30-second answer: First-party intent is buyer behaviour observed on properties a vendor owns: website visits, content downloads, ad engagement on owned channels, demo requests, in-product activity. The vocabulary covers signal grain, identification, scoring, decay, and suppression. This glossary defines 20 first-party intent terms used across B2B revenue stacks.
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Page-level signals capture which pages were viewed, time on page, scroll depth, and exit. Page-level grain is the standard for first-party intent and supports topic-of-interest inference.
Section-level signals roll page activity to product or content sections (pricing, alternatives, customers, docs). Section grain is often more action-relevant than individual pages.
An engagement event is a discrete action: form submit, video played, calculator completed. Engagement events carry more conversion weight than passive page views.
For PLG motions, in-product signals capture feature usage, workspace creation, invite events. They are the strongest first-party intent class for product-led companies.
Channel engagement covers owned-channel ads, email opens and clicks, organic search arrival, paid search arrival. It is first-party because it is observed on or directly into owned properties.
A known visitor is a contact identified through prior form fill, email click, or authenticated session. Known visitors carry high signal precision.
A de-anonymized visitor is identified through reverse IP, cookie-graph match, or third-party identity service. Coverage varies by tool and vertical. See reverse IP lookup and identify in-market accounts.
An anonymous visitor cannot be resolved to an account; signal can still feed cohort and topic analysis but not account-level routing.
Cross-device identification stitches sessions across desktop, mobile, and tablet for a single contact. Stitch quality depends on identity-resolution method.
A score based on how recently an account or contact visited; recency tends to dominate raw count for routing decisions.
A score based on how often an account visits within a window. Frequency captures pattern intent.
Different pages carry different weights: pricing and demo pages weigh more than blog or career pages. Calibrating page weights is the highest-leverage scoring tuning.
The aggregate first-party intent score combining recency, frequency, page weight, and event weight. See how to set up account scoring.
A tool that resolves anonymous visitors to accounts and contacts using reverse IP, cookie-graph, or third-party identity service. See visitor identification glossary.
The process of attaching multiple visitors to a single account through identity resolution. See account graph.
A short retention window for raw events that supports debugging when scoring feels off; without a replay buffer, attribution disputes are unresolvable.
Excluding current customer accounts from the first-party intent pool used for new-business routing prevents misrouting expansion intent into new-logo motions.
First-party intent collection must respect regional cookie-banner consent rules; non-compliance creates regulatory and reputational risk.
A first-party cookie is set by the visited domain itself; first-party cookies remain reliable as third-party cookies decline.
Server-side collection of first-party events, sometimes more reliable than client-side script when ad blockers are common. See first-party data strategy.
Hashing identifiers (email, phone) before transmission to downstream systems is a common practice in privacy-conscious stacks.
Worked example: a SaaS vendor runs server-side event collection on its marketing site and product, identifies known visitors via authenticated sessions and form fills, and de-anonymizes anonymous traffic via reverse-IP plus a third-party identity service. Page weights elevate pricing, demo, customers, and comparison pages above blog and careers. The composite first-party score combines recency, frequency, and weighted page hits. Accounts above the MQA threshold route to sales with a brief assembled from the recent activity.
Counter-example: the same vendor runs client-side-only collection, accepts vendor-default page weights, and routes on raw visit counts. Ad-blocker loss reaches 25 percent in some markets, blog visits dominate the composite, and routing fires on noise instead of intent.
Operating tip: the highest-leverage tuning is page weight, not source coverage. Spend a week recalibrating page weights against historical conversion before adding any new data source.
First-party intent is the foundation under most modern revenue stacks.
It interacts with identity resolution to bind anonymous traffic to accounts, attribution to tie signals to closed pipeline, and personalization to act on signals in-context.
Programs that invest in first-party intent infrastructure consistently outperform programs that lean primarily on third-party feeds.
In product-led motions, in-product first-party signals frequently dominate the composite score.
The conversion correlation of workspace creation, invite events, and feature adoption tends to exceed marketing-site engagement by an order of magnitude.
The first-party data strategy discipline captures the cross-stack work that makes first-party intent reliable.
Programs that build a strong first-party intent foundation usually layer four practices. They run server-side or hybrid event collection so ad blockers and consent modes do not silently lose data. They invest in visitor identification at deterministic quality wherever possible. They tune page and event weights against historical conversion outcomes rather than running vendor defaults. And they suppress customer and employee traffic from the new-business intent pool. Common anti-patterns are weighting all page views equally (which buries the high-leverage pricing and demo events), ignoring identification quality (anonymous accounts cannot drive routing), and acting on raw events without decay (resulting in stale routing). Programs that close these three gaps reliably build sharper first-party intent stacks.
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First-party is observed on owned properties at deterministic identification quality; third-party is observed externally at probabilistic quality. The two classes are complementary, not substitutes. See how to merge first and third party intent and first-party intent data.
It matters a lot for account-level routing and tiering and less for topic and cohort analysis. Programs scaling outbound or running tiered ABM consistently invest in visitor identification.
No. Career pages, blog roll, and unrelated content should weigh near zero or be excluded. The high-leverage signals are pricing, comparison, demo, customers, and product-deep pages.
First-party signal half-lives are typically 14 to 60 days depending on category cycle. Pricing and demo events warrant longer half-lives because intent strength is high.
For PLG motions, in-product activity is the dominant first-party intent class and should weight far above marketing-site engagement. Combining the two views is critical for sales-assist routing in PLG.
Required is strong, but recommended in regions with heavy ad-blocker usage and for stacks running consent-mode-equivalent setups. Client-side-only collection loses 10 to 30 percent of events in some markets.
First-party intent is the strongest signal a B2B revenue stack can collect because it is deterministic, observed in context, and trusted by sales. Combine it with third-party context for breadth and the resulting program tends to outperform either source alone. Use this glossary alongside the intent data glossary when designing first-party scoring rules.
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