Predictive Lifetime
The predictive lifetime calculation feature provides the estimated number of days a user is likely to return to your service. The predictive lifetime can be used to calculate the predictive lifetime value (pLTV), which can offer valuable insight for effective campaign planning.
In the Retention Report, check the [Predictive Lifetime] checkbox and set the calculation period to enable the predictive lifetime calculation.
The predictive lifetime in the Retention Report shows the estimated number of days on average the users in a cohort are expected to return to your service. For example, if the predictive lifetime of a cohort is 10 days, it means that the users in the cohort are likely to return to your service for 10 days on average during the set calculation period.
The predicted lifetime is calculated based on Airbridge data, estimating the probability of a user returning to the service. Following Airbridge's machine learning methodology, only past data that meets validity criteria is used for training. Based on the training results, the system estimates how likely users are to revisit the service within the prediction period set in the report.
The following requirements MUST be met to enable the predictive lifetime feature.
The calculation period must be set.
The date range granularity must be set to “Daily.”
The start date of the date range must be set at least 3 days prior to today.
The number of users in a cohort must be at least 30.
For a more accurate calculation, it is recommended to set the start date of the date range at least 13 days prior to today.
When the number of users of a cohort falls short, “Insufficient User Count” will show instead of the predictive lifetime.
When checking the [Predictive Lifetime] checkbox, you can set the calculation period by entering a value between 1 and 1,000.
For example, when you enter 30, the calculation period is set to Day 30, and the predictive lifetime will show the number of days the cohort is likely to return to your service calculated from Day 0 to Day 30.
The predictive lifetime column shows the calculated predictive lifetime of each cohort. GroupBys can be added to get a more granular view of the predictive lifetime.
Refer to the following example case.
1. Check the [Predictive Lifetime] checkbox and set the calculation period.
2. Set the start event to Install (App) and add Channel as GroupBy.
3. The predictive lifetime of the users whose installs are attributed to ad channel X can be seen in the predictive lifetime column.
With the predictive lifetime in the Retention Report, the pLTV can be calculated using the following formula.
pLTV = Predictive Lifetime * Average Revenue Per Daily Active User (ARPDAU)
If ARPDAU is not available, the ARPU divided by the number of days can be used instead. The ARPU metric is available in the Revenue Report in Airbridge.
The calculation period must be set.
The date range granularity must be set to “Daily.”
The start date of the date range must be set at least 3 days prior to today.
The number of users in a cohort must be at least 30.
For a more accurate calculation, it is recommended to set the start date of the date range at least 13 days prior to today.
When the number of users of a cohort falls short, “Insufficient User Count” will show instead of the predictive lifetime.
根据 Meta 的隐私保护政策，在 Airbridge 报告中设定的日期范围内发生的部分 Meta ads 数据将被掩盖。
Meta ads 通过 渠道集成 或 成本集成 提供的数据中（其 Touchpoint Generation Type 显示为 Self-attributing Network），在以下情况下将发生数据掩盖：
通过 Meta ads 广告系列产生的 Impression 和 Engaged-view 的总和为 1,000 以下
归因于 Meta ads 的 App 安装为 100 以下
被掩盖的数据不会汇总到 Airbridge 报告中。 根据报告设置，数据将按以下方式显示：
标记 | 条件 | 说明 |
|---|---|---|
Privacy Block | 掩盖所有符合报告设置的数据 | 用于代替数值显示 |
+α, ±α | 符合报告设置的数据中，仅掩盖部分数据 | 附在数值后显示 |
更改报告设置可能会允许显示被掩盖的数据。您可以尝试：
更改或延长日期范围。
更改或移除部分分组或筛选条件。
不掩盖的数据
除渠道集成和成本集成外，以其他方式收集的 Meta ads 数据，如通过 Install Referrer 的， 不会被掩盖。您可以在基础报告将 Touchpoint Generation Type 设置为分组条件，按触点生成类型查看数据。Install Referrer 数据的 Touchpoint Generation Type 将显示为 Meta Install Referrer 或 Google Install Referrer (Meta)。
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