As we stressed in the Introduction to User Onboarding, the main ingredient of a great onboarding experience is creating a WOW moment
Note: One of our favorite reads on this topic is David Skok’s on Time to WOW. It influenced both the content of this lesson and how we think about our own WOW moments at Appcues. If you haven’t already, check it out - it’s a solid read.
In this lesson, we’ll give you a three step process to collect the qualitative and quantitative data you need to uncover your product’s WOW moment. Even if you already think you know what your WOW moment is, it’s worth going through these exercises, as the findings may surprise you.
Setting the bar for your WOW or aha moment
So what does a WOW moment feel like? WOW is achieved when a user recognizes, either quite actively or subconsciously, that your product or service is a must have experience that will improve her life. It’s something powerful enough to make users say, “Wow, this is awesome.” And they’re important: when users churn or never fully adopt the product, it’s likely because they never encountered their WOW moment.
Some things to keep in mind about WOW factors:
They’re often not a feature. But rather, what that feature makes possible.
They convey much more value than effort required - (i.e. they are Low Effort, High Value, or LEHV, activities)
They are powerful when communicated with examples or analogies, but even more powerful when the result of real user interaction. Always remember, Do>Show>Tell.
In order to identify your WOW moment, you’ll want to combine qualitative and quantitative data. Here are three things you can start doing right now to find your WOW moment:
Talk to your customers
Collect data from churned users
Identify wins and losses in data
These steps can get a little messy if you don’t have the right process, so it’s important to track your results to ensure you come to the right conclusions. Here’s a sample google doc to help you do that. Note: you may want to customize it to be more applicable to your use.
Talk to your customers
The best place to start is with your customers. Talk to them, and find out what they LOVE about your product or service. Keep in mind that that not every customer is the same: older customers may value your product differently than newer customers, just as power users may derive value in an entirely different way than infrequent users.
So it’s important to track your findings separately based on customer type. In the google doc, we separated it out by new customers and well adopted customers, but there may be a better way for your company (for instance if you segment your user base by the different features they use most, or B2B vs. B2C use cases).
Here’s a short email template to kickstart that conversation:
Hey Jane, Thanks for being a such a great customer! We’re really happy that you love our product, and we’d like to help other customers get the same awesome experience that you have. Would you be open to setting up a 10 minute call this week to help us?
We’re always trying improve our value to new users, and your feedback would be really helpful to us :)
If the customer responds, set up quick call or Skype session. There are four questions you want them to answer:
What do you love most about our product?
What feature(s) do you use most?
When did you decide you wanted to pay?
(New customers) Which competitors were you comparing us against?
After having a handful of these conversations, you’ll recognize patterns in their first run experience and usage of your product. Make sure to document what you hear, as you’ll want to compare it against other conversations and actual data later on. When applicable, it’s nice to ask about which competitors they were looking into as it can be particularly telling about their original motivation for signing up.
Collect feedback from users who churn or never fully adopt
Getting similar feedback from users who never became customers is equally (if not more) important. Did they not convert because your pricing is too high, or your product doesn’t have the functionality they were looking for? Or Perhaps there was too much friction in their first experience, or they were confused on how they could benefit from your product.
Another email template:
Hey Jane, I saw that you signed up for an Appcues free trial last month but never had a chance to publish your first user onboarding flow. It’s cool - I sign up for products all the time that I don’t end up using.
We’re always trying to improve our value to new users. One of the best ways is to get feedback from customers who never fully adopt like you. Would you be open to setting up a 10 minute call this week to help us? I promise I won’t try to sell you anything! In fact, I’d be happy to recommend another product or service that will better suit your needs if you’re still looking for the right solution.
These emails will likely have a low response rate, so you’ll want to use your favorite marketing automation software to automatically send emails to the right people. When you do have conversations with churned customers, there are three things you want to ask:
What were you looking for when signing up for our product?
Why was our product not the right solution?
What could we have done different to keep you around?
With these conversations, you want to know whether the customer ever could have discovered value in your product. Many times, the answer is no; the user was simply looking for something different or a use case you do not yet support (this is a good exercise for prioritizing product development needs as well). When the answer is yes, you goal is to determine what prevented that user from seeing the light.
Identify wins and losses in data
Information from your customers is insightful, but it only paints half the picture. We need to paint the other half ourselves by using real customer data. Every time you speak to a customer (no matter their status), you want to validate what you hear against the user’s data history.
Use an event tracking platform like KISSmetrics or Mixpanel (above) to look into usage patterns for each customer and fill in the rest of the Google doc. On the highest level, you want to answer these questions:
How does each customer’s data differ from what they said in their interview?
Which onboarding steps do my well adopted users share?
Is there just one path to becoming a well adopted user or are there several?
Which onboarding steps are my churned customers not reaching?
With the right data, you should be able to compare the experiences of your well adopted customers with those of your churned customers to identify your assumed WOW moment(s). What are the major differences in usage? Do successful customers activate a specific feature in their initial experience that churned customers do not? How often did they interact with support?
Analyzing the results
Remember that all the analysis you’ve done thus far is on a small subset of your total user base. The final step is to take your assumed aha moment and look beyond only the customers with whom you spoke.
For every user that has ever tried your product, how was your WOW moment experienced (or not experienced, for that matter)? On an aggregate level, how does user behavior change after this activity? What percentage of users get there and how long does it take them? And what is the upgrade rate (or other key metric) for users that do and do not reach this WOW moment?
If all of your qualitative and quantitative data is pointing in the same direction, you have found your WOW moment. Now what you have to do is analyze the steps users go through to reach this WOW moment, and figure out how to get them there quicker. We’ll cover much more on that in future Academy lessons.
But don’t worry if you haven’t found anything conclusive just yet—there are no silver bullets in onboarding. Customers might say they love something without fully understanding why they love it. Look at this as an ongoing experiment to continuously improve the the way users discover and derive benefit from your product. If there’s no clear story in your data, make note of the themes among the feedback and data that you can refine over time with more analysis and testing.