Croct vs Dynamic Yield vs Explore (2026): Built for Shopify
Croct is a React and Next.js personalization and A/B testing platform, component-level and code-first. Dynamic Yield is an enterprise personalisation and recommendations engine owned by Mastercard, used at retail scale by IKEA and McDonald's. Neither is built for a standard Shopify store. Omniconvert Explore is the Shopify-native eCommerce CRO platform, measured in revenue per visitor.
- Croct is a React and Next.js personalization and A/B testing platform, component-level and code-first, with a 4.7 out of 5 G2 rating across 47 reviews. [G2, 2026]
- Dynamic Yield is an enterprise personalisation and recommendations engine owned by Mastercard, with a 4.5 out of 5 G2 rating across 200 reviews. [G2, 2026]
- The two sit at opposite ends of the market: Croct needs a headless React or Next.js storefront; Dynamic Yield needs a five to six-figure contract and a dedicated retail personalisation team.
- Neither is packaged for a mid-market Shopify brand on a standard Liquid theme, and neither reports outcomes in revenue per visitor on the product-to-checkout path.
- Omniconvert Explore is a Shopify-native eCommerce CRO platform: it runs A/B, multivariate, and checkout experiments through a visual editor and measures the result in revenue per visitor.
Teams comparing Croct vs Dynamic Yield are usually choosing between two very different testing platforms built for opposite buyers. Croct is engineering-first: a personalization and A/B testing SDK for React and Next.js applications, component-level and code-only, popular with headless eCommerce teams. Dynamic Yield is enterprise-first: a mature personalisation and recommendations engine owned by Mastercard, used by IKEA and McDonald's on catalogs measured in millions. This page covers what each does well, the mid-market Shopify gap they share, and when Omniconvert Explore is the right layer for a standard Shopify store.
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 well 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 is Dynamic Yield, and what is it actually good at?
Dynamic Yield is an enterprise personalisation and A/B testing platform owned by Mastercard since 2022, used by major retailers including IKEA and McDonald's. It supports product recommendations, experience optimisation, multivariate testing, and cross-channel personalisation across web, mobile, and email. It holds a 4.5 out of 5 G2 rating across 200 reviews. [G2, 2026]
Dynamic Yield's core strength is real-time personalisation tied to customer data at retail scale. Its recommendation engine, experience APIs, and multivariate testing suit brands running dedicated personalisation programs on large catalogs. The Mastercard acquisition in 2022 brought additional payment and behaviour data resources for retail applications.
The platform ships with a visual editor, so marketers can build variants without an engineering ticket, and it exposes server-side APIs for teams that want deeper control. Pricing is custom and typically starts in the five-figure annual range, with implementations that expect a dedicated internal team.
Retail personalisation tailors product, content, and experience per visitor at catalog scale, driven by a unified customer profile and machine-learned intent. Dynamic Yield's engine is one of the most mature in retail, proven at IKEA and McDonald's. It is an execution and delivery layer for large personalisation programs, distinct from running a controlled revenue experiment on Shopify product, cart, and checkout pages through a self-serve store workflow.
Where Dynamic Yield is genuinely strong
- Recommendation engine: mature product recommendations tuned for retail catalogs and cross-sell paths.
- Cross-channel personalisation: one engine serving web, mobile, and email from a shared customer profile.
- Multivariate testing: tests multiple element combinations in a single experiment, native to the platform.
- Enterprise retail scale: proven at IKEA, McDonald's, and other high-traffic brands under Mastercard ownership.
Where Dynamic Yield hits its ceiling for a mid-market store
- Enterprise pricing: custom contracts that typically start in the five-figure annual range and climb from there.
- High implementation effort: setup and integration expects a dedicated internal team and a formal project.
- Not self-serve for mid-market Shopify: a growth lead cannot spin up a checkout test in a week without a project plan.
- Over-specified for straightforward testing: a full personalisation stack for teams that mainly need A/B on the funnel.
- Generic outcome model: reports skew to click, engagement, and segment lift rather than revenue per visitor on the store funnel.
What Croct and Dynamic Yield cannot do for an eCommerce store
Croct is the headless-React answer and Dynamic Yield is the enterprise-retail answer, and a mid-market Shopify store falls between them. Neither is built around the surfaces where store revenue is won or lost, product pages, cart, and checkout on a standard Liquid theme, or around the metric that matters there: revenue per visitor.
Croct is built for React and Next.js developers and cannot be used on standard Shopify Liquid themes without significant development work. It has no visual editor, and every experiment is configured in code. Teams running a stock Shopify store cannot use Croct without migrating the storefront to a headless architecture first, which is a rebuild project, not a testing decision.
Dynamic Yield is an enterprise platform built for large retail organisations with dedicated personalisation teams. It is not designed for mid-market Shopify brands running self-serve CRO experiments. The implementation complexity and pricing put it out of reach for eCommerce teams that need to run tests on product pages and checkout flows without a five to six-figure contract and a developer team.
The two gaps differ in shape but point to the same missing layer. Both are capable testing platforms at opposite ends of the market, one that needs a headless rebuild and one that needs an enterprise budget. Neither treats the standard Shopify checkout as the primary surface, and neither reports the result in revenue per visitor. For the framing that connects testing to customer value, see Has personalization replaced A/B testing?
The deeper issue is that ownership of the test never quite lands on the person who owns the revenue number for the store. With Croct a frontend engineer owns it, once the storefront is rewritten in React; with Dynamic Yield a personalisation strategist owns the setup and a project team owns the implementation. In both cases a mid-market Shopify growth lead cannot self-serve the next checkout test. Omniconvert Explore collapses that loop: a visual editor for product page and checkout variants, native Shopify integration on the standard Liquid theme, on-site surveys and overlays in the same platform, and the outcome measured in revenue per visitor. The person who owns the store's growth number owns the test.
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. 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 a mid-market Shopify team
- Did the win move revenue and margin. Whether a variant raised revenue per visitor and order rate, and held its margin once discounts and returns are counted, not just moved a component-level click or a segment lift.
- Which surface to test first. Which pages in the store funnel (product, cart, checkout) carry the highest revenue impact if tested next.
- How it behaves in Shopify checkout. How an experiment interacts with the Shopify catalog, variants, and checkout flow natively, without a headless rebuild or a custom personalisation implementation.
- 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 or a segment picked by a recommendations engine.
Omniconvert benchmarks more than 7,000 eCommerce websites in its CROBenchmark Report 2026, across 248+ audit criteria. The findings show where stores actually lose orders: 99.6% fail to make guest checkout visible and prominent, 94.2% never show checkout progress, and 85.1% never show the full order cost before the final step. [CROBenchmark Report 2026, Omniconvert]
These are fixes a CRO lead can hypothesise, mock up, and want to test today. With Croct the same fix waits behind a headless-storefront project. With Dynamic Yield it waits behind an enterprise procurement cycle and a personalisation implementation team. Explore runs the experiment on the real Shopify revenue surfaces and reports the outcome in revenue per visitor, without a rebuild or a five to six-figure contract between the hypothesis and the result.
This is what the title means by built for Shopify. Two capable platforms, at opposite ends of the market, and both looking past the surface where a standard Shopify store's revenue is actually decided. Explore optimizes for revenue per visitor 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. Explore also reaches Shopify-specific levers most generic testing tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
Croct vs Dynamic Yield vs Explore: the capability comparison
Side by side, the three tools sit at different points on the headless-to-enterprise-to-store axis. Croct is the component-level layer for React and Next.js engineers on a headless storefront. Dynamic Yield is the enterprise personalisation engine for retail giants with dedicated programs. Explore is the eCommerce CRO layer for the mid-market Shopify team that owns product, cart, and checkout, and is judged on revenue per visitor. See A/B testing with Explore for how those experiments run natively on the Shopify funnel.
| Capability | Croct | Dynamic Yield | Omniconvert Explore |
|---|---|---|---|
| Primary function | React and Next.js personalization and A/B testing | Enterprise retail personalisation and A/B testing | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes React component-level, code-only | Yes visual editor and personalisation engine | Yes visual editor plus code |
| Multivariate testing | No | Yes | Yes |
| Server-side testing | Yes via SDK | Yes | Yes |
| Visual editor | No React code only | Yes | Yes WYSIWYG for marketers |
| On-site surveys and overlays | No testing and flags only | Partial overlays yes, no native surveys | Yes surveys and overlays built in |
| Shopify integration | Medium requires React or Next.js rebuild | High connector available, but heavy setup | Yes native on standard Liquid themes |
| eCommerce focus | Low React frontend platform | High built for enterprise retail | High built for mid-market store revenue |
| Pricing model | Usage-based, free tier available | Custom enterprise, five to six figures annually, contact sales | Session-based, built for store traffic, free trial |
| Best for | Frontend engineers on a headless Next.js storefront | Enterprise retail personalisation teams at catalog scale | Mid-market Shopify and eCommerce growth teams |
| User rating | 4.7 out of 5 (G2, 47 reviews, as of 2026) | 4.5 out of 5 (G2, 200 reviews, as of 2026) | 4.6 out of 5 (G2, 191 reviews, as of 2026) |
AliveCor used 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. Croct is a React and Next.js platform on usage-based pricing with a free tier; Dynamic Yield is enterprise-priced with a contact-sales model. 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 Dynamic Yield?
Decide by rebuild cost and buyer. If your engineering team is already building a headless Next.js storefront and wants component-level personalization inside React, Croct fits well. If your enterprise retail team is running a dedicated program on a Mastercard-scale catalog, Dynamic Yield fits. For a mid-market Shopify store where product, cart, and checkout decide revenue, run your next test in Explore and measure the outcome in revenue per visitor, not clicks.
Croct and Dynamic Yield are both strong at what they do. Croct brings a React and Next.js SDK for frontend engineers who want variants inside the component tree, with feature flags in the same platform and a free tier to start. Dynamic Yield brings deep retail personalisation, a mature recommendations engine, and cross-channel experience optimisation proven at IKEA, McDonald's, and other Mastercard-owned retail scale.
The question for a store is narrower: once you have a hypothesis about the cart or checkout on a standard Shopify Liquid theme, can a CRO lead ship the variant, measure the result in revenue per visitor, and answer whether the win holds for high-value repeat customers, without a headless rebuild or a five to six-figure retail personalisation contract. 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