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.
30-second answer: Third-party intent describes account research behaviour observed across external publisher and ad networks, then reported back to vendors under cooperation agreements. The vocabulary covers source types, taxonomy, scoring, suppression, and the cookieless adjustments that have reshaped the category. This glossary defines 20 third-party intent terms.
30-second answer: An intent signal is an observable behaviour or content interaction that suggests an account is researching a category, vendor, or problem. The vocabulary covers source classes, signal grain, scoring, decay, merge, and false-positive control. This glossary defines 22 terms revenue operators encounter in intent-data platforms and orchestration tools.
Signal orchestration is the discipline of ingesting every relevant buying signal a B2B revenue team can observe (third-party intent, first-party engagement, CRM events, product usage, conversation analytics, executive and technographic changes), routing each signal to the right team and channel through a defined trigger logic, and tracking what happened so the system gets smarter over time. It is the operational discipline that turns noisy signal feeds into coordinated revenue action; without it, the signal arrives but nothing actually changes in the buyer's experience.
Cookieless attribution is the practice of measuring which marketing touches contributed to a B2B revenue outcome without relying on third-party cookies, browser fingerprinting, or cross-site tracking. The discipline became operationally urgent as Intelligent Tracking Prevention in Safari, Total Cookie Protection in Firefox, and the evolving Privacy Sandbox proposals in Chrome collapsed the cookie-based attribution model that B2B marketers leaned on for fifteen years. In 2026 the working answer is a layered model combining server-side tracking, first-party identity, deterministic matching where consent allows, modeled attribution, and account-level rather than person-level measurement.
Bombora intent is third-party B2B intent data produced by Bombora, the company that operates the Data Co-Op, a network of business publishers and content sites where buyer behavior is observed across thousands of sources and aggregated into account-level topic surge signals. Bombora measures B2B research patterns at the company level (not the individual level) by tracking content consumption across its co-op, baselining each account's normal research volume, and flagging when an account's volume on a specific topic spikes meaningfully above its baseline. The output is the Bombora Surge Score, a per-account, per-topic signal that signals "this company is researching this topic right now" and is the most widely integrated third-party intent signal in B2B martech.
Zero-party data is information a buyer has voluntarily and intentionally shared with a brand, including stated preferences, declared intent, role and goals self-reported in a form, survey, or product configurator. In a B2B context, it is what a prospect tells you on purpose, distinct from what you observe (first-party data), what you infer (predicted intent), or what you buy from a third-party data vendor. The category was popularized by Forrester analyst Fatemeh Khatibloo in 2017 and has surged in relevance as third-party cookies depreciate and zero-party signal becomes the cleanest, most consented data input a B2B revenue team can collect.
30-second answer: Account fit scoring rates how closely a company matches an ideal customer profile, expressed as a number or letter grade so revenue teams can rank-order accounts. The vocabulary covers model classes (rule-based, weighted, machine-learned), input classes (firmographic, technographic, behavioural, intent), output classes (fit score, engagement score, propensity, MQA), and operating terms (decay, calibration, threshold, retraining, lookalike). This glossary defines 22 fit-scoring terms.
30-second answer: Firmographic data describes companies. The vocabulary covers core attribute classes (industry, employee count, revenue, geography, ownership, founded year), classification systems (NAICS, SIC, GICS, ISIC), enrichment terms (waterfall, match rate, refresh cadence, deterministic versus probabilistic match), and operating terms (reverse IP enrichment, real-time enrichment, append, hygiene). This glossary defines 22 firmographic terms B2B revenue teams need to read data vendor documentation and instrument targeting.
30-second answer: Marketing attribution credits revenue to the touches that produced it. The vocabulary divides into model classes (first-touch, last-touch, linear, time-decay, U-shaped, W-shaped, data-driven), measurement classes (sourced, influenced, lift), data terms (UTM, server-side tagging, identity graph, deterministic match), and operating terms (path length, attribution window, view-through, cookieless). This glossary defines 22 attribution terms B2B marketers need to read vendor documentation and design measurement frameworks.
30-second answer: Account-based marketing metrics measure programs at the account level rather than the contact or lead level. The vocabulary spans coverage metrics (TAL coverage, contact density, buying-committee coverage), engagement metrics (account engagement score, surge accounts, in-market rate), pipeline metrics (MQA conversion, opportunity creation rate, win rate), and revenue metrics (account-level pipeline contribution, ARR influenced, deal velocity). This glossary defines 24 metrics every ABM program needs to instrument.
30-second answer: Intent data is signal data that indicates a B2B account is researching a problem your product solves. The vocabulary breaks into source classes (first-party, second-party, third-party, predictive), signal types (content consumption, search, technographic shift, funding, hiring), processing concepts (surge, decay, baseline, signal merge), and operating terms (in-market, signal-based selling, account-level identification, intent score). This glossary defines the 22 terms a B2B revenue team needs to read intent vendor documentation and run an in-market account play.