Mobile Gaming Use Cases/Data ScienceData Scientist

Which version of our purchase propensity model performs best on the holdout set across acquisition channels?

Compare ROC-AUC and precision-recall curves across model versions segmented by channel

Metrics & KPIs

AUC-ROCPR-AUClift at top decileF1 scorelog loss

Required Data

model prediction logsholdout labelsacquisition channel metadatapurchase events

Data Sources

MMPData WarehouseA/B TestingMonetization

Works with tools like

AppsFlyerAdjustSingularBranchKochavaSnowflakeBigQueryRedshiftDatabricksClickHouseOptimizelyFirebase Remote ConfigLaunchDarklySplit.ioRevenueCatStripeGoogle Play ConsoleApp Store Connect

How Bruin answers this

Bruin

Bruin AI Data Analyst

Which version of our purchase propensity model performs best on the holdout set across acquisition channels?

Bruin connects to your MMP, Data Warehouse, A/B Testing, Monetization and runs the analysis automatically.

It tracks AUC-ROC, PR-AUC, lift at top decile and delivers the answer in seconds — in Slack, Discord, Teams, or your browser.

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350+ use cases across every team in your studio — from monetisation to LiveOps, product to engineering. One AI that speaks your data.

Ads Monetization ManagerC-LevelCRM / Lifecycle ManagerData ScientistEconomy DesignerEngineeringFinance / FP&AGame DesignerLiveOps ManagerPlayer Support / CommunityProduct ManagerQA EngineerUA Manager

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