Definition

Third-party intent data is information collected from external sources about prospect research and content consumption across the wider internet, revealing potential buying interest before prospects engage directly with your company.

What is Third-Party Intent Data?

Third-party intent data emerged as a distinct sales intelligence category in the early 2010s as organizations sought visibility into prospect research activities beyond their owned digital properties. Early intent data providers typically monitored limited publisher networks with basic topic categorization and minimal account identification capabilities.

Today, third-party intent data has evolved into a sophisticated intelligence resource spanning vast digital ecosystems. Modern intent data encompasses millions of websites, advanced topic taxonomies, and precise account identification technologies that provide unprecedented visibility into external research activities. Sales intelligence platforms like Saber enhance third-party intent data by integrating signals from diverse providers, applying artificial intelligence to identify meaningful patterns in research behavior, and delivering actionable insights about which accounts are actively researching solutions in relevant categories before they've engaged with your marketing or sales teams.

How Third-Party Intent Data Works

Third-party intent data identifies accounts showing interest in specific topics through their content consumption and research activities across external websites, publications, and platforms.

• Digital Behavior Monitoring: Intent providers track user activities across thousands of B2B websites, publications, forums, review sites, and content platforms to identify research related to specific business topics and solution categories.

• Account Identification: IP address matching, cookie pools, registration information, and other identity resolution techniques connect anonymous web activities to specific companies, revealing which organizations are conducting relevant research.

• Topic Classification: Advanced taxonomies categorize content consumption into specific topics and subtopics aligned with product categories, business challenges, and industry issues to identify exactly what interests each account.

• Intent Signal Analysis: Algorithmic processing evaluates the recency, frequency, and intensity of research activities to distinguish between casual information gathering and serious purchase research indicating genuine buying intent.

• Comparative Baselines: Sophisticated intent systems compare current research volume against historical baselines for each account, identifying significant increases or surges" that suggest heightened interest and potential buying activity.

Example of Third-Party Intent Data

A B2B cybersecurity company implements third-party intent monitoring to identify accounts actively researching security solutions before they reach out to vendors. Their intent platform tracks research activities across thousands of technology websites