It's a familiar story: you've built an onboarding tour, launched it, and now you're wondering if it's actually working. Is that ten-step monster scaring users off? Would a shorter, punchier tour convert better? The only way to truly know isn't by guessing or gut feeling – it's by A/B testing.
At its core, A/B testing means comparing two versions of something (A and B) to see which performs better against a specific goal. For onboarding tours, that goal is almost always getting users to complete the tour and, critically, take the next desired action in your product. Let's break down how to set up a simple experiment using Beacon's powerful analytics to improve your onboarding flows.
First, Define Success: What Are We Measuring?
Before you start tinkering, you need to know what "better" even means. For most onboarding tours, success boils down to a few key metrics:
- Tour Completion Rate: The percentage of users who start the tour and make it to the very last step. This is your primary North Star for tour effectiveness.
- Time-to-Value (TTV) Events: Did users complete a specific action within or immediately after the tour? This might be creating their first project, inviting a teammate, or configuring a critical setting. Beacon lets you track these if you're emitting custom events via the SDK or using
clickwaitsteps. - Feedback Scores: If you include a
feedbackstep, are users rating the experience higher for one version over another?
Focus on the completion rate as your main objective for an A/B test. It's the cleanest, most direct measure of engagement with the tour itself.
Getting Baselines with Beacon Analytics
Beacon's dashboard is where you'll spend a lot of time reviewing the performance of your tours. For any published tour, you get immediate access to:
- Views: How many times the tour was initiated.
- Completion Rate: The percentage of views that resulted in a full completion.
- Drop-off by Step: This is incredibly valuable. It shows you exactly where users are abandoning your tour. Is it a confusing step? Too much text? A broken element selector? This data helps you form your hypothesis for what to test.
Before you run any A/B tests, make sure you have a baseline for your current tour. Let it run for a while, collect data, and identify specific pain points. Perhaps Step 3 has a massive drop-off – that's a prime candidate for an experiment.
The A/B Test Blueprint for Your Onboarding Tours
Okay, we know what success looks like and we've got our baseline. Now, how do we actually run an A/B test?
Step 1: Formulate Your Hypothesis
This is where the engineering mindset comes in. Don't just randomly change things. Based on your analytics, what do you think will improve the tour?
Examples of Hypotheses:
- "By reducing our onboarding tour from 10 steps to 6, we will increase the completion rate by 15%." (Focus: Length)
- "Changing the CTA button text on Step 4 from 'Next' to 'Create Project' will reduce drop-off on that step by 10%." (Focus: Call to Action)
- "Adding a short video explanation to Step 2 will improve its completion rate by 5%." (Focus: Content format)
Specificity matters here. It forces you to think about why you're making a change and what outcome you expect.
Step 2: Create Your Tour Variations in Beacon
In the Beacon dashboard, it's straightforward to create variations. Find your existing tour, click the ... menu, and select "Duplicate Tour." Now you have an exact copy.
Rename the original "[Tour Name] - Control (A)" and the copy "[Tour Name] - Experiment (B)." Make your changes only to the Experiment (B) version. This ensures your changes are isolated and easy to track. For instance, if your hypothesis is about tour length, delete steps from version B until it's 6 steps. If it's about a CTA, edit only that CTA.
Keep these tours in "Draft" mode until you're ready to deploy them.
Step 3: Implement Your Traffic Split
Beacon currently doesn't have a built-in automated A/B test traffic splitter. This is a deliberate design choice; we want to give you maximum flexibility. Instead, you'll manage the traffic split yourself, most robustly through the Beacon SDK.
If you're using the Beacon SDK to start tours (which is highly recommended for proper onboarding flows), you can implement a simple client-side split.
Here's a basic JavaScript example:
// Assuming you have a way to identify your user, e.g., userId
// This is a simplified example; in a real app, you'd likely persist
// the assigned variant to ensure consistency for the user.
const userId = 'user_12345'; // Replace with actual user ID
const controlTourId = 'tour_abc123'; // Your Tour A ID from Beacon dashboard
const experimentTourId = 'tour_def456'; // Your Tour B ID from Beacon dashboard
// Simple coin flip for demonstration. For persistent results, store in localStorage or user profile.
const variant = Math.random() < 0.5 ? 'A' : 'B'; // 50/50 split
if (variant === 'A') {
Beacon.startTour(controlTourId);
console.log('Starting Control Tour (A)');
} else {
Beacon.startTour(experimentTourId);
console.log('Starting Experiment Tour (B)');
}
// You might also want to send this variant choice to your analytics platform
// if you're using one in addition to Beacon's built-in analytics.
Publish both Tour A and Tour B in Beacon. Then, deploy your code with the traffic split. Ensure that you have roughly equal numbers of users experiencing each tour. Consistency is key: once a user is assigned to Variant A or B, they should always see that variant if they re-trigger the tour.
For smaller, internal-only tests, you could technically use the Chrome Extension to manually trigger different tours for different testers, but this won't give you statistically significant data for quantitative A/B testing. Stick with the SDK for proper results.
Step 4: Measure and Compare the Results
Let your experiment run for a sufficient period – enough to gather a statistically significant number of completions for both tours. "Significant" varies depending on your traffic, but aim for hundreds, if not thousands, of views per tour if possible. Running a test for a week or two is often a good starting point.
Head back to the Beacon dashboard. You'll now have separate analytics for your Control (A) and Experiment (B) tours. Look at the key metrics we defined:
- Completion Rate: Which tour has a higher percentage of completions?
- Drop-off by Step: Are there specific steps where one tour performs significantly better or worse?
If your Experiment (B) tour shows a higher completion rate, congratulations – your hypothesis was likely correct! The changes you made improved the onboarding experience. If not, that's okay too! You've learned something important about what doesn't work, and you can iterate.
Step 5: Iterate and Optimize
Rarely does a single A/B test provide the silver bullet. This is an ongoing process. If your Experiment (B) won, make it your new control. Then, brainstorm new hypotheses based on its analytics and repeat the process.
If Experiment (B) lost, don't just revert. Analyze why. Was the change too radical? Not enough? Your step-level drop-off data in Beacon is your best friend here. Maybe reducing the tour length was a good idea, but you removed a critical piece of information that caused confusion later.
Don't get too hung up on needing perfect statistical significance on every single micro-test. While important for enterprise-scale platforms, if you're a lean team trying to move fast, sometimes a clear, observable improvement with hundreds of data points is enough to make a call and ship it. Just be rigorous in your approach and honest about your findings.
Improving your onboarding isn't a one-and-done task; it's a continuous journey of testing, learning, and refining. Beacon gives you the tools to measure that impact with precision and clarity.
Ready to put your onboarding theories to the test? Build your first tour for free and dive into the analytics at dobeacon.com/signup.