Shoprenter vs. UNAS 2026

200 stores. Two platforms. Very different strengths.

One of the clearest findings is that Shoprenter and UNAS show strength in different areas. The Shoprenter sample scored higher in SEO and slightly higher in performance. The UNAS sample scored higher in GEO/AI, security and conversion. Product-data scores were nearly identical.

ClearSiteScore combines multiple technical assessment areas. This study is a descriptive benchmark and should not be treated as a statistically representative estimate of all Hungarian ecommerce stores.

Mobile performance is one of the biggest issues across the full sample

192 of the 193 successfully measured stores had a mobile lab LCP above 2.5 seconds.

• 99.5% mobile LCP > 2.5 s
• 13.91 s full-sample median
• 191/193 mobile PageSpeed scores below 90

Median mobile LCP was 14.23 seconds for Shoprenter and 13.50 seconds for UNAS. More important than the difference between platforms is the shared pattern: the overwhelming majority of stores showed substantial room for mobile performance optimization.

One of the strongest findings is not the difference between Shoprenter and UNAS, but how widespread mobile performance problems are on both platforms.

Blocking AI search crawlers is extremely rare in this sample

For OAI-SearchBot, PerplexityBot and Claude-SearchBot, 199 of 200 stores did not block access in robots.txt.

AI visibility issues in this sample cannot be explained simply by crawler blocking. Structured data, business/entity clarity, content structure, JavaScript rendering and product-data quality appeared far more often as issues.

Technical crawler access alone does not mean an AI system will cite, rank or recommend a store.

A larger gap appeared in AI/GEO readiness

Average GEO/AI score: Shoprenter 53.2 · UNAS 66.4 · gap: 13.2 points.

• 85.5% – no suitable FAQ section detected
• 72.0% – client-side JavaScript rendering delay
• 70.0% – incomplete business identifiers on the homepage
• 46.0% – unclear entity identification

AI/GEO readiness is not a single technical switch. Search and AI systems need both access and clarity: who the business is, what it offers, which customer questions it answers and how the information is structured.

Product data tells a more nuanced story

We analyzed 1,671 product pages: 889 on Shoprenter and 782 on UNAS.

Product-markup coverage was almost identical across the two platforms, while machine-readable stock availability showed a substantial difference. Product-data quality increasingly matters as search, shopping and AI systems rely on structured, machine-readable information.

Most common product-page issues

Across the 192 stores where product pages could be analyzed:

• 100%no product-linked customer review was detected in the sampled product pages
• 95.8%no visible star rating was detected
• 80.7%missing or overly short product descriptions were detected
• 71.9%brand name was missing from product markup
• 60.4%missing or inadequate product-image markup was detected

These findings refer to the product pages sampled by ClearSiteScore and do not claim that such content is absent everywhere in the store.

Security configuration showed very different platform patterns

Average ClearSiteScore security score: Shoprenter 70.7 · UNAS 90.6.

The aggregate score hides very different HTTP security-header patterns. Missing HSTS, CSP and clickjacking protection were much more common in the Shoprenter sample, while Referrer-Policy and X-Content-Type-Options issues were far more common in the UNAS sample.

Security-header findings can represent hardening gaps of different severity. A missing header alone is not the same as an exploitable vulnerability.

The 10 most common issues

The most frequent findings span performance, security, GEO/AI and product-page quality. Percentages always use the actually measurable sample as the denominator.

How does your store compare?

Run a free ClearSiteScore scan and see which technical, SEO and AI/GEO issues we detect on your own website.

Shoprenter vs. UNAS – what does the benchmark show overall?

Methodology

• 200 Hungarian ecommerce stores: 100 Shoprenter and 100 UNAS.
• 1,671 product pages analyzed; product-page analysis succeeded for 192 stores.
• Areas assessed: SEO, GEO/AI readiness, security, performance, conversion elements and product data.
• ClearSiteScore analyzed only publicly accessible website data.
• No store-admin access, Google Analytics, Search Console or other private data was used.
• Where a check was not technically measurable, percentages use the number of actually measurable stores as the denominator.
• This is a descriptive benchmark, not a statistically representative sample of the full Hungarian ecommerce market.
• Performance measurements are time- and environment-dependent.
• Crawler access does not imply automatic AI recommendation, citation or ranking.

Want to use the research data?

Journalists, researchers, ecommerce professionals, agencies and creators may use the aggregated findings and charts with attribution to ClearSiteScore.

Individual charts

Every chart as a high-resolution, publication-ready image in the selected language.

• Average ClearSiteScore category scores by platformOpen chart
• 13.91 s full-sample medianOpen chart
• robots.txt access by AI search crawler (200 stores)Open chart
• Machine-readable product data across the analyzed product pagesOpen chart
• Share of stores with each security-header finding, by platformOpen chart
• The 10 most common issuesOpen chart

Domain-level, per-store results are not published; the media kit contains aggregated data only.

About ClearSiteScore

ClearSiteScore analyzes websites and ecommerce stores across SEO, AI/GEO readiness, security, performance, conversion and product data. Its goal is to show site owners the specific issue, the evidence behind it and the next step to fix it.

Rudolf Gábor · Founder, ClearSiteScore

Research, interview or data requests: hello@clearsitescore.com