A/B testing is an essential part of a product manager’s toolkit. It enables teams to make choices based on data and gradually enhance their products. Beyond the immediate results from individual experiments, A/B testing can provide valuable insights that shape a product’s future direction and decision-making.
Let’s explore what product managers can discover through A/B testing and how these insights can lead to strategic, informed decisions.
What To Learn from A/B Testing?
An A/B test can offer information about what users prefer. It can reveal which version of a text that’s being used in a call-to-action (CTA), which page layout, or which feature positioning resonates the most with them.
Let’s say I’m assigned a product goal to boost signup conversions by 10%. Now, there are different directions to explore based on what’s causing the low sign-up numbers that do not always lead to an A/B test.
However, for this example, let’s assume data shows many people are reaching the signup button but not clicking it. This makes us think the problem could be with the button’s text, where it’s placed, or how it looks.
To test this hypothesis, my team will run an A/B test to see if changing the CTA text makes a difference. For example, “Sign Up Free” might bring in fewer users than “Start Your Free Trial”.
What will this test help me understand?
1. What users prefer
If “Start Your Free Trial” works better than “Sign Up Free,” it could show that users prefer clear information about what they’re getting. They appreciate the clarity and the benefit of the offer. This insight helps create messaging that matches what users want.
2. The user behavior
Remember that running an A/B test doesn’t guarantee that users will strongly react to one option. This might point to more significant issues like users not feeling too trusting or motivated.
3. The audience segmentation
Looking at the test results in different segments (like device type, location, or user type) can highlight how different groups react. If mobile users click one version more than desktop users, that shows me that we may need to tweak the design or wording for mobile. Understanding these differences will help the team know how to personalize the experience for various audiences.
4. Potentially actionable insights
By checking how many people moved on from the button click to actually signing up, we can spot friction in the process that needs fixing. If users click the new button but still aren’t signing up due to issues with the following steps, that’s something to work on.
Before fully committing to launching a new feature, A/B testing helps me be sure we’re not spending time and resources on things that won’t pay off. Read more on how to run an A/B test here.
What’s Next After Running the A/B Test?
When running A/B tests, it’s very important to keep track of what you discover along the way. So, make a habit of jotting down how you set things up for your tests, what the results were, and any interesting insights you picked up. You’ll thank yourself later if you gather this kind of documentation because it will be helpful for future tests. On top of that, this is how you can make sure everyone on the team can learn from your experiences.
If you find that one version of your test performs much better than the original, don’t hesitate to roll it out to all your users. On the other hand, if your results are a bit unclear, it might be a good idea to tweak your original idea and perhaps run another test to dig deeper.
So, if, for example, you don’t see any significant increase in sign-ups after launching the test, consider testing other critical parts of the sign-up process.
Always use what you learn to brainstorm new experiments. This means that if changing your signup form boosted conversions, think about testing other design changes in the next steps of the user experience.
Finally, don’t omit to share what you’ve found out with your team and other stakeholders. Keeping them in the loop not only helps everyone understand what you’ve learned but also shows how your tests fit into the bigger goals of the business. It’s all about working together and making informed decisions based on the data you gather.
Should You Explain Results to Stakeholders?
Stakeholder management is a big part of what product managers do, and sharing A/B testing results should definitely be included. Let me explain why it matters and how you can present your findings.
Discussing the results helps build trust in the data. Sharing what you’ve learned shows that your decisions are based on facts rather than guesses. This transparency lets everyone see how the outcomes align with the company’s goals.
Now, clearly, the A/B test and its results must tie into something the company is aiming for. Otherwise, you might struggle to get people on board. When you explain the reasoning behind your findings, stakeholders are much more likely to back any changes you want to make.
Let’s not forget the importance of encouraging a culture that values data. Making successful evidence-based decisions strengthens the trust in future experiments.
So, what do I do to present my results? I start by revisiting the original question or hypothesis of my test. In this case, my question was: “Why do users who get to the Sign-Up button not click on it?”
Then, I focus on the actual test: what was the new version that was tested and the results. This is the number that everyone cares about.
I use charts and graphs when I present numbers as they can make complex data easier to digest and highlight the differences between options at a glance.
Alright, this sounds great! But does it help me get a buy-in?
Not without addressing the key point.
One of the most important topics in this discussion is clarifying why the results are significant and how they align with the broader goals of your product strategy. You need to always provide enough context that allows everyone to understand the relevance of your findings. Check out my article on storytelling, which can help you structure this discussion and any future conversations with your stakeholders.
What we do after is just as important as the test results. Whether I’m going with the best choice or setting up more tests, I always make sure I outline this clearly so everyone’s in the loop about what to expect.
How A/B Testing Informs Future Decisions
A/B testing helps us rely more on facts and numbers. When we use data from these tests, it feels more solid and means we’re making choices that reflect what users want.
Over time, we begin to identify user preference trends that can inform our product design and marketing strategies. For instance, in the earlier example where we tested whether “Start Your Free Trial” attracts more users than “Sign Up Free,” it becomes obvious that clearly communicating that users who sign up are getting a free trial resonates more effectively with our audience.
Each test we run, successful or not, teaches us something. If a test doesn’t go as planned, maybe we realized our initial idea wasn’t quite right, and that can tell us to dig deeper into understanding what users want. On the other hand, if a particular change works well, it could open up new ideas for further testing.
One more thing to consider is refining how we set up our tests. Sometimes, if the results are a bit confusing, that might mean we didn’t set things up quite right, like not having enough people in the test or not considering other factors. Fixing these issues will lead to more reliable results in the future.
So, as you gather all these learnings, you’ll get better at designing tests that hit the mark and help you achieve your main goals.
Final Thoughts
A/B testing is more than a tool for optimization. It’s a way to gain deep insights into user behavior, confirm concepts, and minimize uncertainty.
For product managers, these insights are invaluable for guiding both current decisions and the product’s future direction.
And one more thing to remember: A/B test doesn’t always bring you the expected results. But that’s okay; don’t give up on finding the correct hypothesis to work with.