Crazy Egg vs Optimizely vs Explore (2026): Light, Enterprise, Revenue
Crazy Egg is a heatmap and session recording tool with light two-variant A/B testing, built for small businesses. Optimizely Web Experimentation is the enterprise A/B testing standard, with a visual editor and full-stack SDK. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs experiments on product, cart, and checkout, measured in revenue per visitor.
- Crazy Egg is a heatmap and session recording tool with light two-variant A/B testing, built for small businesses, with a 4.2 out of 5 G2 rating. [G2, 2026]
- Optimizely Web Experimentation is the enterprise A/B testing standard, with a visual editor, full-stack SDK, multivariate testing, and the largest enterprise customer base in the category, at 4.2 out of 5 on G2. [G2, 2026]
- The two tools sit at opposite ends of the testing market, light diagnostics and enterprise rigor, and solve very different buying problems.
- Neither has native Shopify integration, built-in on-site surveys or overlays, or revenue-per-visitor measurement on the store's product-to-checkout path.
- 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 Crazy Egg vs Optimizely Web Experimentation are usually choosing between two very different ends of the testing market. Crazy Egg packages heatmaps and session recordings with a light two-variant A/B test, aimed at small businesses and non-technical marketers. Optimizely runs enterprise-grade A/B testing with a visual editor and a full-stack SDK, built for product and engineering teams at large organizations. Neither is designed around the surfaces where a Shopify store wins or loses revenue, which is where Omniconvert Explore enters the comparison.
What is Crazy Egg, and what is it actually good at?
Crazy Egg is a heatmap and session recording tool with lightweight A/B testing. It shows where visitors click, scroll, and engage on a page, and includes a basic two-variant test in every plan at a low entry price. It is accessible to non-technical marketers, with pricing from around $49 per month. [Crazy Egg, 2026]
Crazy Egg is a behavior analytics tool first and a testing tool second. It holds a 4.2 out of 5 rating on G2 across 144 reviews. [G2, 2026] Its strength is making click, scroll, and engagement data easy to see for teams without an analyst, at an entry price that keeps it approachable for small businesses.
The A/B testing it ships is deliberately simple: single-page, two-variant tests, with no multivariate or server-side capability. That suits a marketer who wants to try one headline against another, not a program of experiments across a Shopify funnel.
A heatmap aggregates where visitors click, move, and scroll on a page into a visual overlay, so a team can see attention and friction at a glance. Session recordings replay individual visits for the same purpose. Crazy Egg does both well and layers a light test on top. It is a diagnosis layer, distinct from running a controlled revenue experiment on product, cart, and checkout pages.
Where Crazy Egg is genuinely strong
- Heatmaps and scroll maps: a clear visual read on where clicks and attention land.
- Session recordings: watch real sessions to see where visitors hesitate.
- Low entry price: accessible plans small businesses can start on quickly.
- Basic A/B testing included: a simple two-variant test in every plan, usable without a developer.
Where Crazy Egg hits its ceiling for an eCommerce store
- Testing is basic: no proper multivariate testing, no server-side testing, no advanced audience targeting.
- No checkout experiments: it cannot run tests on Shopify product, cart, or checkout flows.
- Analytics first: teams serious about CRO outgrow its experiment features quickly.
- Generic conversion focus: no concept of revenue per visitor as a tested outcome.
What is Optimizely Web Experimentation, and what is it actually good at?
Optimizely Web Experimentation is the enterprise A/B testing standard, with a visual editor, a full-stack SDK, feature flags, multivariate testing, and advanced audience targeting. It is built for product and engineering teams running experimentation at scale, and integrates with a full enterprise data stack. [G2, 2026]
Optimizely holds a 4.2 out of 5 rating on G2 across 401 reviews. [G2, 2026] It has the largest enterprise customer base in the A/B testing category and is often the default choice for organizations that run experimentation as a shared discipline across many product and engineering teams.
The category Optimizely sits in is enterprise experimentation. Deep statistical rigor, full-stack coverage, and an integrated data stack are the point of the product. That is also the reason large product organizations choose it over mid-market tools.
Enterprise web experimentation means running controlled A/B tests at organizational scale: dozens or hundreds of concurrent experiments, strict statistical rigor, both web and server-side variants, and tight integration with a central data stack. It is a program discipline operated by a dedicated experimentation or product team, distinct from a Shopify store running a focused test on its checkout.
Where Optimizely Web Experimentation is genuinely strong
- Enterprise-grade statistical rigor: a stats engine and reporting built for high-volume, high-stakes programs.
- Visual editor plus full-stack: both front-end web tests and server-side experiments inside one platform.
- Multivariate and advanced targeting: complex factorial designs and granular audience rules at scale.
- Enterprise data stack integration: ties experiments into the warehouse, BI, and the wider analytics stack.
Where Optimizely hits its ceiling for an eCommerce store
- No native Shopify integration: no built-in connector to the Shopify catalog, variants, or checkout flow.
- Priced for enterprise contracts: custom pricing, no public starting price, no free trial for store teams to try first.
- Heavy implementation investment: the platform rewards technical expertise and dedicated program resources.
- Generic conversion focus: results are reported as conversion lift, not revenue per visitor on store surfaces.
What Crazy Egg and Optimizely Web Experimentation cannot do for an eCommerce store
Crazy Egg and Optimizely sit on opposite ends of the testing market, light diagnostics and enterprise rigor. Both leave the same hole for a Shopify store: neither is built around product, cart, and checkout, and neither measures outcomes in revenue per visitor on store surfaces.
Crazy Egg is primarily a heatmap and session recording tool. Its A/B testing is limited to simple two-variant tests on single pages, and it cannot run experiments on Shopify checkout flows, product pages, or cart sequences. Teams using Crazy Egg for eCommerce CRO can diagnose problems visually but cannot run the revenue-focused experiments needed to fix them.
Optimizely is built for enterprise engineering and product teams managing complex experiment programs. It is not designed for eCommerce revenue workflows and has no native Shopify integration or checkout experiment templates. Operating it for Shopify CRO still asks for substantial developer involvement and custom integration work, and the pricing is set for enterprise contracts rather than store budgets.
The two gaps meet in the same place. Crazy Egg stops at diagnosis plus a headline test. Optimizely runs real experimentation, but on generic web pages and inside enterprise programs. Neither is built around the surfaces where store revenue is actually decided, or around the Customer Value Optimization question: whether a result holds for high-value, repeat buyers.
There is a second gap underneath the first. Crazy Egg has behavior data but no real testing engine. Optimizely has a testing engine but leaves heatmaps, session recordings, and on-site surveys to third-party tools. In either case, insight and test live in separate places, with handoffs and reconciliation in between.
Omniconvert Explore closes that gap. Heatmaps, session recordings, and surveys sit next to the experiment, and the same behavioral and customer data defines the segments you test against. Insight and test live together on the store's 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, not just lifted a click, a scroll depth, or a conversion event on a generic web page.
- 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 buyers, 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 lose orders at the checkout surface itself: 94.2% never show checkout progress, 99.6% fail to make guest checkout visible, 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 light heatmap tool and an enterprise web tester are not set up to run experiments on. Explore runs the experiment on the store's real revenue surfaces and reports the outcome in revenue per visitor.
A winning headline test or an enterprise-grade conversion lift can still 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. Explore also reaches Shopify-specific levers most testing tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
Crazy Egg vs Optimizely vs Explore: the capability comparison
Side by side, the three tools serve different layers. Crazy Egg shows behavior and runs a light test. Optimizely runs enterprise-grade A/B tests on generic web pages. Explore adds native Shopify experiments, built-in surveys and overlays, heatmaps, and revenue-per-visitor measurement on the product-to-checkout path; see A/B testing with Explore.
| Capability | Crazy Egg | Optimizely Web Experimentation | Omniconvert Explore |
|---|---|---|---|
| Primary function | Heatmaps with basic A/B testing | Enterprise A/B testing on web and server-side | eCommerce CRO on product, cart, and checkout |
| A/B testing | Partial basic two-variant, single page | Yes visual editor plus full-stack | Yes visual plus code editor |
| Multivariate testing | No limited two-variant only | Yes | Yes |
| Server-side testing | No | Yes full-stack SDK | Yes |
| Visual editor | No code-based variant setup | Yes enterprise-grade, less intuitive than mid-market | Yes polished editor |
| Heatmaps and session recordings | Yes its core strength | No needs third-party tools | Yes built in beside the experiment |
| On-site surveys and overlays | No heatmaps and recordings only | No needs third-party tools | Yes surveys and overlays built in |
| Shopify integration | Low installs but no checkout testing | Low no native connector, custom work required | Yes native |
| eCommerce focus | Low small-business diagnostics | Low enterprise, generic web and server-side | High built for store revenue workflows |
| Pricing model | Session-based, from $49/mo, free trial | Custom enterprise, contact sales, no free trial | Session-based, built for store traffic, free trial |
| Best for | Small teams wanting heatmaps and light tests | Enterprise product and engineering teams running large programs | Shopify and eCommerce teams optimizing for revenue |
| User rating | 4.2 out of 5 (G2, 144 reviews, as of 2026) | 4.2 out of 5 (G2, 401 reviews, as of 2026) | 4.6 out of 5 (G2, 191 reviews, as of 2026) |
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. 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 Crazy Egg or Optimizely Web Experimentation?
Decide by whether you need enterprise rigor or Shopify revenue. If your organization runs hundreds of experiments across web and server-side surfaces, Optimizely earns its contract. If you only want cheap heatmaps and a light headline test, Crazy Egg will do the diagnosis. For a Shopify store, run your next test on the product-to-checkout path in Explore, measured in revenue per visitor. The three can coexist, but only Explore owns the checkout where the order closes.
Crazy Egg and Optimizely are both capable inside their categories. Crazy Egg is a low-cost heatmap tool with a light test for small businesses. Optimizely is the enterprise A/B testing standard, with the largest customer base in the category and the statistical rigor an experimentation program at scale asks for.
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 or a generic conversion event. 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