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Machine Learning for Mobile Advertising

Aarki uses proprietary data to build predictive machine learning models that forecast the probability of in-app events at an optimal acquisition price. 



Our Approach

We know that there is no “one size fits all” in mobile marketing. Our expertise across multiple app categories means we can find the best strategy for your particular campaign. We craft custom models to support your goals and then optimize those models across multiple key performance indicators (KPIs).

Ensemble models


Optimizing to multiple KPIs per advertiser


Leverage custom models per app and category learnings to find the best mix for performance


Traditional and dynamic lookalikes

Click here to learn more about Aarki’s approach from our data scientists.

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Components of Machine Learning

Advanced Feature Engineering

In-app Behavior
Post-install Behavior
We extract features from the user profile to capture in-app engagement and spend. Then we correlate certain behaviors in one title to purchases in another.
Segmentation Membership
Conversion Delay
We model the expected number of purchases and the time it will take the user to convert. We use incomplete install cohorts to model early stages of the purchase funnel.
Value users
Active User
We extract a measure of user interest or activity from the user profile and quantify based on recency,  frequency and monetization.
Audience Attributes
We identify segments of users based on pre-lapsed activity and historical user behavior to inform model prediction.
neural network
App Embeddings
Using neural network embeddings we can define a similarity metric within the app space, allowing us to quantify how similar two apps are to each other.