Maximize User Retention

Reactivate dormant users with personalized messaging


In this Project, you'll learn how to define and re-engage dormant users. Create Audiences of dormant users and bring them back to your app using push, email, and remarketing campaigns.

Why it matters

On average 58% of app users churn in the first 30 days after downloading an app. Over the first three months, 75% of app users will churn. App marketing tactics like push notifications, email marketing, and remarketing are critical to reengaging users with relevant, valuable content. You can use these tools to remind high-risk users why they downloaded your app in the first place, and to re-surface new and other useful content.

Think about re-engagement as a cascading funnel of tactics starting with push, following up with email, and tying it together with re-marketing. Approaching reengagement this way provides a variety of channels for users to engage with your offers, while minimizing cost. Executing these marketing campaigns with personalized messaging will increase your odds of having a successful re-engagement.

How-to guide

The following steps will walk you through how to strategically combine engagement channels to effectively re-engage dormant users.

Step 1: Define “dormant users”

The first step in re-engaging users is to define who you consider “dormant” or “inactive.” How you define dormant or inactive users depends largely on your business and what metrics you track internally; businesses that run on monthly subscriptions often label users dormant after 30 days of inactivity, whereas a weekly newspaper business may label users inactive after just seven days.

While defining inactive users ultimately differs business by business, many of our customers define dormant users in one of two ways:

  • 30 days of inactivity. This is often a good definition for users who have been onboarded and become a regular user of your app. If someone goes from using your app a few times a week to less than once a month, it’s time to re-engage.

  • 7 days of inactivity. For users who haven’t completed onboarding or are new to your app, you should re-engage them sooner. Our recent research suggests a strong negative correlation between the number of app Sessions (a basic unit of measurement for apps) and user churn. In other words, the more Sessions app users complete in the first 30 days after downloading an app, the less likely they are to become inactive, so get those push campaigns going!

Step 2: Create an Audience

Once you’ve defined dormancy, create an Audience of these users to apply to all your cross-channel marketing campaigns. Learn how to create an Audience here.

Step 3: Create a push campaign

Push messaging should be a go-to tool for prompting conversions as it surfaces up different offers, content and campaigns to the user. Consider offering one free week of an all-access subscription. Give users a taste of what access to the full app experience would be like - for free! They’ll be interested in the special (free) offer and might find more value out of complete access.

Step 4: Follow up with email

Use email marketing campaigns based on in-app user behavior and data to engage outside of the app and create a more holistic experience. Consider offering 25% off their next purchase on their favorite category of product. Use Profile level data to personalize your message. Consider using such Profile attributes like a user's first name, their favorite category of product, or any other appropriate auto or custom Profile attribute. Not only are you offering your users a discounted item, but you are doing so in a more personal manner, tailoring your message on an individual level. To insert Profile data in an email message, see the graphic below.

Step 5: Bring it home with Remarketing

Enlist Remarketing ads to reach high-risk users on the web or via social networks like Facebook. Remarketing is a great way to target users who have turned off push permissions or even uninstalled your app. Target users in the original audience who didn’t convert on either of the previous two offers. Consider offering one free month of the subscription service. Here’s where you pull out the big guns; Remarketing is the most costly of these marketing tactics, so we advise only spending the additional money to target “holdouts” with one big offer - a free month of your subscription service.

Step 6: Measure & optimize

As with all engagement campaigns, test, test, test! Measure the success of your engagement campaigns using control groups and A/B testing. Optimize based on these results to run even more successful omni-channel re-engagement campaigns.

Key Takeaways

  • To create the best relationships with your users, create cohesive, relevant and interesting cross-channel re-engagement experiences.
  • Minimize cost by creating a drip campaign moving users through a funnel of push, email, and re-marketing campaigns.

Discover what causes users to churn with Predictions


In this Project, you’ll learn how to forecast the likelihood of user churn and how to proactively communicate with those users to keep them engaged before they churn.

Why it matters

No matter the app size, category, or business model, retaining app users is a big problem...but also an opportunity. The Apple App and Google Play Stores each offer over 1.5 million apps vying for the same audiences. These stores are buyer’s markets filled with products that often have high substitutability. Just search for “photo editor” or “subway maps” and brace yourself for literally hundreds of options. The market for app downloads is additionally characterized by cut-rate switching costs -- free-to-download apps and ever-decreasing download times are among the most prominent drivers of shrinking barriers to install.

It goes without saying that user churn is a major concern. And even with a robust, data-driven marketing solution, churn can be a particularly tricky problem to diagnose and treat because it trumps the logic of descriptive analytics: once you’ve observed users to churn, it’s already too late to save them. Wouldn't it be nice to have the ability to forecast which users are likely to churn and preemptively reengage them before they do?

How-to guide

The following steps will walk you through creating and analyzing predictive insights to identify users at risk of churn and their associated behaviors and attributes.

Step 1: Create a new Prediction

Navigate to Predictions under the 'User Insights' section of your dashboard. Once there, click on the green 'plus' icon to bring up the "Create a New Prediction" module. There are several levers at your disposal to define the behavior you want to predict:

  • Churn or Conversion
  • If Churn, not performing any Events OR a subset of Events
  • If Churn, the number of consecutive days that the Events will not be performed
  • The specific Events you want to associate with churn or conversion

The first option you have in defining a new Prediction is deciding if it will be a churn or conversion Prediction. Generally churn Predictions will forecast whether users will not behave in some way while conversion Predictions will forecast whether users will behave in some way. New Predictions are set to be churn Predictions by default. To alter this field, simply click the dropdown and select 'churn' or 'conversion' to specify the Prediction type you want to create.

Next, you'll want to define what Event(s) to associate with the new churn or conversion Prediction you're creating. For churn Predictions, click the dropdown and select whether you want churn to be defined as not performing either:

  • Any Events - to forecast the likelihood that users will not perform any Events at all
  • The following Events - to forecast the likelihood that users will not perform specific Events that you select

For both churn and conversion Predictions, specific Events can be added or removed to detail the behavior you want to forecast for your users. In addition, AND/OR operators can be uniformly applied across multiple Events in a Prediction.

For conversion Predictions only, you may only forecast whether users will perform specific Events.

For churn Predictions only, you can define the number of consecutive days that the Event(s) will not occur in order for a user to churn. You can do this by changing the number in the 'consecutive days' field. The number of consecutive days for churn Predictions can range from 7 to 90 days.

Step 2: Name & save a Prediction

We recommend naming your Prediction by describing the type of user behavior you intend to forecast. This will help avoid confusion later on, and will allow you and your team to more easily interpret and use new Predictions as more are created. For example, a good name for the churn Prediction in the screenshot in Step 1 above is "Churn - no Events in 30 days."

To save your new Prediction, simply click 'Save & Close'. Once you save your Prediction, you will return to the main Predictions screen in the Dashboard, where a card for your newest Prediction will appear with the disclaimer:

All new Predictions will first be generated at midnight ET of the day it was created. The Prediction will subsequently be updated weekly from the time that it was first created. For example, if you create a new Prediction on Tuesday at 2pm ET, it will first become available on Wednesday at 12am ET, and subsequently be updated weekly every Tuesday at 2pm ET.

We are frequently asked why we update Predictions weekly. The predictive modeling techniques we use are carefully tuned to not overreact to new user data being generated. We make forecasts based on an analysis of longer-term trends. As such, the incremental data required to move users from one predictive segment to another is generated over the course of days.

Step 3: Interpret the Predictions dashboard

Once a new Prediction is generated it will appear in the Predictions section of the Dashboard as a Predictions summary card. Each Prediction summary card will include four key pieces of information:

  • Name and type (Churn or Conversion) of the Prediction
  • Number of users in the high churn or low conversion likelihood segment
  • Top Related Behavior
  • Top Related User Attribute

Click on the Prediction summary card to explore detailed insights: Prediction 'Type', 'Criteria Definition', 'Next Update', and 'Baseline Churn Rate'.

Likelihoods: total 'Number of Users' and 'Percentage (%) of Active Users' that are predicted to have a High, Medium, and Low likelihood of churn or conversion.

Related Behaviors: Related Behaviors are the usage patterns that serve as lead indicators of future behavior like churn or conversion. Facebook’s former head of growth discussed how important it was for them to discover their keystone metric: Getting any individual user to add 7+ friends in their first 10 days.

  • These behaviors describe the ‘aha!’ moments in your app when users discover core product value, and we observe the greatest shift in retention or conversion as a result of that discovery. We comb through all of the Event and attribute data sent from your app to find these inflection points over all time, as well as within certain key time bins like the first 1, 3, 7, 14, and 28 days of your users’ lifetime.
  • With these insights, you can better target retention campaigns towards users that exhibit early behavior indicative of churn. For example, if I know that users who view 5+ articles with no video in their first 7 days are 20% less likely to churn, I can now communicate with users who have viewed fewer than 5 articles and encourage them to move past that key gateway to product adoption.
  • By default, Related Behaviors are both ranked by z-score and grouped by Event. To view Related Behaviors in attribute-level detail, uncheck “Group by Event.” To cherry pick the most valuable insights for your app, use search, sort and filter functionality:

Related user attributes: User attributes most related to the predictive target behavior. For each attribute, you will see the proportion of active users observed to have each attribute, and the relative difference in churn or conversion between users observed to have that attribute vs. all active users.

Key Takeaways

  • Predictive analytics are a valuable tool for identifying users that are at a high risk of churn, and offering them an incentive, such as access to a new feature, to continue their relationship with your brand and your app.
  • Use Predictions to discover the key behaviors that are early indicators of user retention; like Facebook's growth team, which found that users who add 7 friends in their first 10 days are more likely to be retained.

Engage and rescue at-risk users before they churn


In this project, you’ll learn how to create a new predictive audience for users at risk of churn and develop a targeted messaging campaign to keep users engaged.

Why it matters

Having insights around users who are likely to churn creates a perfect opportunity to reach out to that audience. Send users at high risk of churn a preemptive re-engagement campaign encouraging them to perform a certain Event or redeem a special offer as an incentive to stay an active user. Because the app store is a buyer’s market with high substitutability, it is up to you to show what it is that makes your app more valuable than the rest.

How-to guide

The following steps will walk you through creating a highly targeted and personalized mobile engagement campaign that will work to preemptively rescue users at risk of churn.

Step 1: Create a new predictive audience

Predictive insights are not useful unless they can be followed up with meaningful and targeted actions. One way of taking action on a Prediction is to target an engagement campaign to an audience of users who are predicted to behave in the way you've defined. Predictions makes this possible by generating a new Profile for each Prediction you create, and then setting three profile attributes corresponding to the High, Medium, and Low likelihood segments.

In the Audiences tab under the Marketing section, click the green 'plus' icon to enter the Audience creation dialogue and select 'Add Profile Conditions'. Select the Profile attribute from the 'choose attribute' dropdown. This is where you will find your pre-defined Audience that was created in the Predictions tab. To create a new Prediction, click here. Select 'is one of' as the operator defining the relationship between the Profile attribute values, and select the 'choose values' dropdown to specify whether this Audience should consist of users in the High, Medium, or Low likelihood segments.

Step 2: Create a new predictive campaign

Once you've created a new predictive audience, you can easily create a new engagement campaign targeted at this saved Audience.

Navigate to the Messaging tab under the Marketing section in the left-hand navigation menu of your dashboard. Click the green 'plus' button and select the marketing channel you plan to use to target this audience: Push, In-App, Email, or Inbox. You can also export your predictive audience for use in proprietary, third-party marketing tools, or directly to Facebook through Localytics Remarketing.

Select a target Audience for your campaign. If you've already completed "Step 1: Create a new predictive audience," select 'Saved Audience' and then select the predictive audience you want to target on. If you have not already created a predictive audience, you can do so directly from the New Campaign workflow by selecting 'New Audience' and following the same steps described in Step 1. Finish the campaign creation process and schedule your marketing campaign.

Step 3: Measure & optimize

Once you've sent your Predictive campaign, begin tracking the effects on retention and conversion with campaign reporting. See how different creatives and calls to action perform against one another with A/B testing and Control Groups. Once you’ve dug in and determined what campaigns are most successful at keeping user engaged, ramp them up and continue to iterate.

Key Takeaways

  • Utilize predictions to create Audiences categorized as having a high/medium/low likelihood of churning to engage them in a personalized, targeted messaging campaign.