AB Smartly vs Optimizely vs Explore (2026): Engineering vs Shopify Revenue
AB Smartly is a real-time SDK-based experimentation platform built by former Booking.com engineers for engineer-led teams. Optimizely Web Experimentation is the enterprise standard for A/B testing and feature management. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs experiments on product pages, cart, and checkout, and measures the result in revenue per visitor.
- AB Smartly is a real-time SDK-based A/B testing platform built by former Booking.com engineers, with a 4.8 out of 5 G2 rating. [G2, 2026]
- Optimizely Web Experimentation is the enterprise industry standard for A/B testing and feature management, with a 4.2 out of 5 G2 rating. [G2, 2026]
- AB Smartly is engineering-first with no visual editor; Optimizely is broader but sized and priced for large-enterprise programmes.
- Neither has native Shopify integration, checkout experiment templates, or revenue per visitor as a first-class metric.
- 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 AB Smartly vs Optimizely Web Experimentation are usually choosing how to run experimentation at technical scale: with a lean, engineering-first tool or with the enterprise category standard. AB Smartly leads with real-time results and warehouse-grade rigor. Optimizely leads with enterprise breadth, feature flags, and full-stack testing. Neither is built around the surfaces where a Shopify store actually wins or loses revenue: the product page, the cart, and the checkout, which is where Omniconvert Explore lives.
What is AB Smartly, and what is it actually good at?
AB Smartly is a real-time A/B testing platform built by former Booking.com engineers. It runs SDK-based server-side experiments, streams results live, and connects natively to the customer's own data warehouse. It is built for engineer-led, high-velocity experimentation programmes. [AB Smartly, 2026]
AB Smartly holds a 4.8 out of 5 rating on G2 across 45 reviews. [G2, 2026] Its strength is engineering-first experimentation: real-time result streaming, a native connector into Snowflake, BigQuery, or Redshift, and statistical methods refined at Booking.com scale. That combination fits organizations where the experimentation programme is run by product engineers and data scientists rather than marketers.
The category AB Smartly sits in is high-velocity SDK-based testing. It runs experiments through code rather than a visual editor, which is how it stays honest about server-side behavior and warehouse joins. That focus is the point of the product, not a gap in it.
Real-time experimentation streams results as events arrive, so engineering teams can see effects at low latency and cut, hold, or scale traffic quickly. AB Smartly builds this into its SDK and warehouse connector. It is an engineering delivery layer, distinct from running a controlled revenue experiment natively on Shopify product, cart, and checkout pages.
Where AB Smartly is genuinely strong
- Real-time result streaming: low-latency reads on lift and significance as events arrive.
- Data warehouse integration: native connection into the customer's own Snowflake, BigQuery, or Redshift.
- Statistical rigor from Booking.com: variance reduction and sequential methods refined at scale.
- SDK-based server-side testing: engineering-led velocity for teams that ship changes through code.
Where AB Smartly hits its ceiling for an eCommerce store
- No visual editor: CRO and marketing teams cannot ship a test without engineering time.
- No native Shopify integration: product page and checkout tests need custom code.
- No multivariate testing: rules out combinatorial experiments common in eCommerce.
- Engineering event metrics: results arrive as event data, not revenue per visitor.
What is Optimizely Web Experimentation, and what is it actually good at?
Optimizely Web Experimentation is the industry-standard enterprise A/B testing platform. It runs web experiments, full-stack tests, and feature management with a visual editor for marketing and SDKs for engineering, plus deep audience targeting and integrations across the enterprise data stack. It has the largest enterprise customer base in the category. [G2, 2026]
Optimizely Web Experimentation holds a 4.2 out of 5 rating on G2 across 401 reviews. [G2, 2026] Its strength is depth: a visual editor for marketers, a full-stack SDK layer for engineers, feature flags for product teams, and audience targeting that survives real enterprise complexity. That combination is why many large organizations consolidate on it.
The category Optimizely sits in is enterprise experimentation and feature management. It is built for programmes running hundreds of concurrent tests across web and code, with strong statistics and the ability to graduate a winning test into a permanent feature. That scope is the point of the product.
Enterprise experimentation is a platform layer that unifies A/B testing, feature flags, and audience targeting across web and code, so a large organization can run hundreds of concurrent experiments with strong statistical rigor. Optimizely builds this into its full-stack platform. It is an enterprise capability, distinct from running a controlled revenue experiment natively on Shopify product, cart, and checkout pages.
Where Optimizely Web Experimentation is genuinely strong
- Full-stack experimentation: web plus SDKs across services, mobile, and backend code.
- Feature flags and feature management: graduate a test into a controlled release without a second tool.
- Enterprise-scale audience targeting: rich segmentation connected to the wider data stack.
- Category standing: the enterprise reference, with the largest install base for large-programme experimentation.
Where Optimizely Web Experimentation hits its ceiling for a single store
- Enterprise pricing and implementation: sized for large contracts, not a single store budget.
- No native Shopify integration: checkout templates and cart-level tests need custom integration work.
- Visual editor trails newer entrants: less intuitive for non-technical marketers than mid-market rivals.
- Generic conversion focus: results framed in events, not revenue per visitor and order rate.
What AB Smartly and Optimizely cannot do for an eCommerce store
AB Smartly and Optimizely target different scales, engineering-led speed and enterprise breadth, but they share the same gap for a store. Neither is built around the surfaces where eCommerce revenue is decided, product pages, cart, and checkout, or around the metric that matters there: revenue per visitor and order rate.
AB Smartly is built for engineering teams running high-velocity server-side experiments. With no native Shopify integration and no visual editor, product page and checkout tests happen only through code, so a CRO or marketing lead cannot ship a test without engineering time. Revenue per visitor is not a native experiment outcome; results arrive as engineering event data for the warehouse.
Optimizely Web Experimentation is built for enterprise engineering and product teams managing complex experiment programmes across web and full-stack. It is not designed for eCommerce revenue workflows and has no native Shopify integration or checkout experiment templates. Running it for Shopify CRO takes substantial developer involvement and custom integration, and results are framed in conversion events rather than order rate and revenue per visitor.
The two gaps sit at different points on the same axis but land in the same missing layer. Both platforms optimize the mechanics of a test at scale on a generic page. Neither is built around where a store's revenue is actually decided, or around the Customer Value Optimization question: whether a lift holds for high-value, repeat buyers. For the wider debate behind acting on experiment data, see Has personalization replaced A/B testing?
There is a second gap underneath the first: the data insights layer that tells a team what to test and whether the win moved revenue. AB Smartly ships engineering event streams, not on-site heatmaps or surveys, so a marketing team stitches those in from third-party tools. Optimizely holds deep audience data but locks it behind enterprise implementation. Omniconvert Explore builds that data insights layer in: heatmaps, session recordings, and surveys sit next to the experiment, and the same behavioral and customer data defines the segments you test against. The insight and the test live in one place, on the store's real revenue surfaces.
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, and held its margin once discounts and returns are counted, not just moved a click or an event.
- 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 actually lose orders: 99.6% fail to make guest checkout visible and prominent, and 85.1% never show the full order cost before the final step. [CROBenchmark Report 2026, Omniconvert]
These are checkout-surface problems, exactly the surfaces a code-first testing tool or an enterprise experimentation platform is not built to experiment on directly. Explore runs the experiment on the store's real revenue surfaces and reports the outcome in revenue per visitor.
This is what the title means by engineering versus Shopify revenue. A test won at engineering speed on a generic page can 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. A variant that wins on revenue per visitor and holds for high-CLV customers protects profit; a variant that only lifts a click event often does not. That is the revenue question AB Smartly and Optimizely are not built to answer, and the one Explore is. 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 Optimizely vs Explore: the capability comparison
Side by side, the three tools sit at different points on the experimentation stack. AB Smartly runs code-first server-side tests for engineering teams. Optimizely runs enterprise A/B and feature-flag programmes at scale. Explore adds native Shopify experiments, built-in surveys 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 | AB Smartly | Optimizely Web Experimentation | Omniconvert Explore |
|---|---|---|---|
| Primary function | Real-time SDK-based experimentation | Enterprise A/B testing and feature management | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, no visual editor | Yes visual and full-stack | Yes visual plus code editor |
| Multivariate testing | No | Yes | Yes |
| Server-side testing | Yes SDK-native | Yes via full-stack SDK | Yes |
| Visual editor | No engineering only | Yes | Yes |
| On-site surveys and overlays | No engineering event streams only | Partial overlays via campaigns, no built-in surveys | Yes surveys and overlays built in |
| Shopify integration | Low no native connector | Low custom integration required | Yes native |
| eCommerce focus | Low engineering-first, category-agnostic | Low enterprise-wide, not store-specific | High built for store revenue workflows |
| Pricing model | Custom, contact sales | Custom enterprise, quote on request | Session-based, built for store traffic, free trial |
| Best for | Engineering teams wanting real-time results with warehouse rigor | Enterprise programmes running hundreds of concurrent experiments | Shopify and eCommerce teams optimizing for revenue |
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. Both AB Smartly and Optimizely use custom pricing quoted on request. 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 Optimizely Web Experimentation?
Decide by who runs the tests and what surface they reach. If an engineering team wants real-time warehouse-linked experiments, keep AB Smartly. If your organization needs enterprise feature management at scale, keep Optimizely. For a Shopify store, run your next test on the product-to-checkout path in Explore, measured in revenue per visitor. The three are complementary at scale, but only Explore is built for the checkout where the order closes.
AB Smartly and Optimizely Web Experimentation are both capable inside their categories. AB Smartly is a real-time engineering-led platform built by Booking.com veterans. Optimizely is the industry standard for enterprise A/B testing and feature management.
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 an engineering 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.