How does the SKAN Cohorts feature work?
SKAN群组功能是如何操作的？
Singular has developed estimation and statistical algorithms that improve the SKAdNetwork dataset by leveraging additional datasets we are collecting as the MMP. We use ad spend, IDFA, and in-app event data to estimate cohort metrics, for example d7 and d30 revenue.
The estimation benefits from the existing SKAdNetwork conversion model used by your app — better models creating more accurate data sets will yield better accuracy in estimating cohort metrics.
Against each metric, Singular will provide the confidence interval to help marketers evaluate the accuracy and in order to make informed decisions around campaign optimization.
Here’s an example:
- Let’s take a marketer who is using a measurement period of 24 hours for their conversion model.
- At the end of the first 24-hour window, which starts when SKAdNetwork is first called, every iOS device will get an additional random timer, and after which Singular will collect all captured conversion values, encoded via SKAdNetwork and sent to Singular by the ad networks.
- Singular will then decode the conversion values back to the original metric, for example Revenue, which at this stage is not cohorted.
- Now, comes the data science - Singular will then take the original Revenue metric and using technology will estimate the d7 Revenue. Each value will be provided with a confidence interval to allow the marketer to get a sense of accuracy.
- For example, when $100 is estimated with a confidence interval of $10, this means we estimated the d7 Revenue at $100 with 90% accuracy!
通过利用我们作为MMP收集的其他数据集，Singular开发了能改进SKAdNetwork数据集的 估计和统计算法。 我们使用广告支出、IDFA和应用内事件数据来估计群组指标，例如d7和d30收入。
现有SKAdNetwork转化模型能有效影响估计 - 更佳的模型能创造更准确的数据集，而此准确度能提供更准确的估计群组指标。
针对每个指标，Singular会提供置信区间，以帮助营销人员评估准确性，并在营销广告做出明智的优化决策。
举个例子：
- 有一位营销人员在他们的转化模型使用24小时测量周期。
- 在首次调用SKAdNetwork时开始的第一个24小时窗口结束时，每个iOS设备将获得一个额外的随机计时器。之后，Singular将收集所有转化值。此转化值会通过SKAdNetwork进行编码，并由广告渠道发送给Singular。
- 之后Singular会将转化值解码为原始指标，例如收入。在这个阶段，收入不是群组数据
- 接下来，Singular将使用原始收入指标估计d7收入。每个值都将提供一个置信区间，使营销人员能够获得准确性。
- 例如，当以10美元的置信区间估计100美元时，这意味着我们估计d7收入为100美元，准确率为90%！