Beyond Clicks: How to A/B Test 'Joy' and 'Intuition' on Your AI Landing Page

Imagine this: You're testing two demo videos for your new AI product.

Video A is a clean, step-by-step tutorial. It gets a 10% click-through rate to your sign-up page.Video B is a cinematic showcase of the magical results your AI can produce. It only gets an 8% click-through rate.

Traditional A/B testing would declare Video A the winner. But a month later, you notice something strange. The users who came from Video B are more engaged, convert to paid plans faster, and share their creations more often. They got the vision. They felt the magic.

This is the exact moment when you realize that clicks and conversion rates are telling you only half the story. In the age of AI and experience-driven products, the most important metrics aren't just about what users do—they're about how they feel.

The Problem with 'Good Enough' Metrics

For years, we've relied on a toolkit of standard metrics: click-through rate (CTR), time on page, bounce rate. These are incredibly useful for measuring user actions. They tell us what happened.

But they fail to answer the deeper, more important questions:

• Why did a user hesitate before clicking?
• Did they feel a sense of delight and possibility, or confusion and friction?
• Did the design feel intuitive, or did they have to consciously work to understand it?

UX authorities like the Nielsen Norman Group have built the bedrock of our industry by explaining the importance of "Emotional Design." They give us a powerful vocabulary for why aesthetics and feeling matter. Yet, a gap remains. We know why joy is important, but not how to set up a test to prove one landing page generates 20% more "creative optimism" than another. This is where we move from theory to practice.

Deconstructing 'Feelings': Turning Vague Concepts into Hard Data

The idea of measuring a "vibe" feels subjective and messy. But it doesn't have to be. The secret is to break these big, fuzzy concepts down into small, measurable indicators. We can create a report card for our design's emotional performance.

Let's start by defining our terms in a way we can actually test:

• Intuition: This isn't some magical sixth sense. It's simply the degree to which a user's expectations match the system's behavior. We can measure its proxies: low hesitation (e.g., quick, confident clicks) and high self-reported ease of use.
• Joy/Delight: This is the positive emotional response a user has after a successful interaction. We can measure it as a combination of: high task success, low perceived effort, and a high rating on a post-interaction survey.

This approach allows us to connect the foundational ideas of emotional design—like Don Norman's three levels (Visceral, Behavioral, Reflective)—to a quantitative A/B testing framework. We can devise specific tests for each level:

• Visceral Test: Which thumbnail image creates a more powerful gut reaction of "wow"?
• Behavioral Test: Which user flow makes completing a task feel effortless and satisfying?
• Reflective Test: Which version of the copy makes users feel more optimistic about what they can achieve with the product?

[]

Your New Toolbox: How to Measure Emotion in an A/B Test

To capture this emotional data, you need to expand your toolkit beyond standard analytics. These methods are simple, powerful, and focus on asking users for their feedback at the moment of impact.

1. Targeted Post-Interaction Surveys

Instead of long, boring surveys, ask a single, powerful question immediately after a key interaction (like watching a demo video or using an interactive tool).

• Single Ease Question (SEQ): After a task, ask, "Overall, how easy or difficult was this task to complete?" on a 7-point scale. This is a fantastic measure of behavioral satisfaction.
• User-Delight Scale: After a user sees the "magic" of your AI, ask a simple question like, "How delightful did you find that experience?" using a 5-star or smiley-face scale.

Common Mistake Callout: Avoid leading questions! Instead of "Did you love our amazing new feature?" ask a neutral question like "How would you describe your experience with this feature?"

2. Behavioral Proxies: What Clicks Don't Tell You

Sometimes, user behavior tells you more than their words. Modern analytics and session recording tools can reveal emotional states through "behavioral proxies."

• Hesitation Metrics: How long does a user hover over a button before clicking? Do they move their mouse back and forth in a pattern of indecision? This can signal confusion or lack of confidence.
• Scroll Velocity: A smooth, steady scroll down a page can indicate engagement. Erratic, fast scrolling up and down can signal frustration as a user desperately searches for information.

3. Qualitative Gold: Sentiment Analysis

Add an optional open-ended feedback box after a survey question: "Anything you'd like to add?" The words users choose are packed with emotional data. You can manually categorize them or use simple tools to perform sentiment analysis, classifying responses as positive, negative, or neutral. Words like "wow," "confusing," "finally," or "cool!" are invaluable signals.

The Blueprint: Your First Emotional A/B Test

Ready to try it? Here’s a simple, four-step blueprint for running a test that measures both action and emotion.

Step 1: Formulate an Emotional Hypothesis

Start with a clear hypothesis that goes beyond clicks. Instead of guessing, you'll form a testable emotional question.

• Old Hypothesis: "A blue button will get more clicks than a green button."
• New Emotional Hypothesis: "A demo video focused on the magical output of our AI will generate a higher 'trust' score than a video focused on the technical UI, even if clicks are similar."

Step 2: Choose Your Metrics & Tools

Select a balanced diet of metrics. For our video test hypothesis, we might track:

• Traditional Metric: Click-through rate on the "Get Started" button below the video.
• Emotional Metric: A one-question survey after the video finishes: "How confident are you that this tool can help you achieve your goal?" (1-5 scale).

Step 3: Set Up the Test & Define 'Winner'

Run a standard A/B test showing 50% of your audience Variation A and 50% Variation B. Crucially, define your winning conditions upfront. Is the winner the one with the highest clicks? Or the one with the highest confidence score, provided clicks are within 10% of the other variation?

Step 4: Analyze the Full Story

This is where the magic happens. You might find that the UI-focused video got 15% more clicks, but the "magic output" video resulted in a 40% higher average confidence score.

[]

This data tells a powerful story. The UI video grabs attention, but the magic video builds trust and intent. That's an insight that can shape your entire marketing strategy, and it's one you would have completely missed with a traditional A/B test. For more ideas on how to create compelling presentations of your work, exploring different ways of [showcasing AI projects] can provide a wealth of inspiration.

Case Study: A/B Testing the 'Vibe' of an AI Demo Video

Let's make this even more concrete. Imagine an AI photo animation tool called "Timeless Memories."

• Variation A (The "Utility" Vibe): A screen recording. We see a cursor upload a photo, click "Animate," and watch a progress bar. It's clear, direct, and focuses on the how.
• Variation B (The "Magic" Vibe): No UI at all. The video opens on a faded, black-and-white photo of a grandparent. With a soft musical swell, the person in the photo slowly blinks and smiles. It focuses on the why.

The Test: We measure sign-ups (the traditional metric) but also ask one post-video question: "How optimistic do you feel about bringing your own photos to life?" on a 1-5 scale.

The Hypothetical Result: Variation A gets 5% more sign-ups. However, Variation B scores 30% higher on the "optimism" scale. A follow-up analysis shows that users from Variation B are twice as likely to become paying customers. They didn't just sign up for a tool; they bought into a feeling. This emotional resonance is a hallmark of many [inspiring vibe-coded products] that prioritize user experience and feeling.

Frequently Asked Questions (FAQ)

What's the difference between emotional design and regular UX?

Think of it this way: Regular UX ensures a product is usable (can someone complete the task?). Emotional design ensures it's enjoyable (did they feel good while doing it?). Great products do both.

Isn't measuring feelings too subjective for a real test?

It can be, which is why a rigorous framework is so important. By breaking down "joy" into measurable components like task success and perceived ease, we move from subjectivity to quantifiable data. We're not reading minds; we're analyzing signals.

What tools do I need to run these kinds of tests?

You can start simply! Most A/B testing platforms (like Optimizely, VWO, or even Google Optimize) can be paired with simple survey tools (like Hotjar, Typeform, or a custom-built widget) to collect emotional feedback.

How many people do I need for a reliable test?

It depends on the expected effect size, but for survey-based emotional metrics, you'll often need a larger sample size than for simple click tests. The key is to run the test long enough to gather statistically significant results for both your traditional and emotional metrics.

Your Next Step: From Measurement to Mastery

Shifting your focus from just clicks to include feelings isn't about using "soft" metrics. It's about gathering more precise, more human data to build products people don't just use, but truly love.

You don't need to overhaul your entire process overnight. Start small. For your next A/B test, add just one targeted question. Ask about ease, or confidence, or delight. Listen to what the data tells you—both the numbers and the words. You'll unlock a much deeper understanding of your users and begin to intentionally design experiences that resonate on a human level.

To see what this looks like in the wild, explore the projects on the [Vibe Coding Inspiration platform]. It’s a curated space to see how developers are building AI-assisted products that get the "vibe" just right.