Convert vs Split vs Explore (2026): Flags vs Shopify Revenue
Convert is a privacy-first A/B testing platform for CRO agencies and general websites. Split is a feature-flag platform for engineering teams measuring code releases server-side, now part of a larger developer-tools suite. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs product, cart, and checkout experiments and measures the result in revenue per visitor rather than feature-release metrics.
- Convert is a privacy-first A/B testing platform for CRO agencies and general websites, with a 4.7 out of 5 G2 rating. [G2, 2026]
- Split is a feature-flag platform for engineering teams running server-side SDK experiments and controlled rollouts, now part of a larger developer-tools suite.
- Convert and Split solve different jobs, agency testing and engineering feature flags, and are not substitutes for each other.
- Neither is built around the Shopify checkout, has a marketer-usable visual editor on store pages, or measures results in revenue per visitor.
- Omniconvert Explore runs experiments on product, cart, and checkout natively and measures results in revenue per visitor: pick it for Shopify revenue surfaces.
Teams comparing Convert vs Split are weighing very different jobs: a privacy-first A/B test on a general website, or a feature-flag rollout managed inside application code. Convert answers the testing question for CRO agencies; Split, now part of a larger developer-tools suite, answers the controlled-release question for engineering teams. Neither is built around the surfaces where a Shopify store wins or loses revenue: the product page, cart, and checkout. This page covers what each does well, the gap they share, and when Omniconvert Explore is the right layer.
What is Convert, and what is it actually good at?
Convert is a privacy-first A/B testing platform for CRO agencies and mid-market teams. It supports multivariate testing and advanced targeting, with integrations across Google Analytics and major analytics platforms. It is known for transparent session-based pricing and a no-data-sharing stance. [Convert, 2026]
Convert sits among the most highly rated tools in the category, with a 4.7 out of 5 rating on G2 across 139 reviews. [G2, 2026] It has a strong following among CRO agencies thanks to transparent session-based pricing, agency-friendly account controls, and support that reviewers consistently single out.
The category Convert sits in is practitioner-led web testing. It runs A/B and multivariate tests on general websites, with a privacy posture that appeals to teams handling sensitive traffic. That focus is the point of the product.
Privacy-first testing means the platform avoids sharing experiment data with third parties and minimizes what it collects, which matters for teams working under strict compliance rules. Convert bakes that into its product and pricing. It is a separate concern from whether a test runs natively on a store's checkout.
Where Convert is genuinely strong
- Transparent, session-based pricing: predictable costs that agencies and mid-market teams can plan around.
- Strong privacy stance: no data sharing with third parties, useful under strict compliance requirements.
- Multivariate and advanced targeting: capable experimentation with granular audience rules.
- Highly rated support and integrations: well-regarded support, with Google Analytics and major analytics connections.
Where Convert hits its ceiling for an eCommerce store
- No native Shopify integration: no eCommerce-specific experiment templates, so store setups are manual.
- Editor is functional, not polished: the visual editor trails VWO and Optimizely for ease of use.
- No built-in behavioral analysis: no native heatmaps or session recordings, so teams add third-party tools.
- Generic conversion focus: results are framed around general conversion, not revenue per visitor.
What is Split, and what is it actually good at?
Split is a feature flag and experimentation platform for engineering teams, now part of a larger developer-tools suite following acquisition. It uses server-side SDKs to gate features and measure the business impact of each release, with granular targeting rules and release controls built around the application code path. [Split, 2026]
Split's category is engineering-controlled experimentation. Every variant is an SDK call. Every rule is a code change. That control is the point of the product, and the reason platform and backend teams choose it.
It is widely used by engineering and product teams who ship behind flags: they can roll a feature to 1%, 10%, or a specific cohort, and read the business impact (not just the deployment outcome) through Split's built-in measurement layer. The platform is quote-priced for larger teams and does not publish a free trial.
A feature flag is a toggle inside application code that gates a feature on or off, often by user segment, cohort, or rollout percentage. Feature flag platforms let engineering teams release code safely, run SDK-based experiments on back-end logic, and roll back without a redeploy. It is an engineering control plane, distinct from a marketer-run A/B test on a store's product page.
Where Split is genuinely strong
- Server-side SDKs with release impact: every flagged release carries built-in measurement on business and operational metrics.
- Gradual rollouts and targeting: ship to a percentage, a cohort, or a segment, and roll back without a redeploy.
- Fits the engineering stack: integrates with CI/CD, monitoring, and feature-management workflows that engineering teams already run.
- Backed by a larger enterprise platform: acquired into a continuous-delivery suite, so it slots alongside release tooling for teams standardizing on that stack.
Where Split hits its ceiling for an eCommerce store
- No visual editor: every experiment is written in application code, so marketers cannot ship a test without engineering time.
- No multivariate testing: variant structures are feature flags, not full MVT designs across multiple page elements.
- No native Shopify integration: the Shopify catalog, variants, and checkout sit outside the platform.
- Engineering-only measurement: results are read in release and feature metrics, not revenue per visitor, and pricing is quote-only.
What Convert and Split cannot do for an eCommerce store
Convert and Split serve very different buyers, CRO agencies and engineering teams, but share one gap for a store. Neither is built around the surfaces where eCommerce revenue is won or lost: product pages, cart, and checkout. Neither measures outcomes in revenue per visitor.
Convert is built for CRO practitioners running tests on general websites. With no native Shopify integration or purpose-built eCommerce flows, checkout testing happens through manual implementation, and revenue per visitor is not a native experiment metric. Connecting a winning variant to actual checkout revenue stays a manual exercise.
Split is a feature-flag platform that measures code releases server-side, now part of a larger developer-tools suite. It was built for engineers: there is no visual editor, no eCommerce-specific tooling, and pricing is by quote. A marketer cannot ship a product-page test without engineering, and the platform is not designed to express results in eCommerce revenue terms.
The two gaps start in different places and converge on the same missing layer. Convert optimizes test execution on a generic page. Split gates software releases inside the application. 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.
There is a second gap underneath the first: the data insights layer that tells a team what to test and whether it actually worked. Convert ships no native behavioral analysis, so heatmaps, session recordings, and on-site surveys come from third-party tools. Split ships none of that behavioral layer at all, because it was never designed to run a conversion experiment on a page a shopper sees.
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. Insight and test live together: the difference between guessing at a hypothesis and reading it off the store's own data.
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, not just lifted a click or micro-conversion.
- 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 lose orders at the checkout surface itself: 94.2% never show checkout progress, 99.6% fail to make guest checkout visible, 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 general tester or an engineering feature-flag platform is not built to run an experiment against. Explore runs the experiment on the store's real revenue surfaces and reports the outcome in revenue per visitor.
That is the revenue question Convert and Split are not built to answer, and the one Explore is. A higher click rate or a successfully rolled-out feature 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 also reaches Shopify-specific levers most testing tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
Convert vs Split vs Explore: the capability comparison
Side by side, the three tools serve different layers. Convert runs privacy-first tests on general websites. Split manages feature flags and SDK-based release experiments for engineering teams. Explore adds native Shopify experiments, built-in surveys and overlays, and revenue-per-visitor measurement on the product-to-checkout path; see A/B testing with Explore.
| Capability | Convert | Split | Omniconvert Explore |
|---|---|---|---|
| Primary function | Privacy-first general-website A/B testing | Feature flag management and server-side release experiments | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes visual and code editor | Partial SDK-based feature-flag experiments only | Yes visual plus code editor |
| Multivariate testing | Yes | No | Yes |
| Server-side testing | Yes | Yes SDK, strong at this | Yes |
| Visual editor | Yes functional, not polished | No SDK required | Yes polished editor |
| On-site surveys and overlays | No needs third-party tools | No engineering platform only | Yes surveys and overlays built in |
| Shopify integration | Medium not native, manual setup | None SDK-only, no native connector | Yes native |
| eCommerce focus | Medium agency and mid-market web | Low engineering-first platform | High built for store revenue workflows |
| Pricing model | Session-based, from $299/mo, free trial | Usage-based, quote only, no free trial | Session-based, built for store traffic, free trial |
| Best for | CRO agencies wanting privacy-first testing | Engineering and product teams shipping features behind flags | Shopify and eCommerce teams optimizing for revenue |
| User rating | 4.7 out of 5 (G2, 139 reviews, as of 2026) | No public G2 rating | 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. Split's paid pricing is quote-only and the product is now part of a larger developer-tools suite following acquisition. 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 Convert or Split?
Start with the surface where your revenue is actually decided. If you run agency-side experiments on general websites, keep Convert. If your engineering team gates software releases behind flags and wants release impact measurement, Split earns its slot. For a Shopify store, run your next test on the product-to-checkout path in Explore, measured in revenue per visitor. The three can coexist, but only Explore owns the checkout where the order closes.
Convert and Split are both capable tools inside their categories. Convert is transparent, privacy-first, and agency-friendly. Split pairs server-side SDKs with built-in release impact measurement, and fits the engineering stack that platform and backend teams already run.
The question for a store is narrower: can your team run a controlled experiment on the Shopify product, cart, and checkout, and read the result in revenue per visitor rather than a click or a feature-flag event. 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