AB Smartly vs Amplitude Experiment vs Explore (2026): SDK vs Store Revenue
AB Smartly is a real-time experimentation platform built by former Booking.com engineers, with SDK-based assignment and warehouse integration. Amplitude Experiment is a testing layer built on Amplitude Analytics that requires the Amplitude stack. Both need developer work. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs product, cart, and checkout experiments and measures the result in revenue per visitor.
- AB Smartly is a real-time experimentation platform built by former Booking.com engineers, with a 4.8 out of 5 G2 rating across 45 reviews. [G2, 2026]
- Amplitude Experiment is a testing layer built on Amplitude Analytics, with a 4.5 out of 5 G2 rating across 2,200+ reviews. [G2, 2026]
- Both are engineer-owned platforms: neither has a visual editor, and every experiment starts as an SDK integration.
- Neither runs experiments natively on the Shopify product page, cart, or checkout, and neither reports the outcome in revenue per visitor.
- Omniconvert Explore is a Shopify-native eCommerce CRO platform: it runs A/B, multivariate, and checkout experiments through a visual editor and measures the result in revenue per visitor.
Teams comparing AB Smartly vs Amplitude Experiment are usually choosing an engineer-owned experimentation platform. AB Smartly leads with real-time results and Booking.com-grade methodology, built for high-velocity server-side testing. Amplitude Experiment leads with a testing layer bolted onto Amplitude Analytics, so experiments live in the same dashboard as the rest of the product data. Both need developer work to ship a variant, and neither is designed for a Shopify marketing team to run product page or checkout experiments. This page covers what each does well, the gap they share for eCommerce, and when Omniconvert Explore is the right layer.
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 a batch job, and connects 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 running hundreds of parallel experiments 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 metrics that already live 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 Amplitude Experiment, and what is it actually good at?
Amplitude Experiment is an experimentation layer built on Amplitude Analytics. Experiments read from the same event data your product team already tracks in Amplitude, and results are analysed inside the Amplitude environment using existing cohorts. It is built for product teams already invested in the Amplitude analytics stack. [Amplitude, 2026]
Amplitude Experiment is a product experimentation platform bolted onto one of the most widely deployed analytics tools in SaaS. It holds a 4.5 out of 5 rating on G2 across 2,200+ reviews. [G2, 2026] Its strength is continuity with the analytics layer: experiments target Amplitude cohorts, and results are read against the same event definitions the product team already trusts, in the same dashboard.
Assignment happens through SDKs, with feature-flag style splits and cohort-based targeting. There is a free tier and seat-based pricing, which is helpful for product teams instrumenting a new application.
Analytics-native experimentation runs experiment assignment inside the same platform that already tracks product events, so results appear against existing event definitions and user cohorts without a separate data integration. Amplitude Experiment does this well for teams already on Amplitude Analytics. It is a product analytics and experimentation layer, distinct from running a controlled revenue experiment on Shopify product, cart, and checkout pages through a marketer-accessible interface.
Where Amplitude Experiment is genuinely strong
- Analytics continuity: experiments read the same events as the rest of the product analytics, in one dashboard.
- Cohort targeting: advanced targeting using Amplitude cohorts already defined by the product team.
- Free tier: seat-based pricing with a real free tier, useful for product teams starting an experimentation habit.
- Established install base: 2,200+ reviews and a mature ecosystem around Amplitude's analytics platform.
Where Amplitude Experiment hits its ceiling for an eCommerce store
- Amplitude Analytics required: the value proposition assumes the store already runs Amplitude, which most $1M to $50M ARR stores do not.
- No visual editor: every variant ships through code and SDKs, not a WYSIWYG for marketers.
- No native Shopify integration: nothing wired to product pages, cart, or checkout out of the box.
- No multivariate testing: A/B experiments only, not factorial designs.
- Product analytics framing: event counts and funnel steps, not revenue per visitor or order rate as the primary outcome.
What AB Smartly and Amplitude Experiment cannot do for an eCommerce store
AB Smartly and Amplitude Experiment approach experimentation from opposite ends, engineering infrastructure and product analytics, but they share the same shape for a Shopify store. Both are developer-owned, both need code to ship a variant, and neither is built around the surfaces where eCommerce revenue is won or lost: product pages, cart, and checkout.
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.
Amplitude Experiment is an add-on layer for existing Amplitude Analytics customers. It has no visual editor and requires developer implementation, and it is not designed for eCommerce checkout flow experiments. A marketer cannot ship a product page test through the Amplitude stack without engineering support, and the value proposition itself assumes the store is already paying for Amplitude Analytics, which most $1M to $50M ARR stores do not run.
The gap the two share is the eCommerce one. Both are strong experimentation platforms with credible analytics attached, but 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 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 either tool, needs an engineer to write assignment code, define an event, and later stitch results back to order rate. Omniconvert Explore collapses that loop: a visual editor for product page and checkout variants, native Shopify integration, 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 an Amplitude cohort count.
- 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 an Amplitude event pipeline.
- Whether it holds for valuable customers. Whether the result holds for repeat, high-value customers, the Customer Value Optimization question, not just for 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, 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 sitting in the engineering queue. In Amplitude Experiment it is a code change that also assumes the Amplitude stack already tracks the checkout surface. Explore runs the experiment on the real revenue surfaces and reports the outcome in revenue per visitor, without an SDK integration or an analytics dependency between the hypothesis and the result.
This is what the title means by SDK vs store revenue. Real-time engineering rigour, and analytics-linked experimentation, are both valuable on the products those teams own. Neither is the same as a lift on 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 engineering-first tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
AB Smartly vs Amplitude Experiment vs Explore: the capability comparison
Side by side, the three tools sit at different points on the experiment lifecycle. AB Smartly is the real-time execution and analysis layer for engineering teams shipping many parallel tests. Amplitude Experiment is the experimentation add-on for product teams already living inside Amplitude Analytics. 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 | Amplitude Experiment | Omniconvert Explore |
|---|---|---|---|
| Primary function | Real-time engineering-led experimentation | Experimentation layer on Amplitude Analytics | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, no visual editor | Yes SDK-based, requires Amplitude Analytics | Yes visual editor plus code |
| Multivariate testing | No | No | Yes |
| Server-side testing | Yes core capability | Yes core capability | Yes |
| Visual editor | No code and SDKs only | No code and SDKs only | Yes WYSIWYG for marketers |
| On-site surveys and overlays | No | No | Yes surveys and overlays built in |
| Shopify integration | Low no native connector | Low no native connector | Yes native |
| eCommerce focus | Low built for engineering teams | Low built for product teams on Amplitude | High built for store revenue workflows |
| Pricing model | Custom, contact sales, enterprise | Seat-based, free tier available | Session-based, built for store traffic, free trial |
| Best for | Engineering teams wanting real-time server-side experimentation | Product teams already on Amplitude Analytics | 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. Both AB Smartly and Amplitude Experiment are engineer-owned experimentation platforms rather than marketer-accessible eCommerce CRO tools. 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 Amplitude Experiment?
Decide by who runs your tests. If your engineering team wants real-time experiments with Booking.com-grade statistics, AB Smartly serves them. If your product team lives in Amplitude Analytics and wants experiments in that dashboard, Amplitude Experiment serves them. A Shopify CRO lead cannot ship product page or checkout variants in either without developer work. For a store, run your next test in Explore, self-serve, and read the result in revenue per visitor.
AB Smartly and Amplitude Experiment are both strong at what they do. AB Smartly brings real-time engineering rigour shaped by the Booking.com programme. Amplitude Experiment brings experimentation directly into the analytics environment product teams already trust.
The question for a store is narrower: once you have a hypothesis about the cart or checkout, can a CRO lead ship the variant, measure the result in revenue per visitor, and answer whether the win holds for high-value repeat customers, without waiting on an engineering or analytics ticket. 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.