Maybe you've noticed that shorter copy performs better than long explanations. Or perhaps you've found that embedded guides get more clicks than overlays.

But here's the thing: those hunches are often based on anecdotal evidence or sequential testing where you try one approach, wait a few weeks, then try another. What if you could validate your instincts, with actual data, and without the guesswork?

That's exactly what Guide experiments lets you do. And if you're already using Pendo Guides, you can start experimenting today. Here are three practical ways to get started.

1. Test what actually drives action

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 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.

2. De-risk your launches 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 to approach it:

Start your experiment with a 10-20% rollout to minimize risk
Let it run for at least a few days to gather meaningful data
Monitor both conversion rates and any unintended consequences (like increased dismissals)
Scale up the winning variant once you've validated performance
Use the "Guides will remain public" option when experiments complete so you have time to review results before anything gets disabled

3. Build a learning system, not just individual tests

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 talks about the technical capabilities: "Our new reporting engine lets you create custom dashboards with drag-and-drop widgets." Variant B focuses on the outcome: "See exactly what you need, when you need it, without waiting for someone else to build the report."

Variant B wins, driving higher feature adoption. Great. But don't stop there.

Now you can create segments for users who saw Variant A vs. Variant B, and track their behavior over time. Do users who saw the benefit-focused message continue to use the feature more consistently? Do they expand to related features? Do they have higher retention rates?

This long-term view helps you understand not just what drives initial action, but what leads to sustained behavior change. You're building institutional knowledge about what resonates with your users.

Why this matters:

You stop treating every guide as a one-off project
You identify patterns across experiments that inform your overall messaging strategy
You can apply learnings from one experiment to future campaigns
You build credibility with stakeholders by showing that your approach is based on evidence, not intuition

To create experiment-based segments:

Navigate to Segments and create a new segment
Choose "Experiment" as your rule type
Select your completed or active experiment
Choose which variant (A or B) you want to segment by
Add any additional filtering rules as needed

Remember: these segments can't be used for guide targeting (to avoid contaminating future experiments), but they're incredibly valuable for analytics and understanding long-term user behavior.

Getting started: keep It simple

If you're new to experimentation, don't overthink it. Start with a guide you're already planning to launch. Create two variants with one meaningful difference. Maybe you test a short headline vs. a longer one, or a static image vs. an embedded video.

Set a clear conversion goal (page view, feature click, or track event), give it a two-week duration, and let it run. Then look at the results and ask: "What did I learn? How can I apply this to other guides?"

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.