Kochava Summit Series Session 2, OTT Masterclass, is coming February 1, 2022.
Analyze trends in customer uninstall behavior correlated across audiences, behavioral events, messages, and campaigns.
Identify low-quality users more likely to churn, and reengage them to prevent drop-off, increase ROI, and optimize marketing spend.
Increase retention by leveraging detailed insights into behavior to lower churn rate. Engage current users more effectively and tailor future marketing efforts by excluding those who exhibit behaviors similar to past uninstallers.
An average app loses 77% of its active daily users in the first 3 days
95% of new, opt-in app users churn within the first 90 days
Kochava Predictive Churn Modeling helps brands determine the likelihood a user will churn based on a proprietary algorithm. Kochava links your users to behaviors and characteristics shown most related to churn or conversion, giving them a statisically reliable churn score.
Kochava’s Predictive Churn Scores Provide 90% Accuracy
(13% above the average modeling system)
Grow User Engagement
Leverage each churn score to reengage users at risk with relevant messaging through owned media. Grow user engagement and drive them further down the funnel to increase user lifetime value (LTV).
Automatically Segment Audiences
Automatically segment audiences according to churn-likelihood scores and syndicate “Medium-high” and/or “High” likelihood segments to activate with your most trusted reengagement partners for focused targeting campaigns to drive retention.
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1Heslop, Brent. “By 2030, Each Person Will Own 15 Connected Devices — Here’s What That Means for Your Business and Content,” MarTech Advisor. 4 Mar 2019. Accessed 4 April 2020.
Data within the Kochava Collective is sourced from Kochava’s first-party SDK footprint via Free App Analytics customers only, who exchange free services for data licensing rights. Verified third-party data providers also contribute data to the Collective. All data in the Collective is compliant with all consent regulations.