WhyIQ Research · Updated July 2026
Landing page statistics for 2026.
The average landing page scores 57 out of 100, only 0.7% of pages score above 80, and the single most common conversion killer is pricing that visitors cannot find. Those numbers come from WhyIQ's own scan corpus: 300 scored scans of 326 real websites, each running 50 simulated visitor archetypes, 15,000 simulated sessions in total, collected between April and July 2026.
Below: the corpus numbers, then the key visitor-behavior and AI-search statistics from published research, every one with a named source. Journalists and writers are welcome to cite any figure on this page with a link (see the citation note at the bottom).
What 300 scored scans say about the average page.
Original WhyIQ data. Every figure in this section is simulated visitor behavior from 50-archetype behavioral scans, not analytics of real visitors. The simulation is calibrated against 200+ peer-reviewed papers; the methodology is public.
57/100
Average WhyIQ Score across 300 scored pages
Mean 56.6, median 58. Half of all pages land between 49 and 65.
0.7%
Pages that scored 80 or higher
2 pages out of 300. A genuinely excellent landing page is rare.
27%
Pages that scored below 50
81 of 300 pages failed more simulated visitors than they persuaded.
62%
Median simulated bounce rate
The 90th-percentile page loses 84% of its simulated visitors.
1 in 9
Pages flagged for invisible pricing
About 12% of scans surface some variant of 'no pricing visible', the single most common named confusion in the corpus.
64.9%
Simulated mobile bounce rate
Versus 60.6% on desktop. Mobile visitors are measurably less forgiving.
Exit reasons across 15,000 simulated sessions.
The headline finding: most visitors do not reject the page. They leave undecided or under-stimulated. Clarity and attention failures outnumber price objections roughly 11 to 1.
36%
Left still evaluating
The largest exit group never rejected the page. They left without enough clarity to decide.
31%
Lost interest and left
Boredom exits: the page never earned continued attention.
17%
Left over a trust failure
Missing social proof, weak credibility signals, or claims that did not land.
6%
Rejected the price
Price rejection is a distant fourth behind clarity, attention, and trust.
5%
Left confused
Outright confusion exits: the visitor could not work out what the page offered.
2%
Reached conversion intent
Of 15,000 simulated sessions, 2% ended ready to act.
What published research says about how visitors decide.
50ms
How fast a first impression of a web page forms
Lindgaard et al., 2006
~28%
How much of real behavior stated intentions explain
Sheeran, meta-analysis, 2002
67%
Shoppers who abandon after an unresolved form error
Baymard Institute
10x
Purchase-rate difference between 6 choices and 24 choices (30% vs 3%)
Iyengar & Lepper, 2000
AI search and citation statistics, 2026.
The numbers behind answer engine optimization. For the mechanics, see how AI search decides what to cite and the AI Citability Playbook.
r = 0.664
Correlation between third-party brand mentions and AI citation, roughly 3x the correlation for backlinks (r = 0.218)
Ahrefs, 75,000 brands, 2026
4-7%
Share of AI citation behavior that backlinks predict
Ahrefs / Princeton GEO analyses
46.7%
Perplexity citations that point at Reddit
Profound 30M-citation panel, 2025
44.2%
LLM citations extracted from the first 30% of a page's body text
AirOps, 548,000 pages
~3 months
Half-life of an AI citation before it decays without fresh corroboration
AirOps
14.2% vs 2.8%
Conversion rate of AI-referral visitors versus organic search visitors
Exposure Ninja, 2026
9.5/10
Evidence score for crawler accessibility, the #1 AI citation factor across 54 studies. llms.txt scored 2.0/10
Zyppy meta-analysis, May 2026
+41%
AI visibility lift from adding statistics to content. Quotes add 28%; citing sources adds up to 115% for low-ranked sites
Princeton GEO, KDD 2024
69%
AI crawler requests that cannot execute JavaScript
searchVIU, 1.3B requests
80.9%
B2B SaaS AI citations that land on third-party content, not the vendor's own site
Goodie, 2025
+1,328%
Growth in Reddit's Google search visibility, 2023 to 2024
Multiple SERP analyses
May 7, 2026
The day Google retired FAQ rich results. AI engines extract Q&A content, not FAQPage markup
Google Search Central
Methodology and how to cite these statistics
Corpus statistics (sections 01 and 02) are aggregates from WhyIQ's production scan corpus: 300 scored page scans across 326 unique real websites, April to July 2026. Each scan simulates 50 distinct visitor archetypes with individual trust thresholds, cognitive profiles, and device contexts, calibrated against 200+ peer-reviewed papers. These figures describe simulated visitor behavior. They are not analytics of real visitor sessions, and we say so wherever they appear.
External statistics (sections 03 and 04) are from the named published sources and were verified against the original publications before inclusion. Where a study has been superseded, we cite the newer figure.
Citing this page: any figure here may be quoted with attribution to WhyIQ and a link to this page. For corpus figures, a formulation like "according to WhyIQ's analysis of 15,000 simulated visitor sessions" is accurate. For press enquiries or the underlying aggregate data, email [email protected].
This page is refreshed as the corpus grows. Statistics current as of July 17, 2026.
See where your page lands in this distribution.
Run the same 50-visitor simulation these statistics come from. Free first scan, about 2 minutes.