A/B testing helps marketers see what works by comparing different campaign versions, and artificial intelligence (AI) makes the process easier than ever.
The right machine learning and generative AI tools let you create test variations in seconds, run experiments without coding and gain clear insights to drive better business outcomes.
This guide shows you how to use AI A/B testing for smarter campaign decisions, without needing data analyst skills. Learn the differences between traditional and AI experiments and how to run your first AI-driven test.
What is A/B testing, and why does it matter for small teams?
A/B testing, sometimes called split testing, is a way to compare two versions of an email, web page, ad or other piece of marketing content to see which one performs better.
One group sees version A, and another considers version B. You track which gets the best results based on a clear goal, such as clicks, sign-ups or purchases.
For example, this VWO graphic shows two versions of a web page. Version A (“example.com/a.html”) is text-heavy and has a 22% conversion rate. Version B (“example.com/b.html”) balances visuals and text and has a 52% conversion rate.
A/B testing is a smart way to improve conversions without overhauling your marketing strategy. Rather than guessing what might work, you’re using data to learn what actually works, before committing lots of resources.
Let’s say you’re running an email marketing campaign. Your goal is to increase user engagement.
Instead of sending the same version to all subscribers, you test two subject lines on a smaller sample group:
Half receive version A: “Save hours every week with smarter sales automation”
Half get version B: “Book more meetings. Close more deals. Grow faster.”
After a week, the test results for version B showed that it achieved 23% more opens and 15% more clicks, driving valuable traffic. Now proven more effective, it becomes your new default for the campaign.
Over time, these small, measured changes help you steadily improve conversion rates without needing more traffic or budget.
Email subject lines are a classic example, but you can apply A/B testing across your B2B marketing funnel. Here are some more ideas:
Type of experiment | What to test |
Landing pages | Test headlines, visuals or call-to-action (CTA) buttons to improve user experience (UX) |
Pricing pages | Experiment with messaging, layouts or how you present offers to aid buyers’ decision-making (and make sales more likely) |
Retargeting ads | Use different hooks or formats to improve CTRs and grow your return on investment (ROI) |
Product descriptions | Test technical vs. outcome-focused language to see what resonates across customer segments |
Navigation menus | Experiment with item order or dropdown vs. static menus to boost user engagement and cut bounce rates. |
You can A/B test different campaign variables simultaneously to optimize spending faster. This process is called multivariate testing.
For example, you might test many combinations of subject lines, CTA copy and landing page visuals in one experiment – potentially showing how elements work together to get the best results.
Note: Multivariate testing requires a larger sample size and more careful organization, so it’s better for teams with more testing experience. Run some simpler tests first to understand the A/B testing process.
Traditional vs. AI A/B testing: what’s the difference?
Traditional A/B testing involves choosing a single test element, manually creating different versions and waiting days or weeks to collect enough data. It works but is time-consuming, resource-heavy and often limited to teams with technical know-how.
A/B testing with machine learning and AI is much more time- and cost-efficient. With little or no data experience, small teams on tight budgets can use AI tools to generate content variations, automate testing workflows, create reports and make sense of the results.
Here are three ways AI supports the A/B testing workflow.
1. Content creation: find variations faster with GenAI
Creating different versions of your copy used to take hours. Now, you can use generative AI tools like ChatGPT and Jasper to brainstorm and draft multiple variants in minutes.
Say you want to test different landing page CTAs. You might prompt ChatGPT to suggest alternatives to your current CTA, like this:
Don’t like all the suggestions? Tweak your prompt until you have a solid list of contenders for your A/B test.
Other GenAI tools to consider for content creation include Claude, Google Gemini, Copy.ai and Perplexity. Just be sure to vet the outputs, as quality may vary.
Note: Here’s our example prompt for easy copying and pasting (find a more detailed prompt later): “Here’s my current CTA: ‘Start your free trial today.’ Can you give me five alternative CTAs that are clear, action-focused and encourage users to sign up? Make them more casual than the current CTA.”
2. Experimentation setup: automate your testing process
Advanced AI A/B testing tools help you launch and run experiments with just a few actions. You don’t need coding skills or developer help.
For example, VWO’s AI Copilot feature will take your page’s URL and instantly generate personalized optimization ideas based on your goals, like simplifying form fields or adding trust signals. Here’s what that feature looks like:
Other A/B testing apps, such as Kameleoon and Looppanel, can qualify users for tests based on pre-set conditions and sort variations into groups based on your chosen metrics.
If you already use an email marketing tool like Pipedrive’s Campaigns to group your audience by common characteristics, you can A/B test marketing assets on those segments.
Features like these benefit time-strapped teams who want to run experiments without dwelling on technical details.
3. Insights and decision-making: act on test results faster
AI can help you understand the test results you collect. Machine learning algorithms analyze large datasets in real time, spotting trends, flagging anomalies and suggesting next steps.
For example, Optimizely uses AI to automatically summarize experiment results in clear, plain language, like this:
The app provides a simple trend analysis and recommends next steps (e.g., rolling out a specific feature or conducting another test). You can then prompt the app’s AI agent, Opal, for deeper insights without combing through data.
AI agents like this help across the customer journey. For example, while Optimizely targets marketers and designers on testing projects, Pipedrive incorporates agentic AI into customer relationship management (CRM) to give salespeople always-on support. Both tools help companies build stronger relationships and close more deals.
How to start with AI A/B testing: a beginner’s experimentation playbook
Ready to run your first AI-enhanced A/B test?
Here’s a five-step, repeatable roadmap you can apply to various marketing campaign experiments, from email blasts to lead-gen forms.
1. Pick a simple test to start with
Make your first A/B test straightforward to set up and easy to measure so you can get to know the iteration process. Once you become more confident, you can always scale up to bigger or multivariate tests.
Great starter tests include:
Email subject lines. Compare two different approaches to writing email newsletters or promotional email subjects (e.g., short vs. long, formal vs. casual)
CTA button text. Test different wording on your most essential conversion buttons (e.g., “sign up” vs. “register now”, “place order” vs. “buy now”)
Headline copy. Try alternative versions of your homepage’s main headline (e.g., attention-grabbing vs. subtle, funny vs. serious)
Form length. Compare a longer contact form against a shorter one to balance engagement with data collection (e.g., two fields vs. five fields, character limit vs. no limit)
Choose an experiment that impacts your sales pipeline. If email marketing drives most of your leads, start there. If your e-commerce website’s conversion rate is the priority, focus on the checkout flow.
Pick a test you can finish fast. Aim for experiments that’ll reach statistical significance within a couple of weeks so you can build momentum and start forming good test habits.
Statistical significance tells you whether your test results are real or just random. For example, if version B of your subject line gets 20 more opens than version A, that might seem meaningful. If you sent the email to 50 people, that’s a significant shift. If you sent it to 20,000, it’s probably nothing.
Email tests usually show results within 1-3 days, since most people open right after receiving. A landing page test will take weeks or more, as traffic won’t be immediate. Either way, a decent testing tool will help you know when to act.
2. Set your success metrics
Decide exactly how you’ll measure success. It’ll stop you from subconsciously cherry-picking favorable results later and ensure your experiments contribute to business goals.
Choose metrics that tie directly to revenue or lead generation. Here are some examples:
AI A/B test subject | Suitable KPI metrics |
Email campaigns | Open rates, click-through rates, customer retention rate and demo requests |
Landing pages | Form submissions, trial sign-ups and call-back requests |
Product pages | Add-to-cart rates, checkout completion rate and purchase conversions |
Blog and case study content | Time on page, social shares, newsletter subscriptions and lead magnet downloads |
Get specific about the improvements you want. Don’t just aim to “grow customer retention”, aim to “grow customer retention by 10%”. This clarity helps you determine whether test results justify implementing the winning variation.
Also, consider where you’ll get this data. You may need to source extra tech. Knowing that in advance will help you set up or get buy-in.
A strong CRM is ideal for sales performance and account management data (e.g., retention, purchases, sales demo requests, etc.). Also consider website analytics (e.g., web traffic, time on page, etc.) and email marketing software (e.g., open rates and subscriptions).
Download Your Guide to Sales Performance Measurement
3. Generate test variations with GenAI
When you know what you want to test, use a generative AI tool to create compelling alternatives.
You won’t be alone using AI this way: 75% of our State of AI in Business survey respondents said they use AI tools to create text and content.
Start with a clear prompt that gives context on your audience and goals. In other words, tell the app exactly what you’re doing, like this:
“I’m testing email subject lines for a monthly newsletter sent to small business owners interested in CRM software. Create five variations of this subject line, emphasizing different benefits: ‘Streamline your sales process with these automation tips’. Focus on pain points like time management, revenue growth and team coordination.”
When we gave Google Gemini that exact prompt, we got this response:
You don’t need to use everything the app suggests. Tweak or discard as appropriate, refine your prompt and go again.
GenAI tools’ functions vary, so let this guide your choice. For example, Gemini recommends actions based on user behavior (“Draft and refine an email…” above). Meanwhile, ChatGPT offers custom GPTs: tailored app versions for performing particular tasks.
4. Run the test using an AI-powered platform
Choose a testing platform that matches your technical confidence and budget.
The market is vast, but here are a few options that don’t require formal data analysis skills.
VWO: for drag-and-drop accessibility
VWO provides an intuitive visual editor for creating webpage variations without coding. It uses AI to help optimize test duration (for statistical significance) and provide easy-to-understand user behavior insights.
VWO pricing: VWO’s free plan offers unlimited experimentation and 30-day data retention. Growth and Pro plans start from around $400, with fewer restrictions and more features (including integrations).
Kameleoon: for testing automation
Kameleoon has robust targeting capabilities and real-time user behavior analytics. Its A/B testing machine learning feature automatically tweaks traffic allocation to get the best possible insights from every experiment.
Kameleoon pricing: There are separate licences for web and feature experimentation. Quotes for both are available on request.
AB Tasty: for conversational AI support
AB Tasty is an accessible testing platform with drag-and-drop editing and AI optimization features. Tweak web page assets by telling its AI copilot which changes you want to test. The app will do all the coding behind the scenes and confirm before validating.
AB Tasty pricing: Quotes for all AB Tasty plans are available on request.
Bringing it all together with email marketing software
You’ll need a dedicated email marketing tool to experiment with email content. Even if it doesn’t have a traditional A/B testing feature, the right platform can still support testing-like experiments through smart segmentation.
Pipedrive’s Campaigns is ideal to manage your sales, contacts and outreach in one place. Full CRM integration means salespeople can pull customer data across tools to build personalized campaigns in minutes.
While Campaigns doesn’t offer built-in A/B testing, you can create testing-like experiments using email and market segmentation. Here’s how segmentation becomes your testing toolkit:
Industry segments. Send different messaging to retail vs. SaaS prospects to see which resonates better with each sector
Deal stage segments. Test formal language with late-stage prospects and casual tone with early-stage leads
Company size segments. Compare feature-focused emails for enterprise contacts and benefit-focused content for SMBs
Track open rates and engagement across segments in Campaigns’ email analytics dashboards to see which approach works best for each audience. Then, analyze which content drives the most demo requests or moves prospects further down your sales pipeline.
Clear visualizations show how well each version resonates with your audience, allowing you to make fast decisions about what to roll out.
5. Analyze and act on the results
Once your test reaches statistical significance and there’s a clear difference in performance, explore the data to learn what happened.
First, check your primary metric to determine the best performer. Then you know which version to roll out to your audience or apply to similar campaigns.
Many AI A/B testing tools will make your winner obvious, and may even forecast broader performance improvements, like VWO does here:
However, don’t stop at just finding a winner. Take your learning to the next level by digging deeper into other related metrics.
Why did the best version have more conversions?
If time on the page was low, your messaging must have been clear and direct. Visitors should have gotten what they needed fast and kept moving through the sales funnel.
If your CTA’s click-through rate jumped, the wording matched audience intent. Let’s say “get a quote” outperformed “learn more”. Users were close to a buying decision. They wanted pricing upfront, and you made their path obvious, so they converted.
Ultimately, these patterns help you learn what your audience responds to. Apply the findings at other sales and marketing touchpoints to boost engagement across the board.
9 quick AI A/B testing tips for when time and resources are tight
It doesn’t take a big team or an Amazon-sized budget to run practical A/B tests.
Here are nine easy ways to swap marketing guesswork for data-driven decisions:
How to run A/B tests | Why it works |
1. Start small, learn fast | Small-scale tests build confidence. Focus on early wins and creating a culture of experimentation. Then, scale up as abilities and resources allow. |
2. Test one thing at a time to avoid data overwhelm | Isolating a single variable makes understanding what’s working and why easier. Fewer moving parts = more precise results. |
3. Lean on AI but validate your findings | AI tools speed things up, but they can’t replace your expert judgment. Consistently sense-check results and make sure they align with your goals. |
4. Document your results and reuse winning variations | Record what works and where. Repurposing proven content layouts or styles saves time while helping you build consistent customer experiences. |
5. Use real-time insights to iterate faster | AI A/B testing tools with real-time dashboards and custom reports help you course-correct mid-test – they’re ideal for short campaigns. |
6. Prioritize tests tied to business goals | Focus on areas directly impacting conversions and lead quality, not just cosmetic tweaks and low-quality clicks. Keep that bottom line in mind. |
7. Don’t get stuck waiting for perfect results | Using intuition is okay when the data doesn’t flow as you’d hoped. Trust your instincts and keep moving – you’ll be testing again anyway. |
8. Use GenAI to brainstorm variant ideas | Most of the best GenAI tools are low-cost. Try a few to learn which provides the most helpful inspiration, and then treat it as a testing partner. |
9. Share learnings across teams | Don’t silo your results. What works in one channel may strengthen others, especially in small teams with overlapping responsibilities. |
Apply even a few of these ideas to get miles ahead of companies still relying on gut instinct and historical data.
Final thoughts
A/B testing has long been one of the most reliable ways to make better marketing decisions. Now, with AI in your corner, it’s accessible too.
Modern tools can help you create, run and analyze tests without needing deep technical skills or a ton of data.
It’s not all about dedicated AI A/B testing features either. You’ll also need great sales and marketing tools, like Pipedrive’s CRM, to organize customer data and target your audience effectively.
Remember, the most innovative marketers aren’t waiting for perfect conditions. They’re experimenting, adapting and improving one small test at a time. Do the same, and sustainable growth will follow.
