Explore Frequently Asked Questions

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How does Assetario personalization work?
Product
Why should I choose Assetario over an in-house solution?
Product
What distinguishes Dynamic from Contextual personalization?
Product
Is data shared between clients?
Data and Models
What data is utilized for personalization?
Data and Models
Can Assetario adapt to in-game or app changes, user behavior alterations, or UA changes?
Data and Models
How can I evaluate or track the effectiveness of Assetario's personalization?
Data and Models
How long is the integration process?
Integration process
What are the steps involved in integration?
Integration process
What's the resource requirement for integrating Assetario on our end?
Integration process
How challenging is it to integrate Assetario's personalization?
Integration process
What amount of data is required for Assetario personalization?
Integration process
Is there a need to integrate an SDK?
Integration process

Product

Dynamic Personalization

Contextual Personalization

Purpose

Get the best SKU offer at any point in the user's journey based on the content and price.

Scale prices of all your in-app purchases based on the country and device of each user to maximize user LTV.

ML Model

Trained on user historical and real-time behavioral data are used as input parameters.

Trained on user historical and real-time contextual data are used as input parameters.

Usage

API is called each time the game needs to offer something for a user.

API is called just at the start of the first user session.

Suitability

Suited for games with a vast range of offers.

Ideal for all apps and games mainly monetizing through subscriptions, store offers, and hard currency purchases.

Benefits

Aids in predicting user purchase preferences and eases the live ops team's workload due to outsourcing segmentation to Assetario’s models.

Earlier and higher revenue uplift as all game or app revenue is personalized from the moment of going live.

Data Requirement

Requires at least 6 months of historical data.

Requires at least 3 months of historical data.

Product

Dynamic Personalization

Purpose

Get the best SKU offer at any point in the user's journey based on the content and price.

ML Model

Trained on user historical and real-time contextual data are used as input parameters.

Usage

API is called each time the game needs to offer something for a user.

Suitability

Suited for games with a vast range of offers.

Benefits

Aids in predicting user purchase preferences and eases the live ops team's workload due to outsourcing segmentation to Assetario’s models.

Data Requirement

Requires at least 6 months of historical data.

Product

Contextual Personalization

Purpose

Scale prices of all your in-app purchases based on the country and device of each user to maximize user LTV.

ML Model

Trained on user historical and real-time contextual data are used as input parameters.

Usage

API is called just at the start of the first user session.

Suitability

Ideal for all apps and games mainly monetizing through subscriptions, store offers, and hard currency purchases.

Benefits

Earlier and higher revenue uplift as all game or app revenue is personalized from the moment of going live.

Data Requirement

Requires at least 3 months of historical data.

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