Example analysis
best running shoes
Show data table
| Area | Value |
|---|---|
| Content & topic gaps | 6 |
| UX & structure | 8 |
| AI-readiness score | 68 |
Executive summary
Site A ranks with genuine authority and unmatched testing depth for "best running shoes" — named expert editors, a 14-time Boston finisher, and 300+ wear-testers logging 100+ miles per shoe, plus clear category framing, existing JSON-LD, and strong 2026 freshness signals. However, it trails competitors on data depth, breadth, and usability. RunRepeat wins on objective lab metrics (weight, stack height, drop, durometer) and transparent pricing/discounts, while a competing YouTube guide covers every major brand — notably Asics, Hoka, and Puma, all of which Site A omits. The page itself is a long, unbroken wall of prose reviews with no top-of-page comparison table, no sticky navigation, plain-text (rather than priced CTA) shop links, inconsistent Pros/Cons/Key Specs blocks, and apparent truncation plus malformed headings that hurt both readability and answer-engine parsing. Its AI-overview readiness scores 68/100: solid E-E-A-T, freshness, and headings, but no FAQ section, incomplete spec tables, and narrative that buries extractable verdicts. The priority is to convert Site A's strong original content into a scannable, comparison-friendly, citable experience — add a summary comparison table and per-shoe Key Specs blocks, broaden brand coverage, surface priced CTAs and media, and layer in FAQ schema and concise per-shoe verdicts to improve extractability against list-heavy competitors.
Content & topic gaps
Site A offers strong first-hand, editorially credible tester narratives (E-E-A-T) covering major brands like Adidas, Brooks, Saucony, Nike, New Balance, and On. However, it trails competitors on data depth and breadth: RunRepeat wins on objective lab metrics and transparent pricing/discounts, while the YouTube competitor covers every brand (notably Asics, Hoka, Puma) that Site A omits. To close the gap, add quantified specs (weight, drop, stack, price) in standardized comparison-friendly blocks, broaden brand coverage, and strengthen trail, stability, and marathon/racing guidance plus a buyer's how-to-choose section.
- High Objective lab-test metrics (weight, stack height, drop, heel/toe measurements, durometer)
- Medium Price and current discount/deals information
- High Coverage of Asics, Hoka, and Puma models
- Medium Best shoe per brand breakdown
+ 2 more in the full report
UX & structure
Site A has excellent authority, original testing depth, and detailed narrative reviews, but its UX lags commercial-intent competitors on scannability and comparison. The page is a long prose wall lacking a comparison table, sticky navigation, prominent price/CTA buttons, and consistent per-shoe Pros/Cons/Specs blocks. Structural inconsistencies (malformed headings, apparent truncation) hurt both readability and answer-engine parsing. Prioritise a top-of-page comparison table, anchored TOC/filters, prominent priced CTAs, per-shoe media, and consistent structured verdict blocks to convert its strong content into a more usable, extractable experience.
- High Structure — The article is a long, unbroken wall of prose reviews with no comparison table or at-a-glance summary matrix; competitors like RunRepeat present shoes in a filterable/scannable catalog with prices and savings, and The Run Testers/REI use structured lists.
- Medium Readability — Reviews rely heavily on first-person anecdote and long testimonial quotes; key specs and verdicts are buried in narrative paragraphs rather than surfaced in scannable bullets.
- Medium Navigation — Only a short 'Best Running Shoes Preview' list acts as navigation; there is no visible sticky table of contents or category filter to jump between training/racing/trail sections in a very long page.
+ 5 more in the full report
AI Overviews readiness
AI-readiness score: 68/100
Site A has strong foundational answer-engine signals: genuine E-E-A-T (named experts, 300+ testers, testing since 1966), clear freshness (dated 2026 editor's note and awards), descriptive headings, and existing JSON-LD. Gaps that limit AI Overview readiness are the absence of an FAQ section, inconsistent concise-answer formatting (long narrative reviews without extractable verdict lines), incomplete spec tables across all shoes, and structured data that likely doesn't yet expose per-product Review/ItemList markup. Prioritizing FAQ schema, uniform spec tables, and concise per-shoe verdicts would meaningfully improve citability against list-heavy competitors like RunRepeat.
Top priority actions
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Add standardized Key Specs blocks to every shoe
Impact: High Effort: Medium
Introduce a consistent Pros/Cons/Key Specs block (weight, heel-to-toe drop, stack height, price) for every shoe, not just some. This closes the biggest data-depth gap versus RunRepeat's lab-driven metrics, gives answer engines precise citable figures, and dramatically improves scannability. Site A currently relies on subjective tester narrative without quantified specs across all models.
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Add a top-of-page comparison table
Impact: High Effort: Medium
Insert a scannable summary matrix near the top (shoe, category, price, weight, drop, best-for) with quick jump-links. This matches RunRepeat's filterable catalog and lets users compare picks without reading every full review — the single biggest UX gap flagged as high severity.
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Broaden brand coverage to include Asics, Hoka, and Puma
Impact: High Effort: Medium
Site A omits three major brands that competitors cover (a YouTube competitor frames content as 'Best Running Shoe From Every Brand'). Add credible tester-backed picks for Asics, Hoka, and Puma to close a high-importance topical breadth gap and capture brand-comparison search intent.
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Add per-shoe concise verdict/'Best for' summary lines
Impact: High Effort: Low
Lead each shoe review with a one-line verdict and use-case label (e.g., 'Best for beginners: Brooks Ghost 18 because...'). This lets generative engines lift direct answers without parsing long narrative, directly improving AI Overview extractability where the current partial concise-answer signal limits citability.
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Add an FAQ section with FAQPage schema
Impact: High Effort: Low
The page has no FAQ (absent signal). Add common buying questions — 'How do I choose running shoes?', 'How often should I replace them?', 'What's the difference between neutral and stability shoes?' — marked up with FAQPage structured data to capture question-based AI Overview queries.
+ 8 more in the full report