> For the complete documentation index, see [llms.txt](https://docs.metica.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.metica.com/readme-1.md). # What is Metica? *** Metica is an advanced platform engineered to significantly boost in-app purchase (IAP) revenue for games by integrating personalization and optimization technologies with a unique approach to making in-game shops more dynamic. Leveraging a near real-time data infrastructure and state-of-the-art machine learning, Metica delivers personalized interventions for every player, optimizing engagement and monetization strategies. Key to Metica's innovation is its comprehensive experimentation framework that transcends traditional A/B testing. It supports continuous testing, multi-armed bandits, and contextual multi-armed bandits through reinforcement learning, enabling precise and effective optimization of player experiences based on evolving behaviors and preferences. Furthermore, Metica enhances the dynamic nature of game shops by offering: * Greater flexibility in bundling items together, allowing developers to create tailored offer bundles either as IAP or purchasable with virtual currencies. * The ability to hyper-personalize each offer, ensuring that every player receives the most relevant and enticing deals. * The capacity to experiment with and continuously optimize what’s presented in the shop, ensuring that offerings remain fresh and effective in driving revenue. Metica also provides a Predicted Life Time Value (pLTV) model for players to help understand and maximize the long-term revenue potential from each player. The platform is expanding its predictive modeling capabilities to include other player states such as churn and spend propensity, further empowering developers to proactively engage and retain players. By enabling the operationalization of both first-party and third-party machine learning models, Metica allows for unparalleled customization and optimization of the gaming experience. This comprehensive approach ensures that game developers and publishers can leverage cutting-edge data science and machine learning technologies not only to increase IAP revenue but also to significantly enhance player satisfaction and retention through a more dynamic and personalized in-game shop experience. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.metica.com/readme-1.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.