Amplitude Experiment vs Dynamic Yield vs Explore (2026): Shopify Reality
Amplitude Experiment adds A/B testing on top of Amplitude Analytics: it needs the analytics stack and developer time. Dynamic Yield is an enterprise personalisation platform used by IKEA and McDonald's, priced for retail at scale. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it tests product pages, cart, and checkout, measured in revenue per visitor.
- Amplitude Experiment is an SDK-based testing layer on top of Amplitude Analytics, with a 4.5 out of 5 G2 rating across 2,200 reviews. [G2, 2026]
- Dynamic Yield is an enterprise personalisation and A/B testing platform owned by Mastercard, used by IKEA and McDonald's, with a 4.5 out of 5 G2 rating. [G2, 2026]
- Amplitude Experiment needs Amplitude Analytics and developer time; Dynamic Yield needs a six-figure contract and an implementation project.
- Neither is designed for a mid-market Shopify store running CRO self-serve on product, cart, and checkout, and neither reports in revenue per visitor.
- Omniconvert Explore is Shopify-native, session-priced, and measured in revenue per visitor: pick it when the store is the buyer.
Teams comparing Amplitude Experiment vs Dynamic Yield are usually weighing two very different tools that share a testing label. Amplitude Experiment is an experimentation SDK layered on top of Amplitude Analytics, built for product teams already invested in that stack. Dynamic Yield is an enterprise personalisation and A/B testing suite built for retailers at the scale of IKEA and McDonald's. This page covers what each does well for its own audience, the gap they share when a mid-market Shopify store is the buyer, and where Omniconvert Explore is the right layer.
What is Amplitude Experiment, and what is it actually good at?
Amplitude Experiment is Amplitude's experimentation layer, built on top of Amplitude Analytics. Tests fire through an SDK, feature flags gate the variants, and results are analysed as events inside the same Amplitude environment. It holds a 4.5 out of 5 G2 rating across 2,200 reviews. [G2, 2026]
Amplitude Experiment is an experimentation layer for teams already running Amplitude Analytics. Its strength is that experiments and product-analytics events live in the same environment, so a team can target Amplitude cohorts and read results next to every other product event without a separate integration.
The trade-off is a hard prerequisite. Amplitude Experiment requires Amplitude Analytics, has no visual editor, and runs experiments through an SDK. Every test needs a developer to ship, and for a Shopify store the analytics stack has to be Amplitude first, not Shopify's own funnel data.
Product-analytics experimentation is a testing layer built on top of a product analytics platform. Experiments run through an SDK, and results are analysed against events already flowing into the analytics tool. Amplitude Experiment does this well for teams already on Amplitude. It is a testing layer inside a product-analytics workflow, distinct from running a controlled revenue experiment on product, cart, and checkout pages.
Where Amplitude Experiment is genuinely strong
- Analytics-native results: experiments and product events sit in one platform, with no separate data integration.
- Cohort targeting: reach segments already defined inside Amplitude Analytics.
- Server-side testing: run experiments on backend flows without client-side flicker.
- Free tier available: start experimenting at no cost once Amplitude Analytics is in place.
Where Amplitude Experiment hits its ceiling for an eCommerce store
- No visual editor: every variant needs developer time, so marketing ideas queue behind engineering capacity.
- Amplitude Analytics required: the value only lands once your stack is already Amplitude-first.
- Not built for Shopify: no native checkout experimentation, and no revenue per visitor metric out of the box.
- Product-team ergonomics: designed around product events, not checkout revenue, so store questions read as event trends rather than order-rate lift.
What is Dynamic Yield, and what is it actually good at?
Dynamic Yield is one of the most advanced eCommerce personalisation platforms in market, owned by Mastercard and used by IKEA, McDonald's, and other enterprise retailers. It combines product recommendations, experience optimisation, and A/B testing across web, mobile, and email. It holds a 4.5 out of 5 G2 rating across 200 reviews. [G2, 2026]
Dynamic Yield is not a testing tool with personalisation bolted on; it is a personalisation platform that also runs experiments. The recommendation engine, real-time segmentation, and cross-channel orchestration are the point. Mastercard's 2022 acquisition brought additional data resources and enterprise credibility, and the reference customer list reads as a who's-who of global retail.
Reviewers rate the personalisation depth and enterprise-grade infrastructure, and flag setup complexity as the tradeoff. The platform is designed around large retail stacks with dedicated personalisation teams, not around a mid-market Shopify brand running CRO self-serve.
Enterprise personalisation is the practice of tailoring on-site content, product recommendations, and offers in real time to a segmented visitor, backed by a customer data model that spans channels. Dynamic Yield does this well for retailers at scale. It is a delivery and orchestration layer, distinct from running a controlled revenue experiment on the product, cart, and checkout surfaces of a specific store.
Where Dynamic Yield is genuinely strong
- Product recommendation engine: real-time algorithms for cross-sell, upsell, and category-page merchandising at enterprise scale.
- Cross-channel experience optimisation: web, mobile app, and email personalisation from a single audience model.
- Enterprise data resources: since the Mastercard acquisition, the platform has access to signals most personalisation vendors cannot match.
- Full experimentation stack: server-side testing, multivariate, visual editor, and personalisation experiments in one console.
- Reference customers: IKEA, McDonald's, and large retail brands run on the platform, and the case-study library reflects that scale.
Where Dynamic Yield hits its ceiling for a mid-market Shopify store
- Enterprise-only pricing: custom contracts, typically in the five-figure annual range. A Shopify brand at $1M to $10M ARR cannot get in the door.
- Implementation is a project, not a switch: setup needs dedicated developer resources and a data-modelling phase before the first experience ships.
- Over-specified for testing-only teams: if the job is A/B tests on the checkout, the full personalisation stack is more platform than the team needs.
- Self-serve is not the design: a mid-market growth team cannot operate the platform without an internal specialist or an implementation partner.
- Shopify is a supported path, not the primary one: the reference architecture is built around large enterprise commerce stacks, not the Shopify catalog.
What Amplitude Experiment and Dynamic Yield cannot do for an eCommerce store
Amplitude Experiment and Dynamic Yield come from opposite ends of the market, and they share the same blind spot for a mid-market Shopify brand. One is a developer-first SDK bolted onto an analytics stack; the other is an enterprise personalisation suite behind a six-figure contract. Neither is a self-serve, eCommerce-native experimentation platform priced for a store.
Amplitude Experiment is an add-on layer for existing Amplitude Analytics customers. It has no visual editor and runs tests through an SDK, so every variant on a Shopify product page or checkout queues behind a developer ticket. Experiments read as event trends, not as order-rate lift, and the value only lands once a store's stack is already Amplitude-first rather than Shopify-first.
Dynamic Yield is designed for enterprise retail organisations with dedicated personalisation teams. The pricing and implementation complexity put it out of reach for a $1M to $50M ARR Shopify brand that needs to test on product pages and checkout without a dedicated specialist. Its primary architecture serves large retail stacks, so Shopify is a supported path rather than the native one.
Most experimentation tools are built around a generic web page and a generic conversion event. They optimise the execution of a test. They are not built around the surfaces where eCommerce revenue is actually won or lost (product pages, cart, and checkout), or around the metric that matters to a store: revenue per visitor, not a click. For more on the testing-versus-personalisation split behind this gap, see Has personalization replaced A/B testing?
Omniconvert Explore is built for this layer. Explore runs A/B, multivariate, personalization, and on-site survey experiments on the Shopify product, cart, and checkout, natively, and reports the outcome in revenue per visitor and order rate. Insight and test live in one place, on the store's real revenue surfaces.
eCommerce conversion rate optimization is defined as 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 Amplitude Experiment nor Dynamic Yield 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 moved an event or a personalisation KPI.
- Which surface to test first. Which pages in the funnel (product, cart, checkout) carry the highest revenue impact if tested next. Amplitude surfaces event trends; Dynamic Yield's prioritisation is built for enterprise merchandising, not a mid-market growth team.
- How it behaves in Shopify checkout. How an experiment interacts with the Shopify catalog, variants, and checkout flow natively, without engineering glue work. Amplitude Experiment needs a developer; Dynamic Yield needs an implementation project.
- Whether it holds for high-value customers. Whether the result holds for repeat, high-value buyers, the Customer Value Optimization question, not just first-session visitors. Neither tool reports on it natively.
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 prominent, 94.2% never show checkout progress, and 85.1% never show the full order cost before the final step. [CROBenchmark Report 2026, Omniconvert]
These are checkout-surface problems, the exact surfaces a product-analytics SDK or an enterprise personalisation suite 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, tied back through Customer Value Optimization to the buyers a store keeps rather than the 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.
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]
Amplitude Experiment vs Dynamic Yield vs Explore: the capability comparison
Side by side, the three tools sit on different layers. Amplitude Experiment ties tests to product analytics through an SDK. Dynamic Yield delivers enterprise personalisation at retail scale. Explore adds native Shopify experiments on product, cart, and checkout, with heatmaps and surveys beside them, measured in revenue per visitor. See A/B testing with Explore for how those experiments run on the Shopify funnel.
| Capability | Amplitude Experiment | Dynamic Yield | Omniconvert Explore |
|---|---|---|---|
| Primary function | Product-analytics experimentation via SDK | Enterprise real-time personalisation and testing | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, requires Amplitude Analytics | Yes visual editor plus personalisation engine | Yes visual and code editor, eCommerce templates |
| Multivariate testing | No | Yes | Yes |
| Server-side testing | Yes | Yes at enterprise scale | Yes |
| Visual editor | No SDK and developer-first | Yes enterprise-grade editor | Yes WYSIWYG built for eCommerce templates |
| On-site surveys and overlays | No not in scope for the product | Partial personalisation experiences, not native surveys | Yes surveys and overlays built in |
| Shopify integration | Low via SDK, developer-implemented | High supported, not the primary architecture | Yes native to catalog, variants, and checkout |
| eCommerce focus | Low product-team ergonomics | High enterprise retail personalisation | High eCommerce CRO is the category |
| Revenue per visitor measurement | No product-analytics events | No personalisation KPIs | Yes revenue per visitor and order rate native |
| Pricing model | Seat-based, free tier available (Amplitude Analytics required) | Custom enterprise, contact sales, no free trial | Session-based, built for store traffic, free trial |
| Best for | Product teams already on Amplitude Analytics | Enterprise retail with a personalisation team | Mid-market Shopify and DTC brands, $1M to $50M ARR |
| User rating | 4.5 out of 5 (G2, 2200 reviews, as of 2026) | 4.5 out of 5 (G2, 200 reviews, as of 2026) | 4.6 out of 5 (G2, 197 reviews, as of 2026) |
Yes / Partial / No reflects fit for a mid-market Shopify or DTC store running self-serve CRO. Product teams reading this should weight the Amplitude Experiment column differently, and enterprise retail teams should weight the Dynamic Yield column differently. Competitor ratings, pricing, and plan details reflect publicly listed figures as of 2026 and can change.
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 Dynamic Yield?
Decide by whether a Shopify store is who you serve, and by what scale you operate at. If you are an enterprise retailer with a personalisation team, Dynamic Yield is designed for that job. If product teams on Amplitude Analytics want tests inside that stack, Amplitude Experiment fits. For a mid-market Shopify or DTC brand running CRO self-serve, run your next experiment on the product-to-checkout path in Explore, measured in revenue per visitor.
Amplitude Experiment and Dynamic Yield are both strong products with their audience. Amplitude Experiment is the right experimentation layer for a product team that has already standardised on Amplitude Analytics. Dynamic Yield is the right personalisation platform for an enterprise retailer with the budget and team to operate it. Both are over-specified or under-fit for a $1M to $50M ARR Shopify brand.
The question for a store is narrower: once you have decided to test, can you run a controlled experiment on the Shopify product, cart, and checkout and read the result in revenue per visitor without a developer ticket or an enterprise contract. That is the surface Explore is built for, and the metric it reports natively, tied back through Customer Value Optimization to the buyers a store keeps.
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