make keyword tracking slower, less reliable, and more expensive for everyone. #seo

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Who were the big SEO winners and losers of Google March Core Update? I checked out SERP Summary, our index of 100K Home Services SERPs, and here's some data: 1. Yelp, Lowe's Companies, Inc., and Angi were the three biggest growers, but HomeServe USA came out of nowhere to make it into the top 10. 2. Quora, Reddit, Inc., and JustAnswer were the biggest losers, but they still have a large presence in these SERPs. Guess Google is trying to dial down the Discussions and Forums after going a bit crazy since September. 3. Smaller sites that came out of nowhere appeared to be benefitting from aggressive linkbuilding campaigns (cue Gyi Tsakalakis) 4. On the other hand, some sites that got torched looked like they were perhaps too aggressive with the links. I unpacked the data in a longer post linked to in the comments.

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