Predictive Churn

Predictive Churn analyzes your audience for users that exhibit behaviors indicating they are likely to become inactive, and tags the users as High, Medium, or Low Risk.

Churn is a natural part of engagement ebb and flow, and while a certain amount of churn is normal and healthy, there are ways to identify churn risk factors and take actions to prevent your user base from eroding.

About Predictive Churn

With Predictive Churn, you can identify app and web users by their likelihood to churn, based on risk profiles Airship generates via machine learning, using gradient boosted decision tree methodology.

Risk factors update weekly and are exposed as TagsMetadata that you can associate with channels or Named Users for audience segmentation. Generally, they are descriptive terms indicating user preferences or other categorizations, e.g., wine_enthusiast or weather_alerts_los_angeles. Tags are case-sensitive. for segmentation and analysis in Performance AnalyticsA customizable marketing intelligence tool that provides access to reports and graphs based on engagement data., and exposed as Tag Change events in Real-Time Data StreamingA service that delivers user-level events in real time to your backend or third-party systems using the Data Streaming API..

Example use cases for Predictive Churn:

Target users with offers before they churn.
Run an A/B Test with a single variant and a control group to measure the message’s impact on churn.
Trigger an automation or SequenceA series of messages that is initiated by a trigger. Airship sends messages in the series based on your timing settings, and you can also set conditions that determine its audience and continuation. Sequences can be connected to each other and to other messaging components to create continuous user experiences in a Journey. based on a change in risk group.
Send a message in a Sequence based on a change in risk group.
Create a SegmentA reusable audience group you create by selecting unique or shared user data. that blends risk groups based on the type of messaging and your goals.

The Predictive Churn model

Predictive Churn belongs to Airship’s Predictive suite of products, which uses machine learning to predict user behaviors and optimize engagement strategy for customer lifecycle marketers.

The model is trained on recency and frequency of notification sends and app opens or website visits for a cross-section of anonymized apps and sites. It runs weekly on Mondays. It detects the most relevant risk factors for a churn outcome and assigns a high, medium, or low churn factor to each user who has been active in the past 60 days. By including your app key as an input, the model tailors its predictions to your audience based on your app or website usage.

Notable terms:

Active User

An active user is a member of your audience that has opened your app, had an active web session, or clicked a web notification in the last 30 days.

Inactive User

An inactive user is a member of your audience that had a predictive tag of high, medium, or low and has not opened your app, had an active web session, or clicked a web notification in the last 30 days.

Churn

A churn outcome occurs when a previously active user becomes inactive, i.e., Airship has not seen any activity (measured in app opens, website visits, or web notification clicks) from a user in the last 30 days. Push opt-in status does not factor into the churn outcome, so it is possible that a user who opted out of notifications could still appear active for churn prediction purposes.

Note

A churned user is not the same as an uninstalled user.

Churn Risk

Predictive Churn makes a prediction about the likelihood of a future churn outcome, meaning that a user will go inactive. We assign one of three measures of risk for such an outcome:

High — Users most likely to become inactive
Medium — Users who exhibit signs of potentially becoming inactive
Low — Users least likely to become inactive

Tags and change events

A user’s churn risk profile is represented as a high, medium, or low TagMetadata that you can associate with channels or Named Users for audience segmentation. Generally, they are descriptive terms indicating user preferences or other categorizations, e.g., wine_enthusiast or weather_alerts_los_angeles. Tags are case-sensitive. within the ua_churn_prediction Tag GroupAn array of tags that you can associate with both channels and Named Users.. Changes to that tag are represented as TAG_CHANGE events.

Tag changes return both the change in tag (add or remove) and the current tag. The current tag is the end result of the tag change. There are three scenarios for tag change events:

Add prediction — Adds a new Predictive Churn tag to a channel that did not previously have a prediction. Not all devices begin with a churn prediction; churn prediction is assigned to active users when the Predictive Churn model runs (weekly on Mondays).
Prediction change — Replaces the prediction on a channel.
Remove prediction — Removes the prediction from a channel, typically when a channel becomes inactive.

The following is an example of a Predictive Churn tag change:

{
   "id": "e1559cd7-af96-45ab-bb74-a22cd99a01d5",
   "offset": "1422600",
   "occurred": "2017-01-15T09:26:30.362Z",
   "processed": "2017-01-15T16:15:30.048Z",
   "device": {
      "android_channel": "d5ec96e3-5ced-47b0-a4dd-1b2b6bda442e",
      "named_user_id": "job.bob@example.com",
      "attributes": {
         "locale_variant": "",
         "app_version": "312",
         "device_model": "LG-H811",
         "app_package_name": "com.company.app",
         "iana_timezone": "America/Los_Angeles",
         "push_opt_in": "true",
         "locale_country_code": "US",
         "device_os": "6.0",
         "locale_timezone": "-28800",
         "locale_language_code": "en",
         "location_enabled": "true",
         "background_push_enabled": "true",
         "ua_sdk_version": "6.1.2",
         "location_permission": "ALWAYS_ALLOWED"
      }
   },
   "body": {
      "add": {
         "ua_churn_prediction": [
         "medium"
         ]
      },
      "remove": {
         "ua_churn_prediction": [
         "high"
         ]
      },
      "current": {
         "ua_churn_prediction": [
         "medium"
         ]
      }
   },
   "type": "TAG_CHANGE"
   }

See the Tag change event in the Real-Time Data StreamingA service that delivers user-level events in real time to your backend or third-party systems using the Data Streaming API. API reference.

Use Predictive Churn in messaging

Before you can use Predictive Churn for targeting, you must enable it for your project. It is supported for production projects only. If your app and website both use the Airship SDK, you should enable the feature for both.

Next to your project name, select the dropdown menu (), then Settings.
Under Project settings, select Predictive AI.
Enable Predictive App Churn and/or Predictive Web Churn.

Tags are assigned the first Monday after enabling the feature.

Segments

You can include a Predictive Churn risk profile in your SegmentsA reusable audience group you create by selecting unique or shared user data.. First, search for and select Predicted to Churn, and then select an operator and a risk profile.

Message and experiment audiences

Add a Predictive Churn risk profile as an audience condition for messages and experiments. See Predicted to Churn in Targeting Specific Users.

You can also specify a risk profile for individual Sequence messages. See Conditions in Add messages to a Sequence.

Automation and Sequence triggers

Trigger an Automation or SequenceA series of messages that is initiated by a trigger. Airship sends messages in the series based on your timing settings, and you can also set conditions that determine its audience and continuation. Sequences can be connected to each other and to other messaging components to create continuous user experiences in a Journey. based on changes to a user’s Predictive Churn risk profile. For example, you might set up an automation to send users a special offer when their Predictive Churn risk changes to High, helping retain users at risk of leaving your audience. See Predicted to Churn in Automation and Sequence triggers. For automation using the API, see the next section.

You can also set a churn risk profile as a trigger condition. For Automations, see Conditions in Create an Automation. For Sequences, see Conditions in the Trigger step in Create a Sequence.

Audience definition in the API

With the API, use the audience tag selector object to target by the ua_churn_prediction Tag Group and Tag values low, medium, or high.

For example, the following is a notification to users of all device types whose current churn prediction is medium:

POST /api/push HTTP/1.1
Authorization: Basic <master authorization string>
Content-Type: application/json
Accept: application/vnd.urbanairship+json; version=3

{
   "audience": {
      "tag": "medium",
      "group": "ua_churn_prediction"
   },
   "notification": {
        "alert": "me·di·um, n., an agency or means of doing something."
    },
    "device_types": [
       "ios",
       "android",
       "web"
    ]
}

Analytics

In Performance AnalyticsA customizable marketing intelligence tool that provides access to reports and graphs based on engagement data., the Predictive Dashboard helps you track churn risk factors over time. This Dashboard provides a view into Predictive Churn risk groups, distribution of users across risk groups, and the performance of churn mitigation tactics. If you have both Predictive App and Web Churn enabled, you can set the Device Family filter to Web or Mobile to see churn data for either audience.

Predictive tags update every Sunday, and reports default to the most recent update.

Use cases:

Explore added or removed Predictive tags.
Slice user behavior by churn risk tag.
Export ad IDs, named users, and channel IDs based on their risk category.
Export named users and ad IDs based on app opens, uninstalls, and risk category.
Find churn cohorts and slice by the users’ current tags.
Find churn cohorts, filter, then analyze a funnel of past behavior.