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 elements of a landing page should I A/B test?

Key elements to test include: the principal hero image or video, landing page length, headlines (tone, length, and format), lead-generation forms (design, field count, and placement), product descriptions (copy style and structure), and CTA buttons (color, text, and placement).

How do I know how long to run an A/B test?

You should run the test until you have a substantial sample size sufficient to produce statistically reliable insights. An A/B test calculator can help you estimate the required sample size and duration based on your website's traffic volume and the significance of the changes being tested.

What metrics should I track when A/B testing a landing page?

The most important metrics to track are: click-through rate (CTR), average session duration, bounce rate, conversion rate, abandonment rate (including cart abandonment for e-commerce), retention rate, scroll depth, customer satisfaction score (CSAT), and average order value (AOV).

What are the most common A/B testing mistakes to avoid?

Common mistakes include: running a test without a clear hypothesis, making changes without understanding customer behavior, starting tests before accumulating enough site traffic, ignoring mobile traffic, testing multiple hypotheses simultaneously, running tests for too short a period, and making sweeping site-wide changes based on the narrow findings of a single A/B test.

Can A/B testing improve conversion rates significantly?

Yes. Even the tiniest changes can affect a landing page's performance — conversion rates can increase by over 200% as a result of A/B testing. For example, Humana's A/B test of a redesigned homepage banner with a clearer CTA resulted in 433% more clicks than the previous version.

Is A/B testing a one-time or ongoing activity?

A/B testing is an ongoing process, not a one-time activity. It involves incremental changes that continuously fine-tune a landing page to achieve maximum conversions. Even tests with negative results are insightful because they reveal what works and what doesn't, informing future optimization decisions.

How is scroll depth used in landing page A/B testing?

Scroll depth reveals how far visitors scroll down your landing page, uncovering the most engaging sections and the points where users drop off. Scroll maps use color coding — red for areas of highest engagement and blue for areas with low or no user interaction — allowing you to optimize page layout and content placement accordingly.

Landing Page A/B Testing: Create the Best Version of Your Landing Page

You've invested several days (and nights) perfecting how the landing page for your flagship product looks. But it's just not driving the results you wanted. Could the copy be better? Is the color scheme off? Or is it something to do with the CTA? With landing page A/B testing, you can present visitors with two different versions of your landing page, then retain the one that drives better results.

What Is Landing Page A/B Testing?

Landing page A/B testing is a technique that helps you compare multiple landing page versions to determine which one outperforms the rest. The ultimate goal is to identify the version that gets maximum clicks and conversions.

For example, suppose you're running an A/B test on a landing page for an e-commerce clothing store. Version A features a large red "Buy Now" button and a headline, "Shop the Best Deals on Crew-neck T-shirts Today!" Version B showcases a smaller "Buy Now" button in blue and a headline that says, "Discover Exclusive Offers Just for You!" You split your website traffic equally between these two versions to track metrics like click-through rates and conversions. If Version A gets you 20% more purchases than Version B, Version A is the better-performing page to use as your default.

Why Do You Need A/B Testing For Your Landing Page?

Nailing everything in the first go isn't easy. You need constant tests and trials to figure out what's holding your visitors' interest and actually pushing them to take action. Here are the key reasons why A/B testing is essential:

Provides a Better User Experience

A visitor comes to your landing page with a specific action in mind. You must make this experience as flawless and frictionless as possible — avoiding confusing copies, clashing color combos, overly bright palettes, and murky sign-up buttons. A/B testing can shine a light on such areas so you can switch things up for a more free-flowing user experience.

Helps You Understand Your Target Audience Better

The insights you get from A/B tests help you gauge your target audience's behavior more accurately. You become well-versed in what drives them, which helps you tailor content or product recommendations according to their preferences, customizing their shopping experience and increasing the possibility of conversions.

Gets You a Higher Conversion Rate

Even the tiniest changes can affect a landing page's performance — conversion rates can increase by over 200%. A/B testing allows you to fully and successfully optimize your landing pages by understanding what your target audience wants. The more engaged they are, the higher the chances of them buying from you.

Minimizes Site Exits

Sometimes, visitors arrive at your landing page but leave quickly without exploring other sections, leading to a spike in your bounce rate. Conducting A/B tests allows you to analyze how various elements — such as headlines, CTA buttons, color schemes, and page layouts — impact your landing page's bounce rate. Based on the results, you can identify and implement the version that performs best and keeps visitors engaged.

Is a Fairly Low-Risk Process

A/B testing relies on incremental changes, meaning you make only one adjustment at a time. As a result, your conversion rates are unlikely to plummet during these tests. Moreover, if a change isn't yielding positive results or is negatively impacting conversions, you can quickly revert it and use the insights to inform future decisions.

Can Help Increase Revenue

It can cost a fortune to offer heavy discounts, create new products or services, or give out freebies every time to attract new visitors. A/B testing helps you make the most of existing traffic by optimizing the sales pages using test results, converting more leads — and generating more revenue — without creating new deals or products.

How Does Landing Page A/B Testing Work?

A/B testing is a well-thought-out yet simple process. Create a modified variant of your landing page and alter one element (the variable) that you feel may positively impact your conversion rate — such as changing the size or color of the CTA button or using a different headline font. Then, split the traffic among the original and modified versions, randomly assigning visitors to each in a 50/50 or 60/40 ratio. Run the A/B test until you have a substantial sample size, then compare the accumulated data from both versions to determine whether your alteration has a positive, negative, or neutral impact. If the changes produce positive results, make the modified version your new go-to landing page; if they have a negative impact, continue using the original one.

A/B testing is not a one-time activity. It is an ongoing process involving incremental changes that fine-tune your page to achieve maximum conversions. Every test you conduct holds value — even negative results are insightful, as they reveal what works and what doesn't.

Components of a Landing Page That Need A/B Testing

The decisions about what to test on your landing page carry much weight. Here is a list of all the elements you can experiment with:

1. Principal Landing Page Visual

A hero image or video paints the picture of your landing page's offer — it's usually the first thing a visitor sees. You could test different images or videos in your landing page variants to see which works best. For example, when marketing a fitness app, you might test: Version A — a product screenshot showing the app interface; Version B — a snapshot of someone working out while using the app; Version C — an animated illustration of fitness-related icons like weights and timers. Track metrics like clicks, sign-ups, and time spent on the page to determine which visual resonates most with your target audience.

2. Landing Page Length

Some products or offers may require detailed and longer landing pages to convert more leads, while other products may not need such elaborate designs. The length of your landing page significantly shapes the graph of your conversions. Test different length variations to determine how long or short the sales page should be in the future.

3. Headlines

Headlines can become a hook to grab visitors' attention — a catchy one may appeal to them enough to stay, while a vague headline could repel visitors. Try various forms: precise vs. lengthy headlines, positive vs. negative tones, benefits vs. problems format, and professional vs. playful approaches. Fibr AI's no-code editor lets you create multiple headline versions in a single click; you can then run tests and keep the headline with the highest conversion.

4. Landing Page Forms

Forms are one of the most prominent landing page elements in lead-generation campaigns, but only easy-to-understand-and-fill forms encourage users to complete them. When testing forms, experiment with their design, color, or number of fields. Additionally, you can move the form up or down on the landing page — some pages get the best results with a lead generation form displayed upfront, while others succeed in a form fill-up only after thoroughly convincing the customer about the product or offer.

5. Product Descriptions

If it's a product landing page, your product description needs to make an impact. Begin by crafting a description as the control or initial version, then slightly tweak it for the landing page variation. For instance, try switching a long paragraph to sharp bullet points, experimenting with unique words and witty phrases, highlighting the problem your product resolves, or mentioning the benefits buyers may get. Fibr AI lets you create unlimited versions of a landing page, each suited to unique search intent, increasing the chances of conversion and creating maximum impact.

6. CTA Buttons

CTAs are small but powerful triggers that encourage customers to take action, such as adding an item to the cart. Since they're directly related to revenue, CTAs must be appealing and thoughtfully designed. Experiment with their colors, content, and placement on the landing page. For example, red evokes urgency and encourages immediate action, while bright greens symbolize trust and positivity.

How to Perform A/B Testing for Your Landing Page

The following step-sequence will help you conduct A/B tests for your landing pages and bring necessary changes:

Step 1: Set Measurable Targets

Before you can start A/B testing, decipher what you're trying to achieve. Ask yourself why you need an A/B test — it could be because your landing page is underperforming or you aim for higher numbers. Once your goals are clear, figure out the primary metric you'd work on: most businesses want to improve conversion rates, while others may aim to lower bounce rates or increase newsletter sign-ups. Your website also needs sufficient traffic for an effective A/B test; an A/B test calculator can help you find the sample size and test duration needed to fetch reliable insights.

Step 2: Accumulate Valuable Data

Choosing random elements based on guesswork will degrade your optimization effort. Use analytics tools to assess your page's key metrics — for instance, the Engagement Rate in Google Analytics offers valuable insights into user interactions. A high engagement rate suggests users are actively scrolling, clicking, or exploring your landing page; a low engagement rate may indicate that the content isn't resonating effectively. Zone-based heat maps show where visitors are clicking, scrolling, or moving to, and reveal whether your landing page is intuitive. Surveys let you ask targeted questions to uncover visitors' motivations, obstacles, and decision-making triggers; avoid placing surveys on campaign landing pages, as they might distract users from completing a purchase. Fibr AI integrates with Google Analytics to track campaign results, attributed revenue, and experiments alongside all visitor data, and offers unlimited A/B tests at no cost.

Step 3: Make a Testing Hypothesis

Once you have data in hand, start creating a hypothesis on actions or changes that might improve your page's stats. A useful hypothesis is testable and hinges on reliable data or observations — it involves proposing a potential solution and predicting its outcome based on what you understand about your users. For example, replacing the current headline with one that highlights a clear benefit (e.g., "Save 20% on Your First Order") may increase conversion rates because users will immediately see the value of engaging with the page. Your hypothesis must be based on insights extracted from the previous steps.

Step 4: Create Variants and Run the A/B Test

Based on your hypothesis, create different, modified versions of the control and run the tests for sufficient time to compare and gather enough data about their performance. Give the test ample time to account for differences in visitors' behavior.

Step 5: Measure Impact

Once the test has run long enough, review the gathered insights to see which landing page version performed better. Use statistical tools to compare conversion numbers and other relevant metrics, like bounce rate and average time spent on the page. Dig deeper into why the winning variant gave a better outcome. You can also categorize your data in different audience segments — traffic sources, demographics, or visitor behavior — to reveal insights previously hidden under aggregate data.

Step 6: Implement the Winning Version

Implement the winning version as your new landing page. Document the results of your A/B tests for future reference. To optimize further, keep testing new hypotheses — regular A/B tests will systematically improve your conversion rates and guide better marketing decisions.

Key Metrics to Track While A/B Testing Your Landing Page

1. Click-Through Rate (CTR)

Click-through rate tells you the percentage of clicks on a particular link compared to the total number of times that link was displayed (impressions). You can gauge how relevant clickable elements like CTA buttons and navigation links are to your target audience through CTR.

CTR = (Clicks / Impressions) × 100

2. Average Session Duration

A session's duration is the time a user spends on your website in a single visit, measured from the moment they land on the page until they go inactive or leave. Longer sessions mean people find your website informative or enjoyable, increasing the possibility of conversions.

Avg. session duration = Total session duration / Total sessions

3. Bounce Rate

The bounce rate refers to the percentage of visitors who leave your website after viewing only one page without exploring further or clicking any links (single-page sessions). A high bounce rate can signal a lack of visitor interest or point to issues with your website's design or content.

Bounce rate = Single-page sessions / Total sessions

4. Conversion Rate

The percentage of visitors who convert or take the desired action on your site — such as signing up for your service, clicking on a specific link, or purchasing a product — determines the conversion rate, one of the most crucial metrics for evaluating A/B test success.

Conversion rate = (Number of conversions / Total number of visitors) × 100

5. Abandonment Rate

The abandonment rate shows how many tasks users start but don't finish, like leaving a product in the cart or dropping a survey midway. For e-stores, the cart abandonment rate is especially relevant.

Abandonment rate = (No. of intended tasks completed / No. of intended tasks initiated) × 100
Cart abandonment rate = (No. of carts abandoned / No. of orders initiated) × 100

6. Retention Rate

The retention rate is the percentage of visitors who return to your website or landing page after a specific period. A/B testing different landing page variants provides insights into which design or content drives higher customer retention.

Retention rate = (No. of visitors returning within a timeframe / Total no. of users who visited the page in the same period) × 100

7. Scroll Depth

Scroll depth reveals how far a visitor scrolls down on your site, unveiling your landing page's most engaging and drop-off points. Generate scroll maps to understand scroll depth: red represents areas where users are most engaged, while blue indicates low or no user interaction.

8. CSAT (Customer Satisfaction Score)

Customer satisfaction score gauges customer satisfaction with your product or service and helps you make strategic decisions about improving your landing page. To calculate CSAT, ask shoppers a closed-ended question (e.g., "Did our product solve your problem?") on a scale of 1 to 5, and use the percentage of positive responses (4 to 5) as your CSAT score.

CSAT = (Positive responses / Total responses) / 100

9. Average Order Value (AOV)

The average order value reflects the average amount a customer spends on a single online purchase. This metric indicates whether changes to a landing page have positively or negatively impacted customers' spending behavior.

AOV = Total revenue / Total no. of orders

Landing Page A/B Testing Mistakes to Avoid

Not having a clear hypothesis will leave you with a blurry idea of what variables you must change and why.
Failing to comprehend the customer's perspectives or behavior: Making changes with a bleak understanding of your customer's needs means you may not be able to captivate them enough to convert them.
Running a test without enough site traffic: Low traffic means not getting enough insights into customer behavior and other aspects.
Missing out on mobile traffic: If you don't consider mobile app users or shoppers, you're missing out on a big chunk of visitor traffic.
Testing many hypotheses together will confuse you about what exactly is responsible for the outcome.
Running the test for a very short period can leave you with insufficient data to draw conclusions and see any impact.
Making too many changes based on your results may result in overestimating the test implications — A/B tests only answer a narrow question, and site-wide modifications require more care and caution.

Landing Page A/B Testing Examples

ACT Fibernet

ACT Fibernet boosted customer acquisition by 25% and increased conversions by 12% with Fibr's personalized A/B testing solutions. By tailoring Google search ad landing pages to match user intent — such as city-specific offers, keyword-driven content, and custom visuals — ACT made their campaigns more relevant and engaging. Fibr's no-code platform also optimized CTAs, resulting in a 6% lift in conversions while saving ACT valuable time. These changes enhanced ad performance and supported ACT's goal of expanding beyond southern India.

Humana

Healthcare solution company Humana wanted to improve the performance of their homepage and decided to experiment with a new CTA, colors, and visuals. The company switched the banner design to that of a billboard, making it short and crisp by eliminating unnecessary text, and included a more persuasive CTA with a clearer and more concise message. The new banner received 433% more clicks than the previous one, demonstrating that a simple and concise banner with a clear and directional CTA increases visitor engagement and helps visitors see the purpose at first sight.

Fibr AI: A/B Testing Features

Fibr AI offers free, unlimited A/B testing with an intuitive visual editor and AI-powered suggestions, making it easy to create, run, and analyze A/B tests and gain valuable insights to boost conversion rates. Its seamless integration with Google Analytics 4 and targeting capabilities further enhance its effectiveness. Key features include:

Unlimited Free A/B Testing: Conduct and analyze A/B tests on any webpage at no cost — forever.
AI-Enhanced A/B Testing: Harness AI insights to optimize your website through smarter A/B testing.
AI-Powered Content Suggestions: Get AI-driven recommendations for alternative text to improve your website content.
Custom Text Personalization: Adapt webpage content to better resonate with your target audience.
User-Friendly Visual Editor: Design and adjust variations seamlessly using an intuitive WYSIWYG editor — no IT assistance required.
Simplified UI Element Editing: Quickly customize webpage UI components to match your design goals.
Preview Variations with Confidence: Review and confirm your changes before making them live.
Google Analytics Integration: Track campaign results, attributed revenue, and experiments alongside all visitor data.