SEO SOP: Run a Product Page Attribute Pass AI Will Recommend (Special Edition)

AI assistants now sit between a buyer’s interest and the purchase, and they pick products from whatever the page actually says. Ernesto Ortiz of Structural SEO came back to Unscripted SEO for a Special Edition lightning round, and his one action item was a page-level pass most stores have never run: “You need to grab all the features and attributes of the products and services that you sell and describe them really well and describe who they’re for.” This SOP turns that pass, plus his four questions, into something you can run on your own catalog or a client’s this week.

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Objective

You finish with product and service pages that answer the exact, narrow question a buyer types into an AI assistant: every feature and attribute written out plainly, and a clear line on who the product is for, who it is not for, what it includes and what it doesn’t. Ernesto’s reason is the way people shop now: “There’s a new recommendation intermediary between interest and purchase, and it’s driving people’s decision.” That layer often sends no click, “so people think it doesn’t matter but it’s really influencing decisions.” He doesn’t say the play is new. He adopted it about two years ago, and his view is that “not a lot of people are doing it.”

Key Steps

  1. Pick the pages that sell. List your product pages and your service pages. Ernesto ran this with a couple of clients, “where we just went through most of their product pages.” Start with the pages that bring in the most revenue and work down the list. (the order is inferred – he describes covering most pages, not which ones first)
  2. Pull every feature and attribute into one sheet. One row per product, one column per attribute: size, material, compatibility, what’s in the box, the problem it solves. (the sheet is inferred – Ernesto describes the pass, not the tool) His bar is total coverage: “every attribute and feature needs to be thoroughly covered and explained in a way that it’s very simple to do and understand.”
  3. Write each attribute out in plain words. A spec table alone is not an explanation. Say what each attribute means for the buyer, in a sentence a non-expert can follow. Ernesto’s whole instruction for the pass: “Just being extremely clear.”
  4. Answer the four questions on every page. Ernesto’s checklist, in his words: “Who’s it for? Who’s it not for? What does it include? What doesn’t it include?” He means the last one literally: “Literally what doesn’t it include?” Give each answer its own short block on the page so it is easy to find and easy to quote.
  5. Write who it’s NOT for – when you can say it honestly. This is the half most pages skip. Ernesto: “try to by inference describe or literally if you can, if it’s clear, who they’re not for.” If the product truly fits everyone, leave it out rather than make up a limit.
  6. Turn “who it’s for” into a specific use case. A general audience line doesn’t match the narrow questions buyers now ask. On the episode I gave two examples: a tool for product managers who are using additional analytics platforms, and an interior paint for homes with long winters – anybody in the northern latitudes or snowy Canada. Ernesto’s shampoo example shows why: ask for the best shampoo for a thirty-year-old with a specific problem, climate and hair type, and Google used to return a terrible result while AI gives a precise answer.
  7. Add the bottom-of-funnel detail. This one is my addition from the episode, not Ernesto’s: refund information, shipping and packaging belong on the product page too. And there’s no longer an excuse to skip pages. It doesn’t matter if you have a thousand products; AI makes it efficient to describe every one of them.
  8. Check the page the way a buyer shops. Ernesto described his own routine while furnishing a new apartment and picking a dishwasher: “I ask it for the recommendation and I sort of go with what it tells me.” Then “I just use Google to check availability, pricing and reviews.” Ask an AI assistant the narrow question your buyer would ask, then run the same product through Google. Note whether your product comes back and whether the answer uses your attributes. (the log is inferred)

Cautionary Notes

  • Don’t promise a number. Ernesto says “we’ve just had great results with that” and gives no figures, so this SOP has none either. Record a baseline for each page before you start so you can judge your own result. (inferred)
  • Don’t write a “who it’s not for” line you can’t stand behind. Ernesto’s own qualifier is “if it’s clear.” A wrong exclusion costs sales; a missing one costs nothing.
  • Don’t judge the pass by a rank tracker alone. Ernesto says “we can’t trust tools as much as we could a couple of weeks ago” because results for automated requests aren’t the same as the ones a human sees. He hasn’t made up his mind whether that is a bug or intentional, and his framing of the gap is “how are you gonna trust results that are thirty percent off?” Spot-check by hand.
  • Don’t expect Search Console to show you the long-tail queries this pass wins. Ernesto points out that query hiding in GSC isn’t new: “it’s always been quite annoying.” My dark-click study of 38 Search Console sites found the mean site gets no query data for 63.6% of its clicks, and the median site 72%.
  • Don’t measure the pass on traffic only. The AI layer influences the decision without sending the visit, so watch conversions and sales on the pages you rewrote, not just sessions. (inferred)

Tips for Efficiency

  • Use AI to draft the attribute copy at scale, then have someone who knows the product check every claim before it ships. (the review step is inferred)
  • Run the same pass on service pages. Ernesto’s instruction covers “the products and services that you sell.”
  • Small brands can win this. Ernesto sees small, niche brands show up above massive industry giants “because they’ve actually defined it and talked about it.” Being specific is the edge.
  • Mine your reviews for the “who it’s for” language buyers already use, with the review-language use-case pages SOP, then check what the models say about you with the LLM consensus audit SOP.

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