Audience data plays an essential role in digital advertising, helping brands and agencies understand consumer behaviour, identify relevant target groups and activate campaigns across desktop, mobile, connected TV and other channels.

However, not all audience data providers operate in the same way. Some develop proprietary technology to collect and process behavioural signals, while others combine information from external partners or specialise in specific areas such as identity resolution, search intent, location intelligence and consumer purchasing behaviour.

In 2026, artificial intelligence is creating further differences between providers. Predictive modelling, AI-powered custom audience creation and agentic technologies are changing how advertisers access and use data. The introduction of the Model Context Protocol (MCP) is also making it possible to connect audience-data platforms with compatible AI assistants, bringing audience discovery and campaign preparation into more automated workflows.

This analysis compares 12 leading audience data providers based on their data foundations, product portfolios, AI capabilities, custom audience creation, transparency and activation options. It also examines which providers are already introducing MCP integrations and agentic technologies.

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How We Compared the Audience Data Providers

The comparison focuses on the technology and products behind each provider rather than simply the number of audience segments available.

We evaluated five main areas:

• Technology and data foundation: Whether providers operate proprietary technology and how they collect, process and organise behavioural, consumer, search, identity or location signals.
• Data products and accessibility: The availability of predefined segments, custom audiences, raw or structured data, enrichment and other data solutions.
• Audience creation and transparency: How custom audiences are developed and whether advertisers can review targeting criteria, supporting signals and estimated reach.
• AI and agentic capabilities: The use of predictive modelling, generative AI, natural-language audience creation, MCP integrations and agent-based workflows.
• Devices and activation: Support for desktop, mobile, CTV and other channels, together with delivery through DSPs, SSPs, APIs, data feeds and curated marketplaces.

The companies represent different specialisations within the audience-data ecosystem. The ranking is an editorial comparison of product breadth and capabilities, not an independently tested ranking of advertising performance.

Top 12 Audience Data Providers in 2026: Comparison

The table below summarises the key differences between the providers, including their primary specialisations, data products and AI capabilities.

Provider	Primary specialisation	Main data products	AI and audience creation	Devices and activation
1. OnAudience	Global proprietary behavioural data and audience intelligence	4,100+ segments, Raw Data, Data Enrichment, custom audiences, cross-device data and curation	AI Audiences, natural-language audience creation and MCP integration with Claude	Desktop, mobile, CTV, DSPs, SSPs and curated deals
2. Lotame	Audience marketplace and data collaboration	Data Exchange, Spherical, identity, enrichment and data streams	Predictive modelling, lookalikes and audience expansion	Web, mobile, social and CTV
3. Eyeota (D&B)	Global B2B and B2C audience data	Audience marketplace, Data Desk, demographic and business audiences	Predictive modelling and custom audience development	Display, mobile, video, social and CTV
4. Verve / Captify	Search, mobile and conversational intent	Search intelligence, predictive cohorts and intent indexes	AI-driven intent analysis and predictive audiences	Mobile, in-app, display, video and CTV
5. ShareThis	Behavioural and predictive-intent data	Audience segments, custom audiences, Predictors and data feeds	Machine learning, NLP and predictive engagement models	Programmatic display, mobile, video and CTV
6. ROQAD / Zeotap Data	European identity-powered audience data	Identity graph, onboarding, enrichment and audience segments	Identity modelling, matching and audience extension	Web, mobile, CTV and major DSPs
7. Samba TV / Semasio	TV viewership and semantic intelligence	TV audiences, contextual targeting, semantic audiences and measurement	Semantic AI, audience modelling and developing agentic workflows	CTV, video, mobile and digital
8. Weborama	Semantic, behavioural and intent intelligence	Webo Intent, dynamic audiences, semantic data and contextual targeting	Real-time intent scoring and agentic intelligence	Display, CTV, YouTube, DOOH and CRM
9. TransUnion TruAudience	Identity and consumer intelligence	Identity solutions, audience building, enrichment and analytics	Predictive segmentation, identity modelling and lookalikes	Digital, streaming, social and other addressable media
10. Experian	Consumer, household and identity data	ConsumerView, Experian Audiences, enrichment and curated deals	AI-assisted audience discovery and predictive modelling	Display, mobile, social, TV and CTV
11. Adsquare	Location, visitation and purchase intelligence	ID-Precise Audiences, Geo-Contextual Audiences, location insights and measurement	Location-based intelligence, audience analysis and MCP connectivity	Programmatic, social, mobile and location-based advertising
12. Foursquare	Location and geospatial intelligence	Places, Movement, location audiences and spatial analytics	Geospatial AI, predictive modelling and FSQ Spatial Agent	Mobile, digital, CTV, OOH and data integrations

Comparison based on publicly available company product information and announcements reviewed in October 2026. Capabilities may differ by market, activation platform and commercial agreement.

Leading Audience Data Providers in 2026

1. OnAudience

Best for: Comprehensive audience data, AI-powered custom audiences and connected activation

OnAudience combines proprietary behavioural-data technology with a broad portfolio of data products designed for advertisers, agencies and technology platforms.

Its technology processes signals from more than 25 billion devices across over 200 markets. The company offers more than 4,100 predefined audience segments covering demographics, interests, purchase intentions, brands, events, B2B characteristics and other targeting categories.

OnAudience supports multiple ways of accessing and using data, including ready-made segments, Raw Data, Data Enrichment, custom audience development, desktop, mobile and CTV data, cross-device solutions and OnAudience Curate.

AI-Powered Audience Creation

OnAudience’s AI Audiences platform allows marketers to create custom audiences directly from natural-language campaign briefs.

Users can describe the consumers they want to reach and receive an AI-modelled audience proposal in seconds. The platform provides insights into targeting criteria, demographic characteristics, interests, brand affinities, keywords, supporting behavioural signals and estimated reach.

This approach reduces the manual work involved in developing custom targeting strategies while keeping audience definitions available for review before activation.

MCP Integration and Agentic Advertising

In October 2026, OnAudience expanded its offering with an MCP integration for Claude, bringing its audience intelligence directly into an AI assistant.

Through the integration, media planners and programmatic buyers can search more than 4,100 audience segments, explore audience insights, create AI-modelled custom audience proposals and initiate preparation for activation through supported advertising platforms.

Instead of manually searching data catalogues or moving between different planning tools, advertisers can describe campaign objectives and interact with OnAudience data through natural-language instructions.

The integration does not replace all stages of media buying, but it creates a practical connection between audience discovery, planning and activation preparation within AI-driven workflows.

Why OnAudience stands out: Its combination of proprietary behavioural data based on raw data signals, proprietary segments, enrichment, AI audience creation, cross-device capabilities and MCP integration provides a broad foundation for both traditional and emerging advertising workflows.

Read more: OnAudience MCP Integration Guide for Claude.

2. Lotame

Best for: Audience marketplaces, data collaboration and enrichment

Lotame provides audience intelligence and data collaboration solutions through its Spherical platform and Lotame Data Exchange.

The company connects first-party information with data from external partners, allowing advertisers and publishers to enrich existing customer profiles, create new audiences and extend reach across advertising environments.

Its portfolio includes audience modelling, identity solutions, data enrichment, analytics and access to data streams for more advanced use cases.

Artificial intelligence and machine learning help identify behavioural patterns, develop lookalike audiences and support predictive modelling. These capabilities allow advertisers to find consumers who share characteristics with existing customers or valuable audience groups.

Lotame’s primary strength is its ability to connect multiple data sources and support audience collaboration rather than focusing exclusively on a single proprietary behavioural-data feed.

3. Eyeota, a Dun & Bradstreet Company

Best for: International B2B and B2C audience targeting

Eyeota operates a global audience-data marketplace providing access to demographic, interest, intent and business-related audiences.

As part of Dun & Bradstreet, the company connects international programmatic audience products with business and firmographic intelligence, making its solutions relevant for both consumer and B2B campaigns.

Its Data Desk supports custom audience development by translating advertiser requirements into targeting strategies based on available attributes and data partnerships.

Eyeota also uses predictive modelling and automated audience extension to identify additional consumers who may share characteristics with selected target groups.

Its main advantage is access to a broad range of audience categories through established programmatic distribution channels, particularly for advertisers looking to activate different types of data across multiple markets.

4. Verve and Captify

Best for: Search, mobile and conversational intent intelligence

Verve, incorporating Captify’s search intelligence capabilities, specialises in understanding consumer intent through digital behaviour.

Its technology combines onsite search activity, mobile engagement and other intent signals to identify consumers researching products, services and specific interests.

The company has also introduced conversational intent signals from AI environments, reflecting the growing role of AI assistants in consumer research and product discovery.

Machine learning transforms these signals into predictive audience groups, helping advertisers identify relevant consumers based on current interests and emerging demand.

Verve’s strength lies in interpreting intent across search and mobile environments and translating that intelligence into audience products for programmatic activation.

5. ShareThis

Best for: Behavioural signals and predictive consumer interests

ShareThis develops audience intelligence from online behaviour, including page views, searches, sharing activity and content engagement across its publisher network.

Its data products help advertisers identify consumer interests, purchase intentions and changing behavioural patterns. ShareThis offers ready-made segments, custom audiences, enrichment solutions and curated data feeds.

ShareThis Predictors uses machine learning and natural-language processing to estimate which subjects and product categories consumers may engage with next.

These predictive signals can help advertisers identify emerging demand, improve audience relevance and support data-driven targeting.

The company’s AI capabilities primarily focus on predictive intelligence and audience modelling rather than autonomous advertising workflows.

6. ROQAD and Zeotap Data

Best for: European identity resolution and audience intelligence

ROQAD combines cross-device identity technology with Zeotap Data’s audience intelligence, bringing identity resolution, onboarding, enrichment and activation together.

The company uses identity infrastructure to connect consented audience attributes across devices and advertising environments.

Zeotap Data provides demographic, interest, intent, app-usage and brand-affinity audiences, particularly across European markets.

Advertisers can also combine their existing first-party information with additional audience attributes, improving their understanding of customers and extending targeting capabilities.

Machine learning supports identity matching, attribute validation and audience extension. The combined offering is especially relevant for advertisers seeking European audience data supported by cross-device identity technology.

7. Samba TV and Semasio

Best for: Television intelligence, semantic targeting and cross-screen audiences

Samba TV and Semasio combine television audience intelligence with semantic and contextual targeting technology.

Samba provides insights into television and streaming viewership, while Semasio specialises in analysing content and identifying semantic relationships that support audience and contextual targeting.

Together, these capabilities help advertisers connect media consumption, consumer interests and relevant content environments when creating targeting strategies.

Artificial intelligence supports semantic modelling, audience extension and the identification of relevant advertising contexts.

Samba has also expanded its focus on agentic advertising, introducing technologies intended to connect audience discovery, insights and activation workflows with greater automation.

The combined offering is particularly relevant for campaigns involving CTV, online video and contextual advertising.

8. Weborama

Best for: Semantic intelligence and dynamic consumer intent

Weborama develops audience intelligence from behavioural, semantic and contextual signals.

In April 2026, the company introduced Webo Intent, an agentic platform designed to identify how consumer interests and purchase intentions develop over time.

Rather than relying only on static audience categories, Webo Intent analyses signals continuously, creates dynamic Intent Personas and assigns intent scores that help determine when particular audiences may be most relevant.

These insights can support targeting across display, CTV, YouTube, DOOH and CRM channels.

Weborama describes the platform as agentic because it interprets changing behavioural signals and supports decisions about audience activation. However, it does not independently manage the entire campaign-buying process.

Its differentiation is the ability to identify developing consumer intent and convert those signals into dynamic advertising audiences.

9. TransUnion TruAudience

Best for: Identity-based audience building and consumer intelligence

TransUnion TruAudience combines identity resolution, audience modelling, enrichment, activation and analytics.

Its technology connects consumer, household and device attributes to help advertisers identify audiences and reach relevant users across addressable advertising environments.

Advertisers can combine first-party customer information with additional identity and behavioural attributes, creating tailored segments and extending existing audiences through predictive modelling.

Machine learning supports audience development, lookalike creation and identity-based marketing intelligence.

TransUnion’s main strength is its identity foundation and the ability to connect consumer information across advertising systems. Its publicly documented AI capabilities centre primarily on modelling and analytics rather than a dedicated customer-facing agentic audience platform.

10. Experian

Best for: Consumer, household and identity intelligence

Experian provides consumer intelligence, identity technology, enrichment, audience modelling and activation solutions.

Its products combine demographic, household, behavioural and other consumer attributes to help advertisers understand audiences and develop targeting strategies.

Artificial intelligence supports predictive modelling, audience discovery and the development of more relevant segments.

Experian also provides curated advertising solutions that connect audience intelligence with selected media inventory, helping buyers activate audiences through programmatic deals.

The company’s strength lies in its consumer and household data foundation, supported by identity technology, enrichment and increasingly connected activation capabilities.

11. Adsquare

Best for: Location-based audience data and real-world consumer intelligence

Adsquare specialises in location intelligence, audience targeting and the use of real-world behavioural data to improve digital advertising.

Its technology helps advertisers understand consumers through physical-world activity, including places visited, visitation frequency, dwell time and other location-related signals.

The company’s audience portfolio includes ID-Precise Audiences, which use verified real-world behaviour to define targeting groups, and Geo-Contextual Audiences, which target consumers based on location context without requiring individual identity resolution.

Adsquare also provides audience planning, optimisation and measurement solutions, allowing advertisers to connect digital campaign activity with real-world outcomes such as store visits.

MCP Integration and Connected Workflows

Adsquare publicly documents MCP Server connectivity alongside dashboard and API access, enabling its location intelligence and targeting services to connect with compatible AI-driven applications.

This creates opportunities to bring real-world audience intelligence into more automated campaign-planning and targeting workflows.

The exact functions available through the MCP Server depend on the implementation and integration.

Why Adsquare stands out: Its combination of location-based audiences, real-world behavioural intelligence, programmatic activation and MCP connectivity makes it an interesting provider for advertisers looking to connect physical consumer activity with digital campaigns.

12. Foursquare

Best for: Location intelligence and geospatial analysis

Foursquare specialises in physical-location data, visitation intelligence and geospatial analytics.

Its audience products allow advertisers to understand consumers based on real-world behaviour, including places visited, visitation patterns, geographic activity and proximity to relevant locations.

Beyond audience targeting, Foursquare provides structured places and movement datasets that support analytics, location planning and business intelligence.

In February 2026, the company introduced FSQ Spatial Agent, a multi-agent AI system that allows users to perform complex geospatial analyses through natural-language questions.

The system can select datasets, prepare information, perform spatial queries and generate visualisations, making advanced location analysis more accessible.

Although FSQ Spatial Agent is primarily a geospatial-analysis product rather than a programmatic audience builder, it demonstrates how agentic technology can make specialised datasets easier to use.

AI and MCP Integration: How Audience Data Providers Are Evolving

Artificial intelligence is already widely used across the audience-data industry, but its applications differ considerably between providers.

Some companies use machine learning to identify behavioural patterns, predict purchase intent or expand existing audiences. Others offer natural-language interfaces that help advertisers create custom targeting strategies without manually selecting individual data attributes.

A newer development is the introduction of agentic technology, which allows AI systems to coordinate multiple tasks and interact with external services.

The Model Context Protocol (MCP) is particularly relevant because it provides a standardised way for compatible AI assistants to connect with data platforms and tools.

For advertisers, this can simplify how audience information is discovered, analysed and prepared for activation.

Audience Data Providers: AI, MCP and Agentic Capabilities Compared

Provider	AI capabilities	MCP / agentic integration	Main application
OnAudience	AI-powered custom audience creation, behavioural insights and audience modelling	Documented MCP integration with Claude	Audience discovery, custom segment requests, insights and DSP activation preparation
Adsquare	Location intelligence, audience analysis and optimisation	MCP Server documented	Access to location-based targeting intelligence through connected tools
Weborama	Semantic AI and real-time intent scoring	Webo Intent agentic platform; native MCP not confirmed	Dynamic intent audiences and activation intelligence
Samba TV / Semasio	Semantic AI, TV intelligence and audience modelling	Agentic advertising initiatives; native MCP not confirmed	Audience discovery, semantic targeting and cross-screen intelligence
Foursquare	Geospatial AI and location analysis	FSQ Spatial Agent; native advertising MCP not confirmed	Natural-language geospatial analysis and visualisation
Lotame	Predictive modelling, lookalikes and audience expansion	Native MCP not confirmed	Audience enrichment and predictive segmentation
Eyeota	Predictive modelling and audience extension	Native MCP not confirmed	Custom B2B and B2C audience development
Verve / Captify	AI-driven search and conversational intent	Native MCP not confirmed	Predictive intent targeting
ShareThis	Behavioural prediction, NLP and machine learning	Native MCP not confirmed	Predicting consumer interests and engagement
ROQAD / Zeotap Data	Identity modelling, matching and audience extension	Native MCP not confirmed	Identity-powered audience enrichment
TransUnion TruAudience	Predictive segmentation and identity modelling	Native MCP not confirmed	Identity-based audience development
Experian	Predictive modelling and AI-assisted audience discovery	Native MCP not confirmed	Consumer intelligence and audience activation

Based on publicly documented capabilities reviewed in October 2026. Native MCP not confirmed means that a specific customer-facing MCP integration was not verified from the reviewed product documentation.

From Predictive AI to Connected Advertising Workflows

The distinction between AI-powered and agentic technology is important.

Predictive AI helps advertisers identify likely interests, anticipate consumer behaviour and build relevant audience groups. Generative AI can simplify audience creation by translating natural-language campaign briefs into targeting criteria.

Agentic systems can go further by coordinating multiple tasks, selecting connected tools and carrying out permitted actions. MCP can provide the connection between an AI assistant and external data services, although MCP integration alone does not mean that a platform operates autonomously.

OnAudience demonstrates one practical application of this approach through its Claude integration. Advertisers can interact directly with audience data, identify relevant segments, explore insights and initiate custom audience workflows within an AI conversation.

Adsquare is also introducing MCP connectivity into its location-intelligence ecosystem, while Weborama focuses on agentic interpretation of consumer intent. Samba TV is developing more automated advertising workflows, and Foursquare uses multiple AI agents to simplify geospatial analysis.

These approaches illustrate how the audience data industry is evolving in different directions, with increasing emphasis on accessibility, automation and the connection between data intelligence and activation.

What Makes a Leading Audience Data Provider in 2026?

Strong Proprietary Data and Technology

The quality of audience intelligence depends heavily on the signals used to build it.

A strong provider should be able to explain where its information originates, how frequently it is updated and how behavioural signals are transformed into audience attributes.

Proprietary technology can offer greater control over data processing and product development, while marketplace-based models provide access to information from multiple suppliers. Both approaches require clear standards for data quality, consent and transparency.

Multiple Ways to Access Audience Data

Different organisations need different types of data products.

Media buyers may prefer ready-to-activate segments, while analytics teams and technology platforms may require raw signals, structured attributes or enrichment files.

Providers that offer several data formats can support more use cases, from programmatic targeting to customer intelligence and predictive modelling.

Transparent AI-Powered Audience Creation

Natural-language tools can make custom audience development considerably faster, but advertisers still need visibility into the underlying targeting logic.

Understanding which signals are used, how audiences are defined and what reach is available helps media teams assess whether the resulting segments are suitable for their campaign objectives.

AI should simplify this process without removing essential human oversight.

Cross-Device Coverage and Activation

Consumers engage with brands across desktop, mobile, television and other digital channels, making cross-device availability important for many campaigns.

Advertisers should understand where audience data can be activated, which identifiers are supported and how targeting can be applied across different environments.

The ability to connect audience intelligence with major advertising platforms can also reduce the operational work involved in campaign preparation.

AI and Agentic Readiness

As advertising workflows become more connected, access to data through APIs, MCP servers and other integration technologies is becoming increasingly valuable.

These capabilities can allow AI applications to retrieve audience information, generate targeting recommendations and coordinate selected planning or activation tasks.

However, meaningful agentic advertising also requires clear permissions, reliable data, transparent processes and the ability for users to review important decisions.

Frequently Asked Questions

What are the leading audience data providers in 2026?

Leading providers include OnAudience, Lotame, Eyeota, Verve/Captify, ShareThis, ROQAD/Zeotap Data, Samba TV/Semasio, Weborama, TransUnion, Experian, Adsquare and Foursquare. They specialise in different areas of behavioural intelligence, identity, search intent, location data, audience modelling and activation.

Which audience data providers offer AI-powered custom audiences?

OnAudience offers natural-language custom audience creation through AI Audiences. Other providers, including Lotame, Eyeota, Weborama and Experian, use AI for predictive modelling, audience extension, intent analysis or audience discovery.

The level of automation and user control differs between platforms.

Which audience data providers offer MCP integrations?

As of October 2026, OnAudience offers a documented MCP integration with Claude, while Adsquare also publicly documents MCP Server connectivity within its platform.

Other providers offer various AI and agentic technologies, but equivalent native MCP integrations were not confirmed for all companies in this comparison.

What is MCP in advertising?

Model Context Protocol, or MCP, is a standard that allows compatible AI applications to connect with external tools and data sources.

In advertising, MCP can make it possible to search audience catalogues, retrieve insights or initiate supported workflows directly through an AI assistant.

The functions available depend on the connected service and its permissions.

What is the difference between AI-powered and agentic audience creation?

AI-powered audience creation typically uses machine learning or generative AI to analyse signals, predict interests or translate campaign requirements into targeting criteria.

Agentic systems can coordinate several steps, interact with connected services and initiate permitted actions in pursuit of a defined objective.

These workflows still require appropriate oversight and governance.

What is the difference between raw data and audience segments?

Raw data contains more granular information, such as behavioural events, device attributes and other signals that can be analysed or processed further.

Audience segments organise data into defined groups based on characteristics such as demographics, interests, purchase intent or behaviour, making them easier to use for targeting and activation.

Why is proprietary data important for AI advertising?

AI models depend on the quality of the information they process. Relevant, well-maintained data helps improve audience definitions, predictive modelling and the usefulness of targeting recommendations.

Proprietary data infrastructure can also give providers greater control over signal processing and the development of custom intelligence products.

However, proprietary ownership alone does not guarantee data accuracy, privacy compliance or campaign performance.

Final Verdict: Audience Data Is Becoming More Connected

Audience data providers are evolving beyond traditional segment catalogues. Proprietary technology, behavioural intelligence, predictive modelling, AI-powered audience creation and agent-compatible integrations are changing how advertisers access and use data.

The providers in this comparison take different approaches. Some specialise in identity, search intent, location intelligence or marketplace access, while others combine broader data portfolios with custom audience development and activation solutions.

OnAudience stands out for the breadth of its offering, combining proprietary behavioural data, more than 4,100 ready-made segments, raw data access, enrichment, cross-device capabilities, AI Audiences and programmatic curation.

Its MCP integration with Claude extends these capabilities into connected AI workflows, helping advertisers move from audience discovery to activation preparation through natural-language interaction.

As AI and agentic advertising continue to develop, the value of an audience data provider will increasingly depend not only on the amount of data available, but also on how effectively that data can be understood, customised, connected and activated.

Discover what OnAudience can do for your campaigns.

Explore OnAudience AI Audiences to create custom audiences from your campaign brief, or read the OnAudience MCP Integration Guide for Claude to see how audience intelligence can become part of your AI-powered advertising workflow.