Here is the paradox of intent data in 2026: 98% of marketers say it is fundamental to demand gen, yet only 24% report exceptional ROI. That gap tells you everything. The B2B buyer intent data tools market is estimated at $4.49 billion in 2026 and projected to reach $20.89 billion by 2035 according to Roots Analysis. Billions of dollars flowing into a category where three out of four buyers say the results are just okay.
I spent years at Salesforce and Clari watching teams buy intent data platforms expecting magic. Knowing someone researched "sales intelligence" did not tell them why or when to call. Intent data is powerful, but profoundly misunderstood. Most guides will tell you it is a silver bullet. This one will not.
TL;DR: Intent data reveals which companies are actively researching topics related to your solution, based on content consumption and behavioral signals. It comes in three varieties: first-party (your own properties), second-party (publisher partnerships), and third-party (aggregated web behavior). The real value is not the data itself but how you combine it with other signals. Be warned: 64% of teams collect intent data and struggle to use it well, and high-intent accounts sit in CRMs for 5-7 days before anyone reaches out.
What Intent Data Actually Is
Intent data is behavioral information that indicates a company or individual is researching a specific topic, product category, or business problem. At its simplest, it tracks content consumption: what articles someone reads, what search terms they use, which product pages they visit, and how that activity compares to their normal baseline.
The core idea is straightforward. If an account suddenly starts reading five articles about "CRM migration" in a single week when they normally read zero, something has changed inside that organization. Maybe they got a new CTO. Maybe their contract is up for renewal. Maybe they just had a bad quarter and the board is pushing for change. The intent data does not tell you why. It tells you that something is happening.
Here is the critical distinction most teams miss: intent data measures topic interest, not purchase readiness. A company researching "data security best practices" might be evaluating vendors, or they might be writing an internal policy document. The signal alone does not tell you which. That is why the best teams layer intent data with other indicators, something we will get into later.
According to Forrester's 2025 Wave evaluation of B2B intent data providers, the market now includes 15 significant vendors, and capabilities are advancing rapidly. But the underlying mechanics have stayed remarkably consistent.
“The account and contact signals are key for reaching out at important times, and the value-add messaging it creates unique to every contact helps save time and efficiency.”
Daniel Pitman
Mid-Market Account Executive, Black Swan Data
Types of Intent Data: First-Party, Second-Party, and Third-Party
Not all intent data is created equal. The type you use determines its accuracy, scale, and how much you can trust it.
First-Party Intent Data
This is behavioral data collected from your own digital properties: your website, product pages, pricing page, blog, and email campaigns. If a prospect visits your pricing page three times in a week, that is first-party intent data, and it is the most reliable signal you will find.
Examples: Website visits (especially high-intent pages like pricing or case studies), content downloads, webinar registrations, email opens and clicks, product trial activity.
Strengths: High accuracy, you know exactly who did what. Zero privacy concerns since you collected it directly. Free to generate.
Weaknesses: Extremely limited scope. You only see people who have already found you. According to research from 6sense, buyers stay anonymous for roughly 75% of their research journey. First-party data misses that entire window.
Second-Party Intent Data
Second-party data comes from a partner organization that collects first-party data and shares it with you. The most common example: review sites like G2 or TrustRadius. When a buyer reads reviews in your software category on G2, G2 can tell you that account is actively comparing solutions, even if they never visited your site.
Examples: G2 buyer intent reports, TrustRadius category research signals, publisher content syndication data, event attendance data.
Strengths: Catches buyers who are evaluating your category but have not found you yet. More reliable than third-party because the source is known and the data collection method is transparent.
Weaknesses: Limited to the partner's audience. If your buyers do not use G2, this data set has blind spots.
Third-Party Intent Data
Third-party intent data is collected by aggregating web behavior across thousands of B2B websites, typically through a data cooperative. Bombora is the most well-known example. Their Company Surge data tracks content consumption across a cooperative of 5,000+ websites, categorizing activity into roughly 12,000 topic clusters.
Examples: Content consumption patterns across the open web, search query data, social media engagement patterns, and ad interaction data.
How surge scoring works: Providers like Bombora establish a baseline of normal content consumption for each company across each topic. When consumption spikes significantly above baseline (typically measured as a surge score of 70+ on a 100-point scale), the account gets flagged as "surging" on that topic. Scores are updated weekly.
Strengths: Massive scale. You can identify accounts researching your category even if they have never heard of you. Useful for building target account lists and prioritizing outreach.
Weaknesses: Noisy. IP-based identification means you know the company, not the person. Topic clusters can be broad. A company surging on "cloud security" might be a buyer, a competitor doing research, or a journalist writing a story. Studies show that false positives remain a significant challenge, with researchers, students, and competitors all triggering the same signals as genuine buyers.
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The Dark Funnel Problem Most Guides Ignore
Before we go further, we need to address the elephant in the room. Intent data only captures the visible funnel: content consumption on tracked websites, search behavior, ad clicks. It misses what Chris Walker, founder of Passetto and Refine Labs, calls the "dark funnel."
Walker's research found that 97% of revenue came from channels that attribution software said drove zero revenue. Think about where buyers actually research: Slack communities, private LinkedIn DMs, podcast conversations, peer recommendations at dinners, internal discussions no tracking pixel will ever see.
Most B2B buying decisions start in the dark funnel. A CRO hears about a tool on a podcast, mentions it to their VP of RevOps, who asks a friend at another company. Only after all that does anyone visit a website. By the time intent data picks up the signal, the decision is already half-made.
This does not mean intent data is useless. It means you should be realistic about what it captures. Use it as one layer, not the foundation.
How Intent Data Works Under the Hood
The technical pipeline behind most intent data platforms follows a similar pattern, regardless of the vendor.
Step 1: Data collection. Thousands of B2B publishers embed tracking pixels or share content consumption logs (third-party). For first-party, it is your own analytics stack. For second-party, a direct integration with a partner like G2.
Step 2: Company identification. Web traffic is mapped to companies using reverse IP lookup, cookie matching, and probabilistic modeling. Accuracy varies wildly — the best providers claim 80-90% match rates for enterprise accounts, but smaller companies are harder to resolve.
Step 3: Topic classification. Content consumed is mapped to a taxonomy of topics using NLP. Bombora uses roughly 12,000 topics. The quality of this classification directly affects signal accuracy.
Step 4: Baseline comparison and scoring. Each company's recent consumption is compared against their historical baseline. A score of 85 out of 100 means significantly more content consumption on that topic than usual.
Step 5: Delivery. Scores are pushed to your CRM, sales engagement platform, or ABM tool, typically on a weekly cadence.
The entire pipeline depends on two assumptions: that content consumption correlates with purchase intent, and that IP-to-company mapping is accurate. Both are imperfect, which is why smart teams treat intent data as one input among many.
Salesmotion's Take
I have watched teams buy intent data platforms expecting a pipeline machine and end up with an expensive spreadsheet. The hard truth is that knowing a company researched "sales intelligence" tells you almost nothing about why they are looking or whether they will take your call. Intent data is a layer, not a solution. The teams I have seen get real value from it are the ones who combine intent with account context — what showed up on the earnings call, who just got promoted, what strategic initiative the CEO is publicly committed to. That context is what turns a surge score into an actual conversation.
Semir Jahic
CEO & Co-Founder, Salesmotion
“Salesmotion helps you spot signals from prospect accounts, news items / job hiring alerts etc that indicate that now is a good time to reach out with a well-crafted message.”
Rob Douglas
Director of Sales, icit business intelligence
Intent Data vs. Buying Signals: An Important Distinction
This is where things get interesting for sales teams, and where a lot of confusion lives.
Intent data tells you a company is researching a topic. It measures content consumption and tells you "this account is surging on cloud security." Useful, but inherently anonymous and topic-level. You know the company, not the person. You know the topic, not the trigger.
Buying signals are real-world events that create or indicate buying conditions. A new VP of Sales gets hired. An earnings call reveals a new strategic initiative. A competitor gets acquired. Concrete, verifiable, with clear business implications.
Landbase's research frames the distinction well: "Intent data identifies accounts in active evaluation mode. Signal-based selling identifies accounts entering or approaching that mode." Intent data is reactive — it tells you someone is already shopping. Signals are proactive — they tell you a company is about to start shopping, before the competition knows.
Practical example: intent data says Acme Corp is surging on "sales enablement tools." That is a clue. But a buying signal tells you Acme just hired a new CRO from a company that used your product, and their CEO mentioned "sales productivity" on last quarter's earnings call. That is context you can act on immediately.
The most effective teams use both. Intent gives breadth, signals give depth. For a detailed breakdown, see our guide on buying triggers in B2B sales.
A signal-based feed surfaces verified, source-cited events rather than anonymous topic scores.
How B2B Sales Teams Use Intent Data in Practice
Theory is great. Here is how teams actually put intent data to work.
1. Account Prioritization
The most common use case. Instead of working a static list alphabetically or by company size, reps prioritize accounts showing active research behavior. According to industry data, 96% of B2B marketers report success when using intent data for campaign targeting and prioritization.
Workflow example: Every Monday morning, your SDR team pulls a list of accounts that surged on your core topics in the past week. They cross-reference against your ICP criteria. The accounts that match both filters go to the top of the call list. Simple, but effective.
The catch: high-intent accounts sit in CRMs for an average of 5-7 days before anyone reaches out. In competitive markets, your rivals may be calling within hours. Speed-to-lead matters as much as having the data in the first place.
2. Personalized Outreach Timing
Knowing when an account is researching is as valuable as knowing who. Teams use intent spikes to trigger outreach sequences. Instead of sending cold emails on a calendar schedule, reps reach out when the data says an account is actively looking.
3. Content and Ad Targeting
Marketing teams use intent data to serve relevant ads to surging accounts. If an account is researching "CRM migration," you show them your migration guide, not a generic brand ad. This is the foundation of most ABM display advertising.
4. Competitive Displacement
Some providers detect when accounts research specific competitors. If an account surges on your competitor's brand name, teams trigger competitive displacement plays.
5. Layering Intent with Signals for Maximum Impact
Here is where the real power lies. Intent data alone tells you a company is interested. Buying signals tell you why. Layer them together and you get timing plus context.
For example, you see an account surging on "account intelligence tools" (intent data). Then you see they just hired a new VP of Revenue Operations who previously used your platform (buying signal). Now you know who to call, what to say, and why the timing is right.
Evaluating Intent Data Quality: Signals vs. Noise
Not all intent data is worth acting on. The gap between the best and worst providers is enormous. Here is what to evaluate.
Source transparency. Can the provider tell you exactly where their data comes from? Opaque "data cooperatives" that refuse to disclose member sites make it impossible to assess quality. Forrester's analysts have warned that "some data sets will have major gaps in coverage, major biases in representation, or unseen problems around data collection... most of the time, you won't really know what you're getting until you're six months into a contract."
Recency and refresh rate. Intent data decays fast. A signal from three weeks ago is worth far less than one from yesterday. Most topic-based providers refresh weekly. Real-time event signals (leadership changes, earnings calls, funding rounds) are more actionable because you can verify them independently and act immediately.
Company match accuracy. How reliable is the IP-to-company resolution? Enterprise accounts with dedicated IP ranges are relatively easy. Mid-market companies using shared cloud infrastructure are harder. Ask providers about their match rate methodology and how they handle ambiguous signals. Users of some major platforms have reported that prospects "often deny the activities attributed to them," which raises serious questions about how IP-resolved signals translate to real buying behavior.
Topic specificity. "Cloud computing" is too broad to be actionable. "Cloud security posture management for AWS" is specific enough to trigger relevant outreach. Evaluate how granular the topic taxonomy is and whether it maps to your actual product categories.
Signal-to-noise ratio. Research consistently shows that common false positives include competitor research, academic browsing, journalistic investigation, and employee professional development. The best teams build composite scoring models that require multiple positive signals before flagging an account as high priority.
Actionability. The ultimate test: can a rep do something with this data in under 60 seconds? If acting on a signal requires 20 minutes of additional research, it is not saving time. Verified, source-cited signals outperform anonymous topic scores in practice because reps can reference the actual earnings call or news article directly in outreach.
Verified, source-cited signals give reps immediate context — no additional research needed to craft a relevant message.
Why Frameworks Break at Scale
Here is the uncomfortable truth about intent data. Most teams start with a simple framework: high surge score equals hot account, low score equals cold. That works when you are monitoring 50 accounts. It falls apart at 5,000.
At scale, several problems emerge:
Volume overwhelm. When dozens of accounts surge simultaneously, the prioritization value disappears. If everything is a priority, nothing is.
Decay mismanagement. Weekly refreshes mean you are often acting on stale signals. The buying window for some decisions is measured in days, not weeks.
Single-source dependency. Relying on one intent data provider creates blind spots. Different providers see different slices of the web. An account might show zero intent on Bombora but be actively researching on sites outside their cooperative.
Missing context. A surge score tells you what a company is researching but not why. Without the "why," reps default to generic messaging: "I noticed your company is interested in X." That message is better than pure cold outreach, but barely.
The ROI reality check. Despite the hype, only 24% of teams report exceptional ROI from intent data. The majority — 64% — collect it but struggle to operationalize it effectively. This is not a data quality problem alone. It is a workflow and context problem.
The teams that scale successfully treat intent data as one layer in a multi-signal approach. They combine topic-based intent with event-driven signals (leadership changes, earnings calls, hiring patterns), first-party engagement data (website visits, email opens), and firmographic fit. No single data source carries the full picture.
Key Takeaways
- Intent data measures topic interest, not purchase readiness. It tells you a company is researching, not that they are ready to buy. Always layer it with other signals.
- First-party data is the most accurate but the most limited. Use it to identify high-intent visitors, but do not rely on it alone since it misses 75% of the buyer journey that happens anonymously.
- Third-party intent data provides scale but introduces noise. Surge scores are a starting point, not a verdict. Build composite scoring models that require multiple positive signals.
- The dark funnel is real. Most buying decisions start in channels intent data cannot see — peer conversations, Slack communities, podcast recommendations. Be realistic about what you are measuring.
- Speed matters as much as data. High-intent accounts sitting in your CRM for a week are worse than no data at all. Your competitors are moving in hours.
- Data quality varies enormously between providers. Evaluate source transparency, refresh rate, topic specificity, and company match accuracy before committing. Expect at least six months before you truly understand what you are getting.
Frequently Asked Questions
What is intent data in simple terms?
Intent data is information about the online research behavior of companies that suggests they may be in the market for a product or service. It tracks what content companies consume and how that activity compares to their normal patterns. When consumption spikes, that is captured as an "intent signal." Sales and marketing teams use these signals to prioritize outreach to accounts most likely to be evaluating solutions.
How is first-party intent data different from third-party?
First-party intent data comes from your own digital properties: website, emails, product usage. It is highly accurate but only captures activity from people who have already found you. Third-party intent data is collected across thousands of external websites through data cooperatives and publisher networks. It has much broader reach but is noisier and relies on IP-to-company matching that can be imprecise. Most effective teams use both: third-party to identify accounts early and first-party to confirm engagement as accounts progress.
What is the difference between intent data and buying signals?
Intent data tracks topic-level research behavior — it tells you a company is consuming content about "sales enablement." Buying signals are specific, verifiable business events: a new executive hire, a funding round, or a strategic shift mentioned in an earnings call. Intent data is probabilistic and anonymous. Buying signals are concrete and attributable. The best B2B sales teams combine both. For a complete breakdown of signal-based selling approaches, see our guide.
Is intent data worth the investment for small sales teams?
It depends on your sales motion. If your team is selling into a defined set of target accounts (under 500), the ROI question is straightforward: does knowing which accounts are actively researching save your reps enough time to justify the cost? Industry benchmarks suggest that 61% of teams achieve full ROI within six months. For smaller teams, the bigger question is whether you need traditional topic-based intent data or whether verified buying signals would serve you better. A three-person SDR team does not need 500 surging accounts. They need the 10 accounts where timing and context make outreach worthwhile.
How do you avoid false positives with intent data?
False positives are the biggest practical challenge. Competitors, journalists, academics, and employees doing professional development all trigger the same signals as genuine buyers. The best approach is composite scoring: requiring multiple positive signals before flagging an account as high priority. An account surging on your topic cluster AND showing first-party website visits AND matching your ICP criteria is far more likely to be real than a high surge score alone. Also weigh signal sources differently — verified event-based signals with traceable sources are more reliable than anonymous topic consumption.
What is the dark funnel and how does it affect intent data?
The dark funnel refers to buying activity in channels no tracking pixel can see: private Slack groups, peer recommendations, LinkedIn DMs, podcast conversations, and internal meetings. Most B2B buying journeys start in these invisible channels. Intent data only captures the visible portion — articles read, search queries made, pages visited on tracked websites. By the time intent data flags an account, significant buying activity has likely already occurred. Treat intent signals as confirming evidence, not the start of a buyer's journey.
