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AI visibility for BigCommerce

AI search visibility for BigCommerce, until the SEO apps catch up.

BigCommerce SEO apps don't yet cover AI search. eCommerce Insights plugs the gap with channel-aware audits, drop-in JSON-LD additions, and per-SKU recommendations for ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, and Copilot.

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Works on any storefront URL · No app install required · BigCommerce marketplace app on the way

BigCommerce storefront audit
Theme
Cornerstone · Stencil
SKUs sampled
240 of 1,820
Engines tracked
6
JSON-LD blocks scored
1,820
Custom Fields gaps
7

Illustrative output. Audit reads the public Stencil-rendered HTML.

The BigCommerce ecosystem moves fast on PWA, on Catalyst, on multi-storefront and B2B Edition. The marketplace's SEO apps haven't kept up. Most still focus on title tags and Google rank tracking. Meanwhile ChatGPT Shopping and Perplexity Shopping are already routing shoppers based on what your PDP says, not what your meta description says. eCommerce Insights is the layer that scores BigCommerce PDPs against the AI engines that matter today, and tells your team what to change in product fields and Custom Fields to keep up.

The BigCommerce gap

Five things BigCommerce SEO apps miss on AI search.

AI engines

No coverage of the six engines that matter.

Marketplace SEO apps track Google ranks. AI shopping happens on ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, and Copilot. eCommerce Insights scores each SKU against all six, every week, with citation counts you can sort by engine.

JSON-LD

Partial Stencil schema, no fix path.

The Cornerstone theme and most Stencil themes emit some Product schema, but with gaps that block answer engines: missing brand entity, missing offers block, missing identifier fields. eCommerce Insights identifies the gap and produces a drop-in JSON-LD block you can paste into a Stencil snippet or a script tag.

Custom Fields

Custom Fields aren't being read.

BigCommerce Custom Fields are powerful but invisible to most AI audit tools. eCommerce Insights reads them, scores their AI usefulness (material, country of origin, GTIN, dimensions), and recommends which Custom Fields to add to the product type to fill the gaps Perplexity Shopping cares about.

Variants

Options and Modifiers, treated as SKUs.

BigCommerce's Product Options and Modifiers produce variant SKUs. eCommerce Insights scores each variant on its own, because ChatGPT Shopping and Perplexity Shopping cite them as distinct products. A generalist crawler that scores the parent product loses the signal where the revenue lives.

Multi-storefront

Multi-storefront, one dashboard.

BigCommerce's multi-storefront feature is common in the mid-market. eCommerce Insights groups audits by storefront and rolls them up to the parent brand, so an Enterprise customer running US, UK, and DE storefronts sees per-region citations without switching tools.

B2B Edition

B2B Edition catalogs, audited.

For BigCommerce B2B Edition stores with public-facing catalog pages, eCommerce Insights audits the same way it does B2C. For gated catalogs, the audit reports on what's public and flags the auth wall as a likely AI visibility blocker, which it usually is.

How it works

Storefront URL to PDP diff in four steps.

  1. 01

    Drop in your storefront URL.

    eCommerce Insights crawls a sample of PDPs and category pages from the public Stencil-rendered storefront. No marketplace app install. No API token. The audit uses the same surface AI engines crawl.

  2. 02

    Per-SKU scores, BigCommerce-aware.

    Each SKU gets a 0–100 AI visibility score, broken down by structured data, content depth, review signal, entity clarity, and crawl surface. Recommendations are written for BigCommerce: product fields, Custom Fields, Product Options, and Stencil theme snippets where relevant.

  3. 03

    Apply the diffs through the admin.

    Edit product fields through the BigCommerce product editor, or sync the corrections via the Catalog API if your team already has a PIM pipeline. JSON-LD additions go into a Stencil snippet or a Script Manager script. No theme fork required.

  4. 04

    Re-scan weekly. Track citations over time.

    eCommerce Insights re-crawls on a weekly cadence and tracks citation counts on the six tracked engines. A marketplace app for BigCommerce that adds direct catalog reads and writes is in development (Early access).

Why this beats a generic tool

BigCommerce SEO apps vs. eCommerce Insights on AI search.

Generic SEO app

Title tags and meta descriptions.

The marketplace SEO category is built around Google search rank, sitemap hygiene, and on-page basics. It was designed for the blue-link era. AI search isn't on the radar; per-SKU AI engine citations aren't in the report.

  • No AI engine citation tracking
  • No per-SKU AI score
  • JSON-LD audit is binary (present/absent)
  • Custom Fields ignored as a structured-data surface
eCommerce Insights

AI search readiness, end to end.

eCommerce Insights was built around the assumption that AI engines are now the primary consumer search surface for some categories. Every score, every recommendation, every diff is for that world.

  • Six AI engines tracked weekly
  • Per-SKU 0–100 score
  • Drop-in JSON-LD additions
  • Custom Fields recommendations by product type
Brand profile

A mid-market outdoor brand, $25M GMV on BigCommerce Pro with a Cornerstone child theme.

Eighteen hundred SKUs across 12 product types. The team had three SEO apps installed, none of which surfaced AI search. The first eCommerce Insights audit showed Cornerstone's default Product JSON-LD missing the brand entity, the offers block, and three identifier fields across the entire catalog. A single Stencil snippet fix shipped the JSON-LD additions globally. Within two scan cycles, ChatGPT Shopping citations on the priority SKUs were tracking up. The Custom Fields recommendations (material, country of origin) became the next sprint.

Illustrative brand profile. eCommerce Insights does not publish customer case studies without permission.

Stencil snippet · product/json-ld.html
+ "brand": { "@type": "Brand", "name": "{{product.brand.name}}" },
+ "gtin": "{{ custom_field 'gtin13' }}",
+ "material": "{{ custom_field 'material' }}",
Ships once. Applies to 1,820 SKUs.

BigCommerce gave you the catalog model. eCommerce Insights tells you which fields the AI engines are actually reading.

Audit a PDP now

Drop your BigCommerce PDP URL, get a scorecard.

Free audit covers one PDP. The free trial covers up to 500 SKUs and re-scores weekly across all six AI engines.

Related

Keep reading.

Feature

SKU-level tracking

The tracking primitive eCommerce Insights is built around. Every SKU, every engine, every week.

 Channel

For headless storefronts

Running BigCommerce Catalyst or a custom Next storefront? Use the JavaScript-rendered audit workflow.

 Guide

Schema for AI search

What Product JSON-LD fields the AI engines actually look for. The fields most Stencil themes are missing.

How eCommerce Insights turns an audit finding into a shippable PDP diff

Further reading: the BigCommerce Stencil documentation, which describes the theming framework eCommerce Insights's recommendations target.

Ask AI about eCommerce Insights for BigCommerce

Have your favorite AI engine summarize this page for your specific use case.

Frequently asked questions

Why isn't there a eCommerce Insights BigCommerce app yet?
There is one in development for the BigCommerce App Marketplace (Early access). Today, eCommerce Insights works on BigCommerce stores through the public storefront audit, which doesn't require an app install. When the marketplace app ships, it adds catalog API reads, variant resolution from option sets, and the option to push approved JSON-LD via a script tag injected through the BigCommerce admin.
Does eCommerce Insights work with Stencil themes?
Yes. Stencil is BigCommerce's standard theming framework, and eCommerce Insights reads the public HTML the same way ChatGPT and Perplexity do. Recommendations call out Stencil-specific patterns where they apply: handlebars partials that render product specs, snippet blocks for description content, and the schema.org JSON-LD output that ships with most Stencil themes by default.
What about headless BigCommerce with Catalyst or a Next.js storefront?
Use the headless workflow on Analyze a Page with the "Force JavaScript rendering" toggle enabled. Catalyst, Next Commerce, and Makeswift-driven BigCommerce frontends inject content client-side; eCommerce Insights's static fetch sees an empty shell without rendering. The toggle runs the audit through a headless browser that executes the JavaScript and returns the same DOM the AI engines see.
How is this different from the BigCommerce SEO apps already on the marketplace?
Existing BigCommerce SEO apps focus on title tags, meta descriptions, redirects, sitemap hygiene, and keyword tracking against Google's blue links. Few include AI search at all, and none score per-SKU against ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, and Copilot. eCommerce Insights's audit measures answer-engine readiness: structured data completeness, citation surface, entity clarity, PDP answer-coverage. Different question from traditional SEO.
Can eCommerce Insights push JSON-LD or content into BigCommerce automatically?
Not until the marketplace app ships (Early access). Today, eCommerce Insights produces the JSON-LD additions and PDP copy diffs; your team applies them through the BigCommerce admin product editor or by editing the Stencil theme files. The diffs are formatted as drop-in JSON-LD blocks and copy-paste-ready product description content.
What does the BigCommerce-specific audit cover?
Product Name (used as the H1 on most Stencil themes), Product Description (HTML field that AI engines parse for answers), Product Options and Modifiers (variant signal), Custom Fields (the BigCommerce analog to Shopify metafields and Magento attributes), Categories and Brand entity tagging, and the JSON-LD emitted by the theme. Recommendations land in this vocabulary.

Audit a BigCommerce PDP in 60 seconds.

No app install. No API token. Drop the URL, get the diff.

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