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The short version
B2B intent data is evidence that a company, or a person at it, is researching a problem your product addresses, collected from your own channels (first-party), a partner's (second-party) or the wider web (third-party). RevSure, the agentic GTM platform built on the Full Funnel Data Graph, resolves those signals to accounts, contacts and buying groups so a team can score them and decide who to engage.
In September, a data analyst at a logistics software company was walking RevSure through where their go-to-market signals lived. "We need some place to put all of our signals," they said. Engagement scoring was configured in the company's ABM platform and synced to Salesforce, where the BDRs could see it. LinkedIn engagement was missing from that score. As the team understood it, the LinkedIn data they could reach arrived at the company level with no contact attached, so it had nowhere to land in a model built around people.
The analyst traced the consequence in a single sentence: "if you're not including it in the engagement scoring model, then we don't have an accurate read on where our accounts are in the funnel and if we don't have that, then we can't really take action on it."
Their planned fix was a workaround with the Salesforce admin. "We're gonna have to do some kind of transformation with a dummy contact," they said. RevSure showed them that the transformation already existed. RevSure reads LinkedIn touches at the account level and derives, for each lead, opportunity and account, whether it had a LinkedIn touch at any point in its journey, so an account-level signal shows up on the records the team already reports on.
The team is still deciding where its scoring will live. The problem they described is the one every intent program runs into sooner or later: a signal is collected at one level, the model that should use it works at another, and the people who could act on it never see it.
What is B2B intent data?
B2B intent data is behavioral evidence that an organization is in the market for something you sell. Each recorded action is an intent signal: a page viewed, an article read, an ad clicked, a comparison researched, a question asked on a sales call. Buyer intent signals are the subset that point to a purchase rather than general curiosity, such as a pricing page visit, a read of an integration guide or a demo request.
Intent is different from fit. Fit data describes whether an account looks like your customers: industry, size, region, the technology it runs. Intent describes whether something is happening at the account now, and it goes stale quickly. A team needs both: a high-intent account that will never buy wastes a rep's week.
RevSure's 2025 article on buyer intent data for ABM covers choosing intent topics and building sales playbooks. The step it leaves short is the one in the story above: getting each signal onto the right account, person and journey before anyone scores it.
What are the types of intent data?
Intent data is classified two ways. By who collects it, it is first-party, second-party or third-party. By what it measures, it is behavioral (actions someone took) or topic-based (subjects an account is reading about). The two overlap: first-party intent is almost always behavioral, and most third-party intent is topic-based.
First-party vs third-party intent data: which should you rely on?
Third-party intent, from providers including RevSure partners 6sense and Demandbase, is strongest early: it shows which accounts on or off your target list have started researching a topic, so marketing can decide where to spend before anyone visits your site. First-party intent data becomes the stronger signal once an account is engaging with you, because it is specific to your product and usually tied to people. Inside an open opportunity, the most informative intent often sits in calls and emails, such as a procurement contact joining a thread.
Why is intent data noisy until it is resolved to an account?
A raw intent feed is a list of events. Before it can rank anything, each event needs a company, a person where possible, and a time.
Anonymous visits. Most visitors to a B2B website never fill in a form. Many marketing automation tools record page views only after a visitor is identified, so earlier research is lost or sits under an anonymous ID nobody connects to the person.
IP address vs person. Reverse IP lookup maps a network to a company. It does not say which employee was browsing, and remote work, VPNs and shared offices make the company match itself unreliable. A visit resolved to a company still needs a person before a rep can call anyone.
Lead vs contact. CRMs often hold people as unconverted leads with no account attached, alongside contacts that sit on an account. If a lead's engagement is never matched to its account, the account's intent score undercounts the activity happening there.
Account-level signals with no person. Third-party topic intent, and some ad platform data, arrive at the company level only. A scoring model built around people has no place to put them, which is where workarounds like the dummy contact in the story above start.
Decay. Intent describes a moment. A topic spike from four months ago and a pricing visit from yesterday are different facts. Without a time weight, a model keeps ranking accounts that have already chosen someone else.
How do you resolve intent signals to accounts and buying groups?
Resolution is the work of turning events into an account history a person can read. In practice it runs in five steps:
- Collect every source into one identity model. CRM, marketing automation, website tracking, ad platforms, third-party intent feeds and conversation data need to be read against the same set of accounts and people, continuously rather than in a quarterly spreadsheet.
- Keep anonymous activity and stitch it later. Record unknown visitors under a visitor ID and attach their earlier visits once they identify themselves. RevSure's site script works this way: activity from an unknown visitor is kept under a visitor ID, and when that person becomes known, the earlier visits are stitched to their record.
- Match people to accounts. Match leads to accounts by domain, and roll subsidiaries and name variants up to the account you sell to.
- Attach account-level signals to journeys rather than inventing people. Topic intent and company-level ad engagement belong on the account and on its open opportunities. Deriving a flag, as RevSure does for LinkedIn touches at the lead, opportunity and account level, keeps the signal usable without creating fake contacts.
- Map active people to buying group roles. Once activity is on the right people, check which roles are engaging and which are silent. RevSure's guide to mapping a buying group covers that step in detail.
In RevSure, this resolution happens in the Full Funnel Data Graph, which links leads, contacts, accounts and opportunities across the systems a GTM team already runs, with every signal kept with its timestamp.
How do you score buyer intent signals?
A resolved history can be scored. A practical account intent score follows a few rules:
- Gate on fit first. Score intent only for accounts that match your ideal customer profile, or keep fit and intent as two separate numbers, so a busy non-fit account never reaches the top of the queue.
- Weight by what the action implies. A demo request, a pricing visit and a question about implementation on a call say more than a blog read or a topic spike. Agree the weights with sales.
- Count breadth as well as volume. Several people in different roles researching in the same fortnight is a stronger sign than one person reading twenty pages. It also tells you which roles are involved, which shapes the play.
- Apply decay. Reduce the weight of each signal as it ages, so the score reflects this month. The right half-life depends on your sales cycle: shorter for transactional sales, longer for enterprise deals.
- Keep the evidence with the score. A rep should see the three or four signals that produced a number alongside the number itself. The post on AI lead scoring and the Digital SDR covers why reps ignore scores that do not explain themselves.
- Calibrate against outcomes. Each quarter, compare the scores of accounts that became opportunities with those that did not.
How should sales and marketing act on intent data?
Intent data earns its cost when it changes what someone does this week. The action should match where the account is:
- Topic intent, no first-party engagement. The account is researching but has not found you. Run awareness ads and content on the topic to the roles you expect in the buying group, and hold SDR outreach until something on your side moves.
- First-party engagement from several people. The account is evaluating. This is the moment for SDR outreach that references what they looked at, tailored content for each active role, and an alert to the account owner.
- New activity on an open opportunity. Someone new from security, finance or procurement is researching. Tell the account executive and check whether the forecast date still holds.
Put the signal where the team already works, which for most sales teams means the CRM record. And keep a person in the decision: someone chooses which accounts get a play, who is contacted and what is said, especially at strategic or sensitive accounts.
Where does the Digital ABM Manager use intent data?
RevSure's Digital ABM Manager is a digital worker whose scope includes "monitoring intent and account signals." Its role page lists signal monitoring as one of its jobs: "Detect intent, engagement, product, conversation and trigger signals across the target account list." The outputs of that job are a hot-account queue, trigger alerts and a reason to engage for each account.
Intent also feeds its other jobs. For account selection it will "Identify and rank ICP accounts using fit, propensity, whitespace and revenue potential." For sales, it surfaces hot accounts, recommended actions, stakeholders and talk tracks to account executives. The Digital SDR works the same signals for accounts and individual people, with a job described as: "Monitor account and person intent, engagement, web, product, and trigger signals; rank what deserves action." The post on the Dark Funnel Signal agent shows how an off-site signal reaches the account executive.
People keep the decisions that need judgment. The Digital ABM Manager page lists target-list sign-off and exception accounts, relationship strategy and sensitive contact selection among the work that stays with the customer's team, and states: "A Digital ABM Manager never runs unsupervised." As RevSure's digital workers page puts it: "Your team sets the strategy and approves exceptions. RevSure's own GTM engineers manage the digital worker day to day and are accountable for the completion and quality of the work it delivers."
What the dummy contact was telling the team
The analyst in September was about to create a fake person in the CRM so that a real signal could count. Teams make that trade often, because intent tools collect signals at one level and scoring models run at another. Each workaround makes the score harder to trust, and the BDR reading it cannot tell which parts were improvised.
Buying more intent data does not close that gap. Resolving the data you already have does: anonymous visits stitched to people, people matched to accounts, account-level signals attached to the journeys they belong to, and a score that shows its evidence in the place reps already work. When that is in place, the account that started researching last Tuesday reaches a person who can decide what to do about it while the research is still current.
FAQs
What is B2B intent data?
B2B intent data is evidence that a company, or a person at it, is researching a problem your product addresses. It includes first-party signals from your own channels and sales calls, second-party data shared by partners such as publishers or review sites, and third-party topic research collected across the wider web.
What is the difference between first-party and third-party intent data?
First-party intent data comes from your own channels, such as page visits, form fills, webinar attendance and call questions, and is often tied to a person. Third-party intent data comes from providers that observe research across many sites and usually reports topic interest at the account level. Third-party data helps you find accounts early; first-party data shows who is engaging and how far along they are.
What are examples of buyer intent signals?
Strong buyer intent signals include demo requests, pricing and integration page visits, repeat visits from several people at the same account, questions about security or implementation on calls, and a new procurement contact joining an email thread. Single blog reads and general topic research are weaker, showing interest without a planned purchase.
How do you resolve intent data to accounts?
Bring every source into one identity model, keep anonymous website activity under a visitor ID and stitch it to the person once they identify themselves, match leads to accounts by domain and company, roll subsidiaries up to the parent you sell to, and attach account-level signals to the account and its open opportunities instead of creating placeholder contacts.
How long does intent data stay useful?
It depends on your sales cycle, but intent loses value quickly because it describes a moment of research. Apply a decay so older signals count for less, with a shorter half-life for transactional sales and a longer one for enterprise deals that run over several months.
How does RevSure use intent data?
RevSure resolves intent signals from a team's own channels and from partners such as 6sense and Demandbase to accounts, contacts and opportunities in its Full Funnel Data Graph. Its Digital ABM Manager and Digital SDR monitor those signals, rank accounts and people, and suggest a reason to engage, while the customer's team decides who is contacted and what is said.