AB Smartly vs Conductrics vs Explore (2026): SDK Testing vs Store Revenue
AB Smartly is a real-time experimentation platform built by former Booking.com engineers, with SDK-based assignment. Conductrics is an API-first optimization platform with multi-armed bandit and adaptive targeting. Both require developer work. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs product page, cart, and checkout experiments and measures the result 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]
- Conductrics is an API-first experimentation platform with multi-armed bandit and adaptive optimization, with a 4.3 out of 5 G2 rating across 12 reviews. [G2, 2026]
- Both are engineer-owned platforms: neither has a visual editor, and every experiment starts as an SDK integration or an API call.
- Neither runs experiments natively on the Shopify product page, cart, or checkout, and neither reports the outcome in revenue per visitor.
- 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 Conductrics are usually choosing an engineering-owned experimentation platform. AB Smartly leads with real-time results and a warehouse connector, built by the team behind Booking.com's programme. Conductrics leads with API-first adaptive optimization and multi-armed bandit targeting for teams embedding testing into their own applications. Neither is designed for a Shopify marketing team to run product page or checkout experiments without developer work. This page covers what each does well, the gap they share for eCommerce, and when Omniconvert Explore is the right layer.
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 experiments 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 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 Conductrics, and what is it actually good at?
Conductrics is an API-first experimentation and adaptive optimization platform for technical teams. It exposes multi-armed bandit and adaptive targeting through APIs, so traffic shifts toward winning variants automatically inside the application code that calls it. It is built for developer-led programmes that want experimentation embedded directly into their own product. [Conductrics, 2026]
Conductrics is a programmatic platform first and a marketer's tool a distant second. It holds a 4.3 out of 5 rating on G2 across 12 reviews. [G2, 2026] Its strength is control: technical teams get direct API access to variant assignment, bandit optimization, and adaptive targeting, and can weave that directly into their own product code rather than a standalone testing interface.
The trade-off is scope. Conductrics has no visual editor and no marketer surface, and its public documentation and brand footprint are small compared with the wider testing category. It fits a team that wants a library, not a product.
API-first experimentation exposes variant assignment and adaptive optimization as programmatic endpoints that developers call from their own application code. Conductrics does this well for technical teams that want experimentation logic embedded in the product itself. It is a developer-facing library layer, distinct from running a controlled revenue experiment on Shopify product, cart, and checkout pages through a marketer-accessible interface.
Where Conductrics is genuinely strong
- API-first control: variant assignment and optimization exposed as endpoints for developers to call from their own code.
- Multi-armed bandits: traffic shifts toward winning variants automatically, without a manual traffic-allocation step.
- Adaptive targeting: segmentation rules can react to live user behaviour rather than a fixed pre-test split.
- Embedded programmes: designed for technical teams that want experimentation logic inside their own product, not in a standalone tool.
Where Conductrics hits its ceiling for an eCommerce store
- No visual editor: variants are shipped through API calls, not a WYSIWYG marketers can use.
- No native Shopify integration: nothing wired to product pages, cart, or checkout out of the box.
- Sparse public documentation: a marketer or CRO lead cannot self-onboard the way they can with a mainstream testing tool.
- Priced for enterprise: custom pricing built for technical 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 AB Smartly and Conductrics cannot do for an eCommerce store
AB Smartly and Conductrics sit at different points on the same axis: both are engineer-owned experimentation platforms with no visual editor. Both need code or an API integration to ship a variant, and neither is built around the surfaces where eCommerce revenue is won or lost, product pages, cart, and checkout, or the metric that matters there: revenue per visitor.
AB Smartly is built for engineering teams running high-velocity server-side tests, and the Booking.com pedigree shows in how the platform treats experimentation as continuous infrastructure. On a Shopify store the same shape is a problem. Every product page, cart, and checkout experiment 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.
Conductrics is API-first, which is a real advantage for a technical team embedding optimization into its own application. It supports multi-armed bandits and adaptive targeting, so traffic shifts toward winning variants automatically. On a Shopify storefront the same shape is a wall: a marketer cannot ship a variant through an API integration, and the tool's small public footprint means the store's growth team is often the first internal user learning it.
The gap the two share is the eCommerce one. These are experimentation platforms, but they are not eCommerce CRO platforms. Neither treats the product-to-checkout path as the primary surface, and neither reports the result in revenue per visitor. For the wider context on how testing programmes actually move revenue, 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 lead has a hypothesis about the cart page and, in either tool, needs an engineer to write assignment code or wire up an API call, 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. 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 an engineering event or an API-tracked 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 or API glue work.
- 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 CRO lead can hypothesise, mock up, and want to test today. In AB Smartly or Conductrics, the same fix is an engineering or API-integration ticket, sitting in a queue set by another team. Explore runs the experiment on the real revenue surfaces and reports the outcome in revenue per visitor, without an SDK integration or an API glue layer between the hypothesis and the result.
This is what the title means by SDK testing vs store revenue. Real-time rigour or bandit-driven allocation on metrics your engineering 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 engineering-first tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
AB Smartly vs Conductrics vs Explore: the capability comparison
Side by side, the three tools sit at different points on the experiment lifecycle. AB Smartly is the real-time execution and analysis layer for engineering teams shipping many parallel tests. Conductrics is the API-first bandit layer for technical teams embedding optimization into their own application. Explore is the eCommerce CRO layer for the 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 | Conductrics | Omniconvert Explore |
|---|---|---|---|
| Primary function | Real-time engineering-led experimentation | API-first experimentation and adaptive optimization | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes SDK-based, no visual editor | Yes API-first, no visual editor | Yes visual editor plus code |
| Multivariate testing | No | No native MVT not offered | Yes |
| Server-side testing | Yes core capability | Yes core capability | Yes |
| Visual editor | No code and SDKs only | No API calls only | Yes WYSIWYG for marketers |
| On-site surveys and overlays | No | No | Yes surveys and overlays built in |
| Shopify integration | Low no native connector | Low no native connector | Yes native |
| eCommerce focus | Low built for engineering teams | Low built for technical teams | High built for store revenue workflows |
| Pricing model | Custom, contact sales, enterprise | Custom, contact sales, enterprise | Session-based, built for store traffic, free trial |
| Best for | Engineering teams wanting real-time server-side experimentation | Technical teams wanting API-first bandits and adaptive optimization | Shopify and eCommerce teams optimizing for revenue |
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. Both AB Smartly and Conductrics are engineer-owned experimentation platforms rather than marketer-accessible eCommerce 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 AB Smartly or Conductrics?
Decide by who owns the tests. If your engineering team wants real-time server-side experiments with Booking.com-grade rigour, AB Smartly serves them. If your technical team wants programmatic API-first bandits embedded into your own applications, Conductrics serves them. But a Shopify marketing or CRO team cannot ship a product page or checkout variant in either tool without developer work. For a store, run your next test on the product-to-checkout path in Explore, self-serve, and read the result in revenue per visitor.
AB Smartly and Conductrics are both strong at what they do. AB Smartly brings real-time engineering rigour shaped by the Booking.com programme. Conductrics brings API-first programmatic optimization with multi-armed bandits for teams embedding experimentation into their own applications.
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 waiting on an engineering or API-integration ticket. 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.