Amplitude Experiment vs Eppo vs Explore (2026): Analytics Stack vs Shopify
Amplitude Experiment is an experimentation layer on top of Amplitude Analytics, built for product teams on that stack. Eppo is a warehouse-native experimentation platform that reads from Snowflake, BigQuery, or Redshift. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs product, cart, and checkout experiments through a visual editor, measured in revenue per visitor.
- Amplitude Experiment is an experimentation layer on top of Amplitude Analytics, with a 4.5 out of 5 G2 rating; it needs the Amplitude Analytics subscription to work. [G2, 2026]
- Eppo is a warehouse-native experimentation platform that reads from Snowflake, BigQuery, or Redshift, with a 4.7 out of 5 G2 rating; it needs a data warehouse to work. [G2, 2026]
- Both are engineer-first tools with no visual editor: every variant on a Shopify store is an SDK ticket, and both tools assume a stack most $1M to $50M ARR stores do not already run.
- Neither reports results in revenue per visitor on the product-to-checkout path, the surface where store revenue is actually decided.
- Omniconvert Explore runs product, cart, and checkout experiments natively on Shopify, through a visual editor a marketer can drive, measured in revenue per visitor: pick it for store growth without a prerequisite data stack.
Teams comparing Amplitude Experiment vs Eppo are choosing between two engineer-first experimentation platforms that both lean on an existing data stack. Amplitude Experiment is an experimentation layer bolted onto Amplitude Analytics, built for product teams already in that stack. Eppo is warehouse-native: it reads from Snowflake, BigQuery, or Redshift and reuses the metric definitions a data team already owns. Both run rigorous tests, but neither is designed around the surface where a Shopify store earns money, through an interface its marketing team can drive. That surface is where Omniconvert Explore is built to run.
What is Amplitude Experiment, and what is it actually good at?
Amplitude Experiment is an experimentation layer built on top of Amplitude Analytics. It runs SDK-based A/B testing and server-side experiments, uses Amplitude cohorts for targeting, and reads experiment outcomes against the product events already captured in Amplitude. It suits product teams already invested in the Amplitude stack. [Amplitude, 2026]
Amplitude Experiment is Amplitude's experimentation product, tightly bound to Amplitude Analytics. It holds a 4.5 out of 5 rating on G2 across 2,200 reviews. [G2, 2026] Its strength is that experiment results are analysed in the same environment as every other product event.
For a product team already on Amplitude, this removes glue work: no separate warehouse for results, and cohorts flow from analytics into targeting. Success metrics live where the team already reads them. That is a real advantage for a SaaS product team, but it is a specific one, with Amplitude Analytics as a prerequisite.
Product-analytics experimentation runs A/B tests through the analytics platform's SDK, using its event data and cohorts as the targeting and success layer. Amplitude Experiment does this well for teams already on Amplitude. It is a product-analytics delivery layer, distinct from running a controlled revenue experiment on the product, cart, and checkout pages of a Shopify store.
Where Amplitude Experiment is genuinely strong
- Native Amplitude integration: experiment results sit alongside every other product event in one platform.
- Cohort-based targeting: reuse Amplitude cohorts as audience segments without rebuilding them.
- Server-side experimentation: SDKs for backend variants at feature-flag scale.
- Free tier available: teams already on Amplitude Analytics can start on it at no incremental cost.
Where Amplitude Experiment hits its ceiling for an eCommerce store
- Amplitude Analytics prerequisite: the tool has no value without an existing Amplitude Analytics account.
- No visual editor: every experiment needs developer implementation, blocking marketer-led testing.
- No native Shopify integration: it is not built to test the storefront, cart, or checkout.
- No revenue focus: no concept of revenue per visitor as a tested outcome.
What is Eppo, and what is it actually good at?
Eppo is a warehouse-native experimentation platform. It connects directly to Snowflake, BigQuery, Redshift, and Databricks, and analyses experiments against the metrics your data team already defines there. It is built for high-velocity, high-governance programmes where the source of truth for a metric is the warehouse, not the testing tool. [Eppo, 2026]
Eppo is an analysis platform first and an assignment platform second. It holds a 4.7 out of 5 rating on G2 across 41 reviews. [G2, 2026] Its strength is trusting the warehouse: the experiment reads from the same tables and metric definitions the analytics team already owns, which removes the "why does the testing tool disagree with our dashboard" argument.
It supports advanced statistical methods, sequential testing, and CUPED variance reduction, and is priced for enterprise data teams. Assignment happens through SDKs, not a visual editor.
Warehouse-native experimentation reads experiment data from your data warehouse (Snowflake, BigQuery, Redshift) rather than from the testing tool's own tracking. It reuses the metric definitions the analytics team already trusts. Eppo does this well for organisations with a mature data stack. It is an analysis layer, distinct from running a controlled revenue experiment on Shopify product, cart, and checkout pages through a marketer-accessible interface.
Where Eppo is genuinely strong
- Warehouse-native analysis: experiments read directly from Snowflake, BigQuery, Redshift, and Databricks.
- Rigorous statistics: sequential testing, CUPED variance reduction, and confidence intervals built for data teams.
- One source of truth: reuses existing metric definitions, so results match the analytics dashboards.
- High-governance programmes: designed for organisations running many experiments in parallel with strict data controls.
Where Eppo hits its ceiling for an eCommerce store
- No visual editor: variants are shipped through code and SDKs, not a WYSIWYG marketers can use.
- Warehouse required: assumes Snowflake or similar, which most $1M to $50M ARR stores do not run.
- No native Shopify integration: nothing wired to product pages, cart, or checkout out of the box.
- Priced for enterprise: custom pricing built for data teams, not for a self-serve marketing budget.
- Generic outcome model: no concept of revenue per visitor as a first-class metric on the store funnel.
What Amplitude Experiment and Eppo cannot do for an eCommerce store
Amplitude Experiment and Eppo sit on the same axis: both are engineer-first experimentation platforms that need an existing stack to work at all. Amplitude Experiment needs Amplitude Analytics. Eppo needs a data warehouse. Neither has a visual editor, neither is built around the surfaces where eCommerce revenue is won or lost, product pages, cart, and checkout, and neither reports the result in revenue per visitor.
Amplitude Experiment is an add-on layer for existing Amplitude Analytics customers. It has no visual editor and requires developer implementation on every test. It is not built to run product-page or checkout experiments on a Shopify storefront through a marketer-accessible interface, and the Amplitude Analytics subscription it depends on is a stack most $1M to $50M ARR stores do not already run.
Eppo is a warehouse-native experimentation tool built for organisations with mature data engineering. It has no visual editor, assumes Snowflake, BigQuery, Redshift, or Databricks as a prerequisite, and is priced for data teams. A Shopify store without a warehouse is paying for an analysis layer it cannot connect to its real data. The ones that do run a warehouse still find that a marketer cannot ship a variant through Snowflake.
The gap the two share is not a weakness; it is what each was built to do. These are experimentation platforms, but they are not eCommerce CRO platforms. Neither treats the Shopify product-to-checkout path as the primary surface, and neither reports the result in revenue per visitor. For the wider debate behind acting on behavioral data, 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 or growth lead has a hypothesis about the cart page and, in either tool, needs an engineer or data team 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. 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 variant raised revenue per visitor and order rate, and held its margin once discounts and returns are counted, not just moved a product event or a warehouse metric.
- 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 warehouse pipelines.
- 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 94.2% never show checkout progress steps to the shopper. [CROBenchmark Report 2026, Omniconvert]
Those are fixes a growth lead can hypothesise, mock up, and want to test today. In Amplitude Experiment, the same fix is an engineering ticket, and the Amplitude Analytics stack is a prerequisite. In Eppo, the same fix is both an engineering ticket and a warehouse pipeline. Explore runs the experiment on the real revenue surfaces and reports the outcome in revenue per visitor, without an SDK integration or a warehouse between the hypothesis and the result.
This is what the title means by analytics stack vs Shopify. Rigour on a metric your product analytics or data team owns is not 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 stack-dependent testing tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
Amplitude Experiment vs Eppo vs Explore: the capability comparison
Side by side, the three tools are built for different jobs. Amplitude Experiment lives inside product analytics. Eppo lives inside the data warehouse. Explore adds native Shopify experiments, surveys and overlays, and revenue-per-visitor measurement on the product-to-checkout path, through a visual editor a marketer can drive. See A/B testing with Explore for how those experiments run natively on the Shopify funnel.
| Capability | Amplitude Experiment | Eppo | Omniconvert Explore |
|---|---|---|---|
| Primary function | Product-analytics experimentation | Warehouse-native experimentation | eCommerce CRO on product, cart, and checkout |
| A/B testing | Partial SDK-based, requires Amplitude Analytics | Partial warehouse-native, SDK-only, no visual editor | Yes visual plus code editor |
| Multivariate testing | No | No | Yes |
| Server-side testing | Yes SDK-based | Yes SDK-based with warehouse analysis | Yes |
| Visual editor | No dev implementation required | No SDK-only, no WYSIWYG | Yes for marketer-led tests |
| On-site surveys and overlays | No | No | Yes surveys and overlays built in |
| Shopify integration | Low no native app or checkout hooks | Low no native app, warehouse-dependent | Yes native |
| eCommerce focus | Low product analytics stack | Low built for data teams, not stores | High built for store revenue workflows |
| Pricing model | Seat-based, free tier on Amplitude, Analytics subscription required | Custom, quotes from sales | Session-based, built for store traffic, free trial |
| Best for | Product teams already on Amplitude Analytics | Data and engineering teams with mature warehouse infrastructure | Shopify and eCommerce teams optimizing for revenue |
| User rating | 4.5 out of 5 (G2, 2200 reviews, as of 2026) | 4.7 out of 5 (G2, 41 reviews, as of 2026) | 4.6 out of 5 (G2, 197 reviews, as of 2026) |
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. Amplitude Experiment requires an Amplitude Analytics subscription; Eppo does not publish prices, and quotes come from sales. 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 Eppo?
Decide by who runs the test. If your product team already lives in Amplitude or your data team in Snowflake, Amplitude Experiment or Eppo will keep the analysis inside that stack. But those stacks are engineer-owned, and a Shopify store's revenue sits on surfaces its marketing team should own. For a $1M to $50M ARR store, run your next test in Explore on the product-to-checkout path, measured in revenue per visitor.
Amplitude Experiment and Eppo are both capable, rigorous experimentation platforms. Amplitude Experiment keeps testing inside the product-analytics stack a SaaS team already runs. Eppo keeps analysis inside the data warehouse an engineering-led organisation already owns. Both are built for teams whose source of truth is upstream of the store.
The question for a Shopify or DTC store is narrower: once you have a hypothesis about the product page, the cart, or the checkout, can you run the test today, on the real surface, and read the result in revenue per visitor without routing it through an SDK ticket or a warehouse pipeline. 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.
4.6 out of 5 across 197 reviews, G2 , as of 2026