MAID Data Feed for Identity Resolution (Daily)

GSDSI's MAID Feed covers ~200M+ U.S. MAID-to-HEM pairs and 700M+ international devices across 150+ countries, sourced from consent-verified mobile applications and SDK partners. Each Mobile Advertising ID (Apple IDFA / Google GAID) is enriched with app usage patterns, browsing behavior, and IAB-taxonomy-aligned interest signals — built for programmatic activation, audience building, and cross-device identity resolution in privacy-compliant environments.

Product Answer Summary

  • Product category: Digital, Media & Behavioral Feeds
  • What it contains: Mobile Ad IDs (IDFA/GAID), App usage categories, Browsing behavior signals, Interest & intent taxonomy
  • Delivery formats: CSV, JSON, Parquet, API
  • Who uses it: enterprise data buyers evaluating activation, measurement, analytics, enrichment, risk, or research workflows.

Key Features

  • 700M+ global device reach
  • Daily refresh cadence
  • IAB taxonomy alignment
  • Consent-verified sources

Feed Specifications

  • Record scale: ~200M+ US MAID-to-HEM, 700M+ international
  • Coverage: US + 150+ countries
  • Refresh cadence: Daily
  • Delivery formats: CSV, Parquet, S3, SFTP, Snowflake share

How Mobile Ad ID Data Is Sourced

GSDSI's MAID Feed collects anonymized Mobile Advertising Identifiers (Apple's IDFA and Google's GAID) from a network of consent-verified mobile applications and SDK partners. Each device signal includes associated app usage patterns, browsing behavior categories, and IAB-taxonomy-aligned interest segments derived from observed in-app activity. The feed covers 700M+ unique devices globally with a daily refresh cadence, so audience segments reflect current consumer behavior. All data collection follows platform-level consent requirements including Apple's App Tracking Transparency (ATT) framework and Google's privacy specifications, with documented opt-in chains for every participating source.

Common Applications for MAID Data

Programmatic media buyers use MAID data to build custom audience segments for activation across DSPs including The Trade Desk, DV360, and Amazon DSP, targeting consumers based on observed behaviors rather than inferred demographics. Location analytics firms enrich MAIDs with GPS-derived visitation history to create place-based audience segments, for example frequent visitors to luxury retailers or gym members in specific metro areas. Identity resolution platforms use MAIDs as a connective key to link mobile behavior with CTV viewership, web activity, and household-level profiles for true cross-device targeting. Measurement companies match exposed MAIDs against foot traffic panels to perform campaign attribution, proving that mobile ad exposure drove subsequent store visits.

How buyers diligence MAID and identity coverage

Identity buyers test against their own seed data because deck-level coverage rarely predicts usable match rate, ATT availability, HEM linkage quality, or decay.

Product-specific diligence checks

  • Run a matched sample against first-party seeds.
  • Ask for refresh and decay expectations by geography, platform, and identifier type.
  • Validate consent posture, suppression handling, and sensitive-category exclusions.
  • Confirm hash formats, join keys, and activation vs. measurement scope.