Create Firebase Remote Config Experiments with A/B Testing

When you use Firebase Remote Config to deploy settings for an application with an active user base, you want to make sure you get it right. You can use A/B Testing experiments to best determine the following:

• The best way to implement a feature to optimize the user experience. Too often, app developers don't learn that their users dislike a new feature or an updated user experience until their app's rating in the app store declines. A/B testing can help measure whether your users like new variants of features, or whether they prefer the app as it exists. Plus, keeping most of your users in a baseline group ensures that most of your user base can continue to use your app without experiencing any changes to its behavior or appearance until the experiment has concluded.
• The best way to optimize the user experience for a business goal. Sometimes you're implementing product changes to maximize a metric like revenue or retention. With A/B testing, you set your business objective, and Firebase performs the statistical analysis to determine if a variant is outperforming the baseline for your selected objective.

To A/B test feature variants with a baseline, do the following:

1. Create your experiment.
2. Validate your experiment on a test device.
3. Manage your experiment.

Create an experiment

A Remote Config experiment lets you evaluate multiple variants on one or more Remote Config parameters.

1. Sign in to the Firebase console and verify that Google Analytics is enabled in your project so that the experiment has access to Analytics data.

   If you did not enable Google Analytics when creating your project, you can enable it on the Integrations tab, which you can access using settings > Project settings in the Firebase console.

2. In the Engage section of the Firebase console navigation menu, click A/B Testing.

3. Click Create experiment, and then select Remote Config when prompted for the service you want to experiment with.

4. Enter a Name and optional Description for your experiment, and click Next.

5. Fill out the Targeting fields, first choosing the app that uses your experiment. You can also target a subset of your users to participate in your experiment by clicking and, then choosing options from the following list:

   • Version: One or more versions of your app
   • Build number: The version code of the app
   • Languages: One or more languages and locales used to select users who might be included in the experiment
   • Country/Region: One or more countries or regions for selecting users who should be included in the experiment
   • User audience: Analytics audiences used to target users who might be included in the experiment
   • User property: One or more Analytics user properties for selecting users who might be included in the experiment

   • First open: Target users based on the first time they ever opened your app

     User targeting by first open time is available after you select an Android or iOS app. It is supported by the following Remote Config SDK versions: Apple platforms SDK v9.0.0+ and Android SDK v21.1.1+ (Firebase BoM v30.3.0+).

     Analytics must also have been enabled on the client during the first open event.

6. Set the Percentage of target users: Enter the percentage of your app's user base matching the criteria set under Target users that you want to evenly divide between the baseline and one or more variants in your experiment. This can be any percentage between 0.01% and 100%. Users are randomly assigned to each experiment, including duplicated experiments.

7. Optionally, set an activation event to ensure that only the data from users who have first triggered some Analytics event are counted in your experiment. Note that all users matching your targeting parameters will receive Remote Config experimental values, but only those who trigger an activation event will be included in your experiment results.

   To ensure a valid experiment, make sure that the event you choose occurs after your app activates fetched configuration values. In addition, the following events cannot be used because they always occur before fetched values are activated:

   • app_install
   • app_remove
   • app_update
   • dynamic_link_first_open

8. For the experiment's Goals, select the primary metric to track, and add any additional metrics you want to track from the list. These include built-in objectives (purchases, revenue, retention, crash-free users, etc.), Analytics conversion events, and other Analytics events. When finished, click Next.

9. In the Variants section, choose a baseline and at least one variant for the experiment. Use the Choose or create new list to add one or more parameters to experiment with. You can create a parameter that has not previously been used in the Firebase console, but it must exist in your app for it to have any effect. You can repeat this step to add multiple parameters to your experiment.

10. (optional) To add more than one variant to your experiment, click Add another variant.

11. Change one or more parameters for specific variants. Any unchanged parameters are the same for users not included in the experiment.

12. Expand Variant Weights to view or change variant weight for the experiment. By default, each variant is weighted equally. Note that uneven weights may increase data collection time and weights cannot be changed after the experiment begins.

13. Click Review to save your experiment.

You are allowed up to 300 experiments per project, which could consist of up to 24 running experiments, with the rest as draft or completed.

Validate your experiment on a test device

For each Firebase installation, you can retrieve the installation auth token associated with it. You can use this token to test specific experiment variants on a test device with your app installed. To validate your experiment on a test device, do the following:

1. Get the installation auth token as follows:

   Swift

   do {
     let result = try await Installations.installations()
       .authTokenForcingRefresh(true)
     print("Installation auth token: \(result.authToken)")
   } catch {
     print("Error fetching token: \(error)")
   }

   Objective-C

   [[FIRInstallations installations] authTokenForcingRefresh:true
                                                  completion:^(FIRInstallationsAuthTokenResult *result, NSError *error) {
     if (error != nil) {
       NSLog(@"Error fetching Installation token %@", error);
       return;
     }
     NSLog(@"Installation auth token: %@", [result authToken]);
   }];

   Java

   FirebaseInstallations.getInstance().getToken(/* forceRefresh */true)
           .addOnCompleteListener(new OnCompleteListener<InstallationTokenResult>() {
       @Override
       public void onComplete(@NonNull Task<InstallationTokenResult> task) {
           if (task.isSuccessful() && task.getResult() != null) {
               Log.d("Installations", "Installation auth token: " + task.getResult().getToken());
           } else {
               Log.e("Installations", "Unable to get Installation auth token");
           }
       }
   });
   MainActivity.java

   Kotlin+KTX

   val forceRefresh = true
   FirebaseInstallations.getInstance().getToken(forceRefresh)
       .addOnCompleteListener { task ->
           if (task.isSuccessful) {
               Log.d("Installations", "Installation auth token: " + task.result?.token)
           } else {
               Log.e("Installations", "Unable to get Installation auth token")
           }
       }
   MainActivity.kt

   C++

   firebase::InitResult init_result;
   auto* installations_object = firebase::installations::Installations::GetInstance(
       firebase::App::GetInstance(), &init_result);
   installations_object->GetToken().OnCompletion(
       [](const firebase::Future<std::string>& future) {
         if (future.status() == kFutureStatusComplete &&
             future.error() == firebase::installations::kErrorNone) {
           printf("Installations Auth Token %s\n", future.result()->c_str());
         }
       });

   Unity

   Firebase.Installations.FirebaseInstallations.DefaultInstance.GetTokenAsync(forceRefresh: true).ContinueWith(
     task => {
       if (!(task.IsCanceled || task.IsFaulted) && task.IsCompleted) {
         UnityEngine.Debug.Log(System.String.Format("Installations token {0}", task.Result));
       }
     });

2. On the Firebase console navigation bar, click A/B Testing.
3. Click Draft (and/or Running for Remote Config experiments), hover over your experiment, click the context menu (more_vert), and then click Manage test devices.
4. Enter the installation auth token for a test device and choose the experiment variant to send to that test device.
5. Run the app and confirm that the selected variant is being received on the test device.

To learn more about Firebase installations, see Manage Firebase installations.

Manage your experiment

Whether you create an experiment with Remote Config, the Notifications composer, or Firebase In-App Messaging, you can then validate and start your experiment, monitor your experiment while it is running, and increase the number of users included in your running experiment.

When your experiment is done, you can take note of the settings used by the winning variant, and then roll out those settings to all users. Or, you can run another experiment.

Start an experiment

1. In the Engage section of the Firebase console navigation menu, click A/B Testing.
2. Click Draft, and then click the title of your experiment.
3. To validate that your app has users who would be included in your experiment, expand the draft details and check for a number greater than 0% in the Targeting and distribution section (for example, 1% of users matching the criteria).
4. To change your experiment, click Edit.
5. To start your experiment, click Start Experiment. You can run up to 24 experiments per project at a time.

Monitor an experiment

Once an experiment has been running for a while, you can check in on its progress and see what your results look like for the users who have participated in your experiment so far.

1. In the Engage section of the Firebase console navigation menu, click A/B Testing.

2. Click Running, and then click on, or search for, the title of your experiment. On this page, you can view various observed and modeled statistics about your running experiment, including the following:

   • % difference from baseline: A measure of the improvement of a metric for a given variant as compared to the baseline. Calculated by comparing the value range for the variant to the value range for the baseline.
   • Probability to beat baseline: The estimated probability that a given variant beats the baseline for the selected metric.
   • observed_metric per user: Based on experiment results, this is the predicted range that the metric value will fall into over time.
   • Total observed_metric: The observed cumulative value for the baseline or variant. The value is used to measure how well each experiment variant performs, and is used to calculate Improvement, Value range, Probability to beat baseline, and Probability to be the best variant. Depending on the metric being measured, this column may be labeled "Duration per user," "Revenue per user," "Retention rate," or "Conversion rate."

3. After your experiment has run for a while (at least 7 days for FCM and In-App Messaging or 14 days for Remote Config), data on this page indicates which variant, if any, is the "leader." Some measurements are accompanied by a bar chart that presents the data in a visual format.

Roll out an experiment to all users

After an experiment has run long enough that you have a "leader," or winning variant, for your goal metric, you can release the experiment to 100% of users. This lets you select a variant to publish to all users moving forward. Even if your experiment has not created a clear winner, you can still choose to release a variant to all of your users.

1. In the Engage section of the Firebase console navigation menu, click A/B Testing.
2. Click Completed or Running, click an experiment that you want to release to all users, click the context menu (more_vert) Roll out variant.

3. Roll out your experiment to all users by doing one of the following:

   • For an experiment that uses the Notifications composer, use the Roll out message dialog to send the message to the remaining targeted users who were not part of the experiment.
   • For a Remote Config experiment, select a variant to determine which Remote Config parameter values to update. The targeting criteria defined when creating the experiment is added as a new condition in your template, to ensure the rollout only affects users targeted by the experiment. After clicking Review in Remote Config to review the changes, click Publish changes to complete the rollout.
   • For an In-App Messaging experiment, use the dialog to determine which variant needs to be rolled out as a standalone In-App Messaging campaign. Once selected, you are redirected to the FIAM compose screen to make any changes (if required) before publishing.

Expand an experiment

If you find that an experiment isn't bringing in enough users for A/B Testing to declare a leader, you can increase distribution of your experiment to reach a larger percentage of the app's user base.

1. In the Engage section of the Firebase console navigation menu, click A/B Testing.
2. Select the running experiment that you want to edit.
3. In the Experiment overview, click the context menu (more_vert), and then click Edit running experiment.
4. The Targeting dialog displays an option to increase the percentage of users who are in the running experiment. Select a number greater than the current percentage and click Publish. The experiment will be pushed out to the percentage of users you have specified.

Duplicate or stop an experiment

1. In the Engage section of the Firebase console navigation menu, click A/B Testing.
2. Click Completed or Running, hold the pointer over your experiment, click the context menu (more_vert), and then click Duplicate experiment or Stop experiment.

User targeting

You can target the users to include in your experiment using the following user-targeting criteria.

A/B Testing metrics

When you create your experiment, you choose a primary, or goal metric, that is used to determine the winning variant. You should also track other metrics to help you better understand each experiment variant's performance and track important trends that may differ for each variant, like user retention, app stability and in-app purchase revenue. You can track up to five non-goal metrics in your experiment.

For example, say you're using Remote Config to launch two different game flows in your app and want to optimize for in-app purchases and ad revenue, but you also want to track the stability and user retention of each variant. In this case, you might consider choosing Estimated total revenue as your goal metric because it includes in-app purchase revenue and ad revenue, and then, for Other metrics to track, you might add the following:

• To track your daily and weekly user retention, add Retention (2-3 days) and Retention (4-7 days).
• To compare stability between the two game flows, add Crash-free users.
• To see more detailed views of each revenue type, add Purchase revenue and Estimated ad revenue.

The following tables provide details on how goal metrics and other metrics are calculated.

Goal metrics

Other metrics

BigQuery data export

In addition to viewing A/B Testing experiment data in the Firebase console, you can inspect and analyze experiment data in BigQuery. While A/B Testing does not have a separate BigQuery table, experiment and variant memberships are stored on every Google Analytics event within the Analytics event tables.

The user properties that contain experiment information are of the form userProperty.key like "firebase_exp_%" or userProperty.key = "firebase_exp_01" where 01 is the experiment ID, and userProperty.value.string_value contains the (zero-based) index of the experiment variant.

You can use these experiment user properties to extract experiment data. This gives you the power to slice your experiment results in many different ways and independently verify the results of A/B Testing.

To get started, complete the following as described in this guide:

1. Enable BigQuery export for Google Analytics in the Firebase console
2. Access A/B Testing data using BigQuery
3. Explore example queries

Enable BigQuery export for Google Analytics in the Firebase console

If you're on the Spark plan, you can use the BigQuery sandbox to access BigQuery at no cost, subject to Sandbox limits. See Pricing and the BigQuery sandbox for more information.

First, make sure that you're exporting your Analytics data to BigQuery:

1. Open the Integrations tab, which you can access using settings > Project settings in the Firebase console.
2. If you're already using BigQuery with other Firebase services, click Manage. Otherwise, click Link.
3. Review About Linking Firebase to BigQuery, then click Next.
4. In the Configure integration section, enable the Google Analytics toggle.

5. Select a region and choose export settings.

6. Click Link to BigQuery.

Depending on how you chose to export data, it may take up to a day for the tables to become available. For more information about exporting project data to BigQuery, see Export project data to BigQuery.

Access A/B Testing data in BigQuery

Before querying for data for a specific experiment, you'll want to obtain some or all of the following to use in your query:

• Experiment ID: You can obtain this from the URL of the Experiment overview page. For example, if your URL looks like https://console.firebase.google.com/project/my_firebase_project/config/experiment/results/25, the experiment ID is 25.
• Google Analytics property ID: This is your 9-digit Google Analytics property ID. You can find this within Google Analytics; it also appears in BigQuery when you expand your project name to show the name of your Google Analytics event table (project_name.analytics_000000000.events).
• Experiment date: To compose a faster and more efficient query, it's good practice to limit your queries to the Google Analytics daily event table partitions that contain your experiment data—tables identified with a YYYYMMDD suffix. So, if your experiment ran from February 2, 2024 through May 2, 2024, you'd specify a _TABLE_SUFFIX between '20240202' AND '20240502'. For an example, see Select a specific experiment's values.
• Event names: Typically, these correspond with your goal metrics that you configured in the experiment. For example, in_app_purchase events, ad_impression, or user_retention events.

After you gather the information you need to generate your query:

1. Open BigQuery in the Google Cloud console.
2. Select your project, then select Create SQL query.
3. Add your query. For example queries to run, see Explore example queries.
4. Click Run.

Query experiment data using the Firebase console's auto-generated query

If you're using the Blaze plan, the Experiment overview page provides a sample query that returns the experiment name, variants, event names, and the number of events for the experiment you're viewing.

To obtain and run the auto-generated query:

1. From the Firebase console, open A/B Testing and select the A/B Testing experiment you want to query to open the Experiment overview.
2. From the Options menu, beneath BigQuery integration, select Query experiment data. This opens your project in BigQuery within the Google Cloud console console and provides a basic query you can use to query your experiment data.

The following example shows a generated query for an experiment with three variants (including the baseline) named "Winter welcome experiment." It returns the active experiment name, variant name, unique event, and event count for each event. Note that the query builder doesn't specify your project name in the table name, as it opens directly within your project.

  /*
    This query is auto-generated by Firebase A/B Testing for your
    experiment "Winter welcome experiment".
    It demonstrates how you can get event counts for all Analytics
    events logged by each variant of this experiment's population.
  */
  SELECT
    'Winter welcome experiment' AS experimentName,
    CASE userProperty.value.string_value
      WHEN '0' THEN 'Baseline'
      WHEN '1' THEN 'Welcome message (1)'
      WHEN '2' THEN 'Welcome message (2)'
      END AS experimentVariant,
    event_name AS eventName,
    COUNT(*) AS count
  FROM
    `analytics_000000000.events_*`,
    UNNEST(user_properties) AS userProperty
  WHERE
    (_TABLE_SUFFIX BETWEEN '20240202' AND '20240502')
    AND userProperty.key = 'firebase_exp_25'
  GROUP BY
    experimentVariant, eventName

For additional query examples, proceed to Explore example queries.

Explore example queries

The following sections provide examples of queries you can use to extract A/B Testing experiment data from Google Analytics event tables.

Extract purchase and experiment standard deviation values from all experiments

You can use experiment results data to independently verify Firebase A/B Testing results. The following BigQuery SQL statement extracts experiment variants, the number of unique users in each variant, and sums total revenue from in_app_purchase and ecommerce_purchase events, and standard deviations for all experiments within the time range specified as the _TABLE_SUFFIX begin and end dates. You can use the data you obtain from this query with a statistical significance generator for one-tailed t-tests to verify that the results Firebase provides match your own analysis.

For more information about how A/B Testing calculates inference, see Interpret test results.

  /*
    This query returns all experiment variants, number of unique users,
    the average USD spent per user, and the standard deviation for all
    experiments within the date range specified for _TABLE_SUFFIX.
  */
  SELECT
    experimentNumber,
    experimentVariant,
    COUNT(*) AS unique_users,
    AVG(usd_value) AS usd_value_per_user,
    STDDEV(usd_value) AS std_dev
  FROM
    (
      SELECT
        userProperty.key AS experimentNumber,
        userProperty.value.string_value AS experimentVariant,
        user_pseudo_id,
        SUM(
          CASE
            WHEN event_name IN ('in_app_purchase', 'ecommerce_purchase')
              THEN event_value_in_usd
            ELSE 0
            END) AS usd_value
      FROM `PROJECT_NAME.analytics_ANALYTICS_ID.events_*`
      CROSS JOIN UNNEST(user_properties) AS userProperty
      WHERE
        userProperty.key LIKE 'firebase_exp_%'
        AND event_name IN ('in_app_purchase', 'ecommerce_purchase')
        AND (_TABLE_SUFFIX BETWEEN 'YYYYMMDD' AND 'YYYMMDD')
      GROUP BY 1, 2, 3
    )
  GROUP BY 1, 2
  ORDER BY 1, 2;

Select a specific experiment's values

The following example query illustrates how to obtain data for a specific experiment in BigQuery. This sample query returns the experiment name, variant names (including Baseline), event names, and event counts.

  SELECT
    'EXPERIMENT_NAME' AS experimentName,
    CASE userProperty.value.string_value
      WHEN '0' THEN 'Baseline'
      WHEN '1' THEN 'VARIANT_1_NAME'
      WHEN '2' THEN 'VARIANT_2_NAME'
      END AS experimentVariant,
    event_name AS eventName,
    COUNT(*) AS count
  FROM
    `analytics_ANALYTICS_PROPERTY.events_*`,
    UNNEST(user_properties) AS userProperty
  WHERE
    (_TABLE_SUFFIX BETWEEN 'YYYMMDD' AND 'YYYMMDD')
    AND userProperty.key = 'firebase_exp_EXPERIMENT_NUMBER'
  GROUP BY
    experimentVariant, eventName