AB Smartly vs Croct vs Explore (2026): Engineering vs Shopify Revenue
AB Smartly is an engineering-first experimentation platform built by former Booking.com veterans, with SDK-based testing and warehouse-grade stats. Croct is a React and Next.js personalization and A/B testing tool for developers, component-level, no visual editor. 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.
- AB Smartly is a server-side experimentation platform built by former Booking.com engineers, with real-time results and warehouse-grade stats, at 4.8 out of 5 on G2. [G2, 2026]
- Croct is a developer-first A/B testing and personalization tool for React and Next.js applications, at 4.7 out of 5 on G2. [G2, 2026]
- Both are engineering-owned tools with no visual editor, so marketing teams cannot ship a Shopify test without pulling in a developer.
- Neither runs natively on a standard Shopify Liquid checkout, and neither measures results in revenue per visitor.
- Omniconvert Explore runs product, cart, and checkout experiments natively on Shopify and measures results in revenue per visitor: pick it for standard Shopify store revenue surfaces.
Teams comparing AB Smartly vs Croct are usually engineering-led: they want experimentation control at the code level, and are choosing between server-side testing at scale and component-level testing inside a React or Next.js stack. AB Smartly leads with real-time results and warehouse-grade statistical rigour, built by former Booking.com practitioners. Croct leads with developer-first personalization and A/B testing tied to React components, natural for headless eCommerce teams. Both stop short of the standard Shopify checkout, so this page covers what each does well, the gap they share, and when Omniconvert Explore is the right layer.
What is AB Smartly, and what is it actually good at?
AB Smartly is a server-side experimentation platform built by former Booking.com engineers. It runs real-time A/B tests via an SDK, integrates directly with data warehouses, and applies advanced statistical methods designed for high-velocity, engineering-led programs at scale. [G2, 2026]
AB Smartly is one of the highest-rated tools in the category, holding a 4.8 out of 5 on G2 across 45 reviews. [G2, 2026] Its strength is credibility with engineering: the team behind it built the experimentation program at Booking.com, and the platform reflects that lineage in its statistical rigour and real-time delivery.
Its category is engineering-led experimentation. It ships tests through an SDK, decides variants on the server, and feeds results into the data warehouse where product and engineering teams already live. That focus is the point of the product.
Server-side experimentation runs the variant decision on the backend before the response reaches the browser, so variants can differ at the API and data layer, not only the rendered page. AB Smartly does this at warehouse scale for engineering teams. It is a delivery model for developers, distinct from a marketer-accessible visual test on a store's checkout.
Where AB Smartly is genuinely strong
- Real-time results at warehouse scale: experiment data flows into the warehouse as it happens, ready for analyst work.
- Booking.com statistical lineage: advanced statistical methods designed by practitioners from a high-volume testing program.
- Server-side and SDK-first: variants decided at the API layer, so backend and full-stack changes are testable.
- High-velocity engineering fit: built for product and engineering teams running many concurrent experiments.
Where AB Smartly hits its ceiling for an eCommerce store
- No visual editor: every experiment needs developer time, so marketers cannot ship a test on their own.
- No native Shopify integration: product-page and checkout tests are implemented by hand rather than natively.
- No multivariate testing: the platform focuses on A/B rather than combinatorial variant testing.
- Engineering-priced, engineering-owned: a marketing-led CRO program on a single Shopify store rarely justifies the model.
What is Croct, and what is it actually good at?
Croct is a personalization and A/B testing platform built specifically for React and Next.js applications. It integrates at the component level with a developer-first API, supports feature flags alongside testing, and is popular with headless eCommerce teams. Every experiment is configured in code. [G2, 2026]
Croct holds a 4.7 out of 5 rating on G2 across 47 reviews. [G2, 2026] Its strength is the fit with a modern React stack: it treats variants as component-level swaps, so a frontend engineer can add a test where they already work, without a separate delivery layer or tag manager.
Its category is React-native personalization and testing. It sits inside the component tree, drives variants through a developer API, and pairs testing with feature flags. That focus is what draws headless eCommerce teams building on Next.js.
Component-level testing wraps a React or Next.js component so a variant can be swapped inside the rendered component tree, driven by a developer-first API. Croct does this for React applications. It is a delivery model for frontend engineers on a headless stack, distinct from a marketer-accessible visual test on a standard Shopify Liquid theme.
Where Croct is genuinely strong
- Native to React and Next.js: tests and personalization live inside the component tree where engineers already work.
- Developer-first API: a clean interface for frontend engineers, without a heavy tag or delivery layer.
- Testing plus feature flags: variants and rollouts share one model, which suits a headless deployment workflow.
- Free tier for exploration: teams can validate the fit before committing to usage-based pricing.
Where Croct hits its ceiling for a Shopify store
- Requires React or Next.js: it cannot run on standard Shopify Liquid themes without a headless rebuild.
- No visual editor: all experiments are configured in code, so marketing teams cannot ship a test alone.
- No multivariate testing: the platform focuses on A/B and personalization rather than combinatorial variants.
- Generic conversion focus: results are read as component-level metrics, not revenue per visitor.
What AB Smartly and Croct cannot do for an eCommerce store
AB Smartly and Croct are both developer-first testing tools, and they share one gap for a store. Neither is built around the surfaces where eCommerce revenue is won or lost, product pages, cart, and checkout, or around the metrics that matter there: revenue per visitor, order rate, and the margin a store actually keeps.
AB Smartly is built for engineering teams running server-side experiments at high velocity. It has no native Shopify integration, no visual editor for a marketer, and its metric layer is designed for engineering event data streamed into a warehouse, not store revenue on the Shopify funnel. Every product-page and checkout iteration costs developer time.
Croct is built for frontend engineers on React and Next.js, with variants swapped at the component level. On a standard Shopify Liquid theme it cannot run without a headless rebuild, and even inside Next.js all experiments are code-first. A CRO team on a stock Shopify store cannot use it without migrating the storefront, and there is no visual editor either way.
The two gaps differ in shape but point to the same missing layer. Both tools optimize the execution of a developer-led experiment on a general or React surface. Neither is built around where store revenue is actually decided, or around the Customer Value Optimization question: whether a result holds for high-value, repeat buyers. For the wider debate behind acting on behavior data, see Has personalization replaced A/B testing?
There is a second gap underneath the first: neither ships a built-in behavioral analysis layer, so heatmaps, session recordings, and on-site surveys come from third-party tools a team has to stitch together and reconcile. Omniconvert Explore builds that data insights layer in: heatmaps, session recordings, and surveys sit next to a full experimentation engine, and the same behavioral and customer data defines the segments you test against. The insight and the test live in one place, on the store's real revenue surfaces, which is the difference between watching a problem and proving the fix moved revenue per visitor.
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, and held its margin once discounts and returns are counted, not just moved an engineering event or a component-level click.
- Which surface to test first. Which pages in the 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 checkout flow natively, without engineering glue work.
- Whether it holds for valuable customers. Whether the result holds for repeat, high-value customers, the Customer Value Optimization question, not just first-session visitors.
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, and 85.1% never show the full order cost before the final step. [CROBenchmark Report 2026, Omniconvert]
These are checkout-surface problems, the exact surfaces a server-side engineering tool or a React component library is not built to experiment on for a marketing team. Explore runs the experiment on the store's real revenue surfaces and reports the outcome in revenue per visitor.
This is what the title means by Shopify revenue. A green result on an engineering event, or a lifted click inside a React component, can leave the bank balance flat; what moves it is order rate and average order value along the product-to-checkout path, read as revenue per visitor. Explore optimizes for that number directly, and because Customer Value Optimization ties each result back to repeat, high-value buyers, the lift it confirms is margin the store keeps rather than traffic it rents. A variant that wins on revenue per visitor and holds for high-CLV customers protects profit; a variant that only lifts a top-of-funnel event often does not. That is the revenue question AB Smartly and Croct are not built to answer, and the one Omniconvert Explore is. Explore also reaches Shopify-specific levers most testing tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
AB Smartly vs Croct vs Explore: the capability comparison
Side by side, the three tools serve different owners. AB Smartly serves engineering teams running server-side tests at scale. Croct serves frontend engineers on React and Next.js. Explore adds native Shopify experiments, a visual editor, built-in surveys and overlays, and revenue-per-visitor measurement on the product-to-checkout path. See A/B testing with Explore for how those experiments run natively on the Shopify funnel.
| Capability | AB Smartly | Croct | Omniconvert Explore |
|---|---|---|---|
| Primary function | Engineering-first server-side experimentation | React and Next.js personalization and A/B testing | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, no visual editor | Yes React component-level, no visual editor | Yes visual plus code editor |
| Multivariate testing | No | No | Yes |
| Server-side testing | Yes its core strength | Yes via SDK | Yes |
| Visual editor | No SDK only, code-first | No React code only | Yes marketer-accessible |
| On-site surveys and overlays | No engineering focus | No testing and flags only | Yes surveys and overlays built in |
| Shopify integration | Low no native Shopify support | Medium requires React or Next.js | Yes native |
| eCommerce focus | Low engineering-led experimentation | Low React frontend platform | High built for store revenue workflows |
| Revenue per visitor measurement | No warehouse event data | No component-level metrics | Yes revenue per visitor and order rate native |
| Pricing model | Custom, contact sales | Usage-based, free tier available | Session-based, built for store traffic, free trial |
| Best for | Engineering teams wanting warehouse-grade stats | Frontend engineers on a headless Next.js storefront | Shopify and eCommerce teams optimizing for revenue |
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]
Competitor ratings, pricing, and plan details reflect publicly listed figures as of 2026 and can change. AB Smartly is a server-side experimentation platform priced on request, and Croct is a React and Next.js platform on usage-based pricing with a free tier. 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 AB Smartly or Croct?
Decide by who owns the test. If your team is engineering-led and runs a polyglot backend, AB Smartly earns its place. If your storefront is headless Next.js, Croct fits the React tree. For a standard Shopify store where the CRO team owns the funnel, run your next test on the product-to-checkout path in Explore, measured in revenue per visitor. The three are complementary, and only Explore is built for the store's own checkout.
AB Smartly and Croct are both capable tools within their categories. AB Smartly is a warehouse-grade experimentation platform built by former Booking.com engineers. Croct is a developer-first testing and personalization tool tied to the React and Next.js component tree.
The question for a store is narrower: can your marketing team run a controlled experiment on a standard Shopify product, cart, and checkout without waiting on engineering, and read the result in revenue per visitor rather than an engineering event or a component metric. 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.