Crazy Egg vs LaunchDarkly vs Explore (2026): Heatmaps, Flags, Revenue
Crazy Egg is a heatmap and session recording tool with light two-variant A/B testing. LaunchDarkly is an engineering feature flag platform with SDK-based experimentation. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs product, cart, and checkout experiments without SDK code, and measures each result in revenue per visitor, not click rate or deploy metrics.
- Crazy Egg is a heatmap and session recording tool with basic two-variant A/B testing, at 4.2 out of 5 on G2 across 144 reviews. [G2, 2026]
- LaunchDarkly is the market-leading feature flag platform with SDK-based experimentation, at 4.5 out of 5 on G2 across 712 reviews. [G2, 2026]
- The two tools sit on opposite sides of the stack but share one gap for a store: neither is built for a marketer running controlled experiments on Shopify product, cart, and checkout surfaces.
- Neither reports experiment outcomes in revenue per visitor or order rate, the numbers that actually read a store's profit.
- Omniconvert Explore combines visual, A/B, multivariate, and server-side testing with heatmaps and surveys, native to Shopify and priced for store traffic, so a growth team can run and read a revenue experiment without SDK wiring.
Teams comparing Crazy Egg vs LaunchDarkly are usually not picking between similar tools: they are picking which side of the stack owns experimentation. Crazy Egg is a marketer-friendly heatmap suite that adds a light two-variant test. LaunchDarkly is the market-leading feature flag platform used by engineering teams for gradual rollouts and SDK-based experiments. Neither is built around the surfaces where Shopify revenue is actually won, so this page covers what each does well, the gap they share, and when Omniconvert Explore is the right layer.
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 users click, scroll, and engage on a page, and includes a basic two-variant test in every plan at a low price point. It is accessible to non-technical marketers. [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 starts around $49 per month.
The A/B testing it includes is deliberately simple: single-page, two-variant tests. That suits a marketer who wants to try one headline against another, not a program of experiments across a 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. Crazy Egg does this well and pairs it with session recordings. 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 visits to see where shoppers 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 for light experiments.
Where Crazy Egg hits its ceiling for an eCommerce store
- Testing is basic: no multivariate testing, server-side testing, or 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 LaunchDarkly, and what is it actually good at?
LaunchDarkly is a feature flag management and experimentation platform for engineering teams. It gates every release behind a runtime flag, rolls new code out gradually to a chosen slice of users, and runs SDK-based experiments on code paths. It is built for the engineering toolchain, not a marketer. [LaunchDarkly, 2026]
LaunchDarkly is an engineering platform first and an experimentation tool second. It holds a 4.5 out of 5 rating on G2 across 712 reviews. [G2, 2026] Its strength is controlling how a release reaches users: targeting by segment, ramping traffic in steps, and killing a bad deploy from a dashboard without a redeploy.
The experimentation it supports is server-side and SDK-based. A developer wires the flag, defines the metric event, and reads the lift inside LaunchDarkly. That suits a platform or product team measuring engineering changes, not a marketing team iterating on a product page.
A feature flag is a runtime switch in code that lets a team release a change to a chosen slice of users without a redeploy. LaunchDarkly manages those switches at scale with targeting rules and rollout steps. It is a deployment-control layer, distinct from running a controlled revenue experiment on product, cart, and checkout pages.
Where LaunchDarkly is genuinely strong
- Market-leading feature flag management: the default pick for engineering orgs that gate every release behind a flag.
- Gradual rollouts and targeting rules: ramp a new feature from 1% to 100% with segment-level control.
- Deep CI/CD and monitoring integration: flags live inside the engineering toolchain rather than beside it.
- Server-side experimentation: SDK-based assignment and metric wiring for teams that treat testing as infrastructure.
Where LaunchDarkly hits its ceiling for an eCommerce store
- No visual editor: every variant and metric needs a developer to wire the SDK.
- No native Shopify integration: no app, no checkout reach, no catalog awareness.
- Engineering metrics, not store metrics: no built-in concept of revenue per visitor or order rate.
- Seat-based, contact-sales pricing: priced for engineering orgs, not a store growth team.
What Crazy Egg and LaunchDarkly cannot do for an eCommerce store
Crazy Egg and LaunchDarkly sit on opposite sides of the stack, marketer-friendly diagnostics on one side and developer feature flags on the other, and they share the same 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 metric that matters there: revenue per visitor.
Crazy Egg's A/B testing is capped at simple two-variant tests on single pages. It cannot run experiments on Shopify checkout flows, product pages, or cart sequences. A store using it can diagnose problems visually through heatmaps, but it has no path from that insight to a revenue-focused experiment that fixes them.
LaunchDarkly inverts the problem. It has the experimentation engine, but every test lives behind an SDK call wired by a developer. There is no visual editor for a marketer to change a product-page headline, no native Shopify integration to reach the catalog or checkout, and no concept of revenue per visitor as the primary outcome.
The two gaps meet in the middle: one tool shows the problem without the test, the other runs the test without the store surface. Omniconvert Explore closes both. Heatmaps, session recordings, and surveys sit beside a full A/B, multivariate, and server-side experimentation engine, native to Shopify. The same behavioral data defines the segments a store tests against on the product-to-checkout path, with the outcome read in revenue per visitor rather than a click or a deploy flag. For the wider debate behind acting on behavior data, see Has personalization replaced A/B testing?
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 change raised revenue per visitor and order rate, and held its margin once discounts and returns are counted, not just lifted a click or a deploy metric.
- 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, 94.2% never show checkout progress steps, and 85.1% hide the full order cost until the final step. [CROBenchmark Report 2026, Omniconvert]
A heatmap can show shoppers stalling on that checkout step; a feature flag can roll a backend change out to half of them. Neither reports the lift in revenue per visitor on the store's own checkout, which is what moves the bank balance. Explore runs the experiment on that surface and reads it in that number, and the Customer Value Optimization framing ties each result back to repeat, high-value buyers, so a lift 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 LaunchDarkly vs Explore: the capability comparison
Side by side, the three tools sit at different points of the stack. Crazy Egg shows behavior and runs a light test. LaunchDarkly manages engineering feature flags with SDK-based experiments. Explore adds native Shopify experiments, heatmaps, recordings, 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 | Crazy Egg | LaunchDarkly | Omniconvert Explore |
|---|---|---|---|
| Primary function | Heatmaps with basic A/B testing | Feature flag management and release control | eCommerce CRO on product, cart, and checkout |
| A/B testing | Partial basic two-variant, single page | Yes SDK-based feature flag experiments | Yes visual plus code editor |
| Multivariate testing | No | No | Yes |
| Server-side testing | No | Yes its core strength | Yes |
| Visual editor | No | No SDK only | Yes for marketers and developers |
| On-site surveys and overlays | No heatmaps and recordings only | No not a front-end UX tool | Yes surveys and overlays built in |
| Shopify integration | Low installs but no checkout testing | Low SDK only, no native Shopify app | Yes native |
| eCommerce focus | Low small-business diagnostics | Low engineering-focused release tool | High built for store revenue workflows |
| Revenue per visitor measurement | No behavioral metrics only | No engineering and deploy metrics | Yes revenue per visitor and order rate native |
| Pricing model | Session-based, from $49/mo, free trial | Seat-based, contact sales, no free trial | Session-based, built for store traffic, free trial |
| Best for | Small teams wanting heatmaps and light tests | Engineering teams managing feature releases at scale | Shopify and eCommerce teams optimizing for revenue |
| User rating | 4.2 out of 5 (G2, 144 reviews, as of 2026) | 4.5 out of 5 (G2, 712 reviews, as of 2026) | 4.6 out of 5 (G2, 197 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. Crazy Egg is a behavior analytics tool with light testing; LaunchDarkly is an engineering feature flag platform. 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 LaunchDarkly?
Decide by who is running the test. If you want cheap heatmaps and a simple page test, Crazy Egg will do that. If your engineering team needs flag-based release control at scale, LaunchDarkly owns that job. But for a Shopify store running experiments on product pages, cart, and checkout without SDK wiring, Explore is the one built for it. Pair with LaunchDarkly only if engineering independently needs feature flags; drop Crazy Egg.
Crazy Egg and LaunchDarkly are capable at the jobs they were built for. Crazy Egg gives a small marketing team a visual window on page behavior with a light A/B test. LaunchDarkly gives an engineering team precise release control with SDK-based experimentation on code paths.
The question for a store is narrower: once you can see a shopper stalling on checkout, who runs the test that fixes it, and in which unit do you read the result. If the answer has to be a marketing team, in revenue per visitor, on the Shopify product-to-checkout path, Explore is the surface that supports it.
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 197 reviews, G2 , as of 2026