The more time you spend waiting for campaign results, the more money you may be wasting. Our predictive analytics engine uses machine learning to identify and map correlations between early user engagement indicators and expected user value to give you accurate predictive insights early in a campaign’s lifetime.
Privacy-centric insights that help you make good choices fast
When you respect user privacy you build stronger, longer lasting relationships. Predictive analytics gives you the ability to use aggregate customer behavior without relying on user identity, so that you can base your decisions on comprehensive engagement data while preserving customer privacy.
More data enables better decisions. Our predictive analytics engine gives you an accurate, predictive score based on every measurable event, within a minimal timeframe, so that you can make all of your in-app engagement data count when making optimization decisions.
An ever evolving industry requires forward thinking solutions. Discover how our predictive analytics solution can help you optimize your campaigns, without the wait, while preserving customer privacy.
Get clear data science insights for your marketing efforts from one comprehensive dashboard
Sync with Apple’s SKAdNetwork to easily translate predictive benefit scores into conversion values
Get a detailed, aggregate view of predicted campaign performance scores, broken down by monetization, engagement, retention, and cost
Preserve your customers’ privacy by basing your insights on actual engagement and behavioral data without relying on user identity
Leverage a unique predictive model for your app using distinct LTV logic, first party historical data, and correlations in customer behavior
Enjoy immediate access to predicted scores for accurate feedback on media quality and performance
Advanced data science made available to every app developer
Predictive analytics uses machine learning and advanced data science techniques to give you a competitive advantage in an ever-shifting environment.
AppsFlyer’s SDK measures all of a user’s engagement data in their initial 24 hours post-install, this information is analyzed by the PredictSK engine and broken into three predicted pillars: Retention, engagement, and monetization – constructing the user’s overall benefit score – all ranked from 1-9.
The benefit score will go through an additional compression algorithm to create a conversion value between 0-63 in accordance with SKAdNetwork’s 6 bit requirements. SKAdNetwork will then send the aggregated data alongside campaign information – giving PredictSK the ability to present the aggregate campaign score and relevant media data, and for you to optimize accordingly.
Yes, AppsFlyer’s predictive analytics solution is fully aligned with Apple’s ATT and user-privacy requirements.
PredictSK is currently in beta, and only available for qualified AppsFlyer customers.
Prior to onboarding PredictSK, a machine training period is required in which PredictSK’s AI engine will review an app’s historical data, identify correlations between user engagement points and their eventual value to the app. Developers are also encouraged to add their input in the form of highlighting key events, association of events to one or more of the three KPI pillars and more.