Understanding Bidstream Data: What Intelligence Professionals Need to Know

Commercially available bidstream data from the advertising ecosystem has long been one of the most comprehensive sources of real-time location and device data available. While primarily designed to facilitate ad targeting, this data stream has attracted significant attention from OSINT companies seeking to understand population movements, device patterns, and behavioral analytics.

What Is Bidstream Data?

Bidstream data is information transmitted during real-time bidding (RTB) auctions, the automated process that determines which advertisements appear on websites and mobile applications. When a user visits a webpage or opens an app with available ad space, an auction occurs in milliseconds. During this process, publishers share specific information about the ad opportunity to help advertisers decide whether to bid.

This information typically includes:

• Device characteristics: Type, operating system, screen size, and mobile advertising ID
• Location signals: GPS coordinates, IP address, or cell tower-derived positioning
• Contextual data: Publisher URL, app identifier, and content category
• Ad specifications: Format, size, and placement details
• Timestamp information: Precise timing of the ad request

The scale of bidstream data is substantial. Major ad exchanges process billions of bid requests daily, creating a continuous stream of device and location signals across global populations.

How Bidstream Data Flows Through the Ecosystem

The bidstream operates through a complex network of intermediaries:

Supply-Side Platforms (SSPs) represent publishers and app developers, packaging user and inventory data into bid requests. These requests flow to ad exchanges, which broadcast the information to multiple Demand-Side Platforms (DSPs) representing advertisers. Each participant in the auction receives the bidstream data, regardless of whether they ultimately win the impression.

Because of the nature of this bidding architecture, bidstream data is seen by numerous parties during each transaction. While contractual agreements typically restrict how participants can use this data, the information is transmitted broadly across the advertising technology ecosystem. Once broadcast, bidstream data can be stored, aggregated, and analyzed by any participant in the auction process.

The Data Quality Challenge

Intelligence professionals evaluating bidstream data must understand its fundamental quality limitations. Industry analyses suggest that between 40% and 80% of bidstream location data contains significant inaccuracies or exhibits characteristics consistent with synthetic signals.

The structural characteristics of real-time bidding create specific data quality challenges unique to bidstream. There are four especially worth highlighting:

Cached Location Data: Ad auctions occur in approximately 100 milliseconds, leaving no time for fresh location queries. Bidstream frequently relies on cached location information stored in the phone's settings. Devices may transmit location data from hours or even days earlier, creating signals that reflect where a device was, not where it currently is.

IP Address Geolocation: A substantial portion of bidstream location data derives from IP address reverse geocoding rather than GPS. IP addresses are converted to latitude and longitude coordinates with precision limitations often exceeding one kilometer. In urban environments, this imprecision can render the data insufficient to understand the actual device context or user behavior.

Publisher Incentive Structures: Ad inventory containing location data commands premium prices in programmatic auctions. This creates financial incentives for publishers to include location data in bid requests even when genuine location information is unavailable. Publishers may insert estimated, fabricated, or placeholder coordinates simply to satisfy the technical format requirements of bid requests and increase inventory value.

Declaration Fraud: Some publishers and supply-side platforms engage in declaration fraud, misrepresenting characteristics of their inventory such as ad unit dimensions or placement quality to command higher bid prices. This extends to location data precision claims.

Like all location data sources, bidstream is also vulnerable to broader fraud patterns including spoofed coordinates, anomalous device behavior, and data manipulation by downstream aggregators. However, the structural characteristics of real-time bidding create quality challenges that persist even in the absence of deliberate fraud.

For intelligence applications requiring high confidence in location accuracy, these inherent limitations of bidstream data collection demand sophisticated filtering and validation approaches.

Bidstream Data for Intelligence Operations

Intelligence and OSINT professionals have recognized bidstream data as a significant source of location intelligence, though its use requires careful consideration of both capabilities and limitations.

Operational Advantages

Scale and Coverage: Bidstream data offers unprecedented scale. Billions of location signals are generated daily across millions of devices and applications. This breadth provides visibility into population movements across geographic areas and demographic segments that would be difficult to achieve through other collection methods.

Real-Time Availability: Unlike many intelligence sources that involve collection delays, bidstream data flows continuously in near real-time. For time-sensitive operations or monitoring dynamic situations, this temporal immediacy offers distinct advantages.

Pattern Detection Across Populations: The volume of bidstream data enables population-level analytics. Understanding foot traffic patterns around critical infrastructure, identifying unusual congregation points, or analyzing cross-border movement trends becomes feasible at scale.

Geographic Diversity: Bidstream data provides global coverage wherever mobile advertising operates. This international reach supports investigations and monitoring activities across multiple jurisdictions without requiring separate data collection infrastructure in each region.

Analytical Approaches for Intelligence Contexts

Professionals working with bidstream data in intelligence contexts should consider several analytical best practices:

Implement Rigorous Filtering: Establish processes to identify and exclude cached signals, IP-derived locations with insufficient precision, and default coordinate assignments. This requires analyzing signal characteristics like horizontal accuracy estimates, timestamp freshness, and coordinate clustering patterns. Organizations must develop or access sophisticated validation methodologies that can separate genuine GPS-derived signals from the IP-based approximations that dominate bidstream feeds.

Validate Through Correlation: Cross-reference bidstream signals against other location intelligence sources whenever possible. Convergent evidence from multiple independent sources provides higher confidence than any single data stream alone.

Assess Temporal Patterns: Genuine devices exhibit behavioral patterns consistent with human activity, including sleep cycles, work routines, and movement speeds matching transportation methods. Cached location data and IP-derived signals may not reflect real-time device positions, requiring temporal analysis to identify when location signals represent historical rather than current positions.

Connect with Other Sources: For investigations requiring high confidence, location intelligence derived from SDK-based sources with consent frameworks and fraud prevention mechanisms may enrich the fidelity and accuracy of raw bidstream data. Understanding the trade-offs between scale, accuracy, and compliance helps inform source selection and correlation. 

Conclusion

The intelligence community's interest in bidstream data is continuing to grow to augment traditional intelligence methods. The question is not whether bidstream will be used, but which organizations will develop the technical capability to cut through the noise. This capability, that is, the forensic analysis of location signal provenance, will determine who extracts reliable intelligence and who chases old cached coordinates. Have you and your team been considering using location signals differently? Contact us here to connect with an expert.