Learn how to resurrect your inactive users by comparing their behavioral personas to those of new and current users.
At this point, you’ve studied both your current users (Chapter 5) and new users (Chapter 6). You’ve identified some important user behaviors that drive people through these stages, as well as found your core and power personas. Now we’re down to the last stage of the Retention Lifecycle: resurrected users. In this chapter, you’ll apply many of the methods you’ve already learned, as well as compare the behavior of your resurrected users with that of current and new users.
A resurrected user is someone who has returned to your product after being inactive, or dormant, for a period of time. Resurrected users are often overlooked when people discuss retention strategies, but they can offer a lot of potential for improving your overall retention and active user count.
Think about it this way: all of the users who are dormant in your product have the potential to be resurrected. If you’re like most companies, that’s a pretty big pool of users.
In addition, you’ve already acquired these users—that means you have a better chance of re-engaging them than you do convincing a brand new prospect to try your product. Often, you can spend fewer resources (whether that’s ad dollars or your team’s time) resurrecting users than trying to acquire brand new ones.
Resurrecting your inactive users can help you flatten the retention curve and increase your baseline of active users (and in some cases even inflect the curve upwards, like in Evernote’s ‘smile graph’)
TERMS TO KNOW:
A resurrected user is someone who has returned to your product after being inactive, or dormant, for a period of time. Specifically, we’re defining a resurrected user as someone who is active in the current period, was not active in the previous period, and was active at some point before that.
For example, if you determined that you have a monthly usage interval based on the usage interval calculation in Chapter 2, your resurrected users would be those who were active in the current month, not active in the previous month, and active at any point in time before the previous month.
Think about the experience for a resurrected user—when someone returns to your product after a period of inactivity, what do they see? If it’s an empty state and there’s not much for them to interact with, they might just close the app and never come back.
Here we see 2 examples of empty states. In the screenshot on the left, we see that we have no messages—but there’s nothing else to interact with on the screen. On the other hand, the screenshot on the right shows a travel app. The user has no ‘Recents and favourites’ yet, but the screen encourages them to start with a prominent call to action to ‘Plan a journey’, and even offers a relevant promotion. Think about any empty state as an opportunity to engage users and get them to perform an action you care about. For example, the app on the left could improve this screen with a call to action to send a message to one of your friends.
Take another example from mobile gaming—Words with Friends is a popular game by Zynga in which people play a Scrabble-esque game against their friends in real time. Users who have been inactive on Words with Friends for a prolonged period of time don’t have any active games going on with their friends, which means that if they come back to the app, they don’t have anything to immediately engage with.
The Words with Friends team realized that due to this poor user experience, most resurrected users did not reengage well and didn’t come back to the app. They decided to send push notifications to the person’s friends, encouraging them to invite that user to start a new game. That way, when a user returned to the app, they had game invites waiting for them and were much more likely to start playing again.
Providing a rich experience for resurrected users is an important way to encourage them to reengage and hopefully become current users. In this chapter, we’ll go through different analyses that will help you identify ways to resurrect more users and improve their retention.
The overall goals of resurrected user retention analysis is to learn how you can:
You also want to determine whether resurrected users are a good potential source of growth for your product.
As we go through resurrected user retention analysis, we’ll be answering the following questions. Keep these in mind as you work through this chapter and do your own analysis:
Remember, a resurrected user is someone who is active in the current period, was not active in the previous period, and was active at some point before that.
Just like you did for new and current users, take your resurrected user cohort that you created in Section 3.3 and plot your baseline retention for resurrected users.
It’s helpful to look at longer-term effects, at least 1-2x your product’s usage interval out. Here’s the retention curve of resurrected users for one of our customers, an on-demand delivery company.
You should also compare this with the retention curves of your current users and new users. This will give you a sense of how your resurrected users currently perform relative to these other two groups.
Example: Resurrected users and events
In the chart, we’ve added the retention curves of current and new users for the on-demand delivery company. While resurrected users don’t retain as well as current users, they do retain better than new users during the same time period.
Remember, you should also look at retention for your critical event, not just for “active” users who may not be doing anything valuable in your product. The second chart shows retention during the same time period, but with the company’s critical event, ‘Checkout,’ set as the returning event.
You can see that resurrected users have significantly better rates than new users during the same time period of returning and placing an order, even many weeks after they initially resurrect.
In this case, resurrected users already retain and place orders at much better rates than new users. This indicates that resurrecting users could be a good source for gaining more current users and increasing revenue.
Here’s a different situation for a product where the retention for resurrected users is very low, even lower than for new users. This could mean that the product isn’t doing a good job of showing value to resurrected users, so people are not reengaging and end up just leaving the app.
The good news is that this product has strong current user retention. For a situation like this, we recommend looking at what it would take to increase resurrected user retention, and weighing that effort against the potential benefit. For smaller teams with limited time and resources, it might make more sense to focus on improving new user retention in the short term.
Now that you have your baseline resurrected user retention and know how these users retain relative to current and new users, it’s time to assess whether resurrected users might be a good source of growth for your product. Two calculations that will help you determine the opportunity size are:
Calculate the breakdown of your active users during the current time period you’re measuring. In Amplitude, you can do this using the Lifecycle feature (see Section 3.3). Here’s a hypothetical example of active users during a week:
|User Type||# of Users||% of Total Active|
|Total Active Users||590,084||100%|
The first thing to note is that 73% of the active users during this week are current users—people who have been using the app with some consistency. That’s great—it means this product has a healthy base of users, and isn’t just pouring on new users who quickly churn.
Notice that resurrected users actually make up 14% of the active users, which is about equal to the number of new users during that week. This indicates that the company is already successfully resurrecting users (whether organically or with targeted marketing efforts), and increasing their efforts here could have a positive impact on overall retention.
Next, let’s look at the size of the potential pool of resurrected users. Anyone who has used your product in the past but has not used it in the current period of analysis is a potential resurrected user. A practical way to assess the size of this opportunity is to calculate the number of people who used your product sometime in the preceding 6 months, but have not used it in the current period. Depending on the type of product you have, your usage interval, and any seasonality of your product, you may want to look at a period that’s longer or shorter than 6 months, but it’s a good place to start.
In Amplitude, you can calculate this group of users with a behavioral cohort definition. If you’re looking at the current time period of the week of July 3 – 9, you would create a cohort of users who were active at any time in the last 6 months, but were not active in the current week:
Going back to our example, this product has 1.3 million potential resurrected users. Think about this from an acquisition perspective. This company has 1.3 million people who have downloaded their app in the past 6 months, but are currently inactive. These are people they can try to reengage with a well-timed push notification or email, and are much easier to reach than all of the potential new users that they’re spending money to acquire.
|User Type||# of Users||% of Total Active|
|Total Active Users||590,084||100%|
|Potential Resurrected Users||1,305,242||–|
Just like you did for new and current users, you should examine behavioral personas of your resurrected users. Understanding these patterns of behavior can show you why users might be returning, or what may have triggered their resurrection. For a refresher on behavioral personas, see Chapter 4.
Example: Resurrected personas for on-demand delivery
An on-demand delivery used Amplitude Personas to find clusters of users within their resurrected user cohort. They identified a few interesting personas, listed in the table.
|Persona||Description||2 Month Retention||% Resurrected Users|
|People who know what they want||These people did not have many events related to browsing different vendors or items, but instead found exactly what they want and placed an order||99%||11%|
|Browsers who order||These people did place an order eventually, but did a lot of browsing of different vendors before making their decision||97%||9%|
|Just browsers||Like the previous persona, these people also did a lot of browsing, but ended up not completing an order||75%||21%|
|Discount redeemers||This group of people all did an event called ‘redeem discount’. This was a discount emailed to a subset of users for a few dollars off a delivery||90%||5%|
|Open and leave||People who just opened the app||35%||37%|
The first 2 personas are especially encouraging—people in these personas are placing an order when they return to the app, plus almost all of them are retained 2 months later. These personas, who together make up 20% of resurrected users, are using the product as expected when they return.
The other 2 personas, ‘Just browsers’ and ‘Discount redeemers’, provide some interesting information.
The ‘Just browsers’ persona contains a lot of users, making up 21% of the resurrected user cohort. These people exhibit browsing behavior on a similar level to ‘Browsers who order’, but then ultimately don’t complete an order that day. As a group, they have pretty high 2 month retention at 74.52%, showing that even though they don’t complete an order on that day, there’s a high likelihood they’ll come back later.
Improving the browsing experience could be an effective way to boost resurrected user retention, since 21% of resurrected users are browsing but ultimately not ordering. If the company could get more ‘Just browsers’ to become ‘Browsers who order’, not only would they get more revenue from orders on the day they resurrect, but this data shows that ‘Browsers who order’ have much higher long-term retention as well (97% 2 month retention).
The ‘Discount redeemers’ Persona showed several events related to redeeming a discount on their next order, which was sent to them via email.
Any time you’re looking at the impact of discounts, you need to measure how well they incentivize users to place an order not only in the short-term, but as a repeat customer moving forward. We’ll look at that more in the next section.
In the book “Hooked,” author Nir Eyal talks about two types of triggers: external and internal.
External triggers are things like push notifications, emails, or ads that we use to get users’ or potential users’ attention. Many mobile apps use push notifications to encourage users to come back to their app.
Internal triggers, on the other hand, happen in a person’s mind. According to Nir, an internal trigger occurs when “a product is tightly coupled with a thought, an emotion, or a pre-existing habit.” For example, we open Facebook when we’re feeling bored or lonely—the impulse to open Facebook is cued by emotions.
The best habit-forming products start out with external triggers to initially attract and educate the user, but over time users no longer need the external triggers to keep using the product, relying instead on internal ones.
The next step is to determine any measurable triggers of resurrection. These may be push notifications or emails that you send to your users—for example, if you already have some reengagement campaigns targeted at users who have been inactive for some amount of time. If your product has a social component, these notifications could be based on actions from users’ friends or networks (like the notification you get when someone mentions you on Twitter).
Your product might also have triggers that coincide with outside factors like holidays, sporting events, or weather. For example, you might notice more users placing orders with an on-demand app during a week of heavy snow, when people are less inclined to go outside to run the errands they normally would. It’s hard to confirm these factors in your data, but anytime you notice spikes or dips in usage, don’t forget to think about these outside influences on your users.
For resurrected users, we want to identify any external triggers that might have brought them back to the product. Once we identify them, we can measure how effective they are at reengaging users and think of ways to improve.
Once you identify potential triggers, compare downstream metrics like the critical funnel conversion rates and long-term retention for users who receive these triggers. This will allow you to measure whether they’re having the intended effect.
Tracking messaging data (push notifications and emails) and attribution data in your product analytics platform enables you to measure the impact of these campaigns on later in-product behavior.
We recommend sending messaging and attribution data to your product analytics so that you can get the full picture of user behavior.
If you’re using Amplitude, we partner with best-in-class providers across messaging and attribution so that you can easily integrate different data sources into Amplitude.
Example: The impact of discount email offers
In the previous section, we discussed the ‘Discount redeemer’ persona for the on-demand company. These discounts are sent via email to a subset of users who have been inactive (not completed an order) for some amount of time. We created a behavioral cohort of resurrected users who had received the special offer, and found that 22% of all resurrected users had received a discount offer.
When we compared the critical funnel conversion rates for resurrected users who received the special offer, versus those who did not, we found a huge difference—94.5% of resurrected users who received a special offer completed an order, compared to only 26.5% for the rest of the resurrected users.
Basically, almost every single user who came back to the app after receiving a discount completed an order. The team also looked at the conversion rate from receiving a discount to launching the app, and found that 58% of users who receive a discount offer go on to launch the app within 7 days.
Now that the company knew that the special offer was effective at getting people to come back and place an order, they wanted to look at the long-term impact. Are users just coming back the one time with the special offer, or do they keep placing orders over time?
Looking at monthly retention moving forward, where the returning event is to ‘Complete order’, you can see that people who got the special offer retain at significantly better rates than those who did not — even many months later.
Clearly, the discount program has a significant long-term effect on increasing purchases. The on-demand company decided to try expanding this program to more of their dormant users to encourage resurrection.
It might be tempting to blast your dormant users with notifications or emails, but chances are these will not be effective and will only serve to annoy your users, causing them to unsubscribe or worse, stop using your product forever.
Remember that external triggers, like push notifications, need to be well timed with a user’s internal triggers and existing behavior. Notifications work best when they redirect existing emotions or behaviors to your product. They’re even better when you can personalize them based on something you know about the user—whether it’s preferences they’ve set or prior actions they’ve taken.
Once you identify some triggers and behavioral personas, go back and compare these to your current and new users. We recommend looking at long-term retention and critical funnel conversion rates. You can also measure revenue and use any other methods from the Product Analysis Toolkit in Chapter 4 to help you assess how valuable current users are to focus on relative to other groups.
Example: Compare long-term retention & critical funnel conversion rates
Continuing the on-demand company example from the previous sections, the company split resurrected users into 2 main personas: Organic (did not receive any email reactivation campaign) and Non-Organic (did receive a reactivation campaign). Then, they compared the retention of these two resurrected user personas to current and new user retention.
Here is the weekly retention chart, where the returning event is placing an order (the critical event). Organic resurrected users have retention that is about 65% higher than for new users, while non-organic resurrected users retain far better than new users—they have 260% greater retention. These retention impacts are also long-term, extending 24 weeks out.
Comparing the critical funnel conversion rate for resurrected users compared to new and current users will show you whether resurrected users are currently helping your business goals. You can also identify any critical drop-off points for resurrected users, and see what dropped-off users do instead of converting.
Here’s the critical funnel, comparing the same user groups during the current period. The funnel shows the conversion rate from opening the app to completing an order. Non-organic resurrected users have the highest conversion rate, at 94.6%. Organic resurrected users have a slightly lower conversion rate than new users: 28.2% compared to 33.2%.
For this company, reactivation email campaigns not only increase conversions during the current period, they also have a significant positive impact on retention and repeat orders over at least the next 24 weeks. This is a really good indication that their campaigns are working, and they should try sending campaigns to more of their dormant users.
In addition, this data shows that resurrected users as a whole have higher conversion rates and place more orders in the long-term than new users, especially non-organic resurrected users.
Remember, your critical event is the user action that represents that a user is actually using your product and getting value out of it (e.g. completing a game, placing an order, playing a song). When analyzing your resurrected users, make sure you investigate how much they perform your critical event, not just whether they return to the product.
Comparing critical events and event properties helps you investigate whether resurrected users have different behavioral patterns or perform these events at a different frequency than current users. This can help you determine whether:
One way to compare critical event engagement between different user cohorts is to measure the percentage of users in each cohort that did the event. Remember to also graph any significant personas of resurrected users that you’ve identified.
Example: Critical events for a lifestyle product
Our lifestyle customer’s critical event is booking an appointment. In the chart below, they looked at this critical event as a percentage of active users in each cohort. In other words, the graph is showing what percentage of each cohort (current, new, or resurrected) that booked an appointment on each day.
As you can see, resurrected users have a lower percentage of their users booking an appointment when compared to new and current users.
Another way to look at engagement is the average number of times a user does the event. In the next graph, we see a similar pattern, that resurrected users on average book fewer classes than new and current users.
While the number of bookings that users make is an important metric for this company, the end goal is revenue — how much users actually spend. So, they looked at ‘cart total price’, which is an event property for their booking event.
Graphing the average cart total price for each group of users, they found that resurrected users are actually spending more than new users on average for each transaction.
Even though the total number of booking is less for resurrected users, they spend more per transaction and there is a large pool of potential resurrected users for this company. We recommend dedicating some time and resources to resurrect users.
In addition to looking at critical event patterns and event properties, you can look at stickiness and session metrics to get at other aspects of resurrected user engagement. We covered these metrics in Chapter 4, so feel free to go back and review those sections for more details on how to measure them.
Stickiness and session metrics are another way to compare the engagement of your resurrected users to the behavior of current users. By uncovering any differences, you can form hypotheses about why resurrected users are different and find ways to get resurrected users to behave more like current ones.
If session length is a good indicator for engagement for your product, try graphing the distribution of session lengths for resurrected users and compare that to the usage of current users. Similar distributions will indicate that resurrected users behave similarly to current users, so it should be easier to get them to become current users in the long-term.
Example: Session length as an indicator for engagement
For the mindfulness app that we’ve discussed, session length is a good indicator for engagement. The more time a user spends in the app, the more value they’re getting from the product.
Looking at the average session length, the team found that current users on average have longer sessions that resurrected users.
When looking at the distribution of session lengths, they found that resurrected users have a much higher percentage of sessions that are only less than 30 seconds long. For this product, there’s not much a user can accomplish in less than 30 seconds, so we can assume those users aren’t really using the app during that time.
Current users also have a much higher proportion of sessions that last 10 minutes or longer.
This data shows that current users spend more time in the app and have longer, likely more meaningful sessions than resurrected users. If this company wants to reengage resurrected users, they likely need to improve the resurrected user experience in order to encourage them to behave more like current users.
Of course, the bottom line for most businesses is revenue. Comparing the revenue for resurrected users (and any important personas) relative to current and new users will help you decide whether resurrecting users is a worthwhile pursuit for your team.
As we mentioned before, it will likely cost you far less to resurrect a user than to acquire a new one. So, by comparing the potential monetization of resurrected users compared to new, you can determine the relative ROI and decide how you want to spend your resources.
We recommend comparing revenue metrics like:
In the graph to the right of ARPU, resurrected users spend more per user than new users during the same timeframe, and spend about the same amount as current users.
The ARPPU values are closer together, but still shows that per paying user, resurrected users are spending more than new users and are spending similar amounts to current users.
Remember, the overall goal of resurrected user retention analysis is to learn how you can “resurrect” or reactivate dormant users and get them to become current users of your product. You also want to get a sense of the potential value of resurrected users and whether you should spend your efforts resurrecting more users, especially as compared to your resources spent acquiring new ones.
Although not always the case, we’ve seen that for several of our companies, resurrected users convert and retain better than new users, as well as contribute more revenue per user. And since you’ve already acquired those users, the cost to resurrect a user through a push notification, email, or special offer is likely to be less than the cost of acquiring a new user.
You’ll need to do your own analysis to make sure this is true for your business, but it’s certainly worth investigating as an (often overlooked) source of growth.
You can use the Resurrected User Retention worksheet at the end of this chapter to take notes and keep organized.
Keep the goals of resurrected user retention in mind as you form your metrics:
A resurrected user is someone who is active in the current period, was not active in the previous period, and was active at some point before that.
By analyzing your resurrected user retention, you will learn how you can:
Run through the metrics below to get a baseline understanding of your resurrected users. Refer back to Ch. 4 for a refresher on any of these methods.
Compare the retention of resurrected users to that of your current and new users. This will show you how your resurrected users currently perform relative to these other two groups and how much effort you want to devote to resurrecting users
Answer these 2 questions to get a sense of whether resurrected users can be a good source for boosting overall retention for your product.
Identify any behavioral personas within your resurrected users and list them here.
Remember, you could have internal or external triggers of resurrection (Section 7.3). Here are a few ways to identify triggers:
Ask yourself these questions as you form hypotheses and come up with experiment ideas.
As you start testing some of your hypotheses and trying out ways to improve your resurrected user retention, it’s important to keep track of your metrics to see what is and isn’t working.
Keep the goals of resurrected user retention in mind as you form your metrics:
We suggest tracking these metrics over time to measure your progress: