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!