Programmatic SEO
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Vertical programme
SEO and AI visibility for eCommerce
Retail discovery has fragmented across marketplaces, social and assistants, and a growing share never touches a results page.
At a glance
- Main obstacle
- Marketplaces
- Key work
- Catalogue architecture
- Critical data
- Product schema
- Measured by
- Category, not sitewide
- Includes
- Facet policy
What changes in this vertical
Competing with marketplaces on transactional queries for commodity products is not winnable. The demand worth owning is the research that happens before that.
Marketplaces intercept the search
Category demand often resolves inside a marketplace, not on a results page.
Structured data is the entry condition
Product recommendations lean on specifications, availability and reviews.
Crawl waste is the silent cost
Uncontrolled facets generate millions of near-duplicate URLs competing with each other.
How shoppers actually reach a product page
Comparison, specification and compatibility queries are winnable, and marketplaces answer them poorly.
Research precedes the marketplace
The comparison happens before the transaction moves elsewhere.
Specification queries convert
Compatibility and specification detail is high-intent and under-served.
Assistants read structure
Clean product data is what makes a product recommendable.
Where programmes usually go wrong
The recurring problems are catalogue architecture, not creative.
Manufacturer descriptions on every product
Identical copy across hundreds of retailers gives a search engine no reason to prefer you and a model nothing distinctive to quote.
Differentiated content where it counts: Original detail on the products that drive revenue, rather than thin rewrites across the catalogue.
Uncontrolled faceted navigation
Filter combinations generate near-duplicate URLs that consume crawl budget and compete with each other.
Crawl and index control: A deliberate policy for which facet combinations are indexable, based on real demand.
Category pages treated as grids
Category pages with no content lose to editorial competitors on the queries that convert.
Category pages as buying guides: Structured guidance and comparison alongside the grid.
Product schema left incomplete
Missing availability, price and review markup keeps products out of rich results and AI recommendations.
Complete, validated product data: Full specification and availability markup maintained as inventory changes.
How an engagement runs here
The same five phases we run for every client, with the vertical detail set out at each one. The full model is on our methodology page.
Audit
Day 01 to 10Baseline plus crawl, facet and product-schema audit across the catalogue, and identification of the revenue-driving product set.
6 platforms500+ queriesBaseline reportDiagnose
Day 11 to 21Whether the constraint is crawl waste from uncontrolled facets, duplicated manufacturer copy, or product data too incomplete to be recommended.
Content gapsEntity deficitCorpus gapsArchitect
Day 22 to 30A roadmap with an explicit index policy for facet combinations, and differentiated content scoped to the products that actually earn.
90-day roadmapPillar planEntity planExecute
Day 31 to 180Crawl control and complete product markup first, then category pages rebuilt as buying guides, then comparison content for contested products.
Embedded teamWeekly shipMonthly reviewMonitor
OngoingVisibility and revenue reported by category rather than blended, with schema validity and index bloat watched continuously.
Weekly scansDrift alertsQBR recalibration
What you receive each month
Reported by category and product cohort, since site-wide averages hide where the revenue moves.
01
Category-level visibility
Ranking and traffic by category rather than blended.
02
Product citation
Which products appear in AI recommendations and rich results.
03
Crawl and index health
Facet control, index bloat and coverage changes.
04
Schema validity
Markup errors found and fixed.
05
Revenue attribution
Organic revenue by category.
06
Next-cycle plan
Categories to build out and pages to consolidate.
Services that apply here
Technical Audit & Schema
Crawl control and product data.
AI Content Strategy
Buying guides built to be quoted.
Frequently asked questions
Do we need unique copy on every product page?
On the products that drive revenue, yes. Across thousands, no. The effort is better spent on category pages, comparisons and the top-selling subset than spread thinly everywhere.
How should faceted navigation be handled?
With a deliberate policy: index the facet combinations that have real search demand, block the rest. Left uncontrolled it is the most common cause of crawl waste in ecommerce.
Can we compete with marketplaces?
Not on transactional queries for commodity products. On comparison, specification and buying-guide content, frequently yes.
Does product schema affect AI recommendations?
Materially. Structured specifications, availability and reviews are what a retrieval pipeline reads to decide whether a product can be recommended at all.