StoreMend Research

Shopify stores, audited. Here is what the data says.

Two cohorts of different stores: 1,091 audited in Q2 2026, and 867 measured by code in October 2026. Every page names the cohort its figures come from, and the two are separate measurements, not a trend.

Free to read, free to download, and licensed CC BY 4.0. Attribution lines are published at the foot of each page.

Published research
Index

Shopify AI-Readiness, Q3 2026 cohort

A second, separate cohort: 867 Shopify stores measured by code in October 2026. 96.3% serve the llms.txt Shopify generates, 1.5% block an AI crawler, and 85.4% serve Product schema on the product page. CSV and JSON under CC BY 4.0.

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Benchmark

Apparel benchmark

205 apparel storefronts, the largest single vertical in the corpus. 67% ship no Product or Organization JSON-LD, and 70% fall in the AI-invisible tier. Apparel tracks just under the corpus average on both.

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Benchmark

Beauty benchmark

171 beauty storefronts. 74% ship no Product or Organization JSON-LD, the highest schema-absent rate of any vertical clearing the 50-store floor. App density displaces the structured-data surface.

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Benchmark

Food and CPG benchmark

146 pantry, snack, sauce, and beverage storefronts. 70% ship no schema, and the dietary and allergen filters food buyers actually run are exactly the queries structured data controls.

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Benchmark

Electronics benchmark

108 electronics storefronts, the lowest schema-absent rate of the verticals above the floor at 65%. Spec-comparison buying runs through AI assistants, which read the structured block rather than the page.

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Method

Methodology

How the corpus is built and what each term means: roughly 140 deterministic checks per store, 1,077 of 1,091 classified to a primary cluster, the n=50 statistical floor, and the definitions behind AI-shoppability, schema-absent, and the invisible tier.

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