AB Smartly vs Crazy Egg vs Explore (2026): Two Tools, One Blind Spot
AB Smartly is a real-time experimentation platform built by former Booking.com engineers, with SDK-based assignment and a data warehouse connector. 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.
- AB Smartly is a real-time, engineering-led experimentation platform with SDK-based assignment and a warehouse connector, and a 4.8 out of 5 G2 rating. [G2, 2026]
- Crazy Egg is a heatmap and session recording tool with basic two-variant A/B testing, and a 4.2 out of 5 G2 rating. [G2, 2026]
- The two sit at opposite extremes: AB Smartly is dev infrastructure, Crazy Egg is entry-level analytics with a toy test.
- Neither has native Shopify integration, native checkout experiments, multivariate testing, or revenue-per-visitor measurement.
- Omniconvert Explore is the Shopify-native eCommerce CRO layer that runs product, cart, and checkout experiments and reports revenue per visitor: pick it for store revenue surfaces.
Teams comparing AB Smartly vs Crazy Egg are usually looking at two very different tools and asking which one fits their store. AB Smartly is a real-time, engineering-led experimentation platform priced for teams that treat testing as continuous infrastructure. Crazy Egg is a heatmap and recording tool with a light two-variant test, priced for small teams that want visual behavior data. Neither is built for a Shopify marketer running product page or checkout experiments measured in revenue, and Omniconvert Explore is the layer that fits.
What is AB Smartly, and what is it actually good at?
AB Smartly is a real-time experimentation platform built by former Booking.com engineers. It uses SDK-based assignment, exposes results as they arrive rather than in batch, and connects directly to a data warehouse for downstream analysis. It is built for high-velocity engineering programmes that treat testing as continuous infrastructure. [AB Smartly, 2026]
AB Smartly is an engineering platform built by practitioners of one of the largest experimentation programmes in the industry. It holds a 4.8 out of 5 rating on G2 across 45 reviews. [G2, 2026] Its strength is speed and rigour together: results update in real time rather than in a nightly batch, and the platform is designed for teams running many parallel tests without waiting on a data pipeline.
Assignment happens through SDKs, with server-side splits and advanced statistical methods. The warehouse connector lets a data team join experiment exposure to any downstream metric that already lives in Snowflake, BigQuery, or Redshift.
Real-time experimentation streams experiment assignment and outcome events as they happen, so exposures and metrics update continuously instead of running as a scheduled batch. AB Smartly does this well for engineering teams shipping many parallel tests. It is an execution and analysis layer for developer-owned code paths, distinct from running a controlled revenue experiment on Shopify product, cart, and checkout pages through a marketer-accessible interface.
Where AB Smartly is genuinely strong
- Real-time results: exposures and metrics update continuously, not in a nightly batch.
- Booking.com heritage: statistical methods and program design shaped by one of the largest testing programmes in the industry.
- Warehouse connector: joins experiment exposure to metrics already defined in Snowflake, BigQuery, or Redshift.
- Server-side and SDK based: scales for high-velocity engineering programmes running many parallel tests.
Where AB Smartly hits its ceiling for an eCommerce store
- No visual editor: variants ship through code and SDKs, not a WYSIWYG a marketer can use.
- No native Shopify integration: nothing wired to product pages, cart, or checkout out of the box.
- Engineering-priced: custom pricing designed for engineering-led organisations, not for a self-serve CRO budget.
- No multivariate testing: the platform does not run MVT natively.
- 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.
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 and a basic two-variant test. It is a diagnosis layer, distinct from running a controlled revenue experiment on Shopify 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 that 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, no server-side testing, and 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 AB Smartly and Crazy Egg cannot do for an eCommerce store
AB Smartly and Crazy Egg sit at opposite ends of the same missing layer. One is dev infrastructure for engineering teams; the other is entry-level heatmap software with a toy test. 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.
AB Smartly is built for engineering teams running high-velocity, real-time server-side tests, and the Booking.com pedigree shows in how the platform treats experimentation as continuous infrastructure. On a Shopify store the same shape is a problem. Every product page, cart, and checkout experiment starts as an SDK ticket, and the queue is set by engineering. The A/B test the CRO lead wanted this week ships when engineering ships it.
Crazy Egg sits at the other extreme. It is easy to buy, easy to install, and shows a marketer where shoppers stall on a page. But the moment the team wants to test a real fix on the cart or the checkout, the tool cannot reach it: no multivariate, no server-side capability, no path to the Shopify checkout surface, and no revenue-per-visitor measurement. It diagnoses on the surface and experiments only at the edges.
The gap the two share is the eCommerce one. AB Smartly is an engineering execution layer; Crazy Egg is a diagnosis layer. Neither is an eCommerce CRO platform. Neither treats the product-to-checkout path as the primary surface, and neither reports the result in revenue per visitor. For the wider context on how testing programmes actually move store revenue, see Has personalization replaced A/B testing?
The deeper issue is that ownership of the test sits far from the person who owns the revenue number. A CRO lead has a hypothesis about the cart page and, in AB Smartly, needs an engineer to write assignment code, ship a variant, and later stitch results back to order rate; in Crazy Egg, the hypothesis simply cannot be tested past the top of the funnel. Omniconvert Explore collapses that loop: a visual editor for product page and checkout variants, native Shopify integration, heatmaps, session recordings, 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 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 engineering event or a click.
- 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 checkout. How an experiment interacts with the Shopify catalog, variants, and checkout flow natively, without SDK glue work or a separate testing app.
- 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 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]
Those are fixes a CRO lead can hypothesise, mock up, and want to test today. In AB Smartly, the same fix is an SDK ticket in engineering's queue. In Crazy Egg, the heatmap can show a shopper stalling on the exact step, but the tool cannot reach it to test a change. Explore runs the experiment on the real revenue surface and reports the outcome in revenue per visitor.
This is what the title means by two tools, one blind spot. Real-time rigour on a metric your engineering team owns, and a colourful heatmap on the top of the funnel, both stop short of 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.
AB Smartly vs Crazy Egg vs Explore: the capability comparison
Side by side, the three tools sit at very different points. AB Smartly is real-time execution and analysis for engineering teams shipping many parallel server-side tests. Crazy Egg is heatmaps and recordings with a light headline test for small teams. 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 | AB Smartly | Crazy Egg | Omniconvert Explore |
|---|---|---|---|
| Primary function | Real-time engineering-led experimentation | Heatmaps with basic A/B testing | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, 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 code and SDKs only | No template-based test setup | Yes WYSIWYG for marketers |
| 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 engineering teams | Low small-business diagnostics | High built for store revenue workflows |
| Pricing model | Custom, contact sales, enterprise | Session-based, from $49/mo, free trial | Session-based, built for store traffic, free trial |
| Best for | Engineering teams wanting real-time server-side experimentation | Small teams wanting heatmaps and light tests | Shopify and eCommerce teams optimizing for revenue |
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. AB Smartly is an engineering-owned experimentation platform; Crazy Egg is a behavior analytics tool with light testing. 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 AB Smartly or Crazy Egg?
Choose by whether the person who owns your revenue can own the test. If your engineering team owns experimentation as infrastructure, AB Smartly gives it velocity. If a small team wants cheap heatmaps and a headline test, Crazy Egg fills that niche. For a Shopify store where a CRO lead should run product, cart, and checkout experiments and read the result in revenue per visitor, Explore is built for that surface.
AB Smartly and Crazy Egg are capable tools for their intended buyers. AB Smartly is a real-time, engineering-led experimentation platform with Booking.com heritage. Crazy Egg is a low-cost heatmap suite with a light two-variant test.
The question for a store is narrower: once a CRO lead has a hypothesis about the product page or the checkout, can it be tested on the real Shopify surface, without an SDK ticket or a tool that stops at the top of the funnel, and read as revenue per visitor. That is the layer 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.