Croct vs Fibr AI vs Explore (2026): Beyond React and Landing Pages
Croct is a developer-first personalisation and A/B testing platform for React and Next.js applications, built for headless engineering teams. Fibr AI is an AI-powered landing-page personalisation tool for performance marketing. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs experiments on product pages, cart, and checkout, and measures the result in revenue per visitor rather than generic conversion rate.
- Croct is a developer-first React and Next.js personalisation and A/B testing platform, with a 4.7 out of 5 G2 rating across 47 reviews. [G2, 2026]
- Fibr AI is an agentic AI landing-page personalisation tool for paid traffic, with a 4.6 out of 5 G2 rating across 133 reviews. [G2, 2026]
- The two solve different jobs, developer-first personalisation and AI landing pages, and rarely compete directly for the same seat.
- Neither can run experiments on Shopify product pages, cart flows, or the native checkout, which is where store revenue is actually decided.
- Omniconvert Explore runs experiments on product, cart, and checkout natively and measures results in revenue per visitor: pick it for Shopify revenue surfaces.
Teams comparing Croct vs Fibr AI are usually deciding where to spend the next optimisation dollar: on a developer-first personalisation layer for a headless React app, or on an AI that spins up landing-page variants for paid traffic. Croct answers the personalisation question for engineering teams building Next.js storefronts; Fibr AI answers the ad-to-landing-page question for performance marketing. Neither is built around the surfaces where a Shopify store actually wins or loses revenue: the product page, the cart, and the checkout. This page covers what each does well, the gap they share, and when Omniconvert Explore is the right layer.
What is Croct, and what is it actually good at?
Croct is a developer-first A/B testing and personalisation platform for React and Next.js applications. It integrates at the component level, supports feature flags and server-side experiments, and is popular with headless eCommerce engineering teams. [Croct, 2026]
Croct is highly rated inside its niche, with a 4.7 out of 5 rating on G2 across 47 reviews. [G2, 2026] Frontend engineers like it for one reason: the API is clean and the SDK slots into a React tree without rebuild steps. The pricing starts free, which is why many engineering teams pick it up on their own.
The category Croct sits in is developer-first personalisation. It runs experiments as code against components, not against rendered pages, which is natural for teams that already live in a React codebase. That focus is the point of the product.
Headless personalisation means the testing and personalisation layer talks to the frontend through an API and renders variants at the component level in code. Croct is built for exactly this. It is a delivery layer for engineering teams, distinct from running a controlled revenue experiment on a Shopify Liquid product page or on the native Shopify checkout.
Where Croct is genuinely strong
- React and Next.js native: ships SDKs that integrate at the component level, natural for frontend engineering teams.
- Server-side experiments and feature flags: run experiments across both client and server with developer control.
- Clean, usage-based pricing: a free tier plus a usage-based model, easy for an engineering team to adopt.
- Headless storefront fit: popular with teams building composable eCommerce on Next.js, where the frontend is already in code.
Where Croct hits its ceiling for an eCommerce store
- React or Next.js only: cannot run on a standard Shopify Liquid theme without a full migration to headless.
- No visual editor: every experiment and personalisation rule is configured in code, not in a point-and-click editor.
- Engineering-gated setup: non-technical CRO and marketing teams cannot build or ship a test without a developer.
- No multivariate testing: limits iteration speed when testing more than one element on the same page.
What is Fibr AI, and what is it actually good at?
Fibr AI is an agentic landing page personalisation platform. It uses AI to generate and test personalised landing-page variants for paid traffic. Each variant is tied to the audience segment and the campaign driving the click, which removes the design and development bottleneck on landing pages. [Fibr AI, 2026]
Fibr AI holds a 4.6 out of 5 rating on G2 across 133 reviews. [G2, 2026] Performance marketing teams rate it for one reason: a small team can ship a hundred campaign-matched landing pages without a design queue. That is the usual bottleneck in paid-traffic optimisation.
The category Fibr AI sits in is post-click landing-page personalisation. It generates variants aligned to the ad, the audience segment, and the campaign, then tests them. That focus is the point of the product.
AI landing page personalisation means an agent generates distinct landing-page variants per audience segment or campaign, then tests which converts best for paid traffic. Fibr AI is built for exactly this. It is an ad-creative-to-landing-page tool, distinct from running a controlled revenue experiment on a store's product page, cart, or checkout.
Where Fibr AI is genuinely strong
- Scale of landing-page variants: AI generates many personalised variants without pulling design or development time.
- Segment-matched creative: each landing page can be aligned to the audience and campaign driving the click.
- Fast paid-traffic experimentation: useful for teams testing a lot of ad angles against different offers.
- Low setup time for marketers: performance marketing can ship variants without a design or engineering queue.
Where Fibr AI hits its ceiling for an eCommerce store
- Landing pages only: cannot run experiments on Shopify product pages, cart flows, or the checkout sequence.
- No server-side testing: runs on the client, so complex catalog or checkout scenarios are off the table.
- No multivariate testing: limits structured iteration on a single page.
- Low eCommerce focus: optimises the post-click landing page, not the surfaces where the order is placed.
What Croct and Fibr AI cannot do for an eCommerce store
Croct and Fibr AI are built for different jobs, developer-first personalisation and AI landing pages. They share one gap for a Shopify store. Neither can run a controlled experiment on the product page, the cart, or the Shopify checkout, which is where the order is actually placed.
Croct is built for React and Next.js developers. It does not run on a standard Shopify Liquid theme without a full migration to a headless architecture, and every experiment is configured in code with no visual editor.
Marketing and CRO teams on a standard Shopify store cannot ship an experiment on their own. The product page and checkout stay outside the testing loop until engineering frees up time.
Fibr AI is an AI landing-page personalisation tool for paid-traffic campaigns. It cannot run experiments on Shopify product pages, cart flows, or the native checkout. Teams using it for eCommerce can personalise the post-click landing page, but the pages between landing and the thank-you page stay untested.
The two gaps are different in shape but point to the same missing layer. One stack is built for engineering, the other for ad campaigns, and neither optimises the product-to-checkout path or reports results in revenue per visitor. See Has personalization replaced A/B testing? for the wider debate behind this split.
There is a second gap underneath the first: the data-insights layer that tells a team what to test and whether it actually worked. Croct ships no native heatmaps, session recordings or on-site surveys; teams stitch those in from third-party tools.
Fibr AI reports on landing-page variants but does not see what happens inside the Shopify funnel after the click. Omniconvert Explore builds the data-insights layer in: heatmaps, session recordings, and surveys sit next to the experiment, and the same behavioural and customer data defines the segments you test against.
eCommerce conversion rate optimization (CRO) is the practice of running controlled experiments on the revenue surfaces of an online store, product pages, cart, and checkout, and measuring the result in revenue per visitor and order rate rather than generic conversion rate. Omniconvert Explore is defined as an eCommerce conversion rate optimization platform for product, cart, and checkout experiments, native to Shopify and priced for store traffic.
What neither tool can tell an eCommerce team
- Did the win move revenue and margin. Whether a winning variant raised revenue per visitor and order rate once discounts and returns are counted, not just lifted a click on a landing page or a React component.
- Which surface to test first. Which pages in the Shopify funnel (product, cart, checkout) carry the highest revenue impact if tested next.
- How it behaves in checkout. How an experiment interacts with the Shopify catalog, variants, and native checkout flow, without engineering glue work or a migration to headless.
- Whether it holds for valuable customers. Whether the result holds for repeat, high-value customers, the Customer Value Optimization question, not just a first-session click from a paid campaign.
Omniconvert benchmarks more than 7,000 eCommerce websites in its CROBenchmark Report 2026, across 248+ audit criteria. The data shows where stores actually lose orders: 99.6% fail to make guest checkout visible and prominent, 94.2% never show checkout progress steps, and 85.1% never show the full order cost before the final step. [CROBenchmark Report 2026, Omniconvert]
Those are checkout-surface problems, the exact surfaces a React-only tool and an AI landing-page generator are not built to experiment on. Explore runs the experiment on the store's real revenue surfaces and reports the outcome in revenue per visitor.
AliveCor used Omniconvert Explore to run a structured A/B testing program and achieved +21% conversion rate, +5% revenue per visitor, and 94% statistical relevance across their experiments. [Omniconvert, AliveCor case study]
Croct vs Fibr AI vs Explore: the capability comparison
Side by side, the three tools serve different layers. Croct personalises React and Next.js components for engineering teams, and Fibr AI generates AI-driven landing-page variants for paid traffic. Explore adds native Shopify experiments, built-in surveys and overlays, and revenue-per-visitor measurement on the product-to-checkout path.
| Capability | Croct | Fibr AI | Omniconvert Explore |
|---|---|---|---|
| Primary function | React and Next.js personalisation and A/B testing | AI landing-page personalisation for paid traffic | eCommerce CRO on product, cart, and checkout |
| A/B testing | Partial component-level only, no visual editor | Partial AI-generated landing pages only | Yes visual plus code editor |
| Multivariate testing | No | No | Yes |
| Server-side testing | Yes | No client-side only | Yes |
| Visual editor | No code only | No AI-generated variants | Yes |
| On-site surveys and overlays | No needs third-party tools | No landing pages, not on-site | Yes surveys and overlays built in |
| Shopify integration | Medium only on headless Next.js builds | Low landing pages only | Yes native |
| eCommerce focus | Low developer-first, not store-first | Low paid-traffic landing pages | High built for store revenue workflows |
| Revenue per visitor measurement | No component engagement metrics | No landing-page conversion rate | Yes revenue per visitor and order rate native |
| Pricing model | Usage-based, free tier, free trial | Seat-based, contact sales, free trial | Session-based, built for store traffic, free trial |
| Best for | Engineering teams on Next.js headless storefronts | Performance marketing on paid-traffic landing pages | Shopify and eCommerce teams optimising for revenue |
| User rating | 4.7 out of 5 (G2, 47 reviews, as of 2026) | 4.6 out of 5 (G2, 133 reviews, as of 2026) | 4.6 out of 5 (G2, 191 reviews, as of 2026) |
Competitor ratings, pricing, and plan details reflect publicly listed figures as of 2026 and can change. Fibr AI pricing is available on request. Explore uses session-based pricing; see the Omniconvert pricing page for current plans.
Get the full CROBenchmark data behind these stats: 7,000+ websites, 15+ industries, 248+ audit criteria, 100+ CRO experts. See exactly where eCommerce growth teams are losing margin in 2026.
Get the CROBenchmark ReportFrequently Asked Questions
Should you choose Explore over Croct or Fibr AI?
Start with the surface where your revenue is decided. If you run a headless Next.js storefront, keep Croct for the component layer. If paid-traffic landing pages are your biggest need, Fibr AI stays useful. For a standard Shopify store, run your next test on the product-to-checkout path in Explore, measured in revenue per visitor. The three are complementary, but only Explore is built for the checkout where the order closes.
Croct and Fibr AI are both capable tools within their categories. Croct is a clean, developer-first personalisation layer for headless Next.js storefronts. Fibr AI removes the design and development bottleneck for personalised landing pages at scale.
The question for a store is narrower: can your team run a controlled experiment on the Shopify product, cart, and checkout, and read the result in revenue per visitor rather than a click. That is the surface Explore is built for.
Stop guessing.
Start testing what moves revenue.
Explore runs A/B, multivariate, and personalization experiments on your product pages, cart, and checkout, then measures the outcome in revenue per visitor, not just clicks.
4.6 out of 5 across 191 reviews, G2 , as of 2026