Uber launches Intelligence insights platform powered by LiveRamp clean room

Uber Advertising announced Uber Intelligence on December 8, 2025, a data collaboration platform that enables brands to analyze consumer behavior patterns derived from millions of monthly users across its mobility and delivery services. The platform operates through LiveRamp's clean room infrastructure, allowing advertisers to combine their customer data with Uber's consented signals while maintaining privacy controls and governance standards.

The announcement positions Uber as a significant participant in the data collaboration market, where LiveRamp reported revenue of $194.8 million in the first quarter of fiscal 2026, representing 10.7% year-over-year growth. Uber Intelligence joins an expanding ecosystem where clean room technology has proliferated across retail media networks, streaming platforms, and major advertising channels throughout 2025.

According to Edwin Wong, Global Head of Measurement at Uber Advertising, "For years, brands have been chasing signals that keep disappearing. What they really need is a way to understand people through how they live, not just how they click." The platform processes aggregated, anonymized insights rather than individual user data, revealing patterns about how groups of people move, dine, travel, and order across metropolitan areas.

Technical architecture and capabilities

The platform currently operates exclusively in the United States through what Uber describes as a premium, advanced user interface designed for insight discovery and activation. Advertisers access pre-built use cases developed specifically for the LiveRamp Clean Room environment, eliminating the need for custom query development or data science expertise traditionally required for sophisticated audience analysis.

Audience overlap analysis represents the foundational capability. Brands can determine what proportion of their customer base uses Uber services, enabling targeting precision improvements and identifying untapped growth opportunities within existing market segments. This functionality operates through pseudonymized matching between advertiser customer lists and Uber's user base, with results returning aggregate statistics rather than individual-level matches.

Advanced insights surfaces behavioral patterns unavailable through traditional advertising datasets. Uber's signals capture real-world activities including trip origins and destinations, order timing and frequency, spending patterns across food categories, and mobility preferences between ridesharing and delivery services. These behavioral indicators help brands understand when customers travel, what they order, and how they move through physical environments.

Segmentation tools enable audience construction based on combined datasets. Advertisers can build targeting cohorts that reflect both their own customer attributes and Uber-derived behavioral signals. These segments become activatable across Uber's advertising inventory, which includes Journey Ads in the ridesharing application, post-checkout placements in Uber Eats, and the JourneyTV tablet system deployed in select markets.

The activation capability transforms insights into advertising campaigns. Brands can create joint audiences that combine their customer data with Uber's behavioral signals, then deploy those audiences across Uber's advertising platforms. This closed-loop approach enables advertisers to act on insights within the same environment where they discovered them.

Measurement functionality ties advertising exposures to business outcomes. Advertisers can upload sales data to the clean room environment, then analyze how Uber advertising campaigns influenced purchase behavior. This attribution capability addresses the growing challenge of connecting digital advertising to offline transactions, particularly relevant for quick-service restaurants, consumer packaged goods brands, and retail chains whose customers use Uber services.

Privacy infrastructure and governance

The LiveRamp Clean Room operates as what the company characterizes as a neutral data collaboration platform. According to Vihan Sharma, Chief Revenue Officer at LiveRamp, "LiveRamp is proud to expand the world's most powerful data collaboration network with innovators like Uber Advertising, which brings unparalleled transaction and behavioral insight to the ecosystem."

Clean room technology has faced regulatory scrutiny. The Federal Trade Commission warned in November 2024 that data clean rooms present complicated privacy implications despite marketing claims, emphasizing that most clean room services are not privacy-preserving by default without appropriate constraints. The regulatory body noted that clean rooms can add new avenues for data leaks and breaches by expanding the systems with access to datasets.

Uber Intelligence implements several technical safeguards. The platform processes only pseudonymized signals rather than personally identifiable information. User consent governs data inclusion, with Uber collecting permissions through its applications. Governance controls determine what types of analysis advertisers can perform, preventing queries that could reveal individual-level information. Results aggregate across sufficiently large groups to prevent re-identification.

LiveRamp's technology uses what it calls RampID, a pseudonymized identifier system that replaces personally identifiable information with encrypted tokens. This infrastructure enables customer recognition across platforms while protecting individual privacy. The system operates across over 900 partners in LiveRamp's data collaboration network, facilitating connections between different data sources without direct exposure of raw customer information.

Data never leaves the clean room environment in raw form. Advertisers cannot export Uber's underlying user data or access individual trip records, order histories, or personal information. The platform returns only aggregated statistics that meet minimum threshold requirements, typically requiring groups of at least 1,000 users before revealing insights.

Industry context and competitive positioning

Uber Intelligence enters an advertising technology sector experiencing significant transformation driven by privacy regulation and identifier deprecation. The marketing community has increasingly adopted clean room solutions as third-party cookies phase out and mobile advertising identifiers face restrictions.

Uber's advertising business has expanded substantially throughout 2024 and 2025. The company opened Journey Ads to programmatic buying in June 2024 through partnerships with Google's Display & Video 360, The Trade Desk, and Yahoo DSP, enabling automated purchase of in-app advertising inventory. European expansion followed in June 2025, covering 10 markets including the United Kingdom, Spain, France, Germany, Netherlands, Ireland, Sweden, Poland, Switzerland, and Portugal.

Journey Ads has delivered average click-through rates exceeding 3% and average global view times surpassing 100 seconds since launching in late 2022. The platform achieved particularly strong performance metrics when measured through a custom attention model developed with Adelaide and Kantar. JourneyTV in-ride video scored 11% higher than average tablet video benchmarks, while mobile Journey Video Ads achieved 41% higher scores than mobile video benchmarks.

Uber debuted JourneyTV Presents in September 2025, an enhanced in-ride entertainment platform featuring editorial content from Time Out, Matador Network, Minute Media, The Weather Channel digital, Gallery Media Group, and Cars.com. Advertisements on JourneyTV achieve 98% completion rates with average view times approaching 120 seconds per ride, according to company data.

The ride-hailing sector has intensified competition in mobility advertising. Lyft opened programmatic access via Microsoft Monetize in October 2025, enabling advertisers to purchase in-app inventory through major demand-side platforms. Lyft coordinates 9 million rides per day and has positioned its advertising offerings as privacy-centric alternatives to traditional tracking methods.

Data collaboration market dynamics

The data collaboration platform market has attracted substantial investment and M&A activity. LiveRamp announced the Habu acquisition in January 2024 in a cash and stock transaction valued at approximately $200 million, expanding its clean room software capabilities. The acquisition enabled streamlined cross-cloud collaboration and enhanced measurement across walled gardens, programmatic channels, and media partners.

LiveRamp launched Media Intelligence Tools in November 2024, introducing standardized measurement capabilities across its premium publisher network. The platform's pre-built templates eliminated custom query development requirements while maintaining analytical flexibility for audience overlap analysis, optimal frequency determination, and last-touch attribution metrics.

Recent partnerships demonstrate expanding market applications. LiveRamp enabled retail media networks to measure Meta campaign performance in October 2025, allowing retailers to analyze how Meta advertising drives sales against first-party data. The company powered the first casino media network with Mohegan in January 2025, capturing 200 million monthly impressions across casino properties and digital assets.

Clean room technology has proliferated across the advertising ecosystem throughout 2025, though implementation quality varies significantly. Pinterest integrated with Epsilon Clean Room in November 2025, achieving 5-15% higher match rates and 3-day service level agreement improvements compared to previous activation methods. The partnership addressed key challenges including audience match rate optimization, activation speed, and privacy-compliant data collaboration.

Multiple platforms have launched clean room capabilities during 2025. NIQ introduced its data clean room on Snowflake in October, enabling marketers to enrich first-party data and measure campaign outcomes across global markets. Amazon expanded Prime Video Channel analytics to all eligible marketing partners in November, feeding detailed viewership data into Amazon Marketing Cloud for privacy-safe analysis.

Measurement challenges and industry implications

The launch addresses persistent challenges in digital advertising measurement as traditional tracking mechanisms disappear. Match rate accuracy has emerged as a critical concern, with recent research revealing substantial gaps in deterministic data quality that underpins sophisticated targeting.

Attribution modeling has become more difficult as third-party cookies phase out and mobile advertising identifiers face restrictions. Advertisers have increasingly turned to clean room technologies and first-party data collaborations as alternative measurement approaches, though these solutions require technical expertise and partnership negotiations that have historically limited accessibility.

Uber Intelligence's pre-built use cases lower barriers to entry for brands lacking data science resources. The platform's interface design eliminates SQL query requirements that characterize many clean room implementations, potentially democratizing access to advanced measurement capabilities that previously required specialized technical knowledge.

The platform addresses what Uber describes as a fundamental shift in marketing strategy. Rather than focusing exclusively on digital signals like clicks and impressions, brands can now analyze real-world behaviors including physical movement patterns, dining preferences, and delivery habits. These signals complement rather than replace existing measurement approaches, providing additional context about how customers live beyond their digital activities.

Quick-service restaurants represent a particularly relevant use case. These brands can analyze whether their advertising campaigns influence subsequent Uber Eats orders, connecting media exposure to transaction data within a unified measurement framework. Retail chains can examine whether customers who see Uber advertising subsequently visit physical store locations, though such attribution requires additional data integrations beyond the base platform capabilities.

Consumer packaged goods manufacturers face more complex measurement scenarios. These brands typically sell through retail intermediaries rather than directly to consumers, creating attribution gaps between advertising exposure and purchase behavior. Uber Intelligence enables CPG manufacturers to analyze whether their target audiences use Uber services and how those usage patterns correlate with stated brand preferences, though direct sales attribution requires retailer data collaboration.

The platform's focus on aggregated insights rather than individual tracking aligns with emerging privacy expectations and regulatory requirements. General Data Protection Regulation in Europe and California Consumer Privacy Act in the United States have established strict standards for personal data processing, making aggregate analysis approaches increasingly attractive to compliance-conscious organizations.

Strategic positioning and market expansion

Uber's entry into sophisticated data collaboration platforms reflects broader ambitions within advertising technology. The company has publicly targeted $1 billion in annual advertising revenue, positioning itself as a significant player beyond traditional ride-hailing and delivery operations.

The partnership with LiveRamp provides immediate infrastructure advantages. Rather than building proprietary clean room technology, Uber leverages LiveRamp's existing platform that already connects hundreds of ecosystem participants. This approach accelerates time-to-market while providing advertisers with familiar tools and workflows consistent with other clean room implementations they may already use.

LiveRamp's neutral positioning carries strategic significance. Unlike platforms that operate their own advertising businesses and therefore compete for advertising dollars, LiveRamp provides measurement infrastructure without direct conflicts. This neutrality potentially increases advertiser confidence in measurement accuracy, as results come from an independent third party rather than a platform with financial incentives tied to favorable outcomes.

The United States-only availability suggests initial market testing before potential international expansion. Uber operates globally across mobility and delivery services, with substantial user bases in European, Latin American, and Asia-Pacific markets. Privacy regulations vary significantly across jurisdictions, requiring localized compliance approaches that may explain the phased geographic rollout strategy.

Pre-built use cases represent a deliberate product strategy. By offering standardized analytical frameworks rather than open-ended query capabilities, Uber constrains what advertisers can discover while simultaneously lowering technical barriers. This approach balances accessibility with governance, ensuring analyses remain within acceptable privacy parameters while eliminating the expertise requirements that limit clean room adoption.

The platform extends Uber's advertising value proposition beyond inventory sales. Journey Ads and JourneyTV provide media placements where brands can reach consumers during high-attention moments. Uber Intelligence adds strategic planning and measurement capabilities that operate before and after campaigns run, creating what amounts to an end-to-end advertising platform spanning planning, activation, and measurement phases.

Real-world behavioral signals differentiate Uber's offering from platforms built primarily on digital interaction data. Social media platforms capture engagement patterns and expressed interests. Search engines reveal declared intent through query behavior. Uber's mobility and delivery data reflects actual physical movement and transaction decisions, providing complementary signals about how customers navigate cities and make purchasing choices.

The timing aligns with broader industry shifts toward contextual understanding and away from individual tracking. As deterministic targeting faces technical and regulatory limitations, aggregate behavioral insights become valuable for understanding customer segments and market opportunities. Uber Intelligence positions such insights as strategic intelligence rather than tactical targeting data.

Technical integration requirements

Advertisers require several prerequisites to utilize Uber Intelligence. Access begins with an existing LiveRamp relationship and appropriate contractual agreements governing clean room usage. Organizations upload customer data to the LiveRamp platform, typically through batch file transfers or API integrations that pseudonymize personally identifiable information before matching begins.

Data quality considerations affect matching success rates. Customer lists must include sufficient identifying information for pseudonymized matching to occur, typically email addresses or hashed phone numbers that LiveRamp can translate into RampID tokens. Match rates between advertiser customer lists and Uber's user base will vary based on list quality, overlap between customer populations, and technical matching capabilities.

Results interpretation requires analytical expertise despite the platform's simplified interface. Pre-built use cases generate standardized outputs, but translating those insights into actionable business decisions demands understanding of statistical significance, sampling limitations, and market context. Organizations lacking internal analytics capabilities may require agency support or consulting services to maximize platform value.

Campaign activation through Uber's advertising inventory requires separate agreements and budget allocations. Uber Intelligence operates as a planning and measurement tool rather than a media buying platform, though its insights can inform media strategies executed through existing Uber advertising relationships. The connection between insight discovery and campaign activation creates potential efficiency, but does not eliminate the need for discrete media planning and buying processes.

Measurement capabilities depend on advertiser data contributions. Brands analyzing campaign effectiveness must upload transaction data or other outcome metrics to the clean room environment. This requires data infrastructure capable of extracting, transforming, and transferring business outcome data in formats compatible with LiveRamp's matching and analysis requirements.

Market implications for advertisers

Uber Intelligence represents another data collaboration option in an increasingly crowded field. Retail media networks from Amazon, Walmart, Target, Kroger, and others offer clean room capabilities combining shopping data with advertising measurement. Streaming platforms including Disney, NBCUniversal, and Paramount provide video consumption insights through their measurement tools. Social platforms maintain proprietary analytics environments connecting advertising exposure to on-platform behaviors.

The proliferation of platform-specific measurement tools creates coordination challenges. Advertisers running campaigns across multiple channels must navigate different interfaces, data formats, technical requirements, and contractual terms for each measurement environment. Standardization remains elusive despite industry efforts to establish common frameworks.

Uber's real-world behavioral focus provides differentiation within this fragmented landscape. While many measurement platforms analyze digital behaviors or purchase transactions, Uber captures physical movement and service usage patterns that reflect lifestyle choices and daily routines. This contextual intelligence complements transaction data and digital engagement metrics, potentially revealing insights unavailable through other measurement approaches.

Budget allocation decisions become more complex as measurement options expand. Advertisers must determine which platforms warrant investment in clean room access fees, technical integrations, and analytical resources required to extract actionable insights. Not every brand will find Uber's mobility and delivery data relevant to their business objectives, making strategic prioritization essential.

The platform's value proposition depends significantly on customer-base overlap between brands and Uber users. Luxury automotive manufacturers targeting affluent consumers may find substantial overlap with Uber's ridesharing customers. Quick-service restaurants partnered with Uber Eats for delivery already reach platform users through transaction relationships. Regional brands operating in markets where Uber has limited presence may discover insufficient scale to generate meaningful insights.

Competition with existing measurement providers adds another consideration. Many advertisers already utilize attribution platforms, marketing mix modeling providers, and multi-touch attribution solutions that attempt to connect advertising to business outcomes. Uber Intelligence provides complementary rather than competing capabilities in most cases, but budget constraints force prioritization decisions about where to invest measurement resources.

Timeline

• June 16, 2024: Uber opens Journey Ads to programmatic buying through partnerships with Google Display & Video 360, The Trade Desk, and Yahoo DSP
• January 18, 2024: LiveRamp announces Habu acquisition valued at approximately $200 million
• November 18, 2024: LiveRamp launches Media Intelligence Tools with standardized measurement capabilities across premium publishers
• November 13, 2024: Federal Trade Commission warns data clean rooms not privacy silver bullet, cautioning that most services are not privacy-preserving by default
• January 14, 2025: LiveRamp powers first casino media network with Mohegan, capturing 200 million monthly impressions
• June 17, 2025: Uber opens Journey Ads inventory to programmatic buyers across 10 European markets
• August 6, 2025: LiveRamp reports revenue growth of 10.7% reaching $194.8 million in first quarter fiscal 2026
• September 30, 2025: Uber Advertising debuts JourneyTV Presents with premium content partners
• October 1, 2025: LiveRamp introduces agentic AI tools enabling autonomous agents to access data collaboration platform
• October 23, 2025: LiveRamp enables retail media networks to measure Meta campaign performance through Clean Room platform
• October 26, 2025: Lyft opens programmatic access via Microsoft Monetize platform
• October 31, 2025: Uber launches platform-specific attention metric with Adelaide and Kantar
• November 5, 2025: Pinterest integrates Epsilon Clean Room with improved match rates
• December 8, 2025: Uber Advertising announces Uber Intelligence platform powered by LiveRamp

Summary

Who: Uber Advertising, in partnership with LiveRamp Holdings Inc., launched the platform targeting brand advertisers, marketing agencies, and direct-response marketers seeking behavioral insights derived from mobility and delivery services. Edwin Wong serves as Global Head of Measurement at Uber Advertising, while Vihan Sharma serves as Chief Revenue Officer at LiveRamp.

What: Uber Intelligence is a data collaboration platform operating through LiveRamp's clean room infrastructure that enables brands to combine their customer data with aggregated, anonymized signals from Uber's millions of monthly users across ridesharing and food delivery services. The platform offers five pre-built use cases including audience overlaps, advanced insights, segmentation, activation, and measurement capabilities.

When: The announcement occurred on December 8, 2025, with the platform currently available in the United States market through what Uber describes as a premium, advanced user interface designed for insight discovery and activation.

Where: The platform operates exclusively in the United States at launch, processing data through LiveRamp's cloud-based clean room infrastructure. Advertisers access the platform through the LiveRamp environment, with insights derived from Uber's operations across metropolitan areas throughout the country where the company provides mobility and delivery services.

Why: The platform addresses marketing challenges created by disappearing tracking signals, third-party cookie deprecation, and privacy regulations that restrict traditional measurement approaches. Brands gain access to real-world behavioral patterns including how people move, what they order, and when they travel, enabling marketing decisions grounded in actual consumer activities rather than exclusively digital interactions. The launch supports Uber's broader advertising business expansion and publicly stated goal of reaching $1 billion in annual advertising revenue.