Predictive signals are crucial when granular data on users is unavailable — as with iOS users whose data is restricted under Apple’s ATT.
SKAdNetwork provides limited attribution data, leaving advertisers unable to track individual user behavior or determine which specific ad creative or targeting strategy led to an app install.
This change had a significant impact on app publishers’ measurement efforts. Now, they have to rely on more general information like campaign ID and source app ID to make optimization decisions.
Unfortunately, SKAN is complex to work with, changing considerably between versions and limiting optimization capabilities.
Predictive modeling can restore much of the visibility into channel ROI that marketers lost with SKAN’s implementation.
Get our quick guide to measurement and campaign optimization with SKAN to learn:
- How app marketers using a predictive approach can unlock up to 85% more information
- How a predictive solution enables forecasting ROAS on the campaign level
- How you can use predictions to optimize campaigns — even after just Day 1
Don’t let SKAN leave you in the dark. Instead, get our brief guide to find out how predictive analytics helps marketers extract more information from even the most limited data sets and make more informed optimization decisions.