B2B buyers do much of their research before they contact a vendor. They read up on the problem, compare vendors on review sites and check pricing pages, and by the time they fill out a form, a shortlist often already exists. Intent data shows you that research while it's still happening, so your team can engage before the shortlist is set.
This guide covers where intent data comes from, how to tell a real spike from noise and how to turn a spike into pipeline, including plays where AI agents act on the signal for you. Intent data sits at the center of a signal-led go-to-market strategy, and the teams that act on it first get the first conversation.
What Is Intent Data?
Intent data is behavioral data that shows when a company, or a person at that company, is actively researching a topic tied to a business problem, product or solution. It captures signals such as content consumption, review-site activity, search behavior and visits to your website. Providers compare that activity with each account's normal research levels and flag the accounts whose interest is rising.
You'll also see it called B2B intent data, buyer intent data or sales intent data. Firmographic data shows whether an account fits your market. Technographic data shows what it runs. Intent data adds the timing. It also differs from interest data, which tracks steady curiosity about a subject, because it measures a change in behavior tied to a specific problem.
For example, a logistics company that fits your ICP reads several articles on CRM data enrichment across B2B publishers. In the same week, two of its people compare enrichment vendors on G2 and one visits your pricing page twice. Each signal alone is weak. Together, they show an account moving into active evaluation.
Types of Intent Data
Intent data falls into three types based on where the signal comes from. Each one sees a different part of the buyer's research, so teams get the clearest picture by combining them.
First-party intent data
First-party intent data comes from channels you own or pay for, including your website, emails, events, product and ads. It reflects direct engagement with your brand, which makes it the highest-fidelity signal you have.
Common first-party intent signals include:
Website engagement. Visits to pricing, product and comparison pages, plus repeat visits from the same account. Website visitor identification resolves anonymous traffic to the companies and people behind it.
Content and email engagement. Guide downloads, webinar attendance, email clicks and replies.
Direct requests. Demo requests, contact forms and chat conversations.
Product usage. Trial sign-ups, feature adoption and usage spikes in your own product.
First-party data has two limits. It only captures accounts that already know you, and it often arrives late. ZoomInfo's 2026 GTM Predictions report found that site activity, chat and demo requests usually show up after opinions have formed and shortlists have taken shape. That's why first-party signals work best alongside the two types below.
Second-party intent data
Second-party intent data is another company's first-party data, shared through a direct partnership. Review sites are the clearest example. G2 and TrustRadius sell signals from buyers who view vendor profiles, compare products and read reviews in a category.
These signals sit late in the buying journey, since a buyer comparing three vendors on a review site is usually deep into evaluation. The trade-off is scope, because second-party data only covers research that happens on the partner's own site.
Third-party intent data
Third-party intent data is collected across external websites and content networks, then licensed to sales and marketing teams. It shows research activity before an account ever visits your site, which makes it the main source for finding net-new, in-market accounts.
Third-party intent data comes from three main sources:
Data cooperatives. A network of publishers and B2B sites pools engagement data in a consent-based model. Bombora's data co-op is the best-known example.
Publisher direct. A single publisher, or a group of niche sites, shares data on its own audience. The signal is deep but limited to that publisher's topics.
Bidstream. Data captured during real-time ad bidding. It gives only a brief snapshot of each visit and carries more noise, which makes it the weakest of the three.
ZoomInfo's intent signal types
ZoomInfo adds signal types that sit on top of the standard first-, second- and third-party split:
Derived intent. Combines first-party and third-party signals, such as ad engagement, web activity, topic research and technology use, into one view of an account.
Known intent. Comes from ZoomInfo's research team surveying business professionals about their projects, priorities and pain points. It powers Scoops, which report events such as new initiatives, leadership hires and planned purchases.
Guided intent. Analyzes the topics accounts researched before they became opportunities, then recommends which intent topics to track.
Champion moves. Flags when a past buyer or power user joins a new company, through job change alerts that open a warm path into that account.
Intent data types at a glance
Type | Source | Signal strength | Best used for |
First-party | Your website, emails, events and product | High (known engagement) | Timing outreach to accounts already engaging with you |
Second-party | Partner sites such as G2 and TrustRadius | High (late-stage research) | Competitive deals and late-funnel targeting |
Third-party | Data co-ops, publisher networks, bidstream | Medium (broad, early research) | Finding net-new, in-market accounts |
Guided | Topics researched before past wins | High (predictive) | Choosing which topics to track |
Account-Level vs. Person-Level Intent Data
Intent data works at two levels, and the difference decides how quickly a team can act on it.
Account-level intent tells you that a company is researching a topic. It's the standard for third-party intent and works well for prioritizing target accounts, building ad audiences and scoring. The gap is that a seller still has to work out who at the account is doing the research.
Person-level intent points to the contact associated with the research activity. ZoomInfo offers both Account-Level Intent and Person-Level Intent, which surfaces a named contact tied to that research when available. Reps can then start with the person who's actually reading about the problem, and map the buying committee from there.
The two levels work best together. Account-level intent shows which companies to prioritize. Person-level intent shows where to start inside each one.
How Intent Data Is Collected
Providers collect intent data by tracking content consumption and engagement across their source network, then measuring each account's activity against its own history. The process usually runs in five steps:
Capture behavioral signals. Page views, downloads, searches and review-site activity are captured from consented sources.
Classify topics. Each piece of content is mapped to the topics it covers, such as "sales engagement platforms" or "data warehouse migration".
Resolve activity to accounts. IP addresses, domains and other permitted identifiers tie each signal to a company and, where possible, a contact.
Set a baseline. The provider measures how much each account normally researches each topic.
Flag a surge. When research on a topic rises well above the account's baseline, the provider scores it as an intent spike.
The baseline step matters. A large enterprise reads about cloud security every week, so steady reading is normal for it. A sharp jump in that reading is what signals a buying project.
To score a spike, providers typically weigh the amount of content consumed, the number of people researching, the type of content, time on page and how recently the activity happened. Teams then layer the result with fit data, so the list narrows to accounts that are both in-market and worth pursuing.
How to Read an Intent Signal
Intent data shows that interest is rising. It can't confirm that a specific person has decided to buy or that an account has chosen your product. Reading a signal well means checking six dimensions before acting on it:
Topic relevance. Is the topic close to the problem you solve, or adjacent to it?
Recency. Did the research happen this week or six weeks ago?
Frequency. Is the account researching the topic repeatedly, or was it a single visit?
Velocity. How sharply has activity risen compared with the account's baseline?
Breadth. How many people at the account are researching, and in which roles?
Fit. Does the account match your ideal customer profile?
Signals also gain weight in combination. A pricing page visit alone is weak, since plenty of people check prices out of curiosity. The same visit from an ICP account that is also surging on your category topics, with two contacts comparing vendors on G2, is worth acting on right away.
Florin Tatulea, ZoomInfo's GTM Engineer in Residence, shows what this looks like in practice. In the play below, he stacks G2 review activity with website visits on ICP accounts, then times outreach to the competitors each prospect is researching.
Teams that read signals this way stop chasing noise. As Josh Baez, Sr. Manager of Demand Generation at NetLine, put it in ZoomInfo's 2026 GTM Predictions report, "When sellers trust the signal, prioritization changes, and spend follows."
This is also where intent data meets your other buying signals. Funding rounds, leadership changes, technology installs and hiring spikes all explain why an account is researching now. ZoomInfo's hiring data shows that 1 in 13 companies with a hiring surge are now staffing up to build with AI. When one of those companies also surges on AI tooling topics, the hiring signal tells a rep what the research is for. Our guide to signal-based selling shows how to stack these signals.
How to Use Intent Data
Intent data creates value when it changes what a team does today, and the payoff comes from acting first. ZoomInfo customers reached the buyer before the competition in 27% of new deals, according to ZoomInfo's 2025 Customer Impact Report. These are the main ways sales, marketing and customer success teams put intent to work.
1. Prioritize in-market accounts
Score every account on fit and intent together, then work the accounts that are high on both first. A simple model works well. Tier accounts A to C on ICP fit, then move any A or B account with a high intent score to the top of the rep's list for that week. Reps then spend their time on accounts already researching the problem, which lifts the hit rate of outbound lead generation. Our guide to intent data for sales covers how to build those lists.
2. Time and personalize outreach
An intent topic tells a rep what the account cares about right now, so lead with that problem in the first email. Never tell a prospect you can see their research, since it reads as surveillance and kills the reply. Teams can build sales email sequences by intent topic, so each account gets messaging that matches its research.
Randstad has seen the payoff. “We’re closing deals 40% faster because we can craft better emails and talk about what they care about,” an Area VP at Randstad said in ZoomInfo’s 2025 Customer Impact Report.
Timing matters just as much. Intent decays quickly, so the best time to reach out is the week a spike appears, while the account is still forming its shortlist.
3. Strengthen lead and account scoring
Add intent as a weighted input to your lead qualification and account scoring models, next to fit, engagement and opportunity data. A lead from an account surging on your category deserves a faster response than the same lead from an account showing no research. Intelligent lead routing can then send high-intent leads to the right rep the moment they arrive.
4. Run account-based marketing on live signals
Intent turns static ABM lists into audiences that follow real buying behavior, and ABM software can activate those audiences across channels. Marketing teams running account-based marketing can move surging accounts into paid campaigns, match ad and landing-page content to each account's research topic and pull back spend from accounts that have gone quiet.
Smartsheet lifted MQLs 84% and raised opportunity rates 26% with intent-triggered prioritization.
5. Protect and grow existing customers
Intent data works on your customer base too. A customer researching your competitors, or alternatives to your category, is a churn risk worth flagging to the account team before the renewal conversation. Intent belongs in any churn prediction model for that reason. Feed those signals into your customer health score alongside product usage and support history.
A Motorola regional sales manager saw this firsthand. After an intent alert showed their largest account researching competitors, they set up a meeting and moved the customer to their cloud offering, saving the customer over $2 million. “I would have never known without ZoomInfo and the intent feature,” they said in ZoomInfo’s 2025 Customer Impact Report.
The same signals reveal growth. A customer researching a problem that another one of your products solves is an upsell conversation waiting to happen, which makes intent a core input to customer expansion plays.
6. Automate the response
The fastest teams don’t wait for a rep to check a dashboard. They set workflow automation that fires when an account crosses an intent threshold, such as creating a CRM task, enrolling contacts in a sequence or adding the account to an ad audience.
Safety Services shows what that looks like in practice. It built custom intent topics and used workflows to route its most in-market accounts straight to sales.
"With intent, we're identifying the companies and contacts most ready to buy, warming those leads, qualifying them and then routing them to sales."
The next step is handing the whole play to AI agents, covered in the ZoomInfo section below.
Challenges of Using Intent Data
Intent data has real limits, and knowing them up front keeps a program from stalling.
Signal noise. Students, researchers, job seekers and competitors all consume content, and some topics attract broad, casual reading. Without fit filters and thresholds, teams chase false positives.
Data freshness. Intent decays quickly. A spike from last month may belong to an account that has already picked a vendor.
Account resolution. IP-based matching is weaker for remote workers and small companies, so coverage varies by segment.
Privacy and compliance. Intent data has to come from consented sources and meet GDPR, CCPA and your own governance rules. Ask every provider how it sources data and which certifications it holds.
Integration friction. Intent that lives in a separate dashboard creates manual work. Signals need to reach the CRM platform your team runs. They also need to reach marketing automation and the sales engagement platforms your teams already use.
Data quality underneath. An intent spike is only useful if the contact data behind it is accurate. B2B data decay means outreach to an in-market account can still bounce if the records are stale. Regular CRM hygiene keeps those records usable.
The activation gap. A raw score doesn't tell a rep who to contact or what to say. Without context and a clear play, signals pile up unused while the buying window closes.
Types of Intent Data Providers
Intent data providers fall into five groups. Each one sees a different slice of buyer research and leaves a different amount of work to your team.
Provider type | Examples | What you get | Watch for |
Data co-ops | Bombora | Third-party topic surges across a publisher network | Needs another tool to act on the signal |
ABM platforms | 6sense, Demandbase | Intent plus account scoring, ads and orchestration | Account-level and built mainly for marketing |
Review sites | G2, TrustRadius | Late-stage second-party signals | Only covers research on their own site |
Visitor identification | Leadfeeder, Lead Forensics | First-party company identification | Only covers your own traffic |
All-in-one GTM platforms | ZoomInfo | Intent plus verified contacts, context and AI agents | Broader than a pure intent tool |
When you evaluate providers, ask these questions:
Coverage. Which sources does the data come from, and how much of your market can it see?
Topic quality. How many topics does it track, and can you create custom topics for niche problems?
Freshness. How often are signals updated, daily or weekly?
Resolution. Does it resolve intent to accounts only, or to contacts as well?
Transparency. Can you see why an account is surging and which topics drove the score?
Contact data. Does it include verified contacts for the accounts it flags, or will you source them elsewhere?
Activation. Can signals trigger workflows, sequences, ad audiences and agents without manual exports?
Integrations. Does it connect natively to your CRM and marketing automation, and does it offer an API and MCP server for AI agents?
Compliance. Which privacy certifications does it hold, and how is consent collected?
For a full comparison of 15 providers, see our guide to the best buyer intent data platforms.
From Signal to Action: How ZoomInfo Turns Intent Spikes Into Pipeline
Every challenge above comes back to one gap, the time between a spike and a useful touch. ZoomInfo's GTM Intelligence Report 2025 cites Harvard Business Review research showing that waiting more than five minutes to respond to an inbound lead cuts connect rates by 10x. Intent works the same way, because a spike that waits days for research and outreach has often cooled by the time a rep gets to it.
ZoomInfo is an all-in-one AI GTM Platform built to close that gap, and over half the Fortune 500 already run GTM on ZoomInfo. "An agent is only as good as the context it can reach," says Henry Schuck, ZoomInfo's founder and CEO. So the platform starts with the data and context behind every signal, then puts agents to work on it.
The data and context behind every signal
Every intent signal in ZoomInfo is backed by three layers, so it reaches your team with the contacts, the context and the access needed to act on it.
Data is the foundation. ZoomInfo pairs intent with 500M contacts and 100M companies, including 135M+ verified phone numbers and 200M+ verified business emails. A team of 300+ researchers keeps that data current, with up to 95% accuracy on first-party data. When an account spikes, reps and agents get verified contacts at that account along with the signal, and CRM enrichment keeps those records up to date.
The GTM Context Graph explains the why. It joins intent signals and web visits with CRM records, emails, calls, job postings, funding rounds, champion moves and contracts on one account record, processing 1.5B+ data points a day. Call history comes in from conversation intelligence, so a rep sees that an account is researching and also what the buyer said on the last call. For deals already in motion, intent feeds deal progression management by showing which stakeholders are re-engaging.
Universal access puts it everywhere. Sellers see intent and next best actions in GTM Workspace, while marketers and RevOps teams build intent-triggered audiences in GTM Studio. Data enrichment orchestration keeps signals and records flowing into every connected system. Enterprise data delivery brings the same intent data into your data warehouse.
How to use ZoomInfo intent data
Setting up ZoomInfo intent comes down to four steps:
Choose your intent topics. Pick topics tied to the problems you solve, your product category and your competitors, and add custom topics for niche problems. Guided Intent then recommends the topics your won deals researched before they became opportunities.
Filter by signal strength and fit. ZoomInfo scores each account's signal strength as low, medium or high. Combine that score with your ICP filters so reps only see in-market accounts worth pursuing.
Work Account-Level and Person-Level Intent together. Start with the accounts surging on your topics, then use Person-Level Intent to find the contact behind the research. Verified contact data covers the rest of the buying group.
Push signals into your workflow. Sync intent to your CRM, alert account owners and enroll contacts in sequences with ZoomInfo Workflows, or hand the whole play to an Agent Team.
Intent spikes as an Agent Teams trigger
At the heart of ZoomInfo's execution layer is Agent Teams. It's the multi-agent product in GTM Studio. Give your agents an outcome, not a task, such as turning a surging target account into a booked meeting. A team of specialist agents then carries the play, while a supervisor agent checks each step against your rules.
Every Agent Team starts with a trigger, the event that tells agents where to begin. Triggers can be scheduled, sent by webhook, fired by a CRM event or based on a signal, and an intent spike is one of the strongest signals to start from. Here's how an outbound play opened by intent runs:
The signal opens the play. A target account crosses your intent threshold on a category topic, and the Agent Team picks it up automatically.
Agents research the account. An AI account research agent builds a brief from the intent topics, firmographics, technographics, recent Scoops, CRM history and past call notes.
Agents map the buying group. The team builds a structured list of the key contacts, starting with the person tied to the research when Person-Level Intent is available.
Agents prepare outreach. Messaging is drafted around the topic the account is researching, then the contacts are enrolled in a sequence or routed to the account owner with the brief attached.
The supervisor applies your rules. At each step, the Agent Team decides whether to act, wait, suppress or escalate. It can hold back an account with an open opportunity, wait on a contact who was emailed last week or escalate a strategic account to its owner.
Everything lands in the CRM. Agents act natively in Salesforce, HubSpot, Outreach, Salesloft, Slack and Gmail, and every step is written back to the CRM in one audit trail.
The short walkthrough below shows how to set the trigger that starts an Agent Team in GTM Studio, including a signal-based trigger such as an intent spike.
That turns an intent spike into outbound prospecting that starts while the account is still in research mode. The same pattern works for inbound. When a lead from a surging account fills out a form, an Agent Team can handle inbound lead conversion, researching, qualifying, routing and following up within minutes.
Customer success teams can run the same idea on renewals. An Agent Team can scan every account closing in the next 120 days each morning, flag the ones at risk when competitor research lines up with falling usage, explain why and send the account owner a path forward. RevOps teams describe a new play like this in plain language, and Agent Teams builds the workflow from 50+ ready-made agents.
GTM.AI for teams building their own agents
GTM.AI is ZoomInfo's headless option. Its API, CLI and MCP server let Claude, ChatGPT or your own agents pull intent signals and verified B2B data from wherever your team already works. Teams building their own AI sales agents can run Agent Teams plays through it too, with the same guardrails as in GTM Studio.
Verified data matters even more when an agent acts on its own. In ZoomInfo's GTM Bench test of 450 agent runs, specialist data tools returned verified work emails in up to 94% of contact rows, while web search managed 22%. GTM.AI is free to start, with consumption-based credits.
ZoomInfo maintains SOC 2 controls and is ISO 27001 and ISO 27701 certified.
Turn Intent Into Pipeline Before the Shortlist Forms
Intent data tells you which accounts are researching your category right now. The value comes from what happens next. Read each spike against fit, recency and the account's own baseline, then reach the right person with a message about the problem they're researching, while the shortlist is still forming.
The teams that win with intent close the gap between signal and action. ZoomInfo pairs intent with verified contacts and the GTM Context Graph, and Agent Teams turns each spike into a researched account, a mapped buying group and outreach, all written back to your CRM.
Meet with a GTM expert to see how Agent Teams turns intent spikes into pipeline.
Intent Data FAQs
What is an example of intent data?
A target account reading several articles on CRM data enrichment across B2B publishers is an example of third-party intent data. The same account comparing enrichment vendors on G2 is second-party intent, and a visit to your pricing page is first-party intent. Together, the three signals show an account actively evaluating a purchase.
What is the difference between first-, second- and third-party intent data?
First-party intent data comes from your own channels, such as your website, emails and product. Second-party intent data is a partner's first-party data shared with you, such as review-site activity from G2 or TrustRadius. Third-party intent data is collected across external publisher networks and data co-ops, and it shows research before an account reaches your site.
How accurate is intent data?
Accuracy depends on the provider's sources, topic classification, account matching and how it measures baselines. Intent data is best treated as a prioritization signal. It becomes reliable when combined with ICP fit, first-party engagement and verified contact data.
Is intent data GDPR and CCPA compliant?
It can be, when the provider collects data from consented sources and meets privacy rules in each region. Ask providers how consent is collected, which sources they use and which certifications they hold. ZoomInfo maintains SOC 2 controls and is ISO 27001 and ISO 27701 certified.
Should sales contact every account that shows intent?
No. Filter intent by fit, recency and topic relevance first, then act on the accounts that score high on all three. Lower-scoring accounts are better suited to marketing nurture until their signals strengthen. AI can speed up that triage, as our guide to AI lead qualification explains.
What is the difference between intent data and buying signals?
Intent data is one type of buying signal, based on research behavior. Other buying signals include funding rounds, leadership changes, hiring spikes, technology installs and job changes. Combining them explains both that an account is in-market and why.
How does Bombora intent data work?
Bombora runs a data co-op of B2B publishers and websites that share consented content-consumption data. Its Company Surge score compares each company's research on a topic with that company's normal baseline, then flags accounts whose research rises well above it. Bombora data is account-level and is often delivered through other platforms, so teams usually pair it with a CRM, ABM platform or GTM platform to act on it.
How do AI agents use intent data?
AI agents can treat an intent spike as a trigger. With ZoomInfo Agent Teams, a spike can open a play in which agents research the account, map the buying group, draft outreach and write every step back to the CRM. Teams building their own agents can access the same intent data through GTM.AI and its API, CLI and MCP server. ZoomInfo data also powers the Salesforce Agentforce Prospecting Agent, which ranks prospects by intent for mutual customers.