1. Overview

To understand how Google handles a broad commercial query, we reviewed 20 unique first-page results for “running shoes” in the United States on 14 July 2026.

Google Ranking Snapshot for “Running Shoes”
Screenshot of Google's desktop search results for the query running shoes, showing a Shopping carousel, product filters, and the highest-ranking organic result from Runner's World
Figure 1. Google desktop search results for the query “running shoes” as captured on 14 July 2026. The SERP combines Google Shopping listings, ecommerce features, and organic results, illustrating the mixed commercial, informational, and transactional intent analysed in this benchmark.

The sample includes brands, retailers, editorial publishers, specialist review platforms, a marketplace, and a community discussion. Each page was assessed across content structure, commercial functionality, editorial signals, internal linking, structured data, and user-experience features.

The objective was not to identify a ranking formula or claim that a specific feature causes higher positions. The goal was to understand which page formats Google accepts for the same query, what each format does well, and what SEO teams can apply to their own page strategy.

Access the full dataset: Review the page-level classifications, extracted metrics, feature flags, and benchmark scores in the Running Shoes E-Commerce Ranking Benchmark spreadsheet .

2. A Mixed-Intent SERP

The first page is not dominated by one page type. Google combines:

  • Ecommerce category pages
  • Editorial buying guides
  • Brand homepages
  • Review catalogues
  • A marketplace
  • Community content
SERP Composition by Publisher Type
Horizontal bar chart showing brands at 40 percent, retailers at 30 percent, editorial publishers at 15 percent, and marketplace, review platform, and community platform at 5 percent each
Chart 1. Brands represent 40% of the 20 unique results, retailers 30%, editorial publishers 15%, and marketplace, review, and community platforms 5% each.
Distribution of Page Types
Horizontal bar chart showing eight ecommerce category pages, five editorial guides, three homepages, two review catalogues, one marketplace page, and one community discussion
Chart 2. Ecommerce categories account for 40% of the SERP, editorial guides 25%, homepages 15%, review catalogues 10%, and marketplace and community formats 5% each.

This distribution confirms that “running shoes” combines several forms of intent:

  • Informational: understanding which type of shoe to choose
  • Commercial investigation: comparing brands, models, and use cases
  • Transactional: browsing products and preparing to purchase
  • Experiential: reading opinions and firsthand user experiences

This means the competitive set extends beyond direct commercial competitors. A product category may compete with a buying guide, a specialist review database, a marketplace, or a Reddit discussion.

Practical implication: Before optimising a page, identify which role it is intended to play in the SERP. Page type, content depth, and functionality should follow that role.

3. Why Different Page Types Rank

The strongest distinction in the sample is between editorial pages and ecommerce pages.

Editorial guides

Editorial guides typically include:

  • Named authors
  • Testing methodology
  • Product comparisons
  • Expert input
  • Update dates
  • Affiliate disclosures
  • Supporting guides and model-specific reviews

Ecommerce category pages

Ecommerce categories typically include:

  • Prices
  • Filters and sorting
  • Ratings and reviews
  • Stock information
  • Shipping and return details
  • Wishlists and purchase controls
  • Product and category navigation
Average Scores by Page Type
Grouped bar chart comparing editorial, commerce, brand ecosystem, and community scores across ecommerce categories, editorial guides, homepages, review catalogues, marketplace, and community discussion pages
Chart 3. Editorial guides lead on editorial signals, ecommerce categories lead on commerce functionality, brand homepages lead on ecosystem features, and the community result leads on user-generated experience.

The implication is not that every page should maximise every available feature. Each page format needs to perform its primary function well.

Editorial pages provide decision support. Ecommerce categories reduce friction in product discovery and purchase. Brand homepages connect users to a wider ecosystem. Review catalogues structure large volumes of comparison data. Community content provides authentic user experience.

Hybrid pages remain strategically relevant

Some pages combine editorial depth with commercial functionality. Wirecutter is the clearest example in this sample, combining testing, comparisons, and authorship with pricing, retailer links, and product availability.

This hybrid model is especially relevant for affiliate publishers, specialist review sites, retailer buying guides, and brands creating product-selection resources.

4. Internal Discovery and Feature Prevalence

Related content and navigation appear across almost all ranking pages. Successful results frequently connect users to:

  • Individual product reviews
  • Related product categories
  • Specialist buying guides
  • Brand and model pages
  • Supporting educational content
Feature Prevalence Across the Top 20 Results
Horizontal bar chart showing related content on 20 pages, breadcrumbs on 19, prices on 17, filters and ratings on 15, product schema on 14, named authors on 8, affiliate disclosures on 5, and testing methodology on 4
Chart 4. Related content and breadcrumbs are nearly universal. Commercial features are widespread, while author attribution, affiliate disclosure, and testing methodology are concentrated on editorial pages.

These findings show that leading pages tend to operate within broader product, brand, editorial, or community ecosystems.

Access the full dataset: Review the page-level classifications, extracted metrics, feature flags, and benchmark scores in the Running Shoes E-Commerce Ranking Benchmark spreadsheet .

5. Word Count and Internal Linking

Word count follows page function

Editorial guides are substantially longer than ecommerce categories and brand homepages. That does not mean every ranking page needs more copy.

Median Word Count by Page Type
Horizontal bar chart showing median word counts of 5,338 for the marketplace page, 5,111 for editorial guides, 1,514 for ecommerce categories, 1,224 for homepages, 707 for review catalogues, and 245 for the community discussion
Chart 5. Editorial guides have a median of 5,111 words, compared with 1,514 for ecommerce categories and 1,224 for brand homepages. Median values are used because long editorial pages can distort averages.

Editorial guides require depth because they explain, compare, and recommend. Category pages rely more heavily on product data, filters, ratings, and transactional functionality.

The relevant question is not “How many words should this page have?” It is “Does the content provide everything required for the page’s role?”

Internal link volume reflects architecture

Ecommerce categories have the highest median number of internal links, followed by brand homepages and editorial pages.

Median Internal Links by Page Type
Horizontal bar chart showing median internal-link counts of 240 for ecommerce categories, 143 for brand homepages, 139 for editorial pages, and 5 for the community page
Chart 6. Ecommerce categories have a median of 240 detected internal links, brand homepages 143, editorial pages 139, and the community page 5. The figures describe architectural differences, not ranking causation.

The reason is structural. Ecommerce pages connect products, categories, facets, services, and transactional pages. Editorial pages connect guides, reviews, and topic clusters. Brand homepages distribute users across the wider brand ecosystem.

Build an internal linking architecture that guides users toward relevant next steps, improves content discovery, and strengthens relationships between related topics.

6. Practical Takeaways for SEO Teams

Editorial publishers

Prioritise:

  • Original testing and firsthand evaluation
  • Clear authorship and expert credentials
  • Transparent methodology
  • Structured product comparisons
  • Regular updates
  • Supporting topic clusters and model-level reviews

Ecommerce teams

Prioritise:

  • Useful filters and sorting
  • Accurate pricing and stock information
  • Ratings and reviews
  • Shipping and return transparency
  • Concise category-level buying guidance
  • Strong links between products, categories, and supporting content

Brands

Support product and category pages with:

  • Shoe finders and selection tools
  • Technology explanations
  • Use-case recommendations
  • Athlete or expert content
  • Editorial resources
  • Membership, community, and sustainability information

Review and affiliate sites

Differentiate through:

  • Firsthand product evaluation
  • Clear comparison criteria
  • Expert authorship
  • Accurate pricing and availability
  • Visible affiliate disclosures
  • Clear separation between editorial judgement and monetisation

7. Main Conclusion

The “running shoes” SERP does not reveal one ideal page template.

It shows that Google ranks different formats because they satisfy different parts of the search journey:

  • Editorial guides provide expertise and comparison.
  • Ecommerce categories provide product discovery.
  • Review platforms provide structured evaluation.
  • Brand homepages provide ecosystem navigation.
  • Community pages provide firsthand validation.

For SEO experts, the practical priority is to define the role of the page before selecting its content, features, and internal-linking model.

The strongest page is not necessarily the one with the most content, links, or structured data. It is the one whose structure, content, and functionality are best aligned with the user need it is intended to satisfy.

Optimise the page for its role in the search journey, not for a generic checklist of features.

8. Methodology and Limitations

This benchmark is based on 20 unique first-page Google results for the query “running shoes.”

The pages were reviewed across structural, editorial, commercial, and technical variables, including:

  • Publisher and page type
  • Primary and secondary search intent
  • Editorial and commercial features
  • Word count and heading structure
  • Internal links and related content
  • Schema.org structured data
  • Brand and community signals

The findings are directional insights from one competitive SERP. They should not be interpreted as universal ranking rules or evidence that any individual feature directly caused a page to rank.

The current dataset does not include off-page metrics such as backlinks, referring domains, Domain Rating, URL Rating, page age, or historical ranking movement.

Feature counts are based on the HTML and rendered information available during collection. Some JavaScript-dependent or personalised elements may not have been captured. Percentages describe this 20-page sample only.

9. Frequently Asked Questions

What is the main search intent behind “running shoes”?

The query has mixed intent. Users may want product education, comparisons, direct shopping options, or firsthand opinions. The diversity of ranking formats reflects these different needs.

Which page type appears most often in the SERP?

Ecommerce category pages are the largest group, representing 8 of the 20 unique results, or 40% of the sample. Editorial guides are the second-largest group at 25%.

Do ecommerce category pages need long-form content to rank?

Not necessarily. Category pages in the benchmark generally rely more on product discovery, filtering, ratings, pricing, stock, and transactional usability than on editorial length. Concise buying guidance can still be useful when it supports selection.

Why are editorial guides much longer than ecommerce categories?

Editorial guides need enough depth to explain product differences, testing criteria, use cases, and recommendations. Their page function requires more context than a product-listing page.

Does a higher number of internal links improve rankings?

This benchmark does not establish causation. Higher link counts often reflect site architecture, product inventory, navigation, and page purpose. Internal links should be assessed by relevance, destination, context, and user value rather than quantity alone.

Which features were most common across the top results?

Related content, breadcrumb navigation, prices, filters, ratings, and Product structured data were among the most frequently detected features. Editorial trust signals were concentrated mainly on publisher and review pages.

Does Product Schema explain why ecommerce pages rank?

No. Product Schema was common but not universal. It should be treated as a supporting implementation that helps search engines understand page content and may support enhanced search features, not as a standalone explanation for rankings.

What should ecommerce SEO teams prioritise based on this benchmark?

Teams should prioritise product discovery, filtering, accurate pricing and stock, reviews, shipping and return clarity, useful category guidance, and strong links between products, categories, and supporting content.

What should editorial publishers prioritise?

Editorial publishers should focus on original testing, transparent methodology, expert authorship, structured comparisons, visible disclosures, regular updates, and strong supporting content clusters.

Can these findings be applied to other ecommerce queries?

They can inform hypotheses, but they should not be applied as universal rules. The same framework should be repeated across more queries, industries, devices, and dates before drawing broader conclusions.

Where can I access the complete benchmark data?

The full page-level dataset is available in a public Google Sheet, including page classifications, extracted metrics, feature flags, and composite benchmark scores.

Access the full dataset: Review the page-level classifications, extracted metrics, feature flags, and benchmark scores in the Running Shoes E-Commerce Ranking Benchmark spreadsheet