Your SEO apps track blue links. Your buyers ask ChatGPT.
The BigCommerce marketplace's SEO apps still center on title tags and Google rank. Meanwhile ChatGPT Shopping and Perplexity route shoppers based on what your PDP and its schema say. eCommerce Insights scores every BigCommerce product against the six AI engines and ships the fixes as product-field edits, Custom Fields, and drop-in Stencil JSON-LD.
Good catalog model, partial AI surface.
Stencil ships Product JSON-LD with gaps.
Cornerstone and most Stencil themes emit some Product schema — typically without the brand entity, a complete offers block, or identifier fields. Engines that can't disambiguate your SKU cite a competitor whose schema is complete. One snippet fix usually applies catalog-wide.
Custom Fields are invisible to most audits.
BigCommerce Custom Fields — the analog to Shopify metafields and Magento attributes — can carry material, country of origin, GTIN, and dimensions. Most tools never read them. The audit scores their AI usefulness and recommends which to add per product type.
Options and Modifiers produce citable SKUs.
Product Options and Modifiers generate variant SKUs that ChatGPT Shopping and Perplexity cite as distinct products. A crawler that scores only the parent loses the signal where the revenue lives. Variants are scored individually, with parent rollups for triage.
Both scores, in BigCommerce vocabulary.
| Check | Score | BigCommerce surface |
|---|---|---|
| Product JSON-LD completeness — brand, offers, gtin, material | Both | Stencil theme |
| Custom Fields coverage scored for AI usefulness per product type | Citation | Custom Fields |
| Variant resolution across Product Options and Modifiers | Citation | Options / Modifiers |
| Product Name and Description answer coverage vs. buyer queries | Citation | product fields |
| Review signal — recency plus Review and aggregateRating markup | Citation | reviews app |
| robots.txt admittance for GPTBot, PerplexityBot, ClaudeBot, Google-Extended | Agent | robots.txt |
| Machine-readable price, availability, returns and shipping policies | Agent | schema + web pages |
| Multi-storefront rollup — per-region scores under one brand | Both | multi-storefront |
Theming reference: BigCommerce Stencil documentation. Methodology: citation score.
One Stencil snippet. 1,820 SKUs.
Product-field and Custom Fields diffs apply through the BigCommerce product editor, or sync via the Catalog API if your team already runs a PIM pipeline. JSON-LD additions ship as a drop-in block for a Stencil snippet or a Script Manager script — written once in the theme, applied to every SKU it renders. Until the marketplace app ships (Early access), nothing writes to your store automatically; diffs are reviewed, approved, and applied by your team, with CSV export on every plan.
Weekly re-scans then track citation counts per SKU across the six engines, so the snippet fix shows up as a score delta — not a feeling. See PDP optimization for the full diff workflow.
A Cornerstone catalog, before and after.
Illustrative: a mid-market outdoor brand on BigCommerce with a Cornerstone child theme and 1,800 SKUs across 12 product types. The first audit found the theme's default Product JSON-LD missing the brand entity, the offers block, and three identifier fields across the whole catalog. A single Stencil snippet shipped the additions globally; material and country-of-origin Custom Fields became the next sprint. Citation counts on priority SKUs moved over the following two scan cycles.
Numbers are illustrative; no customer case studies are published without permission.
Related pages.
Product-level tracking
Every SKU, every engine, every week — the primitive under every channel page.
GuideSchema for AI search
The Product JSON-LD fields most Stencil themes are missing, and why engines care.
ChannelFor headless
Catalyst or a custom Next.js storefront? The rendered-audit workflow.
Also: For WooCommerce · best AI visibility tools for D2C · the product overview.
Frequently asked questions
Does eCommerce Insights need a BigCommerce app install?
No. The audit reads the public Stencil-rendered storefront — the same surface AI engines crawl — so there is no app to install and no API token to mint. A BigCommerce marketplace app that adds Catalog API reads and script-tag push is in development (Early access).
What does the BigCommerce-specific audit cover?
Product Name (the H1 on most Stencil themes), Product Description, Product Options and Modifiers as the variant signal, Custom Fields as the structured-data surface, Brand and Category entity tagging, and the JSON-LD the theme actually emits. Recommendations land in that vocabulary, not a generic one.
My Stencil theme already outputs Product schema. What's missing?
Usually the brand entity, the offers block, and identifier fields like gtin — Cornerstone and most Stencil themes ship partial Product JSON-LD. The audit reads what your storefront serves today and produces a drop-in addition for a Stencil snippet or Script Manager script that fills exactly the gaps found.
Does it handle multi-storefront BigCommerce?
Yes. Audits group by storefront and roll up to the parent brand, so a team running US, UK, and DE storefronts sees per-region citation counts in one dashboard instead of three tools. Each storefront keeps its own query bank and scores.
What about headless BigCommerce on Catalyst or Next.js?
Use the rendered-audit workflow: Catalyst and Next.js storefronts inject product content client-side, so the audit runs through a headless browser that executes the JavaScript and reads the same DOM the AI engines see. The headless solutions page covers it in detail.
Ask AI about eCommerce Insights for BigCommerce
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BigCommerce gave you the catalog model
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No app install. No API token. First scan within 24 hours.
6 engines · per-product · weekly