SKAdNetwork Attribution
Regain visibility into cohorted metrics and boost performance on iOS with the industry’s most advanced SKAdNetwork solution.
Powerful SKAN insights. No shady workarounds.
Teams that leveled up with Singular’s next-gen attribution
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Solutions for today’s iOS challenges
Smart Conversion Management
Choose the optimal model for your user behavior so you don’t miss out on key growth indicators. With 7 different models, multiple revenue types, s2s event support, and the ability to account for both conversion and revenue events in a single model – you’ll capture more user insights and make better optimization decisions.
Out-of-the-box SKAN Reporting
Save resources with automated conversion value decoding for KPI insights that tie back to your SDK events. Enrich your SKAN conversion data from Apple with ad network cost and campaign granularity for a granular view of SKAN ROI and decoded performance KPIs.
SKAN Advanced Analytics
Modeled conversion values close the reporting gaps created by privacy threshold censorship – making SKAN analysis a whole lot easier. Improve data accuracy and unlock a comprehensive data set for better ROI with data science driven analytics and cohorted KPIs.
SKAN Instant Campaign Optimization
Enable your ad partners to deliver the best traffic possible by sending predictive D7 LTV for instant campaign optimization. Real-time feedback loops improve SKAN performance signals and server-to-server modes capture offline revenue for accurate pLTV.
Web and Cross-device Attribution
Explore iOS opportunities beyond app-only experiences. Whether you are testing new user journeys or want to ensure your tried and true campaign flows are compliant, Singular covers all your web-to-app and cross-device use cases with a seamless user experience.
All your data, delivered to where you need it, in one fully-managed pipeline
Establish a single source of truth for your SKAN performance data and automatically load aggregate and user-level data sets directly into your databases, storage solution, and reporting tools. Singular supports all leading databases including visualization tools like Tableau and Looker, and file-based storage tools like S3 and SFTP.
SKAdNetwork Attribution FAQ
For each app that you are marketing, you can choose one of the following conversion model types:
– Revenue: Lets you optimize your campaigns based on revenue gained during the measurement period following the install/reinstall. A revenue model can measure three types of revenue, depending on how you set it up: in-app purchase revenue, ad revenue, or both (all revenue).
See also: SKAN Optimized Models FAQ
– Conversion Events: Lets you optimize campaigns based on specific post-install user activity. The model encodes user events into the conversion value if they occur at least once during the measurement period.
– Engagement: Lets you optimize your campaigns by how much the users engaged with the app during the measurement period. The model encodes into the conversion value how many times various events occurred during the measurement period.
– Mixed Models (NEW): Mixed models allow you to get both revenue information and one other type of information about the same SKAdNetwork campaign. Singular offers three mixed models: Conversion Event
For SKAdNetwork campaigns with partial data (due to censored conversion values), Singular can show modeled (extrapolated) metrics for the entire campaign.
Modeled metrics are derived from the installs for which we do have data.
For example:
– A SKAN campaign has 50 installs, out of which 25 came with a conversion value (the Conversion Value Ratio is 50%).
– Based on the existing conversion values, the SKAN revenue is $30.
– The modeled revenue for the campaign, based on the assumption that the installs with censored conversion values behave like the installs with available conversion values, is $60.
SKAdNetwork revenue models are only as effective as the revenue buckets defined for them (see How does a revenue model work?).
Optimized models solve the dilemma of how to define the revenue buckets (how many buckets, how big/small, etc.) by generating the best revenue buckets for your app automatically.
Optimized models are calculated based on real user-level revenue event data for the specific app (as measured by the Singular tracker).
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!
