Subscription churn prediction (preview)

Subscription churn prediction helps predicting whether a customer is at risk for no longer using your company’s subscription products or services. You can create new subscription churn prediction on the Intelligence > Predictions page. Select My predictions to see other predictions that you've created.

Tip

Try the tutorial for a subscription churn prediction using sample data: Subscription churn prediction sample guide.

Prerequisites

  • At least Contributor permissions.
  • Business knowledge to understand what churn means for your business. We support time-based churn definitions, meaning a customer is considered to have churned a period of time after their subscription is ended.
  • Data about your subscriptions and their history:
    • Subscription identifiers to distinguish subscriptions.
    • Customer identifiers to match subscriptions to your customers.
    • Subscription event dates, which define start dates, end dates, and the dates the subscription events occurred on.
    • Subscription information to define if it's a recurring subscription and how often it renews.
    • The semantic data schema for subscriptions requires the following information:
      • Subscription ID: A unique identifier of a subscription.
      • Subscription End Date: The date the subscription expires for the customer.
      • Subscription Start Date: The date the subscription starts for the customer.
      • Transaction Date: The date a subscription change happened. For example, a customer buying or canceling a subscription.
      • Is it a recurring subscription: A boolean true/false field that determines if the subscription will renew with the same subscription ID without customer intervention
      • Recurrence Frequency (in months): For recurring subscriptions, it's the period the subscription will renew for. It's represented in months. For example, a yearly subscription that automatically renews for a customer every year for another year has the value 12.
      • (Optional) Subscription Amount: The amount of currency a customer pays for the subscription renewal. It can help identify patterns for different levels of subscriptions.
  • Data about customer activities:
    • Activity identifiers to distinguish activities of the same type.
    • Customer identifiers to map activities to your customers.
    • Activity information containing the name and date of the activity.
    • The semantic data schema for customer activities includes:
      • Primary key: A unique identifier for an activity. For example, a website visit or a usage record showing the customer viewed a TV show episode.
      • Timestamp: The date and time of the event identified by the primary key.
      • Event: The name of the event you want to use. For example, a field called "UserAction" in a streaming video service could have the value of "Viewed".
      • Details: Detailed information about the event. For example, a field called "ShowTitle" in a streaming video service could have the value of a video a customer watched.

    Note

    You'll need at least two activity records for 50% of the customers you want to calculate churn for.

Create a subscription churn prediction

  1. In audience insights, go to Intelligence > Predictions.
  2. Select the Subscription churn model (preview) tile and select Use this model.

    Subscription Churn model tile with Use this model button

Name model

  1. Provide a name for the model to distinguish it from other models.
  2. Provide a name for the output entity using letters and numbers only, without any spaces. That's the name that the model entity will use. Then, select Next.

Define customer churn

  1. Enter the number of Days since subscription ended that your business considers a customer to be in a churned state. This period is typically liked to business activities like offers or other marketing efforts trying to prevent losing the customer.
  2. Enter the number of Days to look into future to predict churn to set a window to predict churn for. For example, to predict the risk of churn for your customers over the next 90 days to align to your marketing retention efforts. Predicting churn risk for longer or shorter periods of time can make it more difficult to address the factors in your churn risk profile, but this is highly dependent on your specific business requirements. Select Next to continue

    Tip

    You can select Save and close at any time to save the prediction as a draft. You'll find the draft prediction in the My predictions tab to continue.

Add required data

  1. Select Add data for Subscription history and choose the entity that provides the subscription history information as described in the prerequisites.
  2. If the fields below aren't populated, configure the relationship from your subscription history entity to the Customer entity.
    1. Select the Subscription history entity.
    2. Select the Field that identifies the customer in the subscription history entity. It needs to relate to the primary customer ID of your Customer entity.
    3. Select the Customer entity that matches your primary customer entity.
    4. Enter a name that describes the relationship.

      Subscription history page showing the creation of a relationship to customer

  3. Select Next.
  4. Map the semantic fields to attributes within your subscription history entity and select Save. For descriptions of the fields, have a look at the prerequisites.

    Subscription history page showing semantic attributes that are mapped to fields in the selected subscription history entity

  5. Select Add data for Customer activities and choose the entity that provides the customer activity information as described in the prerequisites.
  6. Select an activity type that matches to the type of customer activity you're configuring. Select Create new and provide a name if you don't see an option that matches the activity type you need.
  7. You'll need to configure the relationship from your customer activity entity to the Customer entity.
    1. Select the field that identifies the customer in the customer activity table, which can be directly related to the primary customer ID of your Customer entity.
    2. Select the Customer entity that matches your primary Customer entity
    3. Enter a name that describes the relationship.
  8. Select Next.
  9. Map the semantic fields to attributes within your customer activity entity and select Save. For descriptions of the fields, have a look at the prerequisites.
  10. (Optional) If you have any other customer activities you would like to include, repeat the steps above.

    Define the entity relationship

  11. Select Next.

Set schedule and review configuration

  1. Set a frequency to retrain your model. This setting is important to update the accuracy of predictions as new data is ingested in audience insights. Most businesses can retrain once per month and get a good accuracy for their prediction.
  2. Select Next.
  3. Review the configuration. You can go back to any part of the prediction configuration by selecting Edit under the shown value. Or you can select a configuration step from the progress indicator.
  4. If all values are configured correctly, select Save and run to begin the prediction process. On the My predictions tab, you can see the status of your predictions. The process may take several hours to complete depending on the amount of data used in the prediction.

Review a prediction status and results

  1. Go to the My predictions tab on Intelligence > Predictions.

    View of the My Predictions page

  2. Select the prediction you want to review.
    • Prediction name: The name of the prediction provided when creating it.
    • Prediction type: The type of model used for the prediction
    • Output entity: Name of the entity to store the output of the prediction. You can find an entity with this name on Data > Entities.
    • Predicted field: This field is populated only for some types of predictions, and isn't used in subscription churn prediction.
    • Status: The current status of the prediction's run.
      • Queued: The prediction is currently waiting for other processes to run.
      • Refreshing: The prediction is currently running the "score" stage of processing to produce results that will flow into the output entity.
      • Failed: the prediction has failed. Select Logs for more details.
      • Succeeded: the prediction has succeeded. Select View under the vertical ellipses to review the prediction
    • Edited: The date the configuration for the prediction was changed.
    • Last refreshed: The date the prediction refreshed results in the output entity.
  3. Select the vertical ellipses next to the prediction you want to review results for and select View.

    View of options in the vertical ellipses menu for a prediction including edit, refresh, view, logs, and delete

  4. There are three primary sections of data within the results page:
    1. Training model performance: A, B, or C are possible scores. This score indicates the performance of the prediction, and can help you make the decision to use the results stored in the output entity.
      • Scores are determined based on the following rules:
        • A when the model accurately predicted at least 50% of the total predictions, and when the percentage of accurate predictions for customers who churned is greater than the historical average churn rate by at least 10% of the historical average churn rate.
        • B when the model accurately predicted at least 50% of the total predictions, and when the percentage of accurate predictions for customers who churned is up to 10% greater than the historical average churn rate of the historical average churn rate.
        • C when the model accurately predicted less 50% of the total predictions, or when the percentage of accurate predictions for customers who churned is less than the historical average churn rate.

          View of the model performance result

    2. Likelihood to churn (number of customers): Groups of customers based on their predicted risk of churn. This data can help you later if you want to create a segment of customers with high churn risk. Such segments help to understand where your cutoff should be for segment membership.

      Graph showing distribution of churn results, broken into ranges from 0-100%

    3. Most influential factors: There are many factors that are taken into account when creating your prediction. Each of the factors has their importance calculated for the aggregated predictions a model creates. You can use these factors to help validate your prediction results. Or you can use this information later to create segments that could help influence churn risk for customers.

      List showing influential factors and their importance in predicting the churn result

Fix a failed prediction

  1. Go to the My predictions tab on Intelligence > Predictions.
  2. Select the prediction you would like to view error logs for and select Logs.

    View of results menu bar including close, edit model, and logs buttons

  3. Review all the errors. There are several types of errors that can occur, and they describe what condition caused the error. For example, an error that there's not enough data to accurately predict is typically resolved by loading additional data.

Refresh a prediction

Predictions will automatically refresh on the same schedule your data refreshes as configured in settings.

  1. Go to the My predictions tab on Intelligence > Predictions.
  2. Select the vertical ellipses next to the prediction you want to refresh.
  3. Select Refresh.

Delete a prediction

  1. Go to the My predictions tab on Intelligence > Predictions.
  2. Select the vertical ellipses next to the prediction you want to delete.
  3. Select Delete.

Note

Deleting a prediction will remove its output entity.