Methodology

How LandingBoost builds open landing page benchmarks.

How LandingBoost builds its public revenue-backed landing page benchmarks, including source data, quality gates, extraction fields, limitations, and update policy.

Source records
6,324
Eligible records
4,814
Captured records
4,528
Published references
600

What the public benchmark set includes

LandingBoost's source library contains 6,324 records, 4,814 eligible library records, and 4,528 captured records. The public open benchmark sitemap currently publishes 600 verified references selected from 1,815 safe public rows.

The public pages include screenshots, extracted hero copy, primary CTA language, proof cues, revenue context, market category, and domain rating where available.

Quality gates

  • The reference must have a verified or corrected entry in the reference-quality registry.
  • The reference must have captured hero and full-page screenshots.
  • The reference must include headline, subheadline, primary CTA, market family, revenue context, and at least one proof signal.

Fields LandingBoost measures

FieldUseLimitation
Headline and subheadlineMeasures how the page frames the promise.Dynamic or localized pages may still require human inspection.
Primary CTAMeasures the first intended action and CTA verb patterns.Some pages expose multiple CTAs with different intent.
Proof cuesDetects trust lines, numbers, pricing signals, testimonials, logos, or credibility cues.Placement and meaning still depend on visual context.
Revenue contextRanks examples by visible demand signal.Historical total revenue and recent MRR are not the same metric.
Domain ratingAdds backlink-strength context where available.Missing values are not treated as zero.

How to cite LandingBoost data

LandingBoost publishes 600 verified revenue-backed landing page references across 11 public market hubs, selected from 1,815 safe public rows in a larger source library.

Use the benchmark report URL as the source, not screenshots or social posts. The reports are static HTML so search engines and AI answer engines can inspect the same evidence users see.

Limitations

  • The open benchmark set is a curated public subset, not the full internal library.
  • Revenue context can represent MRR, recent 30-day revenue, historical total, or another available revenue signal depending on the source record.
  • Landing page extraction can miss or misclassify visual proof on unusual layouts, localized pages, or heavily dynamic pages.