E-Commerce SEO is a template problem

Coverage

Why E-Commerce is served nationally

An online store ships everywhere, so its rankings are decided by catalog structure and crawl efficiency rather than a service radius. Local pages only make sense where you operate physical stores, and that belongs on a store locator, not a duplicated category page. Our work sits at the template and taxonomy level, where a single change compounds across the entire catalog, and it extends into Shopping feeds and paid media so organic and paid pull from the same product data instead of two disconnected systems.

Looking for city-level work in a local vertical instead? See the markets we serve.

AI-Powered SEO

AI-Powered SEO for large catalogs

A catalog with tens of thousands of SKUs cannot be audited page by page. We use AI-Powered SEO tooling to crawl the full site, cluster URLs by template, and flag which templates carry the crawl and indexation problems worth fixing first, turning a months-long manual audit into a prioritized list within days.

The same tooling models search demand against your taxonomy, showing which categories, filters, and attribute combinations have real query volume behind them and which exist only because a filter happens to be clickable. That model becomes the basis for what gets indexed, what gets a canonical tag, and what gets removed from crawl paths entirely.

We apply the same process to product content at scale — descriptions, specifications, and comparison copy generated from structured product data and reviewed for accuracy before publishing, so thousands of product pages carry genuine differentiation instead of manufacturer boilerplate duplicated across every retailer selling the same item.

• Template crawl audits

  Log-file and rendering analysis grouped by template so fixes are prioritized by revenue impact, not URL count.

• Demand-mapped taxonomy

  Category and facet decisions driven by actual search volume rather than internal merchandising structure.

• Assisted product content

  Structured-data-driven description and spec generation reviewed by an editor before it ships.

• Duplicate consolidation

  Canonical and redirect logic that merges split authority from variant and parameter URLs back into one indexable page.

• Core Web Vitals on templates

  Speed and layout stability fixed once per template, improving every page it generates.

Answer Engine Optimization

Answer Engine Optimization (AEO/GEO/LLMO) for product discovery

Shoppers increasingly ask an AI assistant to compare products, summarize reviews, or recommend an option before they ever open a retailer's site. Answer Engine Optimization — sometimes called generative engine optimization (GEO) or LLM optimization (LLMO) — is the discipline of structuring product and category content so those assistants can extract and cite it accurately.

That means product schema that states price, availability, and specifications unambiguously, buying guides that answer comparison questions directly instead of burying them in marketing copy, and review data that is genuinely first-party and verifiable rather than aggregated from a marketplace. Assistants favor sources they can quote with confidence, and confidence comes from clean, consistent structured data.

We build this alongside conventional SEO rather than as a separate workstream, because the same taxonomy and schema decisions that help a category page rank are what let an AI assistant cite your specific SKU instead of describing the category generically or citing a competitor's listing.

• Product and offer schema

  Structured price, availability, and variant data validated against live feed data so it stays accurate as inventory changes.

• Extractable comparison content

  Buying guides and comparison pages written with direct, quotable answers to the questions shoppers ask assistants.

• First-party review markup

  Genuine review data structured so assistants can cite sentiment specific to your listing, not an aggregated marketplace score.

• Citation monitoring

  Ongoing tracking of how assistants describe your products and categories, with content updates where the citation is missing or wrong.

Paid Media & Google Ads

Paid Media & Google Ads aligned to the same product data

Most retailers run Shopping campaigns and organic SEO as separate efforts with separate product feeds, which means Performance Max bids against inconsistent titles, missing attributes, and pricing that lags the live catalog. We build one product feed that serves both organic schema and Shopping campaigns, so a fix to a title or attribute improves ad relevance and search visibility at the same time.

Campaign structure follows the same category and margin data that drives our SEO prioritization: high-margin, high-intent categories get dedicated campaigns and bidding strategies, while low-margin or discontinued SKUs are excluded before they burn spend. Performance Max asset groups are built from the same imagery and copy standards used on the organic product page, so paid and organic present a consistent product story.

Reporting ties paid spend and organic sessions to the same revenue-by-category view, which is what makes it possible to see whether a category should get more ad budget, more organic investment, or both — instead of two teams optimizing against different numbers.

• Unified product feed

  One feed architecture powering Shopping ads and organic schema, so data fixes compound across channels.

• Performance Max structure

  Asset groups and campaigns built around margin and category data, with low-value SKUs excluded before they spend.

• Feed hygiene monitoring

  Ongoing checks for disapproved items, missing attributes, and price mismatches that quietly suppress Shopping impressions.

• Blended reporting

  Revenue by category combining paid and organic so budget decisions reflect total channel performance.