AB Smartly vs Convert vs Explore (2026): Two Testers, One Blind Spot
AB Smartly is a real-time experimentation platform built by former Booking.com engineers, SDK-based with no visual editor. Convert is a privacy-first A/B testing tool for CRO agencies and mid-market teams. Neither is Shopify-native. Omniconvert Explore is the eCommerce CRO platform for product, cart, and checkout, measured in revenue per visitor rather than generic conversion rate.
- AB Smartly is a real-time experimentation platform built by former Booking.com engineers, with a 4.8 out of 5 G2 rating and SDK-based assignment. [G2, 2026]
- Convert is a privacy-first A/B testing platform for CRO agencies and mid-market teams, with a 4.7 out of 5 G2 rating and transparent session-based pricing. [G2, 2026]
- AB Smartly targets engineering teams; Convert targets CRO agencies. They rarely appear on the same shortlist except when a Shopify team is unsure which side of that line it belongs on.
- Neither is Shopify-native or measures results in revenue per visitor, the surfaces and metric where store revenue is decided.
- 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 Convert are choosing between two very different testing tools. AB Smartly is engineering-first: SDK-based, real-time, built by former Booking.com engineers for high-velocity programmes. Convert is agency-first: a visual-editor A/B platform with a strong privacy stance and transparent session-based pricing. This page covers what each does well, the eCommerce gap they share, and when Omniconvert Explore is the right layer for a Shopify store.
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 job, and the platform is designed for teams that run hundreds of parallel tests without waiting on a batch 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, 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 Convert, and what is it actually good at?
Convert is a privacy-first A/B testing platform for CRO agencies and mid-market teams. It supports multivariate testing and advanced targeting, integrates with Google Analytics and major analytics platforms, and is known for transparent session-based pricing and a no-data-sharing stance. [Convert, 2026]
Convert is one of the most highly rated tools in the category, with a 4.7 out of 5 rating on G2 across 139 reviews. [G2, 2026] It is a favourite among CRO agencies for transparent, session-based pricing and agency-friendly account features, and its support is consistently praised.
The category Convert sits in is practitioner-led web testing. It runs A/B and multivariate tests on general websites, with a strong privacy posture that appeals to teams handling sensitive traffic. That focus is the point of the product.
Privacy-first testing means the platform avoids sharing experiment data with third parties and minimises what it collects, which matters for teams under strict compliance requirements. Convert builds this into its product and pricing. It is a separate concern from whether a test runs natively on a store's checkout.
Where Convert is genuinely strong
- Transparent, session-based pricing: predictable costs from $299 per month that agencies and mid-market teams can plan around.
- Strong privacy stance: no data sharing with third parties, useful under strict compliance needs.
- Multivariate and advanced targeting: capable experimentation with granular audience rules.
- Highly rated support and integrations: well-regarded support, with Google Analytics and major analytics connections.
Where Convert hits its ceiling for an eCommerce store
- No native Shopify integration: no eCommerce-specific experiment templates, so store setups are manual.
- Editor is functional, not polished: the visual editor trails VWO and Optimizely for ease of use.
- No built-in behavioural analysis: no native heatmaps or session recordings, so teams add third-party tools.
- Generic conversion focus: results are framed around general conversion, not revenue per visitor.
What AB Smartly and Convert cannot do for an eCommerce store
AB Smartly and Convert are built for different audiences, one for engineers, one for CRO agencies, but they share one gap for a Shopify store. 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 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. It is not designed for self-serve eCommerce CRO, and every store surface a marketer wants to test needs a developer to wire it into an SDK first.
Convert is built for CRO practitioners running tests on general websites. It does not have native Shopify integration or purpose-built eCommerce experiment flows. Teams using Convert for Shopify CRO must handle checkout testing through manual implementation and cannot use revenue per visitor as a native experiment metric.
The two gaps differ in origin but land in the same place. AB Smartly optimises the engineering execution of a test; Convert optimises the agency workflow around a test. Neither is built around where store revenue is actually decided, or around the Customer Value Optimization question: whether a result holds for high-value, repeat buyers.
There is a second gap underneath the first: the data insights layer that tells a team what to test and whether it actually worked. AB Smartly ships experiment infrastructure but no on-site behavioural analysis, so heatmaps, session recordings, and surveys come from separate tools a data team has to stitch together. Convert ships a clean test runner but no native heatmaps or session recordings either. Omniconvert Explore builds that insights layer in: heatmaps, session recordings, and surveys sit next to the experiment, and the same behavioural and customer data defines the segments you test against. The insight and the test live in one place, which is the difference between guessing at a hypothesis and reading it off the store's own data.
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 lifted a click or an engineering 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 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 data shows where stores actually lose orders: 99.6% fail to make guest checkout visible and prominent, 94.2% never show checkout progress, and 85.1% do not show the full order cost before the final step. [CROBenchmark Report 2026, Omniconvert]
These are checkout-surface problems, the exact surfaces an engineering SDK or an agency test runner is not built to experiment on. Explore runs the experiment on the store's real revenue surfaces and reports the outcome in revenue per visitor.
This is what "two testers, one blind spot" means. A higher click rate or a lifted micro-conversion 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 optimises 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. 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 Convert vs Explore: the capability comparison
Side by side, the three tools serve different layers. AB Smartly runs real-time server-side experiments for engineering teams. Convert runs clean privacy-first tests for CRO agencies on general websites. 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 | Convert | Omniconvert Explore |
|---|---|---|---|
| Primary function | Real-time server-side experimentation | Privacy-first general-website A/B testing | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, no visual editor | Yes visual and code editor | Yes visual plus code editor |
| Multivariate testing | No not supported natively | Yes | Yes |
| Server-side testing | Yes core capability | Yes | Yes |
| Visual editor | No code and SDK only | Yes functional, less polished than VWO | Yes visual editor built in |
| On-site surveys and overlays | No not in scope | No needs third-party tools | Yes surveys and overlays built in |
| Shopify integration | Low no native connector | Medium not native, manual setup | Yes native |
| eCommerce focus | Low built for engineering programmes | Medium agency and mid-market web | High built for store revenue workflows |
| Pricing model | Custom, contact sales | Session-based, from $299/mo, free trial | Session-based, built for store traffic, free trial |
| Best for | Engineering and data teams wanting warehouse-grade rigour | CRO agencies wanting privacy-first testing | Shopify and eCommerce teams optimising 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. 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 Convert?
Start with the surface where your revenue is decided. If you need real-time server-side experiments in an engineering-led programme, keep AB Smartly. If you want privacy-first agency A/B on general websites, keep Convert. 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, but only Explore is built for the checkout where the order closes.
AB Smartly and Convert are both capable tools within their categories. AB Smartly is engineered for real-time velocity with warehouse-grade rigour. Convert is transparent, privacy-first, and agency-friendly, with a 4.7 G2 rating across 139 reviews.
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 a click or 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.