AB Smartly vs Shoplift vs Explore (2026): Engineers vs Themes
AB Smartly is an engineering-led, SDK-based experimentation platform built by former Booking.com engineers with real-time server-side results. Shoplift is a Shopify-native theme A/B testing app for testing sections and page components without code. Omniconvert Explore is the eCommerce CRO platform: it runs A/B, multivariate, and checkout experiments on the Shopify product-to-checkout path, measured 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 across 45 reviews. [G2, 2026]
- Shoplift is a Shopify-native theme A/B testing app for testing sections, hero variants, and page components, rated 4.8 out of 5 on G2 across 55 reviews. [G2, 2026]
- AB Smartly is engineering-first with no visual editor and no native Shopify; Shoplift is Shopify-native but scoped to theme sections, with no server-side testing and limited statistical depth.
- Neither runs a full-funnel A/B or multivariate test on the underlying product page, cart, and Shopify checkout with outcomes read in revenue per visitor.
- Omniconvert Explore runs A/B, multivariate, and server-side experiments on product, cart, and checkout natively and measures results in revenue per visitor: pick it for full-funnel Shopify CRO.
Teams comparing AB Smartly vs Shoplift are usually looking at opposite ends of the same room: an engineering-first experimentation platform on one side, a Shopify theme section tester on the other. AB Smartly leads with real-time results and warehouse-grade rigor for product engineers. Shoplift leads with zero-setup theme section testing a merchant can run in the Shopify theme editor without a developer. Neither is built to run a controlled A/B test across the full product-to-checkout funnel with multivariate, server-side, and revenue-per-visitor outcomes, 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 Shoplift, and what is it actually good at?
Shoplift is a Shopify-native A/B testing app for testing theme sections, hero variants, product descriptions, and page components. It installs from the Shopify App Store, runs inside the theme editor, and lets a merchant ship a section test without writing code or waiting on a developer. [Shoplift, 2026]
Shoplift holds a 4.8 out of 5 rating on G2 across 55 reviews. [G2, 2026] Its strength is a native, zero-setup workflow on Shopify: install the app, pick a section, define a variant, split traffic. Onboarding takes minutes, and pricing starts at $99 per month with a free trial, which fits DTC merchants who want to try theme changes without a CRO agency in the loop.
The category Shoplift sits in is Shopify theme A/B testing. It is built to help merchants find the best-performing layout for a hero banner, a product page section, or a landing page structure inside the Shopify theme editor. That focus is the point of the product.
Shopify theme testing is the practice of running A/B tests on theme sections and page components inside the Shopify theme editor: hero variants, product page section layouts, and page structures a merchant can toggle without code. Shoplift is built around this workflow. It is a theme-section delivery layer, distinct from a controlled revenue experiment across the full product-to-checkout funnel.
Where Shoplift is genuinely strong
- Native Shopify theme integration: installs from the App Store, runs in the theme editor, no engineering setup.
- Section-level tests without code: merchants test hero images, product descriptions, and page structures directly.
- Fast onboarding: first test live in minutes, with a free trial to prove the workflow before commitment.
- Purpose-built for Shopify merchants: the product model matches how a DTC store team actually ships changes.
Where Shoplift hits its ceiling for an eCommerce store
- Theme-section scope only: cannot test cart logic, dynamic pricing, or backend rules outside the theme editor.
- No server-side testing: rules out experiments on shipping thresholds, discount logic, or catalog rules.
- Limited statistical depth: designed for straightforward two-variant theme tests, not variance-reduced programmes.
- Section engagement, not funnel revenue: results frame around section performance rather than revenue per visitor across the funnel.
What AB Smartly and Shoplift cannot do for an eCommerce store
AB Smartly and Shoplift sit at opposite ends of the testing spectrum: engineering-led server-side experiments on one side, Shopify theme section swaps on the other. Both leave the same gap for a store. Neither runs a full-funnel A/B test across product, cart, and checkout with multivariate depth, server-side reach, and outcomes read in revenue per visitor.
AB Smartly is built for engineering teams running high-velocity server-side experiments. It has no native Shopify integration and cannot run product page or checkout experiments through a visual interface accessible to non-technical users. A marketer cannot ship a test without engineering time, and results arrive as engineering event data for the warehouse rather than eCommerce revenue metrics.
Shoplift is a Shopify theme testing tool. It can only run experiments on theme sections and page components and cannot run multivariate tests beyond simple section variants, server-side experiments, or full-funnel tests beyond basic Shopify checkout extensibility. Teams wanting full-funnel eCommerce CRO beyond theme-level testing outgrow the tool quickly.
The two gaps look opposite but land in the same missing layer. AB Smartly optimizes the mechanics of an experiment at engineering scale on a generic page. Shoplift optimizes theme-section swaps a merchant can ship in the theme editor. Neither is built around the full revenue surface: the underlying product page logic, the cart page, and the Shopify checkout, read as revenue per visitor and tied back to repeat, high-value buyers through Customer Value Optimization. For the wider debate behind acting on experiment data, see Has personalization replaced A/B testing?
There is also a data insights gap underneath the testing gap. AB Smartly ships engineering event streams, not on-site heatmaps or surveys, so a marketing team stitches those in from third-party tools. Shoplift focuses on theme section performance, with limited page-level behavioral analytics across the funnel. 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 a lifted event or a preferred theme section.
- Which surface to test first. Which pages in the funnel (product, cart, checkout) carry the highest revenue impact if tested next, not just which theme section clicked best or which SDK metric moved.
- How it behaves in checkout. How an experiment interacts with the Shopify catalog, variants, and checkout flow natively, without engineering glue work or template forking.
- 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 clicking a new hero image.
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 SDK tool or a theme-section tester 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 engineers versus themes. A server-side test won at engineering speed on a generic page can leave the bank balance flat; so can a theme section swap that a merchant ships in the theme editor without ever reaching the cart or the checkout. What actually moves the store's revenue 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 or a section engagement rate often does not. That is the revenue question AB Smartly and Shoplift 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 Shoplift vs Explore: the capability comparison
Side by side, the three tools cover different jobs. AB Smartly runs code-first server-side tests for engineering teams. Shoplift runs zero-setup theme section tests inside the Shopify theme editor. Explore adds native Shopify A/B, multivariate, and server-side experiments across product, cart, and checkout, with built-in surveys and revenue-per-visitor measurement on the full funnel. See A/B testing with Explore for how those experiments run natively on the Shopify funnel.
| Capability | AB Smartly | Shoplift | Omniconvert Explore |
|---|---|---|---|
| Primary function | Real-time SDK-based experimentation | Shopify theme section A/B testing | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, no visual editor | Partial Shopify theme sections only | Yes visual plus code editor |
| Multivariate testing | No | Partial across theme section variants | Yes across page elements |
| Server-side testing | Yes SDK-native | No | Yes |
| Visual editor | No engineering only | Partial Shopify theme editor, not a WYSIWYG | Yes |
| On-site surveys and overlays | No engineering event streams only | No theme-section scope only | Yes surveys and overlays built in |
| Shopify integration | Low no native connector | High native Shopify app | Yes native |
| eCommerce focus | Low engineering-first, category-agnostic | High Shopify-only, theme-scope | High built for store revenue workflows |
| Pricing model | Custom, contact sales | Session-based, from $99/mo, free trial | Session-based, built for store traffic, free trial |
| Best for | Engineering teams wanting real-time results with warehouse rigor | Shopify merchants wanting zero-setup theme section testing | Shopify and eCommerce teams optimizing for revenue per visitor |
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. AB Smartly uses custom pricing quoted on request. Shoplift starts at $99/month with a free trial. 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 Shoplift?
Decide by scope and team shape. If your team is engineering-led and needs real-time warehouse-linked results, keep AB Smartly. If you are a solo Shopify merchant who wants zero-setup theme section tests, keep Shoplift. For full-funnel A/B, multivariate, and server-side experiments across product, cart, and checkout, measured in revenue per visitor, run those in Explore. The three cover different jobs, but only Explore tests the surfaces where a store's order closes.
AB Smartly and Shoplift are both strong inside their categories. AB Smartly is a real-time engineering-led experimentation platform built by Booking.com veterans, rated 4.8 out of 5 on G2. Shoplift is a top-rated Shopify-native theme A/B testing app, also 4.8 out of 5 on G2, with a workflow a merchant can run without a developer.
The question for a store is narrower: can your team run a controlled experiment across the Shopify product, cart, and checkout, with multivariate and server-side reach, and read the result in revenue per visitor rather than an engineering event or a theme-section click. 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.