How to audit your Shopify catalog for AI readiness.

How people do this without eCommerce Insights

The manual audit is a week of junior-analyst time. You export the Shopify product CSV. You crawl the store with Screaming Frog to capture schema. You pull Google Search Console data for each PDP. You manually spot-check ten or twenty PDPs in ChatGPT and Perplexity. You open a spreadsheet with twelve columns — SKU, URL, schema completeness, word count, review count, title, meta description, canonical, GSC impressions, ChatGPT citation, Perplexity citation, notes — and start filling it in.

By day three you notice you have not touched collection pages or policy pages or the blog. You have not checked whether the llms.txt exists or the robots.txt allows AI crawlers. You have no benchmark to compare against because the category is new enough that public benchmarks are thin. The data quality in the spreadsheet is uneven because three analysts worked on it and each interpreted "schema completeness" differently.

Leadership asks for the one-page summary. You spend another day turning the twelve-column spreadsheet into a slide. The result is honest, incomplete, and out of date the moment it ships.

How to do this in eCommerce Insights

1. Connect your Shopify store to eCommerce Insights. Install the Shopify admin app or use Shopify Partner auth. eCommerce Insights reads your full catalog: every product, every variant, every metafield, every collection, every policy page, your theme's schema output, your robots.txt, your llms.txt if it exists.
2. Run the full-catalog scan. One click. eCommerce Insights parses every PDP, queries the six tracked AI engines with category-typical buyer prompts for your hero SKUs, checks crawl-surface signals, and runs schema validation. A 2,500-SKU catalog scans in about 40 minutes. You can leave the tab open or have eCommerce Insights email the report when done.
3. Review the one-page readiness report. The report opens on a dashboard showing composite AEO score (0-100), five-factor breakdown, catalog distribution (how many SKUs are in each readiness tier: ready, partial, work to do, foundational), top ten gaps ranked by expected score lift, and the category benchmark — the median score across Shopify stores in your industry vertical in eCommerce Insights's audit data.
4. Export PDF or CSV. The PDF is board-ready and branded (for agencies the agency logo replaces eCommerce Insights's in exports). The CSV includes per-SKU scores, per-factor scores, the ranked gap list per PDP, and the Shopify handle so you can join it to any internal data. Most teams use the PDF for leadership reviews and the CSV for the sprint roadmap.
5. Compare against the category benchmark. The report includes the median score for Shopify stores in your vertical. The benchmark is illustrative in categories with small samples, but it gives leadership a reference point beyond "we got a 62."

For agencies running audits across multiple brands: see how to onboard a new brand, which uses this audit as the Week-1 artifact.

What "good" looks like

• Composite AEO score above 70. Below 70 is "work to do," above 80 is "ready." Most mid-market Shopify catalogs start between 50 and 65.
• No single factor below 40. A balanced catalog has no gaping weakness; an unbalanced one (e.g. structured data 85, review signal 25) usually reflects a specific tool or integration gap.
• Less than 10% of SKUs in the "foundational" tier. These are products scoring below 40, and they typically need rebuilds rather than tweaks.
• Quarter-over-quarter lift of 8-15 points at a mid-market catalog committing one engineer-day per week to optimization. Lifts larger than that are possible in the first quarter when starting from a low base.

Archive the audit score every quarter. A twelve-month trend line is where the story lives.

Related jobs

Related tools

• AEO Grader — free, 60-second preview of the full audit.
• Shopify SKU Visibility Grader — five-SKU citation check.