AB Smartly alternative (2026): Engineering testing vs Shopify CRO
AB Smartly is an experimentation platform built by former Booking.com engineers for real-time server-side testing with warehouse-grade statistical rigour. Omniconvert Explore is a Shopify-native CRO platform that runs product, cart, and checkout tests through a visual editor without engineering involvement, measured in revenue per visitor. Different teams, rarely overlapping.
- AB Smartly is an engineering-led experimentation platform built by former Booking.com engineers, with a 4.8 out of 5 G2 rating across 45 reviews. [G2, 2026]
- AB Smartly delivers real-time server-side experiment results, a data warehouse connector, and advanced statistical methods for high-velocity product-engineering programmes.
- AB Smartly has no visual editor, no native Shopify integration, no multivariate testing, and requires developer implementation for every test.
- Omniconvert Explore runs experiments on Shopify product, cart, and checkout pages without an SDK or engineering team, and measures results in revenue per visitor.
- The two rarely compete: many organisations run AB Smartly for backend product experiments and Explore for storefront CRO on the same brand.
Teams comparing AB Smartly vs Omniconvert Explore are usually asking two different questions dressed as one. AB Smartly is an engineering-led experimentation platform built by practitioners from Booking.com, with real-time results and a data warehouse connector for high-velocity server-side testing. Omniconvert Explore is a Shopify-native CRO platform that runs experiments on product, cart, and checkout without engineering plumbing, and measures the outcome in revenue per visitor. This page explains where each fits and where they never really compete.
What is AB Smartly, and what does it actually do?
AB Smartly is a server-side experimentation platform built by former Booking.com engineers. It runs SDK-based A/B tests with real-time results, a direct data warehouse connector, and advanced statistical methods designed for high-velocity engineering-led programmes. It is a product team tool, priced and shaped for organisations that treat experimentation as engineering infrastructure. [AB Smartly, 2026]
AB Smartly is highly rated in its category, with a 4.8 out of 5 rating on G2 across 45 reviews. [G2, 2026] The product is a favourite among engineering and data teams because it collapses SDK-driven exposure, real-time analysis, and warehouse integration into a single workflow used by product engineers at scale.
The category AB Smartly sits in is engineering-led server-side experimentation. Experiments are defined in code, exposures fire from SDKs, results stream back in near real time, and warehouse export lets analytics teams cut the data in their own tools. That focus is the point of the product.
The question this page answers is narrower: is SDK-based server-side experimentation the same job as running conversion experiments on a Shopify store? And if not, where is the gap?
Engineering-led experimentation means the platform ships as SDKs and APIs rather than a visual editor, and every experiment is authored, released, and monitored by engineers inside a product codebase. It is powerful for product-engineering organisations that already deploy multiple times a day. It is a separate concern from whether a marketer can launch a Shopify product page test without a developer.
Where AB Smartly is genuinely strong
- Built by Booking.com veterans: the founding team ran one of the largest experimentation programmes on the internet, and the product reflects that operational discipline.
- Real-time results: exposures and metrics stream back in near real time, so engineering teams shipping many tests per week see impact without a next-day wait.
- Advanced statistical methods: supports sequential testing, variance reduction, and other techniques that improve sensitivity for high-velocity programmes.
- Direct warehouse connector: exposures and outcomes flow into Snowflake, BigQuery, or Redshift so analytics teams can join experiment data with the rest of the business model.
Where AB Smartly hits its ceiling for an eCommerce store
- No visual editor: every experiment requires SDK integration and a developer, which locks marketing and CRO teams out of self-serve testing.
- No native Shopify integration: product page, cart, and checkout tests need custom implementation against the Shopify catalog and checkout flow.
- No multivariate testing: classic A/B is supported, but full MVT designs are not.
- Priced for engineering-led orgs: custom pricing with contact sales and no free trial, shaped for organisations that already staff a dedicated experimentation platform team.
None of this makes AB Smartly a weak product. It makes it a product-engineering tool. The friction shows up specifically when the site under test is a Shopify store and the team running experiments does not have a platform engineer, an analytics engineer, and a release calendar behind every hypothesis.
What AB Smartly cannot do for an eCommerce store
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, so it is not designed for self-serve eCommerce CRO. That is the gap an eCommerce-first platform closes.
Omniconvert Explore is built for the layer AB Smartly leaves open. AB Smartly can run any code-authored experiment well, but a store does not need every experiment authored in code; it needs the product page, the cart, and the checkout tested, and the result expressed in revenue per visitor. Those are not the same task.
Most engineering-led experimentation tools are built around a generic SDK and a generic exposure event. They optimise the velocity and rigour of a code test. They are not built around the surfaces where eCommerce revenue is actually won or lost, or around a marketer-accessible interface for launching a test on a Shopify checkout.
eCommerce 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 AB Smartly cannot tell an eCommerce team
- Did the win move revenue. Whether a winning variant actually raised revenue per visitor and order rate, not just an SDK-fired event.
- Which surface to test first. Which pages in the Shopify 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 plumbing on every page.
- Whether it holds for valuable customers. Whether the result holds for repeat, high-value customers, the Customer Value Optimization question, not just first-session traffic.
Across the 7,000+ eCommerce websites in Omniconvert's CROBenchmark Report 2026, the stores testing fastest are the ones where a marketer or CRO lead can launch a product page or checkout experiment the same week it is proposed; AB Smartly's SDK-first model pushes that work into the engineering backlog, and the benchmark shows testing cadence drops sharply when every experiment needs a developer ticket and a release. [CROBenchmark Report 2026, Omniconvert]
Explore runs the experiment on the store's real revenue surfaces and reports the outcome in 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]
AB Smartly vs Explore: the capability comparison
Side by side, AB Smartly and Explore share almost no overlap in daily job. AB Smartly runs SDK-based server-side experiments with real-time results streamed to engineering dashboards. Explore ships Shopify-native experiments on product, cart, and checkout, adds surveys and overlays, and reports in revenue per visitor. Where they touch is server-side testing, and the fit still splits by team.
| Capability | AB Smartly | Omniconvert Explore |
|---|---|---|
| Primary function | SDK-based server-side experimentation for engineering and product teams | eCommerce CRO on product, cart, and checkout pages |
| A/B testing | Yes SDK-based, no visual editor | Yes visual editor plus code editor |
| Multivariate testing | No | Yes |
| Server-side testing | Yes | Yes |
| Visual editor | No SDK and code required for every test | Yes no developer required |
| On-site surveys and overlays | No not part of the product | Yes surveys and overlays built in |
| Shopify integration | Low no native app, engineering integration required | Yes native |
| eCommerce focus | Low built for engineering-led product orgs | High built for store revenue workflows |
| Pricing model | Custom pricing, contact sales, no free trial | Session-based, built for store traffic, free trial |
| Best for | Engineering and data teams wanting real-time server-side experimentation with warehouse-grade statistical rigour | Shopify and eCommerce teams optimizing product, cart, and checkout for revenue |
Competitor pricing and plan details reflect publicly listed figures as of 2026 and can change. 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?
If your experiments run on a Shopify store and you need a marketer-accessible way to test product page, cart, and checkout without engineering time, choose Explore: it ships a visual editor, native Shopify integration, and measures revenue per visitor. If your engineering team wants real-time server-side experimentation with warehouse-grade statistical rigour built by ex-Booking.com practitioners, AB Smartly is purpose-built for that. The two rarely compete; many organisations run AB Smartly on the product stack and Explore on the storefront.
AB Smartly earns its high ratings. It is fast, statistically strong, and built by practitioners who ran one of the largest experimentation programmes on the internet, which is exactly what a product-engineering org wants from server-side testing.
The question for a store is narrower: are the experiments that move revenue running natively on the product, cart, and checkout pages, without an SDK or a platform engineer in the loop, and are they measured 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.