Metica provides a platform that integrates user acquisition and in-app purchase (IAP) revenue optimization for mobile games through machine learning and real-time personalization. By enhancing lifetime value (LTV) and return on ad spend (ROAS) by up to 30% and 50% respectively, it addresses the challenges of scaling user acquisition and maximizing monetization in a competitive gaming market.
Funding
$14M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Mobile game developers face challenges in efficiently scaling user acquisition (UA) and maximizing in-app purchase (IAP) revenue due to the complexities of personalizing game experiences for diverse player profiles. Traditional methods often lack the real-time adaptability needed to optimize lifetime value (LTV) and return on ad spend (ROAS) in a competitive market.
Solution
Metica provides a unified platform that integrates UA and monetization optimization for mobile games, leveraging machine learning and real-time personalization. The platform analyzes player behavior to deliver personalized IAP bundles, LiveOps events, and rewarded ads, adapting to individual player profiles. By predicting LTV at the player level, Metica ensures that all interventions drive long-term profitability, improving both ROAS and LTV. The platform's AI-driven optimization streamlines ad buying and LTV optimization, providing a comprehensive solution for game growth and increased profits.
Target Audience
Metica primarily targets mobile game developers and publishers looking to enhance user acquisition, optimize monetization strategies, and increase the lifetime value of their players.
Features
- Real-time personalization of IAP bundles with ML optimization for relevant items and prices
- Dynamic LiveOps that customize game modes and rewards for each player
- Optimized in-game ad revenue through reward and frequency adjustments
- A/B testing and contextual multi-armed bandit solutions for fast feature testing
- AI-powered models for non-stationary contextual multi-armed bandits, adaptive off-policy evaluation, neural representation learning, and Bayesian marketing mix modeling
- Cohort-level prediction using player-level understanding and forecasting
- Integration with existing ML models or the ability to build on Metica's models