广告营销工具
SEOSEO付费获客付费获客程序化广告网站变现程序化App 获客App 变现网站变现关键词研究搜索意图App 获客ROASCPAApp 变现CPCLTV联盟营销eCPMRPM零售媒体营销归因转化追踪创意情报MMPHeader BiddingDSPSSPRTB广告可见率填充率ASOSKAdNetworkARPDAU激励视频广告聚合联盟营销创意测试A/B 测试再营销相似受众广告优化品牌安全供应路径
SEOSEO付费获客付费获客程序化广告网站变现程序化App 获客App 变现网站变现关键词研究搜索意图App 获客ROASCPAApp 变现CPCLTV联盟营销eCPMRPM零售媒体营销归因转化追踪创意情报MMPHeader BiddingDSPSSPRTB广告可见率填充率ASOSKAdNetworkARPDAU激励视频广告聚合联盟营销创意测试A/B 测试再营销相似受众广告优化品牌安全供应路径

App 用户获取

AppLovin

付费

全球领先移动广告网络,覆盖数亿 App 用户,以 AI 驱动的 ROI 优化和自动化创意测试见长,支持 ROAS 竞价。

上线移动广告网络ROAS优化AI竞价App买量

主要用途

AppLovin's advertising business, powered by its Axon machine-learning engine, is one of the largest in-app user acquisition channels, particularly for games. Advertisers feed it conversion goals, creative assets, and target economics such as CPI, CPE, or ROAS targets; AppDiscovery then buys inventory across the enormous ad supply of apps using AppLovin MAX mediation, optimizing delivery per impression. SparkLabs provides creative production and iteration services. The flywheel between MAX supply data and Axon's bidding is the platform's edge, and the same engine now extends into ecommerce and web advertising. Reporting is campaign and creative level, with measurement typically validated through an MMP.

所在链路

AppLovin is a launch-stage demand channel in app UA, buying the in-app inventory that publishers expose through MAX and measured externally by MMPs.

核心功能

  • Axon ML-driven bidding toward install, event, or ROAS goals
  • AppDiscovery demand across MAX-mediated in-app supply
  • SparkLabs creative production and testing support
  • Campaign automation via APIs
  • Creative-level performance reporting

适合谁用

  • Game developers scaling UA beyond Google and Meta
  • App advertisers with strong event signals and ROAS targets
  • Teams able to produce playable and video creative at volume

新手提示

  1. The engine optimizes to the goal you set, so feed it post-install events that correlate with revenue, not just installs.
  2. Creative volume drives performance; plan continuous video and playable refresh rather than launching with two assets.
  3. Validate results through your MMP and watch for cannibalization, because in-app networks can claim credit for users you would have acquired anyway.
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