Conductrics vs Crazy Egg vs Explore (2026): The Shopify Gap
Conductrics is an API-first experimentation platform built for engineers embedding bandits and adaptive optimization into their own applications. Crazy Egg is a heatmap and session recording tool with basic two-variant A/B testing. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs product page, cart, and checkout experiments and measures the result in revenue per visitor.
- Conductrics is an API-first experimentation and adaptive optimization platform for technical teams, with a 4.3 out of 5 G2 rating across 12 reviews. [G2, 2026]
- Crazy Egg is a heatmap and session recording tool with basic two-variant A/B testing, with a 4.2 out of 5 G2 rating across 144 reviews. [G2, 2026]
- The two sit at opposite ends of the same axis: Conductrics is too developer-owned for a marketer, Crazy Egg is too light to run real revenue experiments.
- Neither runs multivariate or checkout experiments on Shopify, and neither reports outcomes in revenue per visitor, the metric that decides store revenue.
- Omniconvert Explore is Shopify-native and marketer-accessible: it runs product page, cart, and checkout experiments and reports revenue per visitor as the primary outcome.
Teams comparing Conductrics vs Crazy Egg are usually asking two very different questions. Conductrics is picked by data science and engineering teams that want programmatic control over experimentation. Crazy Egg is picked by marketers who want cheap heatmaps and a light two-variant test on any page. Neither is built to run controlled experiments on the Shopify product-to-checkout path measured in revenue per visitor. This page covers what each does well, the gap they share for an eCommerce store, and when Omniconvert Explore is the right layer.
What is Conductrics, and what is it actually good at?
Conductrics is an API-first experimentation and adaptive optimization platform for technical teams. It exposes multi-armed bandit and adaptive targeting through APIs, so traffic shifts toward winning variants automatically inside the application code that calls it. It is built for developer-led programs that want experimentation embedded directly in their own product. [Conductrics, 2026]
Conductrics is a programmatic platform first and a marketer's tool a distant second. It holds a 4.3 out of 5 rating on G2 across 12 reviews. [G2, 2026] Its strength is control: technical teams get direct API access to variant assignment, bandit optimization, and adaptive targeting, and can weave that directly into their own product code rather than a standalone testing interface.
The trade-off is scope. Conductrics has no visual editor and no marketer surface, and its public documentation and brand footprint are small compared with the wider testing category. It fits a team that wants a library, not a product.
API-first experimentation exposes variant assignment and adaptive optimization as programmatic endpoints that developers call from their own application code. Conductrics does this well for technical teams that want experimentation logic embedded in the product itself. It is a developer-facing library layer, distinct from running a controlled revenue experiment on Shopify product, cart, and checkout pages through a marketer-accessible interface.
Where Conductrics is genuinely strong
- API-first control: variant assignment and optimization exposed as endpoints for developers to call from their own code.
- Multi-armed bandits: traffic shifts toward winning variants automatically, without a manual traffic-allocation step.
- Adaptive targeting: segmentation rules can react to live user behavior rather than a fixed pre-test split.
- Embedded programs: designed for technical teams that want experimentation logic inside their own product, not in a standalone tool.
Where Conductrics hits its ceiling for an eCommerce store
- No visual editor: variants ship through API calls, not a WYSIWYG marketers can use.
- No native Shopify integration: nothing wired to product pages, cart, or checkout out of the box.
- Sparse public documentation: a marketer or CRO lead cannot self-onboard the way they can with a mainstream testing tool.
- Priced for enterprise: custom pricing built for technical teams, not a self-serve marketing budget.
- Generic outcome model: no concept of revenue per visitor as a first-class metric on the store funnel.
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 with a free trial.
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 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 sessions to see where users hesitate.
- Low entry price: accessible plans small businesses can start on quickly, from $49 per month.
- 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 Conductrics and Crazy Egg cannot do for an eCommerce store
Conductrics and Crazy Egg sit at opposite ends of the same axis. One is a developer library, the other is a marketer's diagnostics tool. 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.
Conductrics is API-first, and that is a real advantage for a technical team embedding optimization into its own application. It supports multi-armed bandits and adaptive targeting, so traffic shifts toward winning variants automatically. On a Shopify storefront the same shape becomes a wall: a marketer cannot ship a variant through an API integration, the tool has no native Shopify connector, and its small public footprint means the store's growth team is often the first internal user learning it.
Crazy Egg sits at the other end. It is primarily a heatmap and session recording tool, and its A/B testing capability is limited to simple two-variant tests on single pages. 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.
The gap the two share is the eCommerce one. Conductrics is too far from the marketer, Crazy Egg is too far from a real experiment, and neither treats the product-to-checkout path as the primary surface or reports the result in revenue per visitor. For the wider debate about how testing programs actually move revenue, see Has personalization replaced A/B testing?
The deeper issue is that neither tool sits where the store's growth number is owned. The CRO lead with a hypothesis about the cart page needs an engineer to wire up API calls in Conductrics, or a heavier testing platform beside Crazy Egg to actually run the experiment. Omniconvert Explore collapses that loop: a visual editor for product page and checkout variants, native Shopify integration, heatmaps, session recordings, and on-site surveys 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 an eCommerce 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 an API-tracked event or a click on a heatmap.
- 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 API glue work or a separate analytics tool.
- Whether it holds for valuable customers. Whether the result holds for repeat, high-value customers, the Customer Value Optimization question, not just for first-session visitors.
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 steps, and 85.1% never show the full order cost before the final step. [CROBenchmark Report 2026, Omniconvert]
A heatmap can show shoppers stalling on that checkout step, and an API-first tester can, in theory, split them into variants, but neither can package the fix as a marketer-owned experiment on the Shopify funnel. Explore runs the experiment on the store's real revenue surfaces and reports the outcome in revenue per visitor, without an API integration or a second diagnostic tool between the hypothesis and the result.
This is what the title means by the Shopify gap. Developer-owned bandits or click-level diagnostics are not the same as a lift on the number that pays for the store. 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 testing tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
Conductrics vs Crazy Egg vs Explore: the capability comparison
Side by side, the three tools sit at very different points on the experiment lifecycle. Conductrics is the API-first bandit layer for technical teams embedding optimization into their own application. Crazy Egg is the heatmap and light-testing layer for small teams diagnosing behavior on any page. Explore is the eCommerce CRO layer for the store 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 | Conductrics | Crazy Egg | Omniconvert Explore |
|---|---|---|---|
| Primary function | API-first experimentation and adaptive optimization | Heatmaps with basic A/B testing | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes API-first, no visual editor | Partial basic two-variant, single page | Yes visual editor plus code |
| Multivariate testing | No | No | Yes |
| Server-side testing | Yes core capability | No | Yes |
| Visual editor | No API calls only | No heatmap-first, no editor | Yes WYSIWYG for marketers |
| Heatmaps and session recordings | No | Yes its core strength | Yes built in beside the experiment |
| On-site surveys and overlays | No | No heatmaps and recordings only | Yes surveys and overlays built in |
| Shopify integration | Low no native connector | Medium installs but no checkout testing | Yes native |
| eCommerce focus | Low built for technical teams | Low small-business diagnostics | High built for store revenue workflows |
| Pricing model | Custom, contact sales | Session-based, from $49/mo, free trial | Session-based, built for store traffic, free trial |
| Best for | Data science and engineering teams wanting API-first bandits and adaptive optimization | Small teams wanting heatmaps and light tests | Shopify and eCommerce teams optimizing for revenue |
| User rating | 4.3 out of 5 (G2, 12 reviews, as of 2026) | 4.2 out of 5 (G2, 144 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. Conductrics is priced for technical teams and Crazy Egg is priced for small businesses; neither is designed as a full eCommerce CRO 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 Conductrics or Crazy Egg?
Decide by where the block is. If you need engineer-owned experimentation embedded in a custom application, Conductrics fits. If you want cheap heatmaps and a light test on any page, Crazy Egg fits. Neither is built to run controlled experiments on Shopify product, cart, and checkout, measured in revenue per visitor. For a store on Shopify, run your next test in Explore, then measure the lift in revenue per visitor.
Conductrics and Crazy Egg do very different jobs well. Conductrics gives engineering teams API-first bandits and adaptive optimization inside a custom application. Crazy Egg gives small marketing teams cheap heatmaps and a light two-variant test.
The question for a Shopify store is narrower: once you can either see or programmatically assign traffic on a page, can you run a controlled experiment on product, cart, and checkout and read the result in revenue per visitor. 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