Although product management is a broad discipline, and the exact knowledge needed to do the job well varies depending on a particular context, there are a few concepts that every product manager should know.
One of them is understanding aha moments.
In this guide, we’ll explain what we mean by aha moments, how to identify them, and how to use them to build better products.
In the context of digital product management, an aha moment is a pivotal point in a user’s experience when they truly understand the value and potential of a product. This moment of clarity or realization often results in increased engagement, loyalty, and satisfaction with the product.
In other words, the meaning of aha moment is when the value of your product first dawns on the user.
Although it’s rarely a singular moment in their user journey, the aha moment provides a good barometer for what situations and milestones are most impactful to users’ long-term behavior. By identifying and understanding these moments, we can design our products and map the user journey to maximize our users’ exposure to these moments — which, in turn, means more value delivered, higher retention, and satisfaction.
It’s rare for a product to have a singular aha moment. Not only are there often multiple aha moments, but there might also be different types of those situations depending on the user segment, the stage in their journey, and so on.
I’ve identified three high-level types of aha moments:
We talk about retention aha moments when going through a particular action significantly reduces user churn. A retention aha moment is when the user experiences and realizes the benefit of sticking around and continuing to use your product.
When you hear “aha moment” out of any context, it most likely refers to retention aha moments.
A virality aha moment happens when the user feels a strong urge to invite friends to try out the product.
It might be to cash in on a bragging right, to redeem some benefit from inviting friends, or improve the experience of using the app by inviting friends to join in.
Examples of virality aha moments:
When users feel a strong inner need to convert to paying/more premium users, we talk about conversion aha moments.
A conversion aha moment might occur either when the user tires of limitations associated with their current plan or itches to unlock the benefits of higher-tier plans.
Examples of conversion aha moments:
I’ll be honest; identifying true aha moments is hard. It might take months to discover a truly reliable aha moment in your product analytics. But the search is worth it.
The simplified process of searching for aha moments looks more or less like this:
Instrumentation is the key. The more detailed data you get from your product, the higher the chance you will identify an aha moment.
If you collect only basic data such as “logged in,” “converted,” and “deleted an account,” you won’t get much insight. Truly reliable aha moments come from statistically significant quantitative data rather than qualitative research.
One tool that can help you gather valuable data is LogRocket. LogRocket is a frontend performance monitoring and session replay tool that captures detailed user interactions within your digital product.
Using tools like LogRocket, you can gain insights into user behavior, identify patterns, and pinpoint potential aha moments. This comprehensive view of user interactions can help you focus on the aspects of your product that create the most impact and drive user satisfaction.
There’s a chance you’ll discover an aha moment by accident. To maximize this chance, you need to be proactive.
Use qualitative insights to set up some hypotheses. What could a potential aha moment be? Come up with a few well-informed guesses.
For example, a hypothesis could be, “The more people who use feature X, the higher the retention rate.”
Use the data you have to validate your assumptions.
Remember, you are looking for big spikes. If your hypothesis was “the more people use feature X, the higher their retention,” I can already tell you, it’s true. But obvious insights like this don’t mean much. More engaged users tend to retain more and use features more — that’s a fact of life. You’re looking not for mere patterns, but for significant peaks that deviate from expected trends.
Back to our example, if you believe using feature X might be an aha moment, try various analytical approaches to confirm that. In this case, segmentation should do just fine.
Let’s say you segmented the usage by units of five, and you got the following results:
There’s no aha moment. Yes, retention grows over time, but as already mentioned, that’s a natural phenomenon. More engaged users lead to higher retention. The only actionable insight here is “engage people more so they’ll retain more.” Not a breakthrough, is it?
But let’s say it looks more like this:
That’s something different. The difference between 6–10 and 11–15 users is just too big to ignore. There’s a high chance that around the 10th use, people really begin to understand the value of your product.
After you spot a possible aha moment, you want to confirm two things: impact and causation.
Let’s start with impact. You want to make sure the aha moment is genuinely relevant. To do so, ask yourself two questions:
For example, even if the aha moment can boost your retention by 300 percent, is it relevant if it applies only to 5 percent of your total user base? What impact on the whole product would it make?
Maximizing the potential of your aha moments is often a time-consuming process. If pushing 100 percent of the relevant user segment through the aha moment would give you a 5 percent boost to your overall retention, it’s not that great an opportunity after all.
The second step is to confirm the causation. The fact that something is strongly correlated doesn’t mean there’s causation.
For example, users that use feature X 10 times or more might have a way higher retention, but is it because of feature X? Maybe these users also use feature Y a lot — is that feature the actual driver? It could also be a mere coincidence.
To confirm the causation, you could:
The exact nitty gritty of differentiating causation from correlation is out of the scope of this article, and if you are not gifted in statistical analysis, you might need a help of a skilled data analyst.
Even though, please remember that correlation doesn’t necessarily mean causation, and you should prove causation before doubling down on an aha moment.
Knowing what your aha moments are is only the first step. It’s nothing more than a fun fact if you don’t take action on it. You should use aha moments to guide your product roadmap and build the product around these experiences.
There are three common ways to do this:
After you identify key aha moments that impact users’ retention, virality, and conversion, try to expose as many users to those moments as possible.
If using feature X five times or more triples user retention, it could mean great things for your product — but not if only 3 percent of your user base experiences that situation. You want to gently nudge and guide users through these moments to ensure as many people as possible experience them.
Assuming your main aha moment is “using feature X five times or more,” you could:
That said, the more you push your users to try feature X, the weaker the aha moment will become. Forcefully pushing all your users to test the feature won’t suddenly make all those users stick. But if you’re careful not to overdo it, the outcome should still be positive.
After experiencing an aha moment, your users’ motivation and enjoyment are at their peak. That’s when they are most likely to take the action you want them to take.
For example, if experiencing situation Y is an aha moment, reach out to users right after they go through this aha moment and:
Sometimes, the possible aha moment exposure relies more on the product than users’ behavior.
For example, say you’re building a product that helps people book a handyperson for emergencies. Apart from the normal search, you also have an “instant match” feature: if the search criteria of the user match availability of a particular handyperson, instead of showing search results, you show them a prompt to book the service in a one-click manner and get the service within 30 minutes. You also proved that people who experience that tend to retain 400 percent more.
In such a case, you should ensure that most of your search results are instantly matched. It should become one of your main KPIs.
If only 20 percent of searches result in instant-match, plan your product roadmap to boost this number as much as possible. It could include:
Once you spot an aha moment you can improve, don’t think twice; double down on it.
Identifying and doubling down on your aha moments is one of the best investments you can make. Rather than shipping random features, you can double down on already-proven key experiences in your product that drives the outcomes you wish.
Aha moments can also help you narrow down your focus from vague “improve retention/conversion/virality” to more tangible and actionable objectives, such as “increase the usage of feature X to drive retention.”
Although identifying truly relevant aha moments is a challenging and long journey, it’s worth the effort.
Featured image source: IconScout
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With LogRocket, you can understand the scope of the issues affecting your product and prioritize the changes that need to be made. LogRocket simplifies workflows by allowing Engineering, Product, UX, and Design teams to work from the same data as you, eliminating any confusion about what needs to be done.
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