Amplitude Experiment vs Statsig vs Explore (2026): SDK vs Shopify Revenue
Amplitude Experiment is an experimentation layer for existing Amplitude Analytics customers. Statsig is a feature flag and experimentation platform for engineering teams. Both are SDK-based, require developer implementation, and have no visual editor. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs experiments on product pages, cart, and checkout, and measures results in revenue per visitor.
- Amplitude Experiment is an experimentation layer that requires Amplitude Analytics as a prerequisite, with a 4.5 out of 5 G2 rating across 2,200+ reviews. [G2, 2026]
- Statsig is a feature flag platform with built-in experimentation for engineering teams, with a 4.7 out of 5 G2 rating across 346 reviews. [G2, 2026]
- Both are SDK-first with no visual editor: every experiment needs an engineering ticket, and neither integrates natively with Shopify.
- Neither runs multivariate testing, on-site surveys, or overlays, and neither measures results in revenue per visitor, the metric that decides store outcomes.
- Omniconvert Explore adds a visual editor, native Shopify integration, and revenue-per-visitor measurement on product, cart, and checkout: pick it when marketing and CRO need to ship experiments without an engineering ticket.
Teams comparing Amplitude Experiment vs Statsig are usually choosing an engineering-driven experimentation platform: a way to run tests through SDKs and analyze them alongside product event data. Amplitude Experiment leads by tying tests to Amplitude Analytics cohorts. Statsig leads by pairing feature flags with a built-in analytics layer. Both are strong for product engineering teams, but neither is built around the surfaces where a Shopify store actually wins or loses revenue: the product page, the cart, and the checkout. This page covers what each does well, the gap they share, and when Omniconvert Explore is the right layer.
What is Amplitude Experiment, and what is it actually good at?
Amplitude Experiment is an experimentation layer inside Amplitude Analytics. It runs SDK-based A/B testing and server-side tests, uses Amplitude cohorts for targeting, and analyses results in the same environment as product event data. It requires Amplitude Analytics as a prerequisite. [Amplitude, 2026]
Amplitude Experiment is an add-on for existing Amplitude Analytics customers, not a stand-alone testing tool. Amplitude holds a 4.5 out of 5 rating on G2 across 2,200+ reviews. [G2, 2026] Its strength is a single environment: tests are read against the same events, funnels, and cohorts a product team already tracks.
The testing itself is SDK-first. There is no visual editor and no marketer-accessible UI for building variants on product or checkout pages. Every experiment needs engineering to instrument, deploy, and clean up.
Analytics-native experimentation sits inside a product analytics tool and lets teams run tests against the same event data they already track. Amplitude Experiment fits this pattern: results share the analytics stack, but tests are written and shipped by engineers. It is a layer on the analytics stack, distinct from running a controlled revenue experiment on product, cart, and checkout pages.
Where Amplitude Experiment is genuinely strong
- One environment for tests and analytics: results read next to the events, funnels, and cohorts you already use.
- Advanced cohort targeting: Amplitude's behavioral cohorts drive test audiences directly.
- Server-side testing: SDK-first experimentation across backend surfaces.
- Free tier available: low friction for teams already running Amplitude Analytics to add tests.
Where Amplitude Experiment hits its ceiling for an eCommerce store
- Analytics dependency: value depends on already owning Amplitude Analytics; not a stand-alone platform.
- No visual editor: every variant needs engineering; marketing and CRO cannot ship a test alone.
- No native Shopify integration: not built for product page or checkout flow experiments on a store.
- Analytics-event outcomes: no concept of revenue per visitor as the tested outcome.
What is Statsig, and what is it actually good at?
Statsig is a feature flag platform with a built-in experimentation layer. Every flag release comes with automatic measurement, powered by advanced statistical methods such as CUPED and sequential testing. It scales for engineering-driven experimentation programs and offers a generous free tier. [G2, 2026]
Statsig sits at the feature-flag layer of the engineering stack. It holds a 4.7 out of 5 rating on G2 across 346 reviews. [G2, 2026] Its strength is pairing flag rollouts with automatic experiment measurement, so every gated release is a controlled test by default rather than a manual analytics chase.
Like Amplitude Experiment, the platform assumes engineers write the test. There is no visual editor and no marketer-accessible way to change a product page or a checkout step. Every experiment is an SDK integration, and every rollout follows an engineering release cycle.
Feature flag experimentation ties every code-level feature flag to a controlled test with automatic statistical readout. Statsig fits this pattern: it is engineering-first, and it is not built around eCommerce revenue surfaces. It is a layer on the engineering stack, distinct from running a controlled revenue experiment on product, cart, and checkout pages.
Where Statsig is genuinely strong
- Feature flags and experiments in one platform: every gated release comes with a controlled readout.
- Advanced statistics built in: CUPED and sequential testing raise the sensitivity of results.
- Generous free tier: engineering teams can start without a commercial commitment.
- Scales for engineering programs: handles a high volume of concurrent experiments across product surfaces.
Where Statsig hits its ceiling for an eCommerce store
- Engineering-only workflow: no visual editor; marketing and CRO cannot run a test independently.
- No native Shopify integration: cannot test product pages or checkout flows without significant developer work.
- Event-based outcomes: results are read as engineering event counts, not as store revenue.
- Feature-release framing: designed around shipping product changes, not around the store's product-to-checkout path.
What Amplitude Experiment and Statsig cannot do for an eCommerce store
Amplitude Experiment and Statsig share the same gap for a store. Both are SDK-first engineering tools with no visual editor. Neither is built around the surfaces where eCommerce revenue is won or lost, product pages, cart, and checkout, or around the metrics that matter there: revenue per visitor, order rate, and margin the store keeps.
Amplitude Experiment is an add-on layer for existing Amplitude Analytics customers. It has no visual editor and requires developer implementation for every test. It is not designed for eCommerce checkout flow experiments and cannot run product page tests through a marketer-accessible interface without engineering support. Results are read as product analytics events, not as store revenue outcomes.
Statsig is an engineering-first feature flag and experimentation tool. It has no visual editor and requires SDK implementation for every experiment. It cannot run tests on Shopify product pages or checkout flows without significant developer involvement, and it is not designed to measure experiments in eCommerce revenue terms. Both tools stop at the point where marketing needs to ship a variant without waiting on an engineering ticket.
The two gaps are the same gap in different stacks: analytics-native testing on one side, feature-flag testing on the other, both requiring engineering for every variant. Neither reaches the Shopify checkout as marketing sees it, and neither reports the outcome in revenue per visitor. Both are built for the product engineering funnel, not the store funnel. For the broader debate about acting on experiment data in eCommerce, see Has personalization replaced A/B testing?
The deeper issue is that testing what actually moves store revenue needs to sit next to the team closest to that revenue: marketing and CRO. Tests on the product page or the checkout do not belong in an SDK; they belong next to the merchandiser, the copywriter, and the CRO lead, with a visual editor and a native connection to the storefront. Omniconvert Explore closes that gap: a visual editor, native Shopify integration, and outcomes read in revenue per visitor and order rate, on the product-to-checkout path.
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 change raised revenue per visitor and order rate, and held its margin once discounts and returns are counted, not just fired an event or moved a flagged metric.
- 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 Shopify 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: 94.2% never show checkout progress steps, and 99.6% fail to make guest checkout visible and prominent. [CROBenchmark Report 2026, Omniconvert]
A product analytics event or a feature flag rollout can tell you that shoppers dropped at that step, but neither can test the fix on the Shopify checkout without engineering. 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 SDK vs Shopify revenue. A lifted SDK event or a gated feature 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. Explore also reaches Shopify-specific levers most engineering-first tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
Amplitude Experiment vs Statsig vs Explore: the capability comparison
Side by side, the three tools sit in different stacks. Amplitude Experiment tests inside the product analytics stack. Statsig tests inside the feature-flag stack. Explore tests on the Shopify storefront, with a visual editor, on-site surveys, 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 | Amplitude Experiment | Statsig | Omniconvert Explore |
|---|---|---|---|
| Primary function | Analytics-native experimentation | Feature flags with experimentation | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, requires Amplitude Analytics | Yes SDK-based, no visual editor | Yes visual plus code editor |
| Multivariate testing | No | No | Yes |
| Server-side testing | Yes | Yes | Yes |
| Visual editor | No SDK-only | No SDK-only | Yes for marketing and CRO teams |
| On-site surveys and overlays | No analytics events only | No feature flags only | Yes surveys and overlays built in |
| Shopify integration | Low no native connection | Low no native connection | Yes native |
| eCommerce focus | Low product analytics teams | Low engineering teams | High built for store revenue workflows |
| Revenue per visitor measurement | No analytics event outcomes | No engineering event counts | Yes revenue per visitor and order rate native |
| Pricing model | Seat-based, free tier available | Seat-based, free tier available | Session-based, built for store traffic, free trial |
| Best for | Product teams on Amplitude Analytics | Engineering teams shipping features with flags | 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 Amplitude Experiment and Statsig are engineering-first, SDK-based experimentation platforms rather than storefront 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 Amplitude Experiment or Statsig?
Decide by ownership. If your product team is deep in Amplitude Analytics or already running Statsig for feature flags, keep them there for product analytics and feature releases. But for a Shopify store, the experiments that move revenue live on product pages, cart, and checkout, and they need to move without an engineering ticket. Run those tests in Explore, on the store's revenue surfaces, measured in revenue per visitor.
Amplitude Experiment and Statsig are both capable SDK-first experimentation tools. Amplitude Experiment adds testing to a product analytics stack. Statsig turns feature flags into automatic controlled tests. Both are built around the product engineering funnel.
The question for a store is narrower: once product data or a feature flag shows where shoppers struggle, can marketing and CRO run a controlled experiment on the Shopify product, cart, and checkout and read the result 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.