AB Smartly vs Evergage vs Explore (2026): Engineering vs Enterprise
AB Smartly is an engineering-first experimentation platform built by former Booking.com veterans, with SDK-based testing and warehouse-grade stats. Evergage, now Salesforce Marketing Cloud Personalization, is an enterprise real-time personalization suite. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs experiments on product pages, cart, and checkout, and measures the result in revenue per visitor.
- AB Smartly is an engineering-first server-side experimentation platform built by former Booking.com practitioners, with a 4.8 out of 5 G2 rating. [G2, 2026]
- Evergage, now Salesforce Marketing Cloud Personalization, is an enterprise real-time personalization suite with a 4.3 out of 5 G2 rating. [G2, 2026]
- AB Smartly and Evergage answer to different owners: engineering with a warehouse in one case, enterprise marketing inside Salesforce in the other.
- Neither is built around the Shopify checkout or measures results in revenue per visitor, the surfaces and metric where store revenue is decided.
- Omniconvert Explore runs experiments on product, cart, and checkout natively and measures results in revenue per visitor: pick it when the CRO owner needs the store tested, not the warehouse or the CDP.
Teams comparing AB Smartly vs Evergage are usually choosing between two very different worlds: an engineering-first experimentation platform built by former Booking.com practitioners, or an enterprise real-time personalization suite inside Salesforce. AB Smartly leads with server-side testing, real-time results, and warehouse-grade statistical rigour. Evergage leads with machine-learning personalization across web, email, and mobile, tied to the Salesforce CDP. Neither is built around the Shopify checkout, so this page covers what each does well, the gap they share, and when Omniconvert Explore is the right layer.
What is AB Smartly, and what is it actually good at?
AB Smartly is a server-side experimentation platform built by former Booking.com engineers. It runs real-time A/B tests via an SDK, integrates directly with data warehouses, and applies advanced statistical methods designed for high-velocity, engineering-led programs at scale. [G2, 2026]
AB Smartly is one of the highest-rated tools in the category, holding a 4.8 out of 5 on G2 across 45 reviews. [G2, 2026] Its strength is credibility with engineering: the team behind it built the experimentation program at Booking.com, and the platform reflects that lineage in its statistical rigour and real-time delivery.
Its category is engineering-led experimentation. It ships tests through an SDK, decides variants on the server, and feeds results into the data warehouse where product and engineering teams already live. That focus is the point of the product.
Server-side experimentation runs the variant decision on the backend before the response reaches the browser, so variants can differ at the API and data layer, not only the rendered page. AB Smartly does this at warehouse scale for engineering teams. It is a delivery model for developers, distinct from a marketer-accessible visual test on a store's checkout.
Where AB Smartly is genuinely strong
- Real-time results at warehouse scale: experiment data flows into the warehouse as it happens, ready for analyst work.
- Booking.com statistical lineage: advanced statistical methods designed by practitioners from a high-volume testing program.
- Server-side and SDK-first: variants decided at the API layer, so backend and full-stack changes are testable.
- High-velocity engineering fit: built for product and engineering teams running many concurrent experiments.
Where AB Smartly hits its ceiling for an eCommerce store
- No visual editor: every experiment needs developer time, so marketers cannot ship a test on their own.
- No native Shopify integration: product-page and checkout tests are implemented by hand rather than natively.
- No multivariate testing: the platform focuses on A/B rather than combinatorial variant testing.
- Engineering-priced, engineering-owned: a marketing-led CRO program on a single Shopify store rarely justifies the model.
What is Evergage, and what is it actually good at?
Evergage, now Salesforce Marketing Cloud Personalization, is an enterprise real-time personalization platform. Its machine learning builds a live profile of each visitor to serve individualized product and content recommendations across web, email, mobile, and in-store, connected to the Salesforce CDP. [G2, 2026]
Evergage is a personalization engine first and a testing tool second. It was named a G2 Leader for personalization engines, holding a 4.3 out of 5 rating across 94 reviews. [G2, 2026] Its strength is delivering the right experience to the right visitor in the moment, at scale.
Its A/B and multivariate testing exists to validate personalization campaigns: which rule, audience, or recommendation performs best. That is a different job from running a standalone conversion experiment program on a store's checkout.
Real-time personalization adapts what a visitor sees based on a live behavioral profile, serving individualized content and recommendations as they browse. Evergage does this across channels inside the Salesforce ecosystem. It is an enterprise capability, distinct from running controlled revenue experiments on product, cart, and checkout pages.
Where Evergage is genuinely strong
- Real-time one-to-one personalization: machine learning, not static rules alone, driving individualized experiences.
- Recommendations at scale: machine-learning product and content recommendations across many channels.
- Deep behavioral analytics: rich segmentation connected to the Salesforce CDP and Marketing Cloud.
- Enterprise omnichannel reach: web, email, mobile apps, onsite search, and in-store touchpoints.
Where Evergage hits its ceiling for a single store
- Enterprise implementation: needs technical resources, so it is rarely self-serve for one eCommerce team.
- Custom enterprise pricing: value depends on the broader Salesforce stack, not a standalone store budget.
- Testing serves personalization: A/B testing validates campaigns, not a revenue experiment program.
- No native Shopify checkout: no checkout-level experiment templates for the Shopify funnel.
What AB Smartly and Evergage cannot do for an eCommerce store
AB Smartly and Evergage are built for different owners, engineering and enterprise marketing, but they share one gap for a Shopify store. Neither is built around the surfaces where eCommerce revenue is won or lost, product pages, cart, and checkout, or around the metric that matters there: revenue per visitor, not a warehouse event or a personalization KPI.
AB Smartly is built for engineering teams running server-side experiments at high velocity. It has no native Shopify integration, no visual editor for a marketer, and its metric layer is designed for engineering event data streamed into a warehouse, not store revenue on the Shopify funnel. Every product-page and checkout iteration costs developer time before it can even reach a customer.
Evergage is built for enterprise marketing teams running real-time personalization across many channels, usually inside Salesforce. Its testing exists to validate personalization rules, not to run standalone revenue experiments on product, cart, and checkout. A single Shopify store evaluating Evergage takes on enterprise implementation and custom pricing for a personalization-first model, then still needs a separate answer for its A/B testing program.
The two gaps differ in shape but point to the same missing layer. Both tools optimize the execution of a test or campaign on a surface someone else owns: engineering in one case, an enterprise Salesforce partner in the other. Neither is built around where store revenue is actually decided, or around the Customer Value Optimization question: whether a result holds for high-value, repeat buyers. For the wider debate behind this testing-versus-personalization split, see Has personalization replaced A/B testing?
There is a second gap underneath the first: the data insights layer that tells a team what to test and whether it actually worked. AB Smartly ships engineering event streams, not the on-site behavioral surface a marketer reads, so heatmaps, session recordings, and on-site surveys come from third-party tools stitched together outside the experimentation engine. Evergage holds deep behavioral data, but it lives inside an enterprise Salesforce deployment most single Shopify stores will never run. Omniconvert Explore builds that data insights layer in: heatmaps, session recordings, and surveys sit next to the experiment, and the same behavioral and customer data defines the segments you test against. The insight and the test live in one place, on the store's real revenue surfaces, which is the difference between watching a problem and proving the fix moved revenue per visitor.
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 winning 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 a personalization KPI.
- 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 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: 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]
These are checkout-surface problems, the exact surfaces a server-side engineering tool or an enterprise personalization suite is not built to experiment on for a marketing team. 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 engineering vs enterprise. AB Smartly answers to product and data teams; Evergage answers to enterprise marketing and Salesforce owners. A Shopify store's CRO owner answers to neither, and the work of moving order rate and average order value along the product-to-checkout path stays stuck between them. 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. A variant that wins on revenue per visitor and holds for high-CLV customers protects profit; a variant that only lifts an engineering event or a personalization click often does not. That is the revenue question AB Smartly and Evergage are not built to answer, and the one Explore is. Explore also reaches Shopify-specific levers most testing tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
AB Smartly vs Evergage vs Explore: the capability comparison
Side by side, the three tools serve different owners. AB Smartly serves engineering teams running server-side tests at scale. Evergage serves enterprise marketing teams personalizing experiences inside Salesforce. Explore adds native Shopify experiments, a visual editor, built-in surveys and overlays, 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 | AB Smartly | Evergage | Omniconvert Explore |
|---|---|---|---|
| Primary function | Engineering-first server-side experimentation | Enterprise real-time personalization | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, no visual editor | Yes within personalization campaigns | Yes visual plus code editor |
| Multivariate testing | No | Yes | Yes |
| Server-side testing | Yes its core strength | Yes via SDK | Yes |
| Visual editor | No SDK only, code-first | Yes for personalization campaigns | Yes marketer-accessible |
| On-site surveys and overlays | No engineering focus | No personalization, not surveys | Yes surveys and overlays built in |
| Built-in data insights | No warehouse event streams only | Yes deep analytics, enterprise Salesforce only | Yes heatmaps, recordings, and surveys built in |
| Shopify integration | Low no native Shopify support | Low enterprise implementation | Yes native |
| eCommerce focus | Low engineering-led experimentation | Medium enterprise personalization | High built for store revenue workflows |
| Revenue per visitor measurement | No warehouse event data | No personalization KPIs | Yes revenue per visitor and order rate native |
| Pricing model | Custom, contact sales | Custom enterprise, quote on request | Session-based, built for store traffic, free trial |
| Best for | Engineering teams wanting warehouse-grade stats | Enterprise teams in the Salesforce ecosystem | 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. AB Smartly is a server-side experimentation platform priced on request. Evergage is now Salesforce Marketing Cloud Personalization and uses custom enterprise pricing. 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 Evergage?
Start with who actually owns the next test. If the answer is engineering with a warehouse behind it, keep AB Smartly. If the answer is a Salesforce team running personalization at enterprise scale, Evergage earns its place. For a Shopify store where the CRO owner needs the checkout tested this month, run the experiment in Explore, measured in revenue per visitor. The three are complementary, but only Explore is built for the surface where the order closes.
AB Smartly and Evergage are both capable inside their categories. AB Smartly carries the Booking.com experimentation lineage and delivers warehouse-grade stats to engineering teams. Evergage is a G2 Leader for personalization engines and reaches its full value inside the Salesforce ecosystem.
The question for a Shopify store is narrower: can your team run a controlled experiment on the product, cart, and checkout, and read the result in revenue per visitor rather than a warehouse event or a personalization KPI. 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.