If you're running in-app guides for onboarding, feature announcements, or product education, you've probably wondered: Is this actually working?

Most teams rely on hunches. Maybe you think embedded guides perform better than overlays. Or that video content drives more engagement than text. But without A/B testing, you're guessing—trying one approach, waiting weeks, then trying another. You never really know what moved the needle.

Pendo Guide Experiments changes this. It's an A/B testing capability built directly into Pendo Guides that lets you test two variants simultaneously, measure conversion against a specific goal, and get statistical confidence scores on which approach actually drives results.

The best part? If you already have Guides Pro, you can start running experiments today. Here are three proven ways to use Guide Experiments to validate what works and scale your wins.

1. Test guide formats and messaging to drive feature adoption

Let's be honest: most of us have written a guide that we thought was brilliant, only to watch it get ignored. You spent time crafting the perfect headline, choosing the right colors, maybe even adding some animations. And then... crickets.

With Guide experiments, you can stop guessing and start testing what actually moves the needle.

Here's a real-world example: A product team was promoting user education events through in-product guides. They'd always used text-based announcements with a "Register Now" call-to-action. It worked okay, but they wondered if they could do better.

So they ran an experiment: their control variant was the standard text-based guide they'd always used. The test variant was identical, except it included a 10-second video clip showing a preview of the event with the presenter explaining what they'd cover.

The result? The video variant drove 18% more registrations.

By testing a simple format change, they discovered a repeatable approach they could use for dozens of future events. That's the power of experiments, finding what works once, then scaling it.

Potential test ideas:

  • Copy-focused guides vs. visual-heavy guides
  • Feature descriptions vs. benefit-driven messaging

The key is to focus on your conversion goal. Don't just test to test. Ask yourself: "What user behavior am I trying to change?" Then design your variants around that specific outcome.

How to set it up:

When you create your experiment in Pendo, you'll test two variants (Variant A vs. Variant B, or one guide vs. no guide). For the example above:

  • Variant A: Text-based guide with "Register Now" CTA
  • Variant B: Same guide with 10-second video preview
  • Conversion metric: Feature click on the registration button
  • Attribution window: 14 days 

Pendo tracks conversion rates for each variant and confirms which guide performed better at the end of the experiment period. That's when you promote the winner and scale it to your full audience.

This approach works for testing any guide element: headlines, button copy, or even embedded vs. overlay delivery. 

2. De-risk onboarding changes with controlled rollouts

Here's a scenario you might recognize: You've built what you think is the perfect onboarding guide. It's been reviewed by stakeholders, approved by legal, and polished to perfection. You're ready to launch it to all your users.

But what if it doesn't work? What if users ignore it, or worse, find it annoying?

With Guide experiments, you don't have to make that all-or-nothing bet. You can start with a controlled rollout, say, 10% of your target audience, and gradually increase it as you validate performance.

This approach does two things:

First, it protects your users from a potentially bad experience. If something isn't working, you catch it early with a small sample size rather than annoying your entire user base.

Second, it gives you ammunition for internal conversations. Instead of stakeholders asking, "Why isn't the guide performing better?" you can say, "We tested three approaches, and this one drove 23% more feature adoption. Here's the data."

That shift from "we think this works" to "we know this works" changes how your organization approaches in-product messaging. You move from gut-feel decisions to data-driven strategy.

How controlled rollouts work in Guide Experiments:

This is one of the most powerful features in Pendo. Instead of exposing your entire segment to an untested guide, you can use a rollout percentage to start small:

  1. Create your experiment with the new onboarding flow
  2. Set your initial rollout to 10% of your target segment
  3. Monitor conversion rates and confidence scores as data comes in
  4. Gradually increase the rollout (20%, 50%, 100%) as you validate performance

You can adjust the rollout percentage even while the experiment is active—no need to stop and restart. This means you can start cautious, validate early signals, and scale confidently.

For example, if you're testing a major change to your product tour, you might run it at 10% for 3 days, check your confidence score, then bump it to 50% if the results look positive. If Variant B isn't performing, you can reduce the rollout or complete the experiment early without exposing your full user base to a suboptimal experience.

3. Build a learning system with experiment-based segments

Here's where Guide Experiments gets really interesting: you can create segments based on which variant users saw in an experiment. That means you're not just testing in isolation—you're building a knowledge base about how different user groups respond to different messaging approaches.

Let's say you run an experiment comparing feature-focused messaging vs. benefit-focused messaging:

  • Variant A: "Our new reporting engine lets you create custom dashboards with drag-and-drop widgets"
  • Variant B: "See exactly what you need, when you need it—no waiting for someone else to build reports"

Variant B wins with a 15% higher conversion rate and a 96% confidence score. Great! But don't stop there.

Create segments for ongoing analysis:

Once the experiment completes, go to People > Segments in Pendo and create a new segment using "Guide Interactions > Experiment" as the filter. You can build separate segments for:

  • Users who saw Variant A
  • Users who saw Variant B

Now track their behavior over time. Do users who saw the benefit-focused message continue to use the reporting feature more consistently? Do they expand to related features? Do they have higher retention rates after 30 days?

This longitudinal view helps you understand not just what drives initial action, but what leads to sustained behavior change. Over multiple experiments, you'll identify patterns:

  • "Benefit-driven copy consistently outperforms feature lists for our persona X"
  • "Embedded guides work better for complex workflows; overlays work for quick tips"
  • "Mobile users respond to shorter copy than web users"

These learnings become your institutional knowledge—informing not just future guides, but your entire product communication strategy.

Important note: Segments based on experiment participation can't be used for guide targeting (this prevents contaminating future experiments). But they're incredibly valuable for analytics, retention analysis, and understanding long-term user behavior patterns.

Understanding your results: Confidence scores explained

One of the most powerful features in Pendo Guide Experiments is the confidence score. This tells you whether your results are statistically significant or just random noise.

Here's how it works:

When your experiment runs, Pendo tracks conversion rates for each variant and calculates the probability that the observed difference is real—not just chance. Once your confidence score hits 95% or higher, the experiment is flagged as "Significant." This means you can trust the results.

What you'll see:

Let's say you're testing two onboarding guides:

  • Variant A: 100 views, 15 conversions = 15% conversion rate
  • Variant B: 100 views, 22 conversions = 22% conversion rate
  • Confidence score: 92%

At 92%, you're close but not quite at the 95% threshold. You might let the experiment run longer to gather more data. Once you hit 95%+, you know Variant B's 7-percentage-point lift is real, and you can confidently promote it to your full audience.

Why this matters:

Without confidence scores, you might see Variant B performing 7 points better and assume it's the winner. But with small sample sizes, that difference could easily be random chance. A 95% confidence score gives you statistical certainty that your optimization actually works—so you're not shipping changes based on flukes.

You can hover over the results in your experiment summary to view the confidence score, or export detailed results as a CSV for deeper analysis.

Getting started: keep It simple

If you're new to Guide Experiments, start simple. Here's a proven first-experiment workflow:

Step 1: Pick a guide you're already planning to launch

Don't experiment on something that's already working. Choose a new feature announcement, onboarding flow, or adoption campaign you're about to ship.

Step 2: Create one meaningful variation

Test a single element: embedded vs. overlay delivery, video vs. text, short headline vs. detailed explanation. Don't change multiple things at once; you won't know what drove the difference.

Step 3: Set your conversion metric

Choose the specific action you want users to take: a page view (for awareness), feature click (for adoption), or track event (for completed workflows). Make sure this metric aligns with your actual business goal.

Step 4: Configure your experiment settings

  • Duration: Start with 2-3 weeks for sufficient data
  • Attribution window: 7-14 days depending on your user behavior patterns
  • Audience: Target a specific segment (new users, specific role, etc.)
  • Optional rollout: Start at 10-20% if you're nervous about risk

Step 5: Let it run, then analyze

Don't peek too early. Wait until you hit 95% confidence or your duration expires. Then ask: "What did I learn? What patterns can I apply to other guides?"

The goal isn't to test everything all the time. It's to develop a systematic understanding of what drives action in *your* product with *your* users.

The beauty of Guide Experiments is that it moves you from sequential testing—where you try one thing, wait, then try another—to simultaneous comparison. You're no longer asking, "Did this work better than what we had last month?" You're asking, "Which approach works better right now, with the same users, in the same conditions?"

That's a much better question. And now you can answer it with data.

Final thoughts

The goal of experimentation isn't to test everything all the time. It's to develop a systematic understanding of what drives action in your product. Every experiment should make you smarter about your users and more confident in your approach.

Start with one experiment. Learn something. Apply it. Repeat.

That's how you build a data-driven in-product messaging strategy that actually moves the needle.

Ready to start experimenting? Guide experiments is available for all Guides Pro customers. Check out the full documentation to learn more.