Eppo vs Fibr AI vs Explore (2026): The Shopify Blind Spot
Eppo is a warehouse-native experimentation platform for teams with mature Snowflake or BigQuery infrastructure. Fibr AI generates AI-powered landing page variants for paid traffic. Neither runs experiments on Shopify product pages, cart, or checkout. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it tests the revenue surfaces of a store and measures the outcome in revenue per visitor.
- Eppo is a warehouse-native experimentation platform for data teams on Snowflake, BigQuery, or Redshift, with a 4.7 out of 5 G2 rating. [G2, 2026]
- Fibr AI is an AI-powered landing page personalization tool for paid traffic, with a 4.6 out of 5 G2 rating. [G2, 2026]
- The two sit at opposite ends of the funnel: Eppo in the warehouse, Fibr AI on the post-click landing page. Neither runs an experiment on Shopify product pages, cart, or checkout.
- Neither reports outcomes in revenue per visitor on the store funnel, so the metric that decides store profitability is not the metric they measure.
- Omniconvert Explore runs A/B, multivariate, and server-side tests natively on the Shopify product-to-checkout path, measured in revenue per visitor: pick it for the store surfaces where revenue is actually made.
Teams comparing Eppo vs Fibr AI are usually solving two different problems: how a data team analyzes experiment results from a warehouse, and how a performance marketing team generates landing page variants at speed. Both are capable tools, but they sit at opposite ends of the eCommerce funnel. Neither reaches the product page, the cart, or the checkout on a Shopify store, which is where store revenue is actually decided. This page covers what each does well, the gap they share, and when Omniconvert Explore is the right layer.
What is Eppo, and what is it actually good at?
Eppo is a warehouse-native experimentation platform. It connects directly to a company's data warehouse, Snowflake, BigQuery, or Redshift, and reads experiment results from the metrics library the analytics team already trusts. It is built for data and engineering teams running high-velocity experimentation with strict data governance. [G2, 2026]
Eppo is a data-team tool first and a testing interface second. It holds a 4.7 out of 5 rating on G2 across 80 reviews. [G2, 2026] Its strength is rigor: experiment analysis runs against the same warehouse tables that feed the finance and product dashboards, so a test result and a revenue number reconcile.
What Eppo does not do is provide a visual editor for changing a page, or a self-serve interface a marketer can use without an engineer. Assignment, exposure logging, and metric wiring all live in code and warehouse jobs, which is why the tool suits organizations with mature data infrastructure and a budget for it.
Warehouse-native experimentation runs the analysis of an A/B test directly against a company's data warehouse, using the existing metric definitions rather than a separate experimentation database. Eppo does this well for teams on Snowflake, BigQuery, or Redshift. It is a data-analysis layer, distinct from running a controlled revenue experiment on the product, cart, and checkout of a Shopify store.
Where Eppo is genuinely strong
- Warehouse-native analysis: reads Snowflake, BigQuery, and Redshift metrics directly, so numbers reconcile with the rest of the business.
- Advanced statistical methods: variance reduction, sequential testing, and guardrail metrics for rigorous programs.
- Server-side experimentation: a strong fit for engineering teams running feature-level tests inside product code.
- Metric governance: a single source of metric definitions across experiments, useful for regulated or finance-adjacent teams.
Where Eppo hits its ceiling for an eCommerce store
- No visual editor: every experiment requires engineering effort; a marketer cannot ship a test on a product image or a headline alone.
- No Shopify-native surfaces: it does not reach product pages, cart flows, or checkout steps through a store-side interface.
- Enterprise pricing, sales-negotiated: priced for data-heavy organizations, not for a $1M to $50M DTC store.
- Warehouse required: assumes Snowflake, BigQuery, or Redshift, infrastructure most stores at this scale do not run.
- No native revenue per visitor: the tool reports on the metrics a data team defines, not on store-specific revenue outcomes out of the box.
What is Fibr AI, and what is it actually good at?
Fibr AI is an agentic landing page personalization platform. It uses AI to generate audience-targeted variants of post-click landing pages, so a performance marketing team can produce dozens of tailored pages per campaign without a designer or developer in the loop. [G2, 2026]
Fibr AI is a landing page tool built for the paid-traffic flow. It holds a 4.6 out of 5 rating on G2 across 133 reviews. [G2, 2026] Its strength is speed: an ads team pushing many campaigns can generate a matching landing page per audience segment in a fraction of the design and build time.
What Fibr AI does not do is reach further into the store. Its scope stops at the post-click page. It does not run experiments on Shopify product pages, cart flows, or checkout sequences, and it does not run server-side or multivariate tests on the store funnel.
AI landing page personalization generates variants of a post-click landing page automatically, using an AI model conditioned on audience or campaign data. Fibr AI does this well for teams running paid traffic. It is a paid-media delivery layer, distinct from running a controlled revenue experiment on the product, cart, and checkout of a Shopify store.
Where Fibr AI is genuinely strong
- AI-generated variants at scale: dozens of landing pages per campaign without designer or developer bottlenecks.
- Audience-segment personalization: variants tuned to paid-traffic sources and audience data.
- Fast turnaround per campaign: a real advantage for performance marketing teams running many parallel ad sets.
- Free trial available: a low-friction way for a paid-media team to test the workflow.
Where Fibr AI hits its ceiling for an eCommerce store
- Post-click landing pages only: no reach into product pages, cart, or checkout on Shopify.
- No server-side or multivariate testing: the funnel beyond the landing page is out of scope.
- No native Shopify integration: checkout experiments are not something the tool is built to run.
- Best-fit is paid traffic: organic visitors and repeat shoppers land outside its optimization loop.
- No native revenue per visitor: results are read as landing page conversion, not store-wide revenue per visitor.
What Eppo and Fibr AI cannot do for an eCommerce store
Eppo and Fibr AI sit at opposite ends of the eCommerce funnel. Eppo lives in the data warehouse and needs an engineering team to ship a test. Fibr AI lives at the post-click landing page and does not reach further. Neither runs an experiment on the surfaces where a Shopify store's revenue is actually decided.
Eppo is a warehouse-native tool built for organizations with mature data engineering. It has no visual editor and requires Snowflake, BigQuery, or a similar warehouse in place. That is a reasonable choice for a data team that already runs its metric library there. It is not a fit for a Shopify store's marketing lead who wants to test a product page image or a checkout step. The interface, the pricing, and the data model are all built for a different buyer.
Fibr AI is an AI-powered landing page personalization tool for paid-traffic campaigns. It shortens the loop between an ad and its dedicated landing page, and that is a real speed advantage for performance marketing. But its scope stops at the post-click page. Teams using Fibr AI for eCommerce CRO can personalize what a paid visitor lands on, and cannot optimize the product page, the cart, or the checkout that decides whether the order actually completes.
The gap the two share is the surface where a store's money is made and lost. One team optimizes analysis after the fact; the other optimizes the entry page before the store. Neither runs the controlled experiment on the product-to-checkout path where revenue per visitor is set. For an eCommerce store, that is the layer that matters. For the wider debate behind acting on this kind of data, see Has personalization replaced A/B testing?
Omniconvert Explore is built for that layer. Explore runs A/B, multivariate, and server-side experiments natively on Shopify product pages, cart, and checkout, with heatmaps, session recordings, and on-site surveys beside them. The experiment and the diagnostics live in one place, and results are reported in revenue per visitor and order rate rather than a generic click or a landing page conversion.
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 store revenue. Whether a change raised revenue per visitor and order rate on the store funnel, not just a landing page conversion or a warehouse metric definition.
- Which surface to test next. Which pages in the funnel (product, cart, checkout) carry the highest revenue impact for this store's catalog and traffic.
- How it behaves in checkout. How a change interacts with the Shopify catalog, variants, discounts, and the checkout flow itself, natively, without engineering glue.
- Whether it holds for valuable customers. Whether the result holds for repeat, high-value buyers, the Customer Value Optimization question, not just first-session visitors on a paid ad.
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% of stores 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]
A warehouse-analyzed feature test or a personalized ad landing page can look strong while these checkout failures still cost the store its orders. Explore runs the experiment on the actual surfaces where those orders die, and reports the outcome in revenue per visitor.
This is what the title means by the Shopify blind spot. Data-team analysis and paid-media personalization are both useful, and both stop short of the product-to-checkout path where a store's revenue actually moves. Because Explore ties each result back to repeat, high-value buyers through Customer Value Optimization, a variant that wins on revenue per visitor is margin the store keeps rather than traffic it rents. Explore also reaches Shopify-specific levers most testing tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
Eppo vs Fibr AI vs Explore: the capability comparison
Side by side, the three tools sit at different points from data analysis to landing page delivery to store-side testing. Eppo analyzes experiments in the warehouse. Fibr AI generates AI landing pages for paid traffic. Explore runs native Shopify experiments on product, cart, and checkout, with heatmaps, recordings, and surveys beside them. See A/B testing with Explore for how those experiments run on the Shopify funnel.
| Capability | Eppo | Fibr AI | Omniconvert Explore |
|---|---|---|---|
| Primary function | Warehouse-native experiment analysis | AI-generated landing page personalization | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes warehouse-native, code-driven | Partial AI-generated landing pages only | Yes visual and code editor |
| Multivariate testing | No | No | Yes |
| Server-side testing | Yes core strength | No | Yes |
| Visual editor | No engineering required | No AI-generated only | Yes point-and-click plus code |
| On-site surveys and overlays | No analysis-only tool | No landing pages only | Yes surveys and overlays built in |
| Shopify integration | Low no native store surfaces | Low post-click pages only | Yes native |
| eCommerce focus | Low built for data teams | Low focused on paid landing pages | High built for store revenue workflows |
| Revenue per visitor measurement | Possible if a data team defines it | No landing page conversion only | Yes revenue per visitor and order rate native |
| Pricing model | Custom, contact sales | Seat-based, contact sales, free trial | Session-based, built for store traffic, free trial |
| Best for | Data and engineering teams with mature warehouses | Performance marketing teams generating landing page variants | Shopify and eCommerce teams optimizing store revenue |
| User rating | 4.7 out of 5 (G2, 80 reviews, as of 2026) | 4.6 out of 5 (G2, 133 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. Eppo and Fibr AI serve very different buyers, but neither is built for Shopify checkout experimentation. 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 Eppo or Fibr AI?
Decide by the surface you need to test. If a data team needs warehouse-connected experiment analysis, keep Eppo. If a paid-media team needs AI landing pages, keep Fibr AI. Neither reaches Shopify product, cart, or checkout. For a store optimizing revenue per visitor on the product-to-checkout path, run the next experiment in Explore. It replaces the missing store-side layer, and often the analytics tool a store bought to substitute for it.
Eppo and Fibr AI are both capable within their scope. Eppo is a rigorous warehouse-native experiment analysis platform for a data team. Fibr AI is a fast AI landing page generator for a paid-media team.
The store question is narrower: once you can see traffic and metrics, can you run a controlled experiment on the Shopify product, cart, and checkout and read the result in revenue per visitor. 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