It’s important to note that before rushing into defining your activation metric, you must first focus on the initial Aha! moment.
As mentioned previously, the initial Aha! moment is the magical wow feeling that a user gets when they first realize the value your product brings to the table.
A great example of this is Facebook’s “7 friends in 10 days” rule. Essentially, Facebook’s Growth Team discovered that a user who added 7 friends within their first 10 days using the platform would be way more likely to stick around in the long-term.
That became Facebook’s Aha! moment. Adding friends was intrinsic to the overall Facebook experience, and so users who successfully completed that task were able to see the value.
If you are unsuccessful in instigating this feeling in the user’s first run experience (the first time they interact with your product), then it will be incredibly hard to keep them motivated and go through the journey of activation.
If you are successful, however, in demonstrating the value of your product during that vital first-run, then it will be much easier to push them to activation.
Not only that, but finding the Aha! moment will also help you understand which key features you should be focusing on during the onboarding process, and ultimately this will help you when defining the activation event(s).
For that reason, you must first turn your attention to the initial Aha!.
There are multiple strategies that can help you uncover the Aha! moment. Let’s take a look at two of the most effective techniques.
Ideally, users must experience the initial Aha! moment during their first-ever product run.
Therefore, this would be a pretty good place to start.
A simple way to analyze the first-run is to use tools such as FullStory and Hotjar. These tools can reveal what users are doing and how they are interacting with the product.
For example, you can use FullStory to monitor and analyze the types of actions successful users did during their first-run.
Take a look at a recent segment that is actively using your product, and try to understand how they came to find value in the product.
There’s nothing more powerful than talking to your own successful users to understand their first Aha! moment.
Use in-app surveys, feedback boxes or even email them directly to get the feedback you want.
This will help you understand why successful users find value in your product and methods you can use to drive more users down the same road will present themselves.
Don’t ignore churned users either. Instead, embrace their feedback and find the reason for their failure to find value in your product.
Understanding the initial Aha! moment should now have given you a rough idea on how to approach your first-run onboarding experience. In fact, identifying your Aha! is the first step to mapping the user journey to activation.
It’s very important to distinguish between the two terms, though.
While the Aha! moment is simply an emotional state where a user truly believes in the value of your product, activation is an event where your product actually delivers some tangible value to their business.
Your overall user onboarding strategy must be able to do both. First, deliver that wow moment, and then lay out the path to the activation event(s).
At Userpilot, for example, we have found out that users who install our Chrome extension, create a product experience, and then ‘preview’ it are more likely to experience that first Aha! moment.
You see the difference here?
This is why your first-run onboarding experience should be all about the Aha! as opposed to pushing for activation.
Nevertheless, it’s vital to clearly define an activation metric as the Aha! is just a subjective notion that cannot be measured.
Let’s take a look at a couple of approaches that can help us unfold the activation event(s).
This approach is, by far, the simplest and most basic. Gather your team, and simply choose a major in-app event or set of events that you believe to be a signal that users are actively finding value in the product.
That metric, for Twitter as an example, is when a user sends at least 5 tweets. For a CRM like Drip, it could be as simple as a user sending their first email.
Every business, of course, is different.
You know your product better than anyone else, and you should be able to come up with a logical metric that can be your success indicator.
It’s also useful to constantly conduct customer interviews to try to unveil the paths they take to find value in your solution.
If you’re a bit more technical, and you trust data more than common sense, then this is for you.
This approach relies on tracking every single major event that happens inside your product to uncover which set of actions were the turning point in the user journey to adoption.
The turning point here would be defined as the point where the user is actively and successfully using your solution to solve problems.
Get the help you need from your engineering team, and make sure that key in-app events are being tracked and passed to an analytics software such as Mixpanel or Amplitude.
Once that’s done, you can run regression analysis to find a correlation between certain key in-app events and successful users.
The best way to do this is to take a segment of your users that have signed up recently to the product and are finding great success using it. Then, run a simple correlation test to see which features or behavioral events are most common among that group.
This type of analysis can be a key indicator to which set of actions your activation should include. You just need to make sure that there’s enough data points for this to qualify as significant.
It’s well worth noting that many products tend to attract various personas, and therefore it’s entirely logical in some cases to have more than one activation metric.
For example, a collaboration tool such as Slack is likely to attract a wider variety of users, as opposed to niche tools such as InVision or Sketch App.
If that’s the case, then you should define an activation event for each persona separately. This will help you adjust the user onboarding experience to account for the different use cases.
Let’s say the product team decides to study 3 or 4 key features. Then, they might find out that 2 features, let’s call them A and B, are highly correlated in a certain order. Event A leads to event B:
A → B
It would only make sense to push users to activate event B after event A has already been activated.
Hubspot is a real-life example of a company that has clearly done their research on their product adoption journey.
Let’s take a look at how they push existing user to activate the templates feature.
Hubspot only pushes their templates feature after a user tries to copy and paste text into an email body.
Their formula is basically: whenever a user has yet to try the templates feature (event B) but tries to copy and paste something into an email body (event A), they will push the template feature. This tip keeps coming, until you create at least one template.
Now imagine if they had tried to push this template feature at a random time, or when a user first signs up? It would be absolutely meaningless.
At the end of this lesson, it’s critical to emphasize the importance of tracking and analyzing the user journey properly.
Being able to monitor and understand user behavior inside the product will allow you to efficiently establish your onboarding process.
You will be able to map the path to activation, and understand how each secondary feature should be adopted. Ultimately, this will be the key in building the right onboarding experience.
If you truly understand what an ideal journey should look like, then the onboarding will simply pave the way for it.
Not only that, but it will also allow you to detect where the dropout from the ideal path is occuring.
By now, you should have a clear understanding of your product’s first Aha! moment. Additionally, you should be able to clearly define the activation event(s) and have a clear map of how and when secondary features are adopted.
Having this clear map of the user journey will help you track how successful your onboarding strategy is.
Most importantly, however, it will give you the chance to see where users fall short. This will be the subject of our next lesson.