Populating basic user data, such as a first name, into CRM communications is a common way for marketers to personalize the experience in their channels. It’s so common, that most CRM tools, including Braze, Salesforce Marketing Cloud and Emarsys, have built-in functionality making it as easy as clicking a button to populate a user’s name into an email or push notification.
But this technique is no longer effective on its own. If the user is not becoming numb to seeing their name in every message they receive, it’s because they haven’t trusted the senders of those messages with their name in the first place. I recently heard a panelist talk about a user who contacted their customer service because the app had been populating “asshole” into their communications. The greeting “Hey Asshole!” didn’t seem to give this user the intimate and personal experience that the app was intending to give by populating the user’s name.
As we enter the new decade, I propose we turn over a new leaf on personalization. Let’s leave basic user data population in the 2010s and focus our efforts on leveraging the most unique set of data that we have. Let’s put to use the set of intel that differentiates our products from every other product that our customers use: our users’ actions within our own product.
Below I will lay out three personalization techniques that use behavioral data to differentiate product communications and get your users engaged and retained.
Avoid one-size-fits-all messaging
Not all users are the same.
Say it with me. Not all users are the same.
One more time for the people in the back! Not all users are the same.
And thank goodness for that! If we built a product for a single type of user, we could never scale it, grow it, or make any real money off of it. Once we’d have tapped into our market, we’d have had to call it a day and move onto our next project.
Instead, we build products that appeal to several types of users who use it at completely different cadences for a variety of purposes. We continue developing and iterating on our product to expand this audience as far as possible. The further we expand our audience, the more necessary it becomes that we not treat all users the same.
A good way to start is to develop several user communication flows based on a set of qualitative user personas and drop users in and out of these flows based on which persona they currently fit. These personas could be based on frequency of use, feature adoption, or a variety of other behavioral traits.
Take active and inactive users for example. At Babbel, we differentiate between active and inactive users by looking at how recently they have completed a lesson or review. Consider the following campaigns:
Now imagine if we sent the active campaign to inactive users and the inactive campaign to active users:
Though both of these campaigns lead to high levels of user engagement in their intended segment, they are completely irrelevant (and even provide a bad user experience) to a segment for which they are not intended.
The most impactful personalization technique used in these campaigns is not the population of the user’s name, but instead the fact that the user will only receive these campaigns when they fall into the behavioral segment for which they are relevant. Inactive users will not receive the summary of their week, because we do not want to affirm their dormant behavior. Active users will not receive a message asking where they are because we know exactly where they are – they are in our app.
Segmenting users based on in-product behavior provides a personalized experience for each user. By receiving communications relevant to their interactions with the product, users are prompted toward the right level of engagement for their segment.
Punctuality is key
In order to harness the full power of behavioral data, we need to recognize user action for what it is: intent. In the case of Babbel, when a user opens the app, they are showing an intent to learn a new language, even if they don’t go on to complete a lesson. That’s why pairing CRM communications with even the smallest signs of intent can lead to high rates of engagement. Users who are prompted to learn at a moment when they have shown intent go on to learn at a higher rate than those prompted at a time which is not linked to intent.
For example, at Babbel, users receive either a push notification or an email soon after closing the app in the middle of a lesson:
We recognize the act of a user starting a lesson as a sign of intent. Just because they close the app does not mean that their intent has evaporated into thin air. The user is still in a window of intent; they are willing to learn and we need to prompt them to do so at the right time.
But where do we even start with time testing?
We can start with analysis of behavioral data. Without CRM communications, how much time does it take the users who abandon a lesson to come back and complete it? Analyzing the average and median times between these two events, we can determine that targeting the users who had not come back to learn around 30 minutes after they abandon a lesson would be optimal. This delay would allow most users who would convert on their own to do so, while activating several users who would not have converted on their own.
Testing this 30 minute delay against several other time delays validated our hypothesized “window of intent” and allowed us to optimize the timing of the message to maximize the number of users who returned and completed the abandoned lesson.
Use your information wisely
Now, don’t get me wrong. Just because I bashed name population there for a minute doesn’t mean that I don’t think it’s a worthwhile practice. I just believe that name population alone does not offer a personalized experience – it needs to be paired with other techniques.
Consider again the idea of differentiation through personalization. How can we differentiate ourselves from all other apps on a user’s device? Focus on using information that only we have access to.
At Babbel, our language lessons are bundled into sets called courses. One way that we keep users engaged is by guiding them through the courses. Consider these two course-related campaigns:
Though both populate the name of the user, the second is far more compelling because it is personalized with the name of the course and how many lessons the user has left. These data points help the user conceptualize how far they are from their next milestone and reminds them of exactly what they were learning before dropping off.
For an app with a progressive user experience, helping users remember right where they are in their journey is key to giving them the personalized experience that will keep them coming back.
Go forth and personalize
The unique thing about working with a digital product is that we are inundated with information about our users. This advantage often presents us with a distinct challenge: how do we identify which data is best to base our engagement communications strategy off of?
Though everything is worth a test, I would go out on a limb and claim this: if your goal is to engage users in your app and keep them coming back, you need to set your app apart from all other apps on a user’s device. You need to convince that user to spend their time in your app instead of the others. You should use the data that you have exclusive access to. Focus on personalizing user communications with behavioral data.
Encourage users to take specific actions within your app based on that user’s past actions.Target users at the moment when they already intend to engage. Go beyond name population, and instead populate traits of your users which bring real value to their experiences in your product.