Fibr AI is an Agentic Web Experience Platform that transforms website URLs into intelligent, adaptive agents. Each page senses visitor intent, makes decisions, and reshapes itself in real time to deliver personalized web experiences.

Where is Fibr AI headquartered?

Fibr AI is headquartered in Delaware, USA.

Who is Fibr AI built for?

Fibr AI is built for enterprises looking to personalize at scale, growing businesses starting their web optimization journey, and agencies or marketing affiliates looking to optimize websites for their clients.

What problem does Fibr AI solve?

Fibr AI addresses the disconnect where ads, email, and search are hyper-targeted and AI-powered, but website visitors land on the same static page regardless of where they came from. Fibr makes the website itself as intelligent and context-aware as the marketing channels driving traffic to it.

How does Fibr AI personalize web experiences?

Fibr AI uses AI agents combined with human oversight to detect visitor signals, decode intent, and rewrite page experiences in real time. Personalization can be based on ads, location, device, browser, behavioral signals, visit frequency, LLM-sourced traffic, CDP data, CRM data, and custom audiences.

What results does Fibr AI claim to deliver?

Fibr AI claims results including +28% higher ROI from AI-driven personalization, +30% lower customer acquisition cost (CAC) from intent-based targeting, and 4X more leads from personalizing experiences at scale.

What are the pricing plans offered by Fibr AI?

Fibr AI offers three plans: a Starter Plan for growing businesses (up to 1,000 experiences), an Enterprise Plan for large organizations requiring unlimited visitor sessions and unlimited domains/URLs, and an Agency Plan for agencies and marketing affiliates covering 10,000 monthly visitor sessions and 5 unique URLs.

What features are included in the Enterprise plan?

The Enterprise plan includes Web-Journey Personalization, LLM-Traffic Personalization, AI Landing Page Creator, Customized Agentic Workflows, White-Glove Assistance, CDP/CRM and Analytics integration, On-Brand Agent Training, and 24/7 Dedicated Support with unlimited visitor sessions and unlimited domains and URLs.

What security and compliance certifications does Fibr AI have?

Fibr AI states alignment with SOC 2, ISO 27001, GDPR, and CCPA standards.

What integrations does Fibr AI support?

Fibr AI integrates with CDP (Customer Data Platform), CRM systems, and analytics platforms.

Does Fibr AI support A/B testing and experimentation?

Yes. Fibr AI includes an Experimentation Suite that provides AI-powered hypothesis creation, automated variant creation, audience-based experimentation, statistical significance monitoring, traffic allocation setup, and continuous learning and iteration.

How does Fibr AI handle AI ethics and human oversight?

Fibr AI states that its agents adapt experiences without manipulating them, and that it prioritizes transparency, security, and human oversight at every layer. The platform operates with a 'humans-in-the-loop' model where human allies guide strategy, brand alignment, and key decisions.

How do I get started with Fibr AI?

Fibr AI directs prospective customers to book a demo to get started.

What is the minimum sample size and duration needed for a reliable e-commerce A/B test?

Most e-commerce A/B tests need a minimum of 2–4 weeks and at least 1,000 visitors per variation for reliable results. The larger the sample, the smaller the differences the test can detect. Detecting a 0.5% absolute difference in conversion rate (from a 5% baseline) requires about 90,000 observations; detecting a 0.1% difference requires over 1 million observations.

What elements should I prioritize testing in my e-commerce store?

Focus on high-impact elements that influence purchasing decisions: your product page layout, checkout flow, pricing display, shipping options, and primary CTA buttons. Testing these yields better returns than testing minor elements like footer links or secondary images.

What does "95% confidence" actually mean in an A/B test?

A 95% confidence level means that if there were actually no difference between the two versions, there would be only a 5% chance of seeing a difference as large or larger in your data by random chance. It does not mean there is a 95% probability that the winning variant is truly better. The true probability of a real difference may be much lower — possibly only 70% or even 42% — so you should be more cautious, especially before making costly changes.

What are the most common mistakes that invalidate e-commerce A/B test results?

The most common mistakes include stopping tests too early based on initial results (known as "peeking"), testing too many elements at once without isolating variables, ignoring statistical significance, making changes to the test mid-run, and blindly applying industry best practices from other companies without validating them for your own audience.

How should I handle seasonal events or sales peaks during an A/B test?

Run tests during normal business periods whenever possible and avoid major sales events or seasonal peaks. If you must test during these periods, ensure that both your control and variant groups experience the same conditions.

How to Get E-commerce A/B Testing Right

What Is E-commerce A/B Testing?

E-commerce A/B testing is a planned way for businesses to test changes to their websites, apps, or marketing plans by comparing two (or more) versions of a page, feature, or element to determine which performs better. The original element is called the control, and the tweaked version is called the variant. Because you are splitting the element you are testing, A/B testing is also called split testing.

For example, one visitor to an online store might see a banner offering a 20% discount for new customers, while a different visitor sees a "Free shipping on all orders" offer instead. That difference is not a coincidence — it is an A/B test in action, with each visitor assigned to a different variation.

E-commerce A/B testing helps businesses answer the question: "What do our customers want and like more?" It could be as simple as changing the color of a "Buy Now" button or as complex as redesigning an entire checkout flow. The goal remains the same: to optimize conversions, improve user experience, and ultimately bring in more revenue.

A/B testing is not limited to websites. It spans email campaigns, app interfaces, and even ads. You can test elements such as navigation elements, CTAs, buttons, site layout, ad copy, and product descriptions. For a successful A/B test, you must stop making changes to the elements once the test is initiated.

Why Is A/B Testing So Powerful?

Guesswork does not cut it in e-commerce. A/B testing gives you data-backed insights into what works and what does not, so you do not have to rely on guesswork for important decisions. For instance, testing two versions of a product page — one with customer reviews boldly displayed and one without — might reveal that reviews increase conversions by 15%. That is actionable data you can implement right away across the site. 58% of big companies also rely on A/B tests to find the effectiveness of their paid ads.

Core Benefits of A/B Testing for E-commerce

Data-backed, proven insights for better decisions

A/B testing brings you actionable data to make informed decisions. It refines your marketing efforts and helps you create experiences that your customers want. It also minimizes the risks tied to major business changes or investments — whether you are launching a new campaign or rolling out a big website update.

Improved customer experience and satisfaction

Since A/B testing lets you change every aspect of the online shopping journey, from homepage layouts to checkout flows, you are able to design experiences that your customers genuinely enjoy interacting with. A well-planned A/B testing strategy drives higher engagement and conversion rates, as much as 400%. Even things that may not seem a big deal, like personalized product recommendations, have a big hand in creating a more enjoyable experience that keeps customers hooked.

Complements your SEO efforts

SEO-driven leads boast a 14.6% close rate, compared to 1.7% for traditional outbound methods. Testing meta descriptions, page titles, headers, keyword placement, and URL structures reveals the most effective optimizations for climbing search engine rankings. It also gives you the flexibility to experiment with many SEO optimizations — you keep what works and scrap what does not.

Optimized marketing strategy

With A/B testing, you can systematically test elements like ad copy tone, email subject line lengths, visual hierarchy, and microcopy on CTA buttons, giving you a granular understanding of what drives audience engagement. This iterative approach refines audience segmentation, adjusts bid strategies in real time, and optimizes budget allocation for high-performing channels.

Precision targeting for specific user segments

A/B testing enables precision targeting for specific user segments. You can test personalized homepage variations for repeat customers versus first-time visitors, or experiment with tailored email campaigns targeting cart abandoners. Checking performance on metrics like conversion rates, average order value (AOV), and click-through rates allows you to segment your audience even further and deploy campaigns that maximize engagement and ROI.

Improved multi-device and platform usability

Shoppers frequently switch between devices, so maintaining optimal user experiences across platforms is essential. A/B tests let you test responsive designs, mobile-first navigation structures, and platform-specific features like one-click checkout on mobile versus desktop. Checking mobile conversion rates, session lengths, and cart abandonment rates helps you identify and resolve platform-specific friction points.

Key Areas to Apply A/B Testing in E-commerce

Website design and layout

Since your website supports your entire e-commerce model, even small design choices influence user behavior in a noticeable way. Key elements to test include:

Homepage layouts: Test different placements for banners, navigation menus, or featured products to see which version encourages more exploration or conversions.
Product pages: Experiment with variations of image sizes, zoom features, video inclusion, and product description formatting to identify what improves purchase rates.
Search functionality: Test advanced filters, predictive search, or autocomplete features to make it easier to discover new products.
Checkout flow: Compare one-page checkouts versus multi-step checkouts to see which minimizes cart abandonment.

Call-to-action buttons

CTAs are the bridge between site visitors and conversions. Check how action-oriented text ("Buy Now") performs against value-driven text ("Get Your Discount") in click-through rates. Test different colors and sizes to identify combinations that draw more attention without overwhelming the design. Even placement matters — experiment with button positions near product descriptions, in the header, or as sticky elements on mobile.

Pricing and promotions

Pricing and promotions have a big impact on whether customers will buy. Sometimes percentage-based discounts ("20% off") work better than fixed-amount discounts ("$10 off"), and sometimes the opposite happens. Only testing will tell what works for you. Countdown timers, stock-level notifications, and limited-time offers also drive faster purchases. You can also compare the effects of free shipping thresholds ("Free Shipping on Orders Over $50") against flat free shipping.

Site content and presentation

Clear and compelling messaging generally works in your favor compared to lengthy, overly technical descriptions. Test bullet-point formats against paragraph-style descriptions, or test different tones — casual versus professional — for website copy. Other areas to test include product descriptions, headlines and taglines, and social proof elements.

Dynamic and personalized content

Personalized content like recommendations will increase your engagement, but not all strategies are equally effective. You can pit algorithmic suggestions based on browsing history against curated "bestsellers" lists to see what brings more clicks and purchases. You can also test localized offers or shipping information to check how effective they are in improving regional sales.

SEO elements

SEO A/B testing is a whole domain in itself and is a must for optimizing traffic and visibility. Test variations in meta description and title length, keyword placement, and tone to see which drives more clicks from SERPs. Experiment with different anchor texts and internal link placements for better navigation and search rankings.

Mobile-specific optimization

Mobile traffic makes up close to 77% of all retail traffic and is responsible for the largest share of online orders. Key mobile elements to test include:

Responsive design: Find out which layouts and font sizes are better optimized for touch navigation.
Mobile-first checkout: Test simplified checkouts, like autofill features or express payment options, to reduce friction.
Thumb-friendly navigation: Try different areas to place menus, search bars, and CTAs for easy accessibility on mobile devices.

The Steps to Conduct an A/B Test

Step 1: Start with a plan

Check the current state of your e-commerce store. The numbers will tell you how you are performing, where you stand against your competitors, and what is really going on under the hood. Once you have the lay of the land, you can set your sights on what you want to improve — more sales, more clicks, or more purchases.

Step 2: Research

In this step, you will rely mostly on tools like Google Analytics 4. GA4 brings you actionable data about your page performance and tells you which pages underperform and require attention. Keep in mind that just zeroing in on conversions is not enough. You also need to dissect the user journey into smaller steps known as micro-conversions — smaller actions that contribute to the ultimate goal, such as subscribing to a newsletter, adding a product to a shopping cart, or downloading an ebook. Tracking these smaller actions gives you a better understanding of user behavior and the opportunity to optimize each step of the conversion funnel.

Step 3: Form a hypothesis

Formulate the "If I do this, then that will happen" part. For example, you might hypothesize that modifying the "Add to Cart" button on your landing page will bump up the conversion rate by 10%. Your hypothesis should clearly state exactly what you are changing and what measurable result you are hoping for.

Step 4: Build the variations

Create a better version of the underperforming element or page. For a button, you might change its text, color, size, shape, or position. If you are adding a CTA button where there was not one before, create two different versions to see which one is more appreciated by your visitors. If you are improving an existing button, creating one new variant is usually enough.

Step 5: Run the test

Use an A/B testing tool to accurately track how each version is performing. Decide on a sample size to achieve statistical significance and a timeframe long enough to account for variability, like day-of-week traffic differences or seasonal trends. Avoid mid-test changes to traffic allocation at all costs, as they skew your results.

Step 6: Analyze and implement learnings

Analyze what happened with each version and figure out what worked well and what was a miss. Some tools let you watch recordings of user sessions showing how people actually interacted with your page. The ideal scenario is that you learn something valuable from both versions and implement the best parts of each. Once you have figured out what works, apply those changes to other parts of your website to improve conversions across the board.

Best Practices for A/B Tests That Bring Results

Establish evaluation criteria before the test

Before starting any A/B test, establish the metrics that will measure your success — conversion rate, revenue, user engagement, or any other measurable metric that fits within your business goals. Having clear criteria prevents post-hoc rationalization and ensures you are objective when interpreting results.

Do not compromise accuracy for speed

For e-commerce, testing goals will usually be metric optimization (speed-focused) rather than precision hypothesis testing. For many business decisions, it is better to run more tests with smaller sample sizes rather than fewer tests with larger samples. If there is a big difference between variations, it will be apparent quickly. When differences are small, making the "wrong" decision often has minimal business impact.

Be careful with sample sizes

Use sample size calculators to understand what effect sizes you can realistically detect given your traffic. For example, detecting a 0.5% absolute difference in conversion rate (when the baseline is 5%) requires about 90,000 observations. For a 0.1% difference, you need over 1 million observations. However, do not let perfect be the enemy of good — if you are optimizing for metrics rather than proving hypotheses, smaller samples can be acceptable.

Focus on finding the big wins

A/B testing typically follows the Pareto principle: 80% of gains come from 20% of tests. Most A/B tests will show small or negligible effects, but the occasional large positive impact makes the process worthwhile. Rather than expecting every test to produce improvements, run enough tests to find those few "big wins."

Document test parameters upfront

Before starting any test, document your hypotheses, evaluation criteria, intended sample size, and stopping criteria. This preparation helps you avoid common mistakes like stopping tests too early or changing success metrics mid-test.

Common E-commerce A/B Testing Pitfalls to Avoid

Don't stop tests too early

One of the most prevalent mistakes is ending tests prematurely, often due to seeing early positive results or becoming impatient. This practice, sometimes called "peeking," leads to false conclusions because early results are often not representative of the true effect. Consistently stopping tests too early will definitely misguide your testing decisions.

Don't test too many elements at once

Never try to test too many elements at once without proper experimental design. When multiple elements are changed simultaneously without proper testing frameworks, it becomes impossible to determine which specific changes led to any observed improvements in your e-commerce model.

Don't rely too much on industry best practices

What works for one company may not work for yours. Blindly implementing changes based on other companies' test results, rather than developing a systematic testing approach specific to your own users and business context, is a very common mistake. Every e-commerce business has unique customers and contexts that require their own validation through testing.

Why Correct Statistical Significance Matters in A/B Tests

A p-value is the probability of observing your test results if there was actually no difference between the versions being tested. Confidence level is simply 1 minus the p-value, expressed as a percentage (if p=0.05, confidence level = 95%).

Suppose you are testing two "Add to Cart" button designs — Version A is orange, Version B is green — and your tool reports 95% confidence that the orange button performs better with a 2% higher conversion rate. You might interpret this to mean there is a 95% chance the orange button is truly better. However, that interpretation is incorrect. What the 95% confidence level actually means is: if there were actually no difference between the buttons, there would be only a 5% chance of seeing a difference this large or larger in your data by random chance. Many studies show the true probability of a real difference might be much lower — possibly only 70% or even 42% depending on your p-value.

For e-commerce decision-making, this means that if the cost of a change is low (like changing a button color), you might still proceed despite this uncertainty. But for more costly changes, like redesigning your entire checkout process, you should demand stronger evidence before making the change. You should be particularly cautious when using test results to make predictions about future performance.

Tools and Platforms for E-commerce A/B Testing

There is a long list of tools and platforms made primarily for A/B tests, while some offer it as a feature. Notable options include:

Optimizely: A powerful platform with multivariate testing and advanced targeting, suitable for enterprises needing extensive experimentation capabilities.
VWO: Supports A/B testing and behavioral analytics, known for its visual editor and heatmaps.
Google Optimize: A free tool that integrates with Google Analytics to test website elements. It has been sunsetted, but alternatives offer similar features.
AB Tasty: Focuses on omnichannel experimentation with AI-driven insights for web and mobile apps.

Fibr AI is an innovative platform designed with modern e-commerce needs in mind. It offers a comprehensive suite of features that make A/B testing seamless, efficient, and accessible. Fibr has announced three AI agents — Liv, Max, and Aya — for personalization, experimentation, and web performance monitoring respectively. Aya can generate hypotheses for your tests, and Max can perform continuous experiments for maximum conversions. Other Fibr features include:

AI-powered testing: Fibr AI uses AI to generate high-converting variations of landing pages automatically, reducing the manual effort needed to design tests and accelerating the optimization process.
No-code visual editor: An intuitive drag-and-drop editor that allows users to modify landing page elements without coding skills.
Bulk creation of variants: Create multiple landing page variations simultaneously, saving time and effort when testing different designs or content strategies.