Dynamic Yield vs Fibr AI vs Explore (2026): The Shopify Gap
Dynamic Yield is an enterprise personalization platform owned by Mastercard, used by large retailers like IKEA and McDonald's for real-time recommendations. Fibr AI generates AI-personalized landing page variants for paid traffic campaigns. Omniconvert Explore is the Shopify-native eCommerce CRO platform: it runs experiments on product pages, cart, and checkout, and measures results in revenue per visitor rather than landing-page conversion rate.
- Dynamic Yield is an enterprise personalization platform owned by Mastercard, used by IKEA and McDonald's, with a 4.5 out of 5 G2 rating. [G2, 2026]
- Fibr AI generates AI-personalized post-click landing page variants for paid traffic teams, with a 4.6 out of 5 G2 rating. [G2, 2026]
- Dynamic Yield and Fibr AI serve different jobs, enterprise personalization and paid-traffic landing pages, and are rarely direct substitutes for each other.
- Neither is built around the Shopify checkout or measures results in revenue per visitor, the surfaces where store revenue is actually decided.
- Omniconvert Explore runs experiments on product, cart, and checkout natively and reports the outcome in revenue per visitor: pick it for the Shopify revenue surface.
Teams comparing Dynamic Yield vs Fibr AI are usually deciding what to personalize next: enterprise recommendations across many channels, or AI-generated landing pages for paid traffic. Dynamic Yield answers the recommendation and experience question for large retailers. Fibr AI answers the post-click landing page question for paid campaigns. 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 Dynamic Yield, and what is it actually good at?
Dynamic Yield is an enterprise personalization and A/B testing platform, owned by Mastercard since 2022. It runs real-time product recommendations, experience optimization, and A/B and multivariate testing across web, mobile, and email for major retailers including IKEA and McDonald's. [G2, 2026]
Dynamic Yield holds a 4.5 out of 5 rating on G2 across 200 reviews. [G2, 2026] Its category is enterprise personalization: connecting live customer data to experience decisions across many touchpoints, at retail scale. The Mastercard acquisition added more data resources and connective tissue for large retail programs.
Its A/B and multivariate testing exists inside that personalization engine. The point of the product is orchestrating experiences at scale for a retailer with a dedicated personalization team, not enabling a single Shopify store's next controlled experiment on the cart.
Enterprise personalization means adapting web, email, mobile, and connected retail experiences to a live behavioral profile of the visitor, driven by machine learning at scale. Dynamic Yield does this for major retailers. It is a different job from running a controlled revenue experiment on a store's product, cart, and checkout.
Where Dynamic Yield is genuinely strong
- Real-time personalization at scale: machine-learning recommendations across web, mobile, and email, tied to a live visitor profile.
- Enterprise recommendation engine: product and content recommendations connected to customer and Mastercard data resources.
- Full experimentation stack: A/B, multivariate, and server-side testing available inside the personalization suite.
- Proven enterprise footprint: deployed by large retailers such as IKEA and McDonald's, with the case studies to match.
Where Dynamic Yield hits its ceiling for a mid-market Shopify store
- Enterprise contracts and pricing: custom quotes typically in the five-figure annual range, not accessible to a mid-market DTC brand.
- High implementation effort: needs a dedicated implementation project and developer resources before the first test runs.
- Over-specified for a single store: the platform is priced and scoped for a personalization team, not for one Shopify brand running self-serve experiments.
- Not self-serve for a store: a store team cannot spin up a cart or checkout test without the enterprise stack behind it.
What is Fibr AI, and what is it actually good at?
Fibr AI is an AI-powered landing page personalization platform. It generates and tests personalized post-click landing page variants automatically, based on audience segments and campaign data, so performance marketing teams can spin up hundreds of variants without designer or developer time. [G2, 2026]
Fibr AI holds a 4.6 out of 5 rating on G2 across 133 reviews. [G2, 2026] It targets a specific bottleneck: the designer-and-developer wait that stops a paid team from producing personalized landing pages at the pace their ad campaigns need.
Teams running paid traffic can build hundreds of personalized post-click pages tied to audience segments and campaigns. That is the job the product answers well. It is a different job from running experiments across a store's product-to-checkout funnel.
AI landing page personalization means using generative and machine-learning models to produce personalized post-click landing page variants for paid campaigns, keyed to audience segments. Fibr AI does this without designer or developer time. It is a delivery layer for paid traffic, distinct from running controlled revenue experiments on a store's product, cart, and checkout.
Where Fibr AI is genuinely strong
- AI-generated variants at scale: hundreds of personalized landing page variants tied to audience segments, without a design queue.
- Removes the design and developer bottleneck: paid teams ship personalized post-click pages at campaign pace, not at production pace.
- Segment-driven personalization: variants matched to audience segments and campaign data straight out of the box.
- Accessible entry point: free trial and seat-based pricing that a performance marketing team can adopt without a procurement cycle.
Where Fibr AI hits its ceiling for an eCommerce store
- Landing pages only: no experiments on Shopify product pages, cart flows, or the checkout sequence.
- Low Shopify integration: the tool sits between the ad and the landing page, not inside the store funnel.
- No multivariate or server-side testing: the testing model is AI-generated landing page variants, not a full experimentation stack.
- Paid-traffic KPIs, not store revenue: results are read in landing page conversion, not revenue per visitor across the store.
What Dynamic Yield and Fibr AI cannot do for an eCommerce store
Dynamic Yield and Fibr AI serve different jobs, enterprise personalization and AI landing pages, but 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, measured in revenue per visitor.
Dynamic Yield is built for enterprise retail teams with dedicated personalization staff and six-figure budgets. For a mid-market Shopify brand, the implementation complexity and contract size put it out of reach: a self-serve experiment on the cart or checkout is not the product it is priced for.
Fibr AI is built for performance marketers running paid traffic to post-click landing pages. It cannot experiment on the Shopify product page, the cart, or the checkout, because those are not landing pages. A store using Fibr AI for CRO covers the ad-to-landing-page step and leaves the checkout untouched.
The two gaps differ in nature but point to the same missing layer. Most personalization tools optimize for the experience or the click on a page they own; they are not 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 personalization-versus-testing 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. Dynamic Yield holds deep behavioral data, but that data lives inside a Mastercard-owned enterprise deployment most single Shopify stores will never run. Fibr AI's insights are keyed to landing page performance, not store revenue. 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.
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 click on a landing page 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, on this store's real data.
- 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 served an AI landing page.
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 94.2% never show checkout progress steps. [CROBenchmark Report 2026, Omniconvert]
These are checkout-surface problems, the exact surfaces an enterprise personalization suite or an AI landing page generator 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.
This is what the title means by the Shopify gap. A lifted click on a personalized landing page or an enterprise recommendation carousel 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. Explore also reaches Shopify-specific levers most personalization tools cannot touch, including price testing; see Explore 3.0: pricing testing on Shopify.
Dynamic Yield vs Fibr AI vs Explore: the capability comparison
Side by side, the three tools serve different layers. Dynamic Yield personalizes experiences at enterprise scale. Fibr AI generates personalized landing pages for paid traffic. 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 for how those experiments run natively on the Shopify funnel.
| Capability | Dynamic Yield | Fibr AI | Omniconvert Explore |
|---|---|---|---|
| Primary function | Enterprise personalization at scale | AI landing page personalization for paid traffic | eCommerce CRO on product, cart, and checkout |
| A/B testing | Yes visual editor and personalization engine | Partial AI-generated landing page variants only | Yes visual plus code editor |
| Multivariate testing | Yes | No | Yes |
| Server-side testing | Yes | No | Yes |
| Visual editor | Yes | No AI-driven variant generation instead | Yes |
| On-site surveys and overlays | Partial via personalization campaigns | No landing page focus only | Yes surveys and overlays built in |
| Shopify integration | Partial available, but requires enterprise implementation | Low post-click landing pages only | Yes native |
| eCommerce focus | High enterprise retail personalization | Low paid-traffic landing pages | High built for store revenue workflows |
| Pricing model | Custom enterprise, contact sales | Seat-based, contact sales, free trial | Session-based, built for store traffic, free trial |
| Best for | Enterprise retail teams with dedicated personalization staff | Performance marketing teams producing landing pages at pace | Shopify and eCommerce teams optimizing for revenue |
| User rating | 4.5 out of 5 (G2, 200 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. Dynamic Yield pricing is custom; Fibr AI pricing is quoted on request. 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 Dynamic Yield or Fibr AI?
Start with the surface where your revenue is decided. If you are an enterprise retailer with a dedicated personalization team, Dynamic Yield earns its place. If your bottleneck is AI landing pages for paid traffic, keep Fibr AI. For a Shopify store, run your next test on the product-to-checkout path in Explore, measured in revenue per visitor. The three are complementary, but only Explore is built for the checkout where the order closes.
Dynamic Yield is a mature enterprise personalization platform, connected to major retail programs since the Mastercard acquisition. Fibr AI is a useful AI landing page generator for teams running paid traffic. Both do their jobs; neither is the store's checkout tool.
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 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.
4.6 out of 5 across 191 reviews, G2 , as of 2026