AB Smartly vs Dynamic Yield vs Explore (2026): The Mid-Market Gap
AB Smartly is a real-time experimentation platform built by former Booking.com engineers, with SDK-based assignment and no visual editor. Dynamic Yield is an enterprise personalisation engine owned by Mastercard, used at retail giants like IKEA and McDonald's. Neither is built for a mid-market Shopify store. Omniconvert Explore is the Shopify-native eCommerce CRO platform, measured 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]
- Dynamic Yield is an enterprise personalisation and recommendations engine owned by Mastercard, with a 4.5 out of 5 G2 rating across 200 reviews. [G2, 2026]
- The two sit at opposite ends of the market: AB Smartly is engineer-owned and SDK-based; Dynamic Yield is enterprise-priced with a dedicated retail personalisation team.
- Neither is packaged for a mid-market Shopify brand, and neither reports outcomes in revenue per visitor on the product-to-checkout path.
- 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 Dynamic Yield are choosing between two very different testing platforms built for opposite buyers. AB Smartly is engineering-first: SDK-based assignment, real-time results, built by former Booking.com engineers for high-velocity server-side testing programmes. Dynamic Yield is enterprise-first: a mature personalisation and recommendations engine owned by Mastercard, used by IKEA and McDonald's on catalogs measured in millions. This page covers what each does well, the mid-market 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, contact-sales 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 Dynamic Yield, and what is it actually good at?
Dynamic Yield is an enterprise personalisation and A/B testing platform owned by Mastercard since 2022, used by major retailers including IKEA and McDonald's. It supports product recommendations, experience optimisation, multivariate testing, and cross-channel personalisation across web, mobile, and email. It holds a 4.5 out of 5 G2 rating across 200 reviews. [G2, 2026]
Dynamic Yield's core strength is real-time personalisation tied to customer data at retail scale. Its recommendation engine, experience APIs, and multivariate testing suit brands running dedicated personalisation programs on large catalogs. The Mastercard acquisition in 2022 brought additional payment and behaviour data resources for retail applications.
The platform ships with a visual editor, so marketers can build variants without an engineering ticket, and it exposes server-side APIs for teams that want deeper control. Pricing is custom and typically starts in the five-figure annual range, with implementations that expect a dedicated internal team.
Retail personalisation tailors product, content, and experience per visitor at catalog scale, driven by a unified customer profile and machine-learned intent. Dynamic Yield's engine is one of the most mature in retail, proven at IKEA and McDonald's. It is an execution and delivery layer for large personalisation programs, distinct from running a controlled revenue experiment on Shopify product, cart, and checkout pages through a self-serve store workflow.
Where Dynamic Yield is genuinely strong
- Recommendation engine: mature product recommendations tuned for retail catalogs and cross-sell paths.
- Cross-channel personalisation: one engine serving web, mobile, and email from a shared customer profile.
- Multivariate testing: tests multiple element combinations in a single experiment, native to the platform.
- Enterprise retail scale: proven at IKEA, McDonald's, and other high-traffic brands under Mastercard ownership.
Where Dynamic Yield hits its ceiling for an eCommerce store
- Enterprise pricing: custom contracts that typically start in the five-figure annual range and climb from there.
- High implementation effort: setup and integration expects a dedicated internal team and a formal project.
- Not self-serve for mid-market Shopify: a growth lead cannot spin up a checkout test in a week without a project plan.
- Over-specified for straightforward testing: a full personalisation stack for teams that mainly need A/B on the funnel.
- Generic outcome model: reports skew to click, engagement, and segment lift rather than revenue per visitor on the store funnel.
What AB Smartly and Dynamic Yield cannot do for an eCommerce store
AB Smartly and Dynamic Yield are the engineer-led and the enterprise-led answers to the same generic-web problem. One is priced for a data team running SDK-based tests; the other is priced for a Mastercard-scale retailer running a dedicated personalisation program. Neither is built around Shopify product, 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. On a Shopify store the shape is a problem: every product page and checkout variant 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, and the outcome is reported in whatever event the developer instrumented, not in revenue per visitor.
Dynamic Yield is an enterprise platform built for large retail organisations with dedicated personalisation teams. It is not designed for mid-market Shopify brands running self-serve CRO experiments. The implementation complexity and the pricing put it out of reach for eCommerce teams that need to run tests on product pages and checkout flows without a formal project team and a five to six-figure contract.
The gap the two share is the mid-market Shopify one. Both are capable testing platforms at opposite ends of the market, one engineer-priced and SDK-based, one enterprise-priced and staffed like a retail program. Neither treats the product-to-checkout path as the primary surface, and neither reports the result in revenue per visitor. For the framing that connects testing to customer value, see Has personalization replaced A/B testing?
The deeper issue is that ownership of the test never quite lands on the person who owns the revenue number for the store. In AB Smartly the engineer owns it; in Dynamic Yield a personalisation strategist owns the setup and a project team owns the implementation, but a mid-market growth lead cannot self-serve either one. 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 a mid-market Shopify 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 click or a segment lift.
- 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 a custom personalisation implementation.
- 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 or a segment picked by a recommendations engine.
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, 94.2% never show checkout progress, 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 in an engineering queue. In Dynamic Yield the fix is a project-planned implementation gated by an enterprise contract and a dedicated personalisation team. Explore runs the experiment on the real revenue surfaces and reports the outcome in revenue per visitor, without an SDK integration or a five to six-figure enterprise contract between the hypothesis and the result.
This is what the title means by the mid-market gap. Two capable testing tools, at opposite ends of the market, and both looking past the surface where a Shopify store's revenue is actually decided. 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 generic testing tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
AB Smartly vs Dynamic Yield vs Explore: the capability comparison
Side by side, the three tools sit at different points on the engineer-to-enterprise-to-store axis. AB Smartly is the real-time execution layer for engineering teams running server-side tests. Dynamic Yield is the enterprise personalisation engine for retail giants with dedicated programs. Explore is the eCommerce CRO layer for the mid-market 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 | Dynamic Yield | Omniconvert Explore |
|---|---|---|---|
| Primary function | Real-time engineering-led experimentation | Enterprise retail personalisation and A/B testing | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, no visual editor | Yes visual editor and personalisation engine | Yes visual editor plus code |
| Multivariate testing | No | Yes | Yes |
| Server-side testing | Yes core capability | Yes | Yes |
| Visual editor | No code and SDKs only | Yes | Yes WYSIWYG for marketers |
| On-site surveys and overlays | No | Partial overlays yes, no native surveys | Yes surveys and overlays built in |
| Shopify integration | Low no native connector | High connector available, but heavy setup | Yes native |
| eCommerce focus | Low built for engineering teams | High built for enterprise retail | High built for mid-market store revenue |
| Pricing model | Custom, contact sales, no free trial | Custom enterprise, five to six figures annually, contact sales | Session-based, built for store traffic, free trial |
| Best for | Engineering teams wanting real-time server-side experimentation | Enterprise retail personalisation teams at catalog scale | Mid-market Shopify and eCommerce growth teams |
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. AB Smartly is engineering-owned with contact-sales pricing; Dynamic Yield is enterprise-priced with a contact-sales model. 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 Dynamic Yield?
Decide by scale and buyer. If your engineering team wants Booking.com-grade real-time server-side testing, AB Smartly serves them well. If your enterprise retail team needs a mature recommendations engine and cross-channel personalisation, Dynamic Yield fits. For a mid-market Shopify store where product, cart, and checkout decide revenue, run your next test in Explore and measure the outcome in revenue per visitor, not clicks.
AB Smartly and Dynamic Yield are both strong at what they do. AB Smartly brings real-time engineering rigour shaped by the Booking.com programme, with a warehouse connector for teams that already run their metrics in Snowflake or BigQuery. Dynamic Yield brings deep retail personalisation, a mature recommendations engine, and cross-channel experience optimisation proven at IKEA, McDonald's, and other Mastercard-owned retail scale.
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 an engineering ticket or a five to six-figure retail personalisation contract. 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.