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

归因与分析

Rokt mParticle

付费

移动端专注的 CDP,实时整合 App 用户行为数据并路由到 MMP、广告平台和分析工具

搭建CDP移动端实时数据

主要用途

Rokt mParticle is a customer data platform that ingests events from mobile apps, websites, servers, and back-office systems through a single SDK and API layer, then cleans, validates, and standardizes that data before anything downstream consumes it. Its identity resolution stitches device, login, and anonymous identifiers into unified customer profiles, which power real-time audiences that can be activated to advertising, messaging, analytics, and personalization tools through a large connection catalog. Data quality controls let teams block malformed events and enforce a consistent schema. Following the 2025 merger with Rokt, it continues as a distinct CDP product aimed at multi-channel brands that need governed, real-time data flowing reliably between their collection points and their growth stack.

所在链路

Rokt mParticle sits at the data collection and setup stage, feeding clean, identity-resolved events to the analytics, advertising, and personalization tools downstream.

核心功能

  • Single SDK and API for collecting events across apps, web, and servers
  • Data quality rules that validate and block malformed events
  • Identity resolution into unified real-time customer profiles
  • Audience builder activating segments across the connection catalog
  • Large integration library for ads, analytics, and engagement tools

适合谁用

  • Multi-channel brands needing governed real-time customer data
  • Data teams enforcing event schema and quality at the source
  • Growth stacks activating audiences across many destinations

新手提示

  1. Design your data plan and event schema before instrumenting; the platform's quality controls only help if you define expected events up front.
  2. Start with a few high-value destinations rather than wiring every integration at once.
  3. Use identity resolution settings carefully, since how you map identifiers determines whether profiles merge correctly.
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