Conductrics vs Eppo vs Explore (2026): Data Teams vs Store Revenue
Conductrics is an API-first experimentation platform built for engineering teams embedding tests in their own apps. Eppo is a warehouse-native analysis tool for data teams with Snowflake or BigQuery. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs experiments on product pages, cart, and checkout, measured in revenue per visitor.
- Conductrics is an API-first experimentation tool for engineering teams, built around multi-armed bandit optimization, with a 4.3 out of 5 G2 rating. [G2, 2026]
- Eppo is a warehouse-native experiment analysis platform for data teams on Snowflake, BigQuery, or Redshift, with a 4.7 out of 5 G2 rating. [G2, 2026]
- Both tools are developer- or data-team owned: neither offers a visual editor, native Shopify integration, or marketer-accessible experiment setup.
- Neither measures results in revenue per visitor or runs experiments on the Shopify product-to-checkout path, where store revenue is actually decided.
- Omniconvert Explore gives a growth team self-serve A/B, multivariate, and checkout experiments on native Shopify surfaces, measured in revenue per visitor: pick it when the goal is store revenue, not experimentation infrastructure.
Teams comparing Conductrics vs Eppo are usually making a technical choice: build experimentation into your own stack through an API, or run it as warehouse-native analysis on Snowflake. Conductrics answers the first question for engineering teams. Eppo answers the second for data teams with mature infrastructure. Neither is built around the surfaces where a Shopify store actually wins or loses revenue: the product page, the cart, and the checkout. This page covers what each does well, the gap they share, and when Omniconvert Explore is the right layer.
What is Conductrics, and what is it actually good at?
Conductrics is an API-first A/B testing and adaptive optimization platform. It supports multi-armed bandit algorithms and adaptive targeting, and is designed for engineering and data science teams embedding testing inside their own applications. It is a programmatic tool, not a marketer-accessible interface. [Conductrics, 2026]
Conductrics is one of the quieter tools in the category, holding a 4.3 out of 5 rating on G2 across 12 reviews. [G2, 2026] Its strength is control: engineering teams get programmatic hooks for running experiments, routing traffic through adaptive bandits, and letting a model shift traffic to winning variants as data accumulates.
The category Conductrics sits in is technical-first experimentation. It is used by teams that want testing embedded inside their own apps rather than bolted on through a visual editor. It is not a marketing tool and does not try to be.
API-first experimentation means the platform exposes variant assignment, traffic routing, and result reporting through code endpoints rather than a visual UI. Conductrics builds its product around this and pairs it with adaptive bandits. It is a developer execution layer, distinct from running a controlled revenue experiment on product, cart, and checkout pages.
Where Conductrics is genuinely strong
- Multi-armed bandit optimization: adaptive algorithms that shift traffic to winning variants as data accumulates.
- API-first architecture: programmatic control for engineering teams embedding tests inside their own applications.
- Adaptive targeting: rules that evolve as the model learns which audiences respond to which variant.
- Server-side testing: tests can run anywhere the code runs, not only in a browser DOM.
Where Conductrics hits its ceiling for an eCommerce store
- No visual editor: every test change requires a developer, so a marketing team cannot ship experiments alone.
- No native Shopify integration: no checkout-level templates and no store-specific setup; integration is manual API work.
- No classic multivariate testing: the model centers on bandits, not controlled multi-factor experiments.
- Generic conversion 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, or Redshift and runs experiment analysis against the metrics definitions a data team already maintains. It is built for data engineering, with no visual editor and no marketer-facing setup. [G2, 2026]
Eppo is highly rated among data teams, holding a 4.7 out of 5 rating on G2 across 80 reviews. [G2, 2026] Its strength is statistical rigor: experiment analysis uses the same metrics, dimensions, and segmentation a company's data team already runs in its warehouse, which keeps experiment results consistent with the rest of the business.
The category Eppo sits in is high-velocity experimentation for organizations with mature data engineering. It assumes a warehouse, a data team, and a willingness to maintain metrics as code. That assumption is the point of the product.
Warehouse-native experimentation means the testing platform runs its analysis directly against the company's cloud data warehouse, using the metrics the data team already defines there. Eppo builds its product around this. It is a data-team analysis layer, distinct from running a controlled revenue experiment on product, cart, and checkout pages a marketing team can edit.
Where Eppo is genuinely strong
- Direct warehouse connection: analysis runs on Snowflake, BigQuery, or Redshift, with no duplicate metric pipeline.
- Rigorous statistical methods: sequential testing, CUPED variance reduction, and other advanced methods for mature programs.
- Metrics as code: experiment metrics reuse the data team's existing definitions, keeping company-wide numbers consistent.
- High-velocity support: built for teams running dozens of experiments at once with strong data governance.
Where Eppo hits its ceiling for an eCommerce store
- No visual editor: every variant needs engineering, so a marketing team cannot ship a test without a developer.
- Requires a data warehouse: without Snowflake or an equivalent, most of its advantage disappears.
- Enterprise pricing and setup: priced for data organizations, not for a single mid-market Shopify store.
- No native Shopify checkout: no templates, no app, no marketer-accessible checkout experiment surface.
What Conductrics and Eppo cannot do for an eCommerce store
Conductrics and Eppo are built for engineering and data teams, and they share one gap for a store. Neither is built around the surfaces where eCommerce revenue is won or lost, product pages, cart, and checkout, or around the metrics that matter there: revenue per visitor, order rate, and the margin a store actually keeps.
Conductrics is an API-first tool designed for engineering teams embedding optimization into their own applications. It has no visual editor and cannot run experiments on Shopify storefronts through a marketer-accessible interface. The team that would run the test is not the team that owns the platform.
Eppo is warehouse-native, built for organizations with mature data engineering. It assumes Snowflake or similar infrastructure to deliver its value, and it cannot run experiments on Shopify product pages or checkout flows through a marketer-accessible interface. A single DTC store that lacks a data warehouse inherits most of the cost and little of the benefit.
The two tools differ in technical shape, API-first versus warehouse-native, but share the same shortcut past the store. Both optimize the mechanics of running or analyzing a test. 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 acting on experiment data, see Has personalization replaced A/B testing?
Underneath this is a simpler problem: neither tool is a self-serve CRO platform for a marketing team. Conductrics expects a developer in the loop for every test; Eppo expects a data team that already runs the warehouse. A Shopify brand with a growth lead and a designer wants to ship a product-page test on Tuesday and read the result in revenue per visitor on Friday, with the behavioral evidence that informed it close at hand. Omniconvert Explore is built for exactly that team: heatmaps, session recordings, and surveys sit next to a full experimentation engine on native Shopify surfaces, and the result reads in revenue per visitor, not in a dashboard that needs a SQL query to interpret.
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 lifted a model-selected arm or a warehouse metric.
- 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, 94.2% never show checkout progress steps, 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 an API-first testing tool or a warehouse-native analysis platform 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.
That is what the title means by store revenue. An elegant bandit result or a warehouse-rigorous lift can leave the bank balance flat; what moves it is order rate and average order value along the product-to-checkout path, read as revenue per visitor. Explore optimizes for that number 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 a top-of-funnel metric often does not. That is the revenue question Conductrics and Eppo 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.
Conductrics vs Eppo vs Explore: the capability comparison
Side by side, the three tools cover different layers. Conductrics gives engineers programmatic bandits inside their own app. Eppo gives data teams warehouse-connected experiment analysis. Explore adds native Shopify experiments, heatmaps, recordings, and surveys, 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 | Conductrics | Eppo | Omniconvert Explore |
|---|---|---|---|
| Primary function | API-first experimentation with adaptive bandits | Warehouse-native experiment analysis | eCommerce CRO on product, cart, and checkout |
| A/B testing | Partial API-first, developer-run | Partial warehouse-native, developer-run | Yes visual plus code editor |
| Multivariate testing | No | No | Yes |
| Server-side testing | Yes | Yes | Yes |
| Visual editor | No developer required for every change | No developer required for every change | Yes self-serve for marketing teams |
| On-site surveys and overlays | No | No | Yes surveys and overlays built in |
| Shopify integration | Low API only, no checkout templates | Low no store integration, warehouse required | Yes native |
| eCommerce focus | Low developer-first, generic use cases | Low data-team-first, generic use cases | High built for store revenue workflows |
| Revenue per visitor measurement | No API-defined events only | No warehouse-defined metrics only | Yes revenue per visitor and order rate native |
| Pricing model | Custom, contact sales, no free trial | Custom, contact sales, no free trial | Session-based, built for store traffic, free trial |
| Best for | Engineering teams embedding tests via API | Data teams with Snowflake or BigQuery | Shopify and eCommerce teams optimizing for revenue |
| User rating | 4.3 out of 5 (G2, 12 reviews, as of 2026) | 4.7 out of 5 (G2, 80 reviews, as of 2026) | 4.6 out of 5 (G2, 191 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. Both Conductrics and Eppo use custom pricing and require technical implementation. 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 Conductrics or Eppo?
Decide by who owns experimentation at your company. If engineering runs it from inside a custom app, Conductrics is the right pick. If a data team runs it against a warehouse, Eppo is. If a growth or marketing team runs it on a Shopify store and needs revenue per visitor as the outcome, run your next test in Explore. These tools are not competing for the same seat; they are competing for different teams.
Conductrics and Eppo are both capable tools for the technical teams they were built for. Conductrics brings adaptive bandits inside a custom product. Eppo brings warehouse-native rigor to large experimentation programs. Neither is a self-serve CRO platform for a marketing team at a Shopify store.
The question for a store is narrower: can a growth team ship a product-page or checkout experiment on Shopify this week, read the result in revenue per visitor, and act on it. 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 191 reviews, G2 , as of 2026