Croct vs Eppo vs Explore (2026): Code-Only Testing on Shopify
Croct is A/B testing for React and Next.js applications, with a developer-first API and no visual editor. Eppo is warehouse-native experimentation for data teams with mature infrastructure like Snowflake or BigQuery. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs experiments on product pages, cart, and checkout with a visual editor, and measures results in revenue per visitor.
- Croct is an A/B testing and personalization platform built for React and Next.js applications, with a developer-first API and a 4.7 out of 5 G2 rating. [G2, 2026]
- Eppo is a warehouse-native experimentation platform that connects directly to Snowflake or BigQuery, with a 4.7 out of 5 G2 rating. [G2, 2026]
- Both are developer-first tools: neither has a visual editor, and every experiment requires code and engineering time.
- Neither is built for a standard Shopify store or for marketing teams that need to run experiments without engineering.
- Omniconvert Explore is Shopify-native, has a visual editor, and measures results in revenue per visitor: pick it when marketing owns the experiment loop.
Teams comparing Croct vs Eppo are usually engineering-led. Croct fits a frontend team building a React or Next.js commerce app, with component-level personalization and feature flags in code. Eppo fits a data team with a mature warehouse, running rigorous analysis on metrics it already trusts. Both are credible for those teams, yet neither is built for a standard Shopify store where the marketing team owns experimentation. This page covers what each does well, the gap they share for eCommerce, and when Omniconvert Explore is the right layer.
What is Croct, and what is it actually good at?
Croct is an A/B testing and personalization platform built specifically for React and Next.js applications. It runs experiments and personalization at the component level, with a developer-first API. It suits headless eCommerce teams already building in React. [Croct, 2026]
Croct's strength is that everything happens where a React developer already works. Components carry the test logic, feature flags sit alongside the code, and there is no separate visual editor to keep in sync. It holds a 4.7 out of 5 rating on G2 across 47 reviews. [G2, 2026]
For a headless team building on Next.js, this removes the usual friction between marketing tools and engineering. The tradeoff is that every test lives in code, and a team member who does not write React has no path in. That is a hard fit for a standard Shopify store, where the theme is Liquid and the marketing team owns experimentation.
Component-level testing means the variant lives inside the React or Next.js component itself, so the variant, the flag that gates it, and the code that renders it all sit together in the codebase. Croct does this well for React apps. It is a code-based delivery layer, distinct from running a controlled experiment on Shopify product, cart, and checkout pages that a marketing team can set up without engineering.
Where Croct is genuinely strong
- Component-level React and Next.js: variants live inside the components that render them, natural for frontend engineers.
- Developer-first API: tests, flags, and personalization rules are defined in code alongside the rest of the app.
- Feature flags in the same tool: gate rollouts and experiments together, one platform for both.
- Headless eCommerce fit: a strong choice for teams already on a React or Next.js commerce stack.
Where Croct hits its ceiling for an eCommerce store
- Requires React or Next.js: cannot run on standard Shopify Liquid themes without a headless rebuild.
- No visual editor: every experiment lives in code, so a marketing team cannot set one up alone.
- Engineering-owned experiment loop: a headline or price change ships behind a developer ticket.
- No native eCommerce metric: no built-in revenue per visitor or order-rate outcome view.
What is Eppo, and what is it actually good at?
Eppo is a warehouse-native experimentation platform. It connects directly to a data warehouse like Snowflake or BigQuery and analyzes experiments using the metrics definitions already stored there. It suits data and engineering teams running high-velocity experimentation with strong statistical rigor. [Eppo, 2026]
Eppo's strength is rigor and reuse. Instead of importing events into a separate testing tool, it reads the tables and metrics the data team already trusts. It holds a 4.7 out of 5 rating on G2 across 80 reviews. [G2, 2026]
For a data-mature organization, this closes the drift between experiment metrics and business KPIs. The tradeoff is scope: Eppo is a rigorous analysis platform, not a delivery tool for a merchandiser. Without a data warehouse and engineers to instrument events, most of what makes it powerful is out of reach.
Warehouse-native experimentation runs the analysis of A/B tests directly against a data warehouse like Snowflake or BigQuery, using the metrics definitions already stored there rather than importing events into a separate tool. Eppo does this well for data teams. It is an analysis and infrastructure layer, distinct from running a controlled experiment on Shopify product, cart, and checkout pages that a marketing team can set up without engineering.
Where Eppo is genuinely strong
- Warehouse-native analysis: results run on Snowflake, BigQuery, or Redshift, on tables the data team already owns.
- Shared metric definitions: experiment metrics use the same definitions as the executive dashboard, so KPIs do not drift.
- Advanced statistical methods: sequential testing, CUPED, and heterogeneous treatment effects built in.
- Governance and velocity: designed for data teams running many concurrent tests with strong review workflows.
Where Eppo hits its ceiling for an eCommerce store
- Requires a mature data stack: without a warehouse and clean event tracking, the analysis has nothing to read.
- No visual editor: no way for a marketer to test a live page, every change routes through engineering.
- Enterprise pricing model: custom pricing on request, not accessible to small or mid-market brands.
- Not Shopify-native: no self-serve path to test a product page or a checkout step from the storefront side.
What Croct and Eppo cannot do for an eCommerce store
Croct and Eppo are developer-first tools, and they share the same gap for a standard Shopify store. Both require code to run any experiment, neither has a visual editor, and neither is designed for the surface where store revenue is decided: product pages, cart, and checkout.
Croct is built for React and Next.js developers. It cannot be used on a standard Shopify Liquid theme without significant frontend rework, and all of its personalization and experiment logic lives in code. A team running a standard Shopify store cannot use Croct without moving to a headless architecture first.
Eppo is warehouse-native. It requires Snowflake or a similar data platform, and expects engineers to instrument events and define metrics. It has no visual editor and no marketer-accessible path to change a Shopify product page or a checkout step.
Both tools are built around a generic experiment: a variant defined in code, a flag toggling it, a statistical readout. Neither is built around the surfaces where eCommerce revenue is actually won or lost, and neither reports the metric that matters to a store, revenue per visitor, as its native outcome. That is what a store needs when the marketing team owns the roadmap.
Platforms like Omniconvert Explore are built for this layer. Explore runs A/B, multivariate, and personalization tests through a visual editor on the Shopify product-to-checkout path, and measures every result in revenue per visitor. Marketing can set up the experiment, and engineering does not become the bottleneck for every headline change.
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 fix move store revenue. Whether a change raised revenue per visitor and order rate, not just resolved a click or a code-level metric.
- Which surface to test first. Which pages in the funnel (product, cart, checkout) carry the highest revenue impact if a merchandiser tests them next.
- How it behaves in Shopify checkout. How an experiment interacts with the Shopify catalog, variants, and checkout flow natively, without engineering glue work.
- Whether the marketing team can run it. Whether the experiment can be set up, launched, and iterated by the team that owns the storefront, without every change queuing behind engineering.
Omniconvert benchmarks more than 7,000 eCommerce websites in its CROBenchmark Report 2026, across 248+ audit criteria. The data shows where stores lose orders on the checkout path: 99.6% fail to make guest checkout visible and prominent, and 94.2% never show checkout progress steps. [CROBenchmark Report 2026, Omniconvert]
A developer tool can build the fix in code once the store already knows what to test. Neither Croct nor Eppo will surface where the friction sits on the Shopify path first, and neither will report the result in revenue per visitor. Explore does both, and the storefront team can run the loop themselves.
This is what code-only testing means for a store. A variant defined in a React component or scored inside a warehouse can lift a metric that never shows up in the bank balance; 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-CLV buyers, the lift it confirms is margin the store keeps rather than traffic it rents. Explore also reaches Shopify-specific levers most testing tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
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 Eppo vs Explore: the capability comparison
Side by side, the three tools sit at different jobs. Croct is component-level personalization for React. Eppo is warehouse-native experiment analysis for data teams. Explore runs A/B, multivariate, and personalization tests on the Shopify product-to-checkout path, measured in revenue per visitor. See A/B testing with Explore for how it runs on the Shopify funnel.
| Capability | Croct | Eppo | Omniconvert Explore |
|---|---|---|---|
| Primary function | React and Next.js personalization and A/B testing | Warehouse-native experiment analysis | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes component-level, requires React code | Yes warehouse-native, requires SDK and events | Yes visual plus code editor |
| Multivariate testing | No | No | Yes |
| Server-side testing | Yes | Yes | Yes |
| Visual editor | No code-only, React components | No analysis platform, no delivery UI | Yes visual plus code editor |
| On-site surveys and overlays | No component overlays via code only | No not a delivery tool | Yes surveys and overlays built in |
| Shopify integration | Medium works on headless React commerce, not standard Liquid themes | Low no Shopify-native path, needs a warehouse pipeline | Yes native |
| eCommerce focus | Low general React apps, not eCommerce-specific | Low general data-team tooling | High built for store revenue workflows |
| Pricing model | Usage-based, free tier available | Custom pricing, contact sales | Session-based, built for store traffic, free trial |
| Best for | Frontend engineers on React or Next.js commerce apps | Data teams with a mature warehouse and event tracking | Shopify and eCommerce teams optimizing for revenue |
| User rating | 4.7 out of 5 (G2, 47 reviews, as of 2026) | 4.7 out of 5 (G2, 80 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. Both Croct and Eppo require engineering resources to run any experiment. 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 Eppo?
Decide by whether your team can write code to run every test. Croct fits if your engineers are building a headless Next.js store and want component-level personalization. Eppo fits if your data team owns Snowflake and wants warehouse-native analysis. For a standard Shopify store where marketing owns experiments, run Explore. Its visual editor reaches product pages, cart, and checkout without engineering glue, and results read in revenue per visitor.
Croct and Eppo are both credible, well-rated tools for the teams they were built for. Croct is a solid fit for a frontend team building a headless Next.js commerce app. Eppo is a strong pick for a data team with a mature warehouse and a taste for statistical rigor.
The question for a store is narrower: can the team that owns the storefront run the next experiment, on the Shopify product-to-checkout path, and read the result in revenue per visitor without waiting for engineering. That is the surface Explore is built for, and where these two developer-first tools are not.
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