Understand the differences between A/B testing and split testing. Learn when to use each, key metrics to track, and more.
Ankur Goyal
Say you're in the market to buy a new smartphone. You've narrowed down the top 5 features you're looking for. It must:
Have a long battery life
Run smoothly without any lag
Have a great camera
Look sleek and stylish
Fit within your budget
You start looking for options that tick all the boxes and narrow it down to two phones. But you're struggling to decide which one to buy.
So what do you do? You compare them side by side.
Maybe you test the camera quality in different lighting. You compare technical specifications like battery life and RAM. And see which one feels better in your hand.
That's exactly how split testing and A/B testing work in marketing. But instead of phones, you compare website pages, ads, email subject lines, and more.
The goal is simple: finding out which version performs better based on real user behavior.
But if you, like many other marketers, use these two terms interchangeably, you've come to the right place.
In this article, we'll help you understand the key differences between A/B testing and split testing, their purposes, and when to use which so you can run smarter experiments and make better decisions.
Related Read: A/B Testing: An Ultimate Guide
Summary:
A/B testing tweaks small elements in an email, ad, or webpage to optimize performance. These include headlines, CTAs, images, subject lines, etc.
Split testing helps compare two completely different versions of a webpage, ad, or email for major redesigns or structural changes.
A/B testing is typically used for small enhancements, whereas split testing is suitable for bigger revamps.
While both testing methods are different, they complement each other. Split testing helps validate big changes, and A/B testing fine-tunes them for maximum impact.
What is Split Testing?
Split testing is a conversion rate optimization (CRO) strategy that lets you compare two completely different versions of a webpage, ad, or email to see which one performs better. Instead of making small tweaks to different elements, it tests entirely different designs or layouts.
For example, suppose you're designing a landing page for a product launch. You create two designs:
Version A: A bold design packed with product images and multiple prominently placed CTAs.
Version B: A clean design with minimal elements and subtle CTAs.
When you run a split test, both these versions are shown to different groups of users. Based on their engagement, bounce rates, and conversion rates over time, you can determine which version drove the best results.
Purpose of Split Testing
Split testing comes in handy when you want to test two entirely different ideas instead of making small tweaks.
Say you want to roll out a completely new website or ad campaign. Instead of making guesses or relying on the hottest trends, you can simply run a split test to compare different versions with real users. This way, you can:
Know exactly what clicks with your audience
Avoid committing to a design that might fail
And since split tests provide actual, quantifiable data, you don't just know which version is working best but also why.
Common Elements Tested in Split Testing
Here are some elements you can experiment with to make big, high-impact changes:
1. Landing Pages
The landing page is arguably one of the most important pages of your site as it determines if a visitor will take a leap and convert or drop off altogether. Instead of making small tweaks to the same page, you can use split testing to pit two versions of the landing page against each other to understand which resonates the most with your audience.
For example, you can experiment with the navbar, design hierarchy, AI-driven chat-based lead generation, etc. In fact, AI-driven end-to-end CRO solutions like Fibr AI can help you experiment with personalized landing pages at scale based on your customers’ preferences, locations, and even languages.
2. Product Pages
Another element you can run split tests on is the product page. It needs to be visually appealing but, at the same time, build trust, reduce friction, and nudge visitors to take the next step. Split testing helps ensure all that.
For example, you can experiment with:
Different product images
Scarcity vs social proof
Actionable CTA messages
Personalized vs standard product recommendations
3. Emails
Email marketing is a great way to reach out to new leads, keep them engaged, and gradually nudge them toward conversion. With split testing, you can experiment with two completely different versions of emails, including the subject lines, CTAs, messaging, and images, to find out what moves the needle.
You can also experiment with different versions of drip campaigns, timing, and frequency to see what works best with your audience.
4. Downloadable Assets
Whitepapers, e-books, and guides make for excellent lead magnets. But there's something else they're great at - building customer trust. With split testing, you can not only determine which assets are more popular among your audience but also who is downloading them.
For example, you can experiment with traditional PDFs vs interactive content, gated vs open-access resources, and even guides or eBooks with varying lengths.
When to Run Split Testing
Split testing is suitable when you want to make big, strategic changes to your pages, product, or strategy. You can use it in the following situations:
1. When You Want to Change Your Entire Page Layout
When your landing page isn't converting as much as you'd hoped for or more users are bouncing off your product pages than taking action, making small tweaks won't really cut it. In such situations, you might need a major page revamp. Split testing can help you compare entirely different layouts to see which version delivers the most engagement and conversions.
2. When You're Targeting a New Segment
Say your landing page is performing well. But you're planning to target a new customer persona or tap into a new market. As such, although your landing page is performing well for one segment, it might not deliver the same amazing results for another. Split testing can help you experiment with an entirely different experience to see if a customized strategy performs better.
3. When You Want to Create a Content Strategy
Creating a solid content strategy involves refining several elements. Take the style of content, for example. Do you take a more conversational storytelling approach or cut straight to the value? A simple way of making the right choice is running a split test and letting your audience decide. Whichever strategy gets the most engagement, clicks, and conversions is your winner.
Key Metrics to Evaluate in Split Testing
So, you've run a split test. Great! But what next? How do you identify the high-performing version? That's where key performance indicators (KPIs) come in. Here are some essential metrics you must track in split testing:
1. Bounce Rate
The bounce rate shows many visitors leave your site without interacting further. If, say, your new landing page has a higher bounce rate, something isn't clicking. As a rule of thumb, the lower the bounce rate, the better. But make sure to also consider other metrics like session duration alongside it.
You can calculate it using this formula: (Single page visits / Total visits) x 100
2. Conversion Rate
The end-goal of any marketing strategy is boosting conversions. This could mean more purchases, sign-ups, downloads, or any action you want users to take. The version that drives the highest conversions is the clear winner. But if there's no change in the conversion rate, your approach might be off.
Here's how you can calculate the conversion rate: (Conversions / Total visitors) x 100
3. Customer Experience Metrics
You could've created a solid strategy to keep customers engaged. But to actually know if your strategy has worked its magic, you need to track metrics, such as scroll depth, time spent on the page, etc.
4. Page Load Time
Another key metric to track in split testing is the time it takes for your page to load. In fact, this metric can directly impact your conversions. Research suggests even a one-second delay can drop conversions by 7%. You can measure the page load time using tools like Google PageSpeed Insights.
What is A/B Testing?
A/B testing is a CRO strategy that lets you tweak minor elements in your webpage, ads, or emails, like images, colors, CTAs, subject lines, etc., to see what encourages visitors to take action.
Say you have a landing page. You want to know if a green CTA button will get more clicks than a red one. Instead of relying on guesswork, you can simply run an A/B test:
Version A: Green CTA button
Version B: Red CTA button
Half the visitors will see Version A, and the other half will see Version B. After running the test for some
time, you can track their performance to identify which version drives more action.
Unlike split testing, which involves experimenting with two entirely different versions of a page or email, A/B testing lets you make small, measured changes over time.
Purpose of A/B Testing
A/B testing, like split testing, helps you find the better of the two versions. But it also offers a lot more. You can use it to:
1. Identify Visitors' Pain Points
Visitors could be leaving your page for different reasons. Maybe your CTA isn't clear. Maybe your checkout process is complicated. A/B testing tells you what exactly this reason is.
For example, suppose you test two versions of a pricing page. One has detailed feature breakdowns, while the other one has a short summary. If the detailed version boosts sign-ups, it could mean users need more information before deciding.
2. Make Low-Risk Changes
Making major changes to the layout can not only be time-consuming but may also backfire. With A/B testing, you can experiment with small tweaks without making any big changes.
This gives you more control, ensuring even if the test fails, there's no major disruption. For example, with Fibr AI's A/B testing agent, Max, you can run continuous experiments to refine your strategy and maximize conversions.
3. Reduce Bounce Rates
When you're tracking website performance, bounces are part of the deal. But they exceed your expectations, something could be seriously wrong.
A/B testing helps you find the reason and fix it. For example, adding customer testimonials to a product page might help you reduce bounces by 15%.
4. Boost Conversions
Sure, the final purchase is the absolute goal of all marketing strategies. But to reach this stage, visitors take a series of micro-actions, like reading more resources, downloading a research paper, or signing up for a newsletter.
A/B testing helps you optimize all these small moments so you can nudge visitors to the final sale. Moreover, with Fibr AI's A/B testing agent, Max, you can run continuous tests to enhance engagement, conversions, and overall ROI.
Common Elements Tested in A/B Testing
There are several small and big elements you can tweak using A/B testing. These include:
1. Page Content
Your messaging plays a key role in convincing users to take the next step. You can determine what resonates the most with them by experimenting with different:
Headlines: Do visitors respond better to direct benefit-driven statements or ones that evoke curiosity?
Product Descriptions: Should you focus on features or benefits?
Content Length: Do users engage more with a short message or a detailed explanation?
2. Page Design
Your page design plays a major role in shaping user experience. Right from the color palette to layout and navigation, you can experiment with different elements of your design to see what drives engagement and pushes users to take action.
3. Images and Videos
Visuals are attractive. And they're powerful. In fact, a simple image can do a much better job of building trust, creating emotion, and driving action than plain text. But simply using images or videos doesn't necessarily guarantee success.
You need to understand what kind of images and videos people like.
Do they prefer clean product pictures or images featuring people?
Do they prefer stock photos or real company pictures?
Do they prefer detailed explainer videos or bite-sized GIFs?
A/B testing can give you the answers.
4. Subject Lines
On average, email open rates range from 15% to 40%. But simply personalizing it can boost open rates by 2X! You can use A/B tests to experiment with:
Personalized vs standard subject lines
Subject line length
Emojis in the subject line
Curiosity vs. value-driven subject lines
5. CTA Buttons
For some businesses, subtle CTAs may drive more conversions. For others, bold CTAs might do the trick. A/B testing helps you determine what works best for you.
For example, you can experiment with:
CTA Text: 'Try for Free' vs. 'Start My Free Trial.'
Color: Does a red button drive urgency, or should it align with your brand colors?
Placement: Should you place it above the fold or near testimonials?
Max optimizes these variations dynamically, ensuring you always display the highest-performing CTA.
6. Social Proof
Whether it's buying a pair of socks from Amazon or a full-fledged SaaS solution, users turn to customer reviews before making a decision. It gives them reassurance, encouraging them to take action. You can use A/B tests to elevate your social proof game by experimenting with:
Star ratings vs testimonials
Client names vs logos
User-generated content
7. Forms
Let's be real. Nobody wants to spend 15 minutes (or worse, more) of their day filling up lengthy forms, especially if they require unnecessary information. While it is always to keep your forms short, A/B testing can further help you determine if:
You should break the form into multiple steps
Offer auto-fill facility
Eliminate certain fields
Related Read: How to Perform A/B Testing: 27 Tips and Best Practices
When to Run A/B Testing?
To determine when to use split testing vs. A/B testing, it's important to remember that these two concepts aren't mutually exclusive. In fact, they perform best together.
As such, once you've run split tests to make major changes, you can run A/B tests on the smaller elements to optimize the page further.
Say you ran a split test to find out the best layout for your landing page. You can now run A/B tests to:
Experiment with CTA texts, colors, and placements
Pinpoint friction points that are causing visitors to drop off
Make small adjustments to improve time spent on page or conversion rates
And that's not all.
You can also use A/B tests to improve emails, ad copies, and even social media captions. But remember, A/B tests only work well if you have enough website traffic. If, say, only 100 people visit your site per week, the results may be skewed or unreliable.
Key Metrics to Evaluate in A/B Testing
Track these metrics to identify the best-performing variant in your A/B tests:
1. Open Rate
The open rate tells you how many people opened your email or clicked your ad. A high open rate shows your subject line or ad headline is doing exactly what it's meant to do - encouraging visitors to open the email or ad.
You can calculate it using this formula: (Emails opened / Emails sent - Bounces) x 100
2. Time Spent on Page
This metric shows if visitors are actually engaging with your content or bouncing after a few seconds. If you have a high time on the page but low conversions, it could indicate that your content is interesting, but something is stopping users from taking action.
You can use tools like Google Analytics to measure average session duration.
3. Click-Through Rate (CTR)
A CTR measures if people are taking the desired action after seeing your message. A low CTR on a CTA button might indicate that your copy isn't compelling enough.
You can calculate CTR using this formula: (Clicks / impressions) x 100
4. Conversion Rate
The conversion rate shows the number of visitors who actually complete the desired goal. This could be micro-conversion, like downloading a resource or adding a product to the cart. Or it could be macro-conversion, like requesting a call from the sales team or making a purchase.
Use this formula to calculate the conversion rate: (Conversions / Total visitors) x 100
5. Scroll Depth
Scroll depth tells you how far down the page people actually scroll. If visitors drop off before reaching your CTA, it's either too low on the page, or they're losing interest before getting there.
You can track this metric through session recordings.
Key Differences Between Split Testing and A/B Testing
Now that we've understood A/B and split testing meaning and when to use which, let's compare split testing vs. A/B testing side-by-side to see how they differ in objectives, variations, use cases, and more.
Factor | Split Testing | A/B Testing |
|---|---|---|
Objective | Tests two completely different versions of a webpage, ad, or email | Fine-tune smaller elements to improve performance |
Variations | Two or more entirely different designs (e.g., one minimalistic vs. one image-heavy) | Slight tweaks to a single element (e.g., CTA text or headline) |
Use Case | Ideal for major changes and revamps | Ideal for smaller enhancements |
Drawbacks | High effort and time-consuming | Requires substantial website traffic |
Outcome | performs better than the current version | Helps determine which small tweaks drive better engagement and conversions |
When to Use | When you need a major layout/design change | When your page is performing well but needs fine-tuning |
Run Effective A/B Tests with Fibr AI
Split testing and A/B testing are like two peas in a pod. While they aren't the same, they complement each other. Split testing helps you make big, bold changes, while A/B testing lets you fine-tune the details.
But as simple as this sounds, running hundreds of tests manually can be a huge productivity killer. Not to mention the time it takes to set them up, track their performance, and optimize them.
That's where Max, Fibr AI's A/B testing agent, comes in.
Think of it as a dedicated A/B testing team, all rolled into one efficient, AI-powered tool. It handles end-to-end A/B testing for you so you can focus on growth. Here's how it works:
Hypothesis Generation: Max analyzes your website's content, visuals, and conversion goals to create data-driven test ideas.
Always-on Testing: It runs non-stop A/B tests, continuously optimizing your site for better performance.
Data-Driven Results: It learns from every experiment, refining your website automatically for smarter decision-making.
Focus on ROI: Max identifies high-performing variations that boost engagement, conversions, and revenue.
Stop relying on gut instinct or manually running hundreds of A/B tests. Let Max handle the hard work so you can focus on growing your business.
