At a glance: Measure the incremental lift generated by retargeting campaigns.
Incrementality analysis is used to make sure that remarketing campaigns have a positive contribution. It considers users who would have converted organically without engaging with a remarketing campaign. The AppsFlyer Incrementality solution has its foundations in the medical science intent-to-treat (ITT) experiment methodology.
Using ITT, researchers randomly split a given population between a control group who are not treated and a test group whom researchers intend to treat. It matters not if treatment is given or not. Researchers measure the efficacy of a given treatment by comparing the results of the groups.
Similarly, in remarketing, the lift metric measures campaign efficacy. It doesn't matter if a user in the target group engages with a campaign (receives treatment) because it is the intent that is relevant in calculating incrementality.
Statistical significance (p-value)
To make sure that the lift result isn't due to random events or chance, ITT methodology requires that the data be assessed for statistical significance using a p-value indicator.
The p-value indicates that experiment results are more than 90% likely to repeat themselves if you run a replicate of the experiment. A p-value of less than 0.1 (typically ≤ 0.1) is statistically significant. It indicates strong evidence against the null hypothesis—there is less than a 10% probability the null is correct (and the results are random). Therefore, the null hypothesis is rejected and the alternative hypothesis accepted.
To use the Incrementality dashboard:
- In AppsFlyer, go to Dashboard > Incrementality.
- Set filters to display the required metrics.
- Use display controls to:
- Select different metrics.
- Display options according to those described in the following tables.
Audience selected. Can be used to target more than one app.
|First seen dates||
Date on which a user first matched audience rules and was then added to the audience.
|Targeted app||An app that benefits from the remarketing campaign efforts.|
|Media sources||Ad networks displaying ads to test group members.|
|Trend display chart/table form||
Change the chart/table display with these controls:
Incrementality raw-data reports
Use Incrementality raw data reports to analyze the interaction of users with remarketing campaigns.
- Incrementality raw data reports contain row-level data of users included in Incrementality experiments. Download Incrementality raw data example files
- Report availability:
- Via Data Locker
- Data freshness: Daily 19:00-23:00 UTC.
Data Locker folder
Organic in-app events
In-app events non-organic
In-app events re-attributions
Uninstalls (This report is currently not populated)
- Users participating in the experiment—First-seen report
- In Audiences, rules are set characterizing users to include in the experiment.
- When a given user, is identified as matching the rules, the event is recorded in the first_seen report.
- Users are allocated randomly to a test or control group indicated by the is_control_group field.
- Users in the test group are allocated to a media source (pid_destination) for retargeting.
- User engagement within the app: User engagement with the app during the experiment is recorded in context-specific reports:
- Engagement type: Session or in-app event
- User attribution status when the user is first-seen: Organic, non-organic, re-attribution. For example, in the past, a user installed the app and was attributed to organic. As such, during an experiment, the attribution status is organic.
- Uninstalls: Users uninstalling the app during the experiment. Uninstall measurement must be active.
Data characteristics and fields
Field availability varies according to report type as indicated.
|Field||Description||First seen||In-app events||Sessions||Uninstalls|
|is_control_group||If true, the user is part of the control group||Y||Y||Y||Y|
|pid_destination||The media source the user is sent to||Y||Y||Y||Y|
|joined_audience_date||Date user first joined the audience||Y||Y||Y||Y|
|audience_name||Audience name (not unique)||Y||Y||Y||Y|
|tm||Hour of day||Y||Y||Y|
|timestamp||Event time stamp YYYY-MM-DD HH:MM||Y||Y|
|app_ids||App ids associated with the audience rules||Y|
|Field||Display name*||First seen||In-app events||Sessions||Uninstalls|
|advertising_id||Advertising ID (GAID)||Y||Y||Y||Y|
|customer_user_id||Customer user ID||Y||Y|
|event_revenue_currency||Event revenue currency||Y|
|event_revenue_u_s_d||Event revenue USD||Y|
|is_purchase_validated||Is receipt validated||Y|
|* According to raw data specification|
Traits and limitations
|Ad network access||Not available|
|Agency access||Not available|
|Agency transparency||Not applicable|
Dashboard: Daily at 18:00 UTC for the previous day
Raw data reports in Data Locker: Daily 19:00-23:00 for the previous day.
|Team member access||Yes, per account permissions.|