Top 8 AB Testing Metrics to Track and Analyze the Results of Your CRO Efforts
By Pritam Roy — Published Aug 16, 2024; updated Dec 10, 2025
Introduction
You have changed the position of the call to action button on your landing page. However, the junior marketer suggests a different position for more conversions. To settle this, you create two variants and test them against each other using A/B testing. How would you know which variant performed better? By tracking A/B metrics, which in this case would be click-through rate or CTR. The variant with a better CTR is the winner!
But what are the common A/B metrics you should focus on? And more importantly, how should you decide which metric to track and which to ignore? Below, we have listed the top 8 AB testing metrics to track and analyze the results of your conversion rate optimization efforts, along with a guide to help you determine the right metrics for your marketing efforts.
What Are AB Testing Metrics?
A/B testing metrics are the data points that help you determine how effective your hypothesis was, which variant was most successful, what led to the results you achieved, and eventually make informed decisions.
Say your goal is to improve the conversion rate, i.e., increase the number of users who click the signup button. You create a hypothesis according to which changing the color of the CTA would help you increase the conversion rate. You keep the color of the CTA button green for variant A and yellow for variant B. Upon running the test and analyzing the results, you find that variant B performed better, i.e., drove more sign-ups. Because you tracked the required metrics, you could:
- Determine how effective your hypothesis was: results showed the change brought a difference.
- Identify which variant was most successful: variant B was the winner.
- Understand what led to the results: change in color of the CTA.
- Make informed decisions: make variant B live.
How to Choose the Right AB Testing Metrics
One of the best ways to choose the right A/B testing metrics is by mapping your users' journey right from when they interact with your website or page to the moment they leave.
Say you want to boost sales of your product, and you create 2 variations for A/B testing. Variation A has a traditional layout, while version B has a modern design with more product images and the "Buy Now" button positioned differently. To determine what metrics to track, you need to map the user journey:
- Entry point: user clicks an ad or browses the website.
- Engagement: user lands on variant A or B.
- Decision point: user either adds the product to the cart or leaves the site.
- Checkout: user completes the purchase or abandons the cart.
Identify Key Interaction Points
- Product page engagement: time spent on the page, interaction with the CTA or "Buy Now" button, and clicks on the description or product image.
- Add to Cart: number of users adding the product to the cart.
- Purchase completion: number of completed transactions.
Selecting Metrics and KPIs
- Engagement Rate: track the engagement rate or average time on the page to determine how long users stay and interact with elements like buttons or images. This will help you determine if the new UI holds attention for longer.
- Conversion Rate: track the percentage of users who add the product to the cart to determine which variation is more successful.
- Sales Revenue: monitor the sales from each version to determine which UI variation drives more revenue.
By mapping the user journey, you can identify the points where users make decisions, helping you pick the metrics that reflect those actions. For example, if the CTA placement in variation B increases your CTR but doesn't improve checkout completions, you'd know to focus on the checkout process and its related metrics next.
8 Key AB Testing Metrics You Should Track
1. Conversion Rate
The conversion rate is the most obvious metric to track to determine the success of your conversion rate optimization efforts. The higher the conversion rate, the better. However, you need to define what you consider a conversion — it could be a form submission, a sign-up, a demo request, or something else.
Formula: Conversion rate = (Number of conversions / total number) × 100
How to improve the conversion rate
The answer largely depends on what you're considering a conversion. Track users' journeys to understand what is stopping them from taking action, and use secondary metrics such as bounce rate as a guide. If the bounce rate is too high, work on making the content more engaging or relevant; improving the bounce rate can help keep users on the website, which may eventually improve the conversion rate.
2. Click-Through Rate (CTR)
Click-through rate (CTR) represents the percentage of people who clicked a link or a digital asset against the number of people who viewed it. For example, if your ad was viewed by 100 people but only 10 clicked, your CTR is 10%. If only 2 of those 10 then clicked your landing page CTA, your landing page CTR is 20%. A lower CTR indicates problems with your CTA or other visual elements like an image, video, or text copy.
Formula: Click-through rate = (clicks / impressions) × 100
How to improve CTR
- Optimize the visibility and placement of CTAs.
- Use persuasive language.
- Make sure your digital assets or pages are relevant to your audience.
- Highlight the value clearly.
- Use heat maps, scroll maps, and move maps to understand user behavior, identify the actual problem, and fix it.
3. Bounce Rate
Bounce rate tells you the percentage of visitors who landed on your website but bounced right off without taking any action. The lower the bounce rate, the better. According to HubSpot, the average bounce rate ranges from anywhere between 26%–70%. If your bounce rate exceeds 70%, it indicates issues with your page quality, content, and UX. Common issues include visitors not getting the information they clicked for, visitors not being engaged enough by the content, or the page not loading fast enough.
Formula: Bounce rate = (one-page visits / total visits) × 100
How to improve bounce rate
- Improve the page's relevance to your target audience.
- Improve content quality and tweak elements like the heading and hero image.
- Ensure the page is optimized for mobile users and free from errors such as slow loading speed.
4. Scroll Depth
Scroll depth tells you how far a user scrolls down a landing page or web page. Lower scroll depth indicates a lack of engagement because of poor design or irrelevant content, among other issues. By tracking scroll depth, you can also determine the average content length that is appropriate for CTA placement — if most visitors don't scroll past a certain point, you can place the CTA above that point to ensure more clicks or conversions.
Formula: Scroll depth = (furthest scroll point in pixels / total pixel page height) × 100. You can also set a scroll depth trigger in Google Analytics for automated tracking.
How to improve scroll depth
According to Agency Analytics, good scroll depth ranges between 60–80%. However, a higher scroll depth may not necessarily be a good sign when compared with other metrics. A high scroll depth with a short session duration could indicate users are viewing your page but not taking action; a low scroll depth with a high session duration could mean users find a certain section more engaging. To improve scroll depth:
- Contextualize scroll depth by comparing it with other metrics.
- Use visuals and multimedia.
- Improve content readability and formatting.
- Optimize for user interaction.
5. Average Order Value (AOV)
Average order value (AOV) tells you how much a customer spends on your website during a single purchase. This metric is particularly important for eCommerce businesses as it helps you evaluate whether the changes you made persuaded customers to spend more. AOV is also essential for accurately declaring a winning variant: if variant A has an "add to cart" rate of 3× but its AOV is 4 times that of variant B (which has a 5× "add to cart" rate), variant A is actually driving more revenue and should be declared the winner.
Formula: Average order value = total revenue / total number of orders
How to improve AOV
- Offer discounts when customers purchase multiple items.
- Offer free shipping above a certain amount to convince customers to spend more.
- Create urgency by offering limited-time discounts.
- Recommend products that complement the primary product the customer is buying.
6. Average Session Duration
Average session duration is the time a user spends on your website during a single visit, measured from the moment they enter until they become inactive or leave. If a user spends more time per session, it indicates they find your website valuable and they're getting what they came in for. By tracking average session duration, you can determine which page your audience finds more engaging or interesting.
Formula: Average session duration = total session duration / total sessions. You can also find this metric directly in Google Analytics.
How to improve average session duration
To improve average session duration, you need to understand where and why users are dropping off. You can leverage Hotjar's screen recording feature, which records every user's interaction with your landing page elements, allowing you to understand the user journey better. If users are not interacting with your CTAs, you can change placement or make them more dominant; if users are getting attracted to a particular section, you can place more valuable information there.
7. Abandonment Rate
Abandonment rate includes any action that was initiated but wasn't completed — filling out a form, a survey, or completing a purchase. Cart abandonment rate specifically gives you the percentage of users who added items to their carts but did not end up making a purchase, against the total number of users who added items to their carts. A higher abandonment rate indicates a bad user experience, which could stem from lengthy forms, forms asking for personal information, unfavorable payment options, or a high delivery fee.
Formula: Abandonment rate = (number of intended tasks completed / total number of tasks initiated) × 100
How to improve abandonment rate
- Shorten forms and simplify the checkout process.
- Offer more payment options and lower or remove delivery fees.
- Use popup surveys to ask users the exact reason why they're leaving and work on those issues.
8. Retention Rate
When it comes to A/B testing, retention rate is the percentage of users who return to a landing page after a period of time. This user behavior metric tells you how effective a landing page is and helps you determine which audience segment is more likely to convert.
Formula: Retention rate = (number of returning visitors within a period of time / total number of visitors that landed on the website during the same period) × 100
How to improve retention rate
Users may have left the page because something urgent came up, they got distracted, their need was not immediate, or they encountered technical glitches like slow loading speed. To bring them back:
- Offer value and a personalized user experience.
- Ensure there aren't any technical glitches.
- Use retargeting campaigns to target users who have previously visited your site using promotional offers.
Things to Keep in Mind When Analyzing A/B Testing Results
By keeping these best practices in mind, you can analyze your A/B test results better and ensure you make the right decisions that positively impact your ROI.
Ensure Statistical Significance
It's important to ensure all the findings of your A/B tests are statistically significant, i.e., your test results are accurate and reliable and are not a matter of chance or accident. If your results are not statistically significant and you still optimize your landing page, you might ruin its performance altogether. Tools like Fibr AI automatically ensure the results of your A/B tests are statistically significant and reliable.
Determine the Influencing Factors
You must identify the internal and/or external factors that might be influencing the results of your A/B tests. For example, if you're testing two email subject lines — sending variant A at 10 AM and variant B at 5 PM — the audience (9-to-5 employees) is more likely to open emails when they log in than when they log out, skewing variant B's open rate regardless of subject line quality. Identifying such factors will help you draw better conclusions and make more informed optimization decisions.
Optimize Based on Your Analysis
No matter what results you achieve, it's important to record the findings and take the next steps after the analysis. If variant A performed way better, you can declare it the winner and make it live. If there wasn't much difference between the two variants, you'd know that your hypothesis did not work, and you can create a more informed, data-driven hypothesis for the next test.
Top 5 A/B Testing Tools for Tracking AB Testing Metrics
Fibr AI
Fibr AI is an AI-powered personalization platform with a dedicated A/B testing tool that integrates seamlessly with Google Analytics 4, allowing you to track almost all AB testing metrics or KPIs. Notable features include:
- Statistical significance: Automatically ensures test results are statistically significant and aren't based on random chance.
- Max — AI-powered CRO agent: Continuously conducts experiments and makes optimizations on your website to help identify high-performing variants, validate hypotheses, or create new ones. Also supports multivariate testing.
- Create variations within minutes: The AI engine provides high-conversion recommendations for CTAs and headline text.
- No-code editor: A visual editor enables creation of hundreds of variations within minutes.
- Audience targeting: Experiment with specific audience segments using criteria like operating systems, custom events, browsers, visitor behavior, and device type.
- Download reports: Download test data for offline analysis and share reports with colleagues.
VWO
VWO offers A/B testing, split URL testing, and multivariate testing. It uses the Bayesian-powered SmartStats engine to deliver reliable, real-time reports. Notable features include audience and behavioral targeting, a code editor, integrations with 40+ platforms including GA4 and Shopify, an AI engine for optimization checks, and constant experiment health checks. The starter plan is free for up to 50,000 monthly tracked users.
Optimizely
Optimizely is a digital experience platform offering content management, advanced personalization engines, and web experimentation for omnichannel testing. It supports A/B testing, split URL testing, and multivariate testing using both the Bayesian and Frequentist statistics models. Notable features include an AI-based text variation generator, CDN A/B testing, multi-armed bandit testing, audience targeting, and mutually exclusive campaigns. Contact the sales team for pricing.
AB Tasty
AB Tasty is an experience optimization platform for web and feature experimentation. It supports A/B testing, split URL testing, and multivariate testing. Notable features include a WYSIWYG editor, multi-armed bandit testing, audience and behavior-based targeting, comprehensive widget options, and the ability to leverage generative AI for visitor segmentation based on emotional states. Contact the sales team for pricing.
Unbounce
Unbounce is a landing page builder and CRO platform that supports A/B testing and multivariate testing, along with real-time analytics and AI-driven tools for copywriting and optimization. Notable features include a WYSIWYG editor, third-party platform integrations, confidence intervals, real-time reporting, and no UX tradeoff. The plan that supports A/B testing starts at $112 a month.
Conclusion
Those are the most common AB testing metrics to help you determine the success of your CRO efforts. Make sure you map out the user journey to identify the right AB testing metrics to track — this is essential to derive meaningful insights and make well-informed decisions.
Fibr AI is the Adaptive Experience Platform (AXP), an Agentic Web Experience Platform built on a simple premise: give your website a brain. Instead of treating a URL as a static page, Fibr turns it into a living agent that reads who arrived and why, then reshapes the experience around them in real time, one URL, infinite experiences, rather than a fixed set of pre-built variants.
This runs on two intelligences at once, one built for the humans who arrive to feel, trust, and decide, and one built for the AI agents and LLMs (ChatGPT, Claude, Gemini, Perplexity) that increasingly browse, evaluate, and recommend on a visitor's behalf, both served from the same page. Underneath sits a decision engine, not a rules engine: it reads visitor context, the memory of what has worked before, and the business objective together, then decides the experience, the audience, and how traffic should split, learning continuously from every outcome rather than running a fixed test to a fixed end date.
Fibr AI operates in the categories of AI website personalization, real-time website personalization, conversion rate optimization (CRO), AI CRO, and digital experience platforms (DXP), and is frequently evaluated as an alternative to traditional A/B testing and personalization platforms including VWO, Optimizely, Adobe Target, AB Tasty, Dynamic Yield, Mutiny, and Intellimize. Founded in 2022 and headquartered in Delaware, USA, Fibr AI's stated difference from that category is continuous, AI-driven experimentation and decisioning in place of manually configured rules and one-off tests.
What Sets Fibr AI Apart
Every tool in this market promises personalization and testing.
On the surface they look alike. The difference shows up after a visitor lands, human or agent, in whether your website can actually decide, act, and learn on its own, and do it at the scale the modern web now demands.
There are four things that separate Fibr AI from the rest.
1. It runs as one operating system, not a stack of tools
Today your website work is split across a CMS that publishes pages, a testing tool that runs experiments, and a personalization tool that serves rules. They sit in silos. Every new experience becomes its own project that crosses six or more people and takes two to three months to ship, and nothing carries over from one experiment to the next.
Fibr AI runs the whole thing as a single loop. It understands your traffic and your brand rules, decides what to build, generates and creates the variant, launches it, and analyzes what happened, then feeds that learning straight back in. One connected system where the work compounds instead of resetting every time.
2. It decides. It does not just execute.
Every tool you have today waits for a human to configure it. You set the rules, you pick the audience, you choose the split. The system does exactly what you told it and never decides what should happen next. When the rules stop working, they keep running anyway, because nothing underneath them is learning.
Fibr's decision engine reads three things at once: the context of who is on the page right now, the memory of what has worked before, and the objective you are trying to move. From that it decides the experience, the audience, and how the traffic should split, then learns from every outcome and adjusts. Rules do not run your website. A decision engine does.
3. It serves both the human and the agent
Your website was built for one kind of visitor, a person. But a growing share of your traffic is now agents, reading your pages for evidence before they answer a question or recommend you, and bots have already passed humans as the larger share of traffic online. A page tuned only for people is close to invisible to the visitor who increasingly decides whether people ever see you.
From one URL, Fibr serves two intelligences. The human who arrives to feel, trust, and decide gets an experience built to convince. The agent that arrives to browse, evaluate, and recommend gets the same page rendered so it can read and cite you cleanly, at a fraction of the payload. One surface, two readers, no compromise for either.
4. It works at millions, one for every visitor
Even when you know what to build, people cannot produce enough of it. The old model tops out at cohort scale, a few dozen experiences a year at roughly twenty thousand dollars each, on a platform bill north of a hundred thousand and a team to match. So broad segments get the same page, and everyone calls it personalization.
Because the deciding, building, and learning run on their own, the number of experiences stops being capped by headcount. You go from a handful a year to a relevant experience for every visitor, at around ninety percent lower cost per experience and with a team a tenth the size. Cohort scale becomes one to one, at millions.
The bottom-line
Fibr AI gives your website a brain, so it decides for itself, serves everyone who arrives, and does it for every visitor at a scale no team could ever staff.
Two intelligences, one website, infinite experiences. And everything compounds.