B2B intent data glossary: 36 terms sales teams actually use
Plain definitions of the terms that come up in intent data, buying signals, targeting, enrichment and outbound, from first-party intent and bidstream data to fit scores, enrichment waterfalls and speed to lead.
On this page
- Intent data is any information suggesting a company or person is looking to buy.
- First-party intent comes from your own channels and is the most reliable; third-party intent is broader but fuzzier.
- Person-level signals tell you who to contact. Account-level signals only tell you which company.
- Every signal has a shelf life, so how fast you act matters as much as the signal itself.
B2B intent data is any information that suggests a company or a person is researching a problem or getting ready to buy. It ranges from your own website visits to public activity like comments on LinkedIn posts, job postings and funding announcements. The terms below are the ones that come up most when sales and marketing teams talk about intent, signals, targeting and outbound, with plain definitions and links to our deeper guides.
Types of intent data
Intent data
Information that suggests a company or person is interested in a topic, a problem or a type of product. It's used to decide who to contact and when. Our guide to LinkedIn intent data covers the sources in detail.
First-party intent
Signals from your own channels: visits to your website, pricing page views, content downloads, webinar attendance, product sign-ups and replies to your emails. It's the most reliable kind, because you see the behavior directly, but it only covers people who already found you.
Second-party intent
Another company's first-party data, shared with you. The common example is a review site telling you which companies have been reading about your category or comparing your product with competitors.
Third-party intent
Signals collected across the wider web by a data provider, usually from a network of publisher sites, and sold as scores showing which companies are researching which topics. It covers far more companies than first-party data, but it's less precise and usually only identifies the company, not the person.
Bidstream data
Data taken from the bid requests sent during programmatic ad auctions, which can include the page being read and the reader's IP address. Some providers use it to infer which companies are reading about which topics. It's broad but noisy, and its use has raised privacy concerns.
Topic surge
A spike in how much a company is reading about a topic compared with its usual level. Third-party providers report it as a score. A surge suggests research is happening somewhere in the company, but not who is doing it.
Account-level intent
Intent attributed to a company as a whole. It tells you which accounts to prioritize, and leaves you to work out who inside them to contact.
Person-level intent
Intent tied to an individual, such as a named person commenting on a post about the problem you solve. It tells you both the account and the person, which makes outreach far more specific.
Signals and triggers
Buying signal
Any observable action or event that makes a purchase more likely soon. Our list of LinkedIn engagement signals ranks the common ones by strength.
Trigger event
A discrete event that opens a window for a purchase: a new leader, a funding round, an acquisition, a new office or a product launch. Trigger events are usually company-level and last for weeks.
Engagement signal
A person interacting with content about a relevant topic: reacting to a post, commenting, sharing, or following a relevant voice. See how to turn post engagement into leads.
Job-change signal
Someone starting a new role or getting promoted. New leaders often review tools and vendors in their first months. See the job-change playbook.
Hiring signal
A company posting jobs that suggest a need, such as its first sales hire or a role that uses a specific tool. See hiring signals.
Funding signal
A company announcing a new investment round. Funding usually comes with growth targets and budget, which makes it one of the most common B2B triggers. See funding as a sales trigger.
Technographic signal
A fact about the software a company uses, or a change in it, such as adding or dropping a tool. See technographic signals.
Firmographic data
Facts about a company itself: industry, size, revenue, location and growth. Firmographics define your market; signals decide timing within it.
Signal decay
How quickly a signal loses its value. A comment on a post is fresh for a day or two; a funding round stays relevant for weeks. See speed to lead for buying signals.
Targeting and scoring
Ideal customer profile (ICP)
A description of the kind of company, and the people inside it, most likely to buy and succeed with your product. See how to define an ICP.
Buyer persona
A description of one role involved in the purchase, such as the economic buyer, the user or the champion, including what they care about.
Total addressable market (TAM)
Every company that could plausibly buy your product. In outbound, it's usually expressed as a count of accounts matching your ideal customer profile.
Fit score
A score showing how closely a lead matches your ideal customer profile. It measures who someone is, not whether they're ready to buy.
Lead scoring
A way of ranking leads, usually by combining fit and intent, so the team works the most promising ones first.
Account-based marketing (ABM)
Focusing sales and marketing effort on a defined list of target accounts rather than on everyone who fits a broad profile. Intent data is often used to decide which accounts on the list to work now.
Lookalike accounts
Companies that resemble your best customers in industry, size, stage or behavior. Building a list from your best customers is often more accurate than writing an ideal customer profile from scratch.
Watchlist
A set of people or companies you monitor for signals, such as the voices your buyers follow or your target accounts. See how to build a high-signal watchlist.
Data and enrichment
Data enrichment
Adding missing details to a lead record, such as a work email, phone number, company size or current title.
Enrichment waterfall
Checking several data providers in order until one returns a result, so you find more contact details than any one provider could. See enrichment waterfalls.
Email verification
Checking that an email address exists and accepts mail before sending to it. It protects your sender reputation by keeping bounces low.
Bounce rate
The share of emails that couldn't be delivered. Hard bounces, where the address doesn't exist, damage your sender reputation. See cold email deliverability.
Data decay
Contact data going out of date as people change jobs, companies rename or close, and emails stop working. Any static list loses a meaningful share of its accuracy every year. See the hidden cost of bad data.
Website visitor identification
Matching anonymous website visits to companies, usually from the visitor's IP address, and sometimes to individuals through other data. Company-level matching is common; reliable person-level matching is much harder, especially with remote work.
Outreach and pipeline
Signal-based selling
Reaching out because of a specific, recent signal rather than working through a static list. See our signal-based selling guide.
Speed to lead
The time between a lead showing interest and your first attempt to contact them. The shorter it is, the more likely a conversation.
Positive reply rate
The share of people contacted who reply with interest, as opposed to replying at all. It predicts pipeline far better than open or total reply rates. See outbound metrics that predict pipeline.
MQL and SQL
A marketing-qualified lead (MQL) has shown enough interest to pass to sales. A sales-qualified lead (SQL) has been checked by sales and accepted as a real opportunity worth pursuing.
Multithreading
Building relationships with several people at a target account instead of relying on one contact, so a deal doesn't stall when one person goes quiet or leaves.
FAQ
What is B2B intent data? Information suggesting a company or person is researching a problem or preparing to buy, such as website visits, engagement with relevant content, job changes, hiring and funding.
What is the difference between intent data and a buying signal? The terms overlap. Intent data usually refers to the data category as a whole, often scores from a provider. A buying signal is one specific observed action or event, like a comment or a new hire.
Which type of intent data is most accurate? First-party intent, because you observe it directly. Among outside sources, person-level signals tied to a real, recent action are more precise than account-level topic scores.
What is the difference between fit and intent? Fit describes who a lead is and whether they match your ideal customer. Intent describes whether they're showing interest now. The best leads have both.
Is using intent data legal? Using public business information for B2B outreach is common, but rules differ by country, especially under GDPR in the EU and UK. Check what applies to your market and how your data provider collects its data.
Where Saava fits
Saava works with person-level intent. It tracks the LinkedIn voices your buyers follow, runs intent monitors across LinkedIn posts, job boards, company events and other public sources, scores everyone against your ideal customer profile, and adds a verified email and phone where available. Each lead shows the signal that surfaced it.