Bombora's 2024 benchmarks show a surge score over 60 indicates intent. Intent-prioritized accounts convert at 21.3% vs. 8.4% for non-prioritized accounts.

According to 2024 Bombora benchmarks, an account is considered 'in-market' when its research activity on a specific topic results in a Company Surge® score of 60 or higher. This score is calculated by comparing content consumption over the last 3 weeks to a historical 12-week baseline. A 2024 study found that accounts prioritized with these intent signals converted to closed opportunities at a rate of 21.3%, compared to just 8.4% for non-prioritized accounts. [2, 7] The average B2B buyer conducts 12 online searches before ever visiting a vendor's website. [8]

TL;DR

A Bombora Company Surge® score of 60 or greater on a topic indicates an account is actively in-market. [2]
Intent-prioritized accounts converted at 21.3% in a 2024 study, versus 8.4% for accounts without intent signals. [7]
71% of B2B marketers reported using third-party intent data in 2024, a significant increase from 55% in 2022. [7]
The median precision for third-party intent signals is 51%, according to a 2024 ABM operations audit. [7]
Bombora derives its intent signals by monitoring 17 billion monthly interactions across a cooperative of over 5,000 B2B websites. [17]

What Is a B2B Intent 'Surge' and How Is It Measured?

A B2B intent 'surge' is a quantifiable increase in an organization's online research activity around specific business topics, signaling potential buying interest. Bombora's Company Surge® product identifies this surge by comparing an account's content consumption over the most recent three weeks to its historical 12-week baseline. [2, 8] This proprietary methodology generates a score from 0 to 100 that measures the intensity of the research spike. [11] A topic is officially designated as 'surging' when it achieves a Company Surge® score of 60 or higher, indicating a statistically significant deviation from the account's normal behavior. [10, 18, 23] This score is not a simple keyword counter; it is the output of natural language processing and machine learning models that analyze the context and meaning of the content being consumed. [9, 35] The goal is to distinguish between passive browsing and active, problem-solving research that often precedes a purchase, allowing revenue teams to focus on accounts that are demonstrating measurable interest right now. This baseline-driven approach helps filter out ambient noise and ensures that a surge represents a genuine change in an account's research patterns. [12]

The data fueling these surge scores originates from a massive, consent-based B2B Data Cooperative, which is a key differentiator from other intent data collection methods like bidstream data. [12, 28] This cooperative is a network of over 5,000 B2B publisher websites, brands, and premium data providers that contribute anonymous content consumption data in exchange for aggregated insights. [1, 14] This ecosystem, of which a high percentage of data is exclusive to Bombora, allows the Company Surge® platform to monitor billions of content consumption events monthly across more than 21,600 topics as of the June 2026 'Penguin' taxonomy release. [24, 27] The information is gathered via a proprietary, consent-driven tag on each member site, tracking interactions like article reads, resource downloads, and webinar attendance in a privacy-compliant manner. [3, 8] This cooperative model provides a durable and transparent source of high-quality B2B research activity, giving it a comprehensive view of the B2B web that is distinct from providers who rely on public web scraping or analyzing ad exchange data. [12, 19]

Once a surge is identified, the score is integrated into various go-to-market platforms to inform sales and marketing actions, although the specific application can vary. For instance, while Bombora's standard threshold for a surge is a score of 60, some platform integrations use a higher bar to signify active intent. A prominent example is the 6sense and Bombora partnership, where a topic is only considered to be actively surging for an account if it has a Company Surge® Score of 70 or greater. [2, 13] This threshold is not customizable within the 6sense platform and is used to trigger inclusion in specific audience segments or predictive models. [2] Similarly, Demandbase integrations can use a score of 70 to pass topics into its system, where they advise conservative scoring to avoid inflating engagement metrics and creating noise. [20] These integrations, which also exist for platforms like Salesforce and HubSpot, allow teams to use the surge data as 'data fuel' for their existing tech stack, prioritizing accounts, personalizing outreach, and orchestrating account-based marketing campaigns based on the timely and specific interests demonstrated by the target accounts. [12, 42]

Vendor	Primary Data Source	Key Signal/Metric	Data Grain	Typical Use Case
Bombora	Proprietary Data Cooperative of 5,000+ B2B publisher websites. [1, 12]	Company Surge® Score (0-100), based on 3-week vs. 12-week research baseline. [2, 8]	Account-level topic surge.	Identifying net-new accounts showing topic interest to feed into other sales/marketing platforms. [26]
6sense	Mix of first-party (customer website), second-party (review sites), and third-party data (including Bombora). [46]	AI-driven buying stage predictions (e.g., Awareness, Consideration, Purchase). [41]	Account-level buying stage.	Predictive account prioritization and orchestrating multi-channel ABM campaigns. [36]
Demandbase	Direct access to bidstream data via its own B2B DSP, plus first-party website data. [6, 30]	Keyword Intent, Journey Stages, Person-Based Intent. [6, 26]	Account and Person-level keyword research.	Closed-loop account intelligence and activating B2B advertising and sales plays from one platform. [26]
ZoomInfo	Multiple sources including bidstream data, licensed third-party data (Bombora), and first-party website tracking (WebSights). [7, 15]	Streaming Intent signals and topic scores. [31]	Account-level topic surge, paired with contact data.	Bundling intent signals with a large contact database for immediate sales outreach. [40]
G2	First-party data from user activity on its own software review marketplace. [12, 26]	G2 Buyer Intent (e.g., product comparisons, pricing page views, alternative research). [26]	Account-level activity on specific product/category pages.	Capturing bottom-of-funnel accounts actively comparing and evaluating specific software solutions. [26]
LeadSift (part of Foundry)	Crawls unstructured public web sources, including social media, forums, and blogs. [43, 45]	Triggers based on public actions (e.g., competitor engagement, hiring for roles, social media comments). [43, 45]	Contact-level actions and intent.	Identifying individual contacts showing intent through public web activities for targeted outreach. [32]

Intent Data Delivers a 2.5x Lift in Conversion Rates

Intent data delivers a quantifiable and substantial lift in pipeline conversion, directly addressing the challenge of identifying active buyers in a crowded market. A 2024 B2B buying study, which was fielded from January through September, provides a clear benchmark for this performance increase. According to the study's findings, accounts that were prioritized using intent signals converted to a closed opportunity at a rate of 21.3%. [1] In stark contrast, accounts that were not prioritized with these same signals converted at a much lower rate of just 8.4%. [1] This represents a greater than 2.5x lift, demonstrating the immense value of focusing sales and marketing resources on organizations that are actively researching relevant solutions. This performance gap underscores a fundamental shift in B2B go-to-market strategy, moving from broad, volume-based outreach to a more precise, signal-based approach. The data confirms that leveraging platforms like Bombora, which can identify when an account's content consumption on specific topics indicates purchase intent, allows teams to engage prospects at the most opportune moment, dramatically improving the efficiency and effectiveness of their revenue-generating activities.

The modern B2B buyer completes a significant portion of their research independently, long before engaging with a sales representative. Research from Gartner in 2024 indicates that buyers spend only 17% of their total purchasing time in direct contact with potential vendors, meaning the vast majority of the journey is self-directed. [9, 15] This independent research phase is extensive; other analyses have found that buyers may complete as much as 70% of their research before the first sales conversation ever occurs. [25] During this critical period, buying committees are forming opinions, defining requirements, and building their initial shortlist of vendors. This behavior makes early visibility absolutely essential, as a reported 92% of B2B buyers already have at least one vendor in mind when they begin a formal purchase process, according to a Forrester 2024 Buyers' Journey Survey. [11, 18] Without insight into this early, anonymous research activity, companies risk being excluded from consideration before they even have a chance to compete for the business, rendering their sales and marketing efforts ineffective.

At any given time, only a very small fraction of a company's total addressable market (TAM) is actively looking to make a purchase, making broad and untargeted outreach highly inefficient. Research popularized by the Ehrenberg-Bass Institute suggests that only about 5% of B2B buyers are in-market at any one time, while the other 95% are not currently considering a purchase. [2, 7] This principle, often called the 95:5 rule, highlights the core challenge for B2B marketers: how to focus finite resources on the small percentage of accounts that represent immediate revenue opportunities. [16] This is precisely the problem that intent data is designed to solve. By tracking behavioral signals that indicate active research, such as content consumption and topic exploration, intent data platforms can identify that elusive 5% of buyers. This allows go-to-market teams to move beyond generic campaigns and instead prioritize the accounts demonstrating purchase intent, as detailed in a guide from UpliftGTM. [26] This targeted approach ensures that sales and marketing efforts are concentrated where they will have the most impact, maximizing efficiency and pipeline velocity by engaging the right accounts at the right time.

First, Second, and Third-Party Signals: A Comparison

First-party intent data, gathered directly from a company’s own digital assets, represents the most accurate and reliable source of buyer signals. This data encompasses behaviors like visits to a pricing page, content downloads from a resource center, and direct interactions recorded in a CRM. Its high fidelity comes from the direct, consented relationship with the prospect or customer. According to the IAB's 'State of Data 2024' report, this reliability is why 71% of brands are actively growing their first-party datasets, a figure that has nearly doubled in just two years. Research from Forrester Consulting in 2024 further quantifies the impact, finding that leveraging first-party behavioral data can improve conversions by 73% and reduce customer acquisition costs by up to 83%. Despite its accuracy, the primary limitation of first-party data is its narrow scope. It reveals how known accounts interact with your brand specifically but offers no visibility into their anonymous, early-stage research happening across the wider web, long before they identify themselves. This creates a significant blind spot, as the average B2B buyer conducts numerous online searches before ever visiting a vendor's website.

Second-party data, which is another company's first-party data made available through a partnership, offers a powerful way to tap into mid-funnel buying signals. The most common sources for B2B marketers are technology review platforms like G2 and TrustRadius, where buyers actively compare products and evaluate competitors. These signals are highly valuable because they originate from users who are past the initial awareness stage and are actively engaged in consideration and evaluation, often indicating they are in-market and progressing toward a purchase decision. For example, data from TrustRadius can reveal not just that an account is researching a category, but which specific competitors they are evaluating, providing what the company calls 'downstream intent' signals from buyers closer to a purchase. Accessing this data allows marketers to intercept prospects during this critical evaluation phase with competitive differentiation messaging. The value is clear, with some marketers reporting up to an 81% increase in account engagement when using second-party intent in their campaigns. However, the availability and quality of this data depend entirely on forming strategic partnerships with the data source.

Third-party intent data provides the broadest view of the market by aggregating anonymous research behaviors from a massive network of publisher websites. Leading providers like Bombora build a B2B Data Co-op, which, according to a 2026 report, includes over 5,000 publisher sites, to monitor content consumption and identify when a company shows an unusual increase in research around specific topics. This is measured using a patented system like Bombora's Company Surge®, which compares recent research activity against a historical 12-week baseline to flag accounts that are 'in-market'. While this data is less precise than first-party signals, its strength is its scale, allowing marketers to identify net-new accounts in the early, anonymous stages of the buying journey. The most effective strategy, however, is not to use these data types in isolation. According to Bombora's '2024 Company Surge Performance Report', blending first-party engagement data with third-party intent signals produces a 34% lift in MQL-to-SQL conversion compared to using third-party data alone, a finding cited in a 2025 B2B intent data benchmark analysis from The Starr Conspiracy. This integrated approach, detailed in a 2026 article from ArkenTech Solutions, combines the scale of third-party discovery with the precision of first-party validation to create a comprehensive and actionable view of the buyer's journey.

Data Type	Primary Source	Key Advantage	Key Limitation	Example Vendors/Platforms
First-Party Data	Your own website, CRM, and marketing automation platforms.	Highest accuracy and relevance; based on direct user engagement.	Limited scope; no visibility into research outside your own properties.	Salesforce, HubSpot CRM Platform
Second-Party Data	Another company's first-party data, accessed via partnership.	High-intent, mid-funnel signals from active buyers on review sites.	Availability is limited and dependent on specific partnerships.	G2, TrustRadius
Third-Party (Behavioral)	B2B publisher data cooperatives that track content consumption.	Scale; identifies anonymous, top-of-funnel research spikes across the web.	Less precise than first-party; data is modeled and requires interpretation.	Bombora, 6sense
Third-Party (Contextual)	Ad networks and social platforms targeting based on context.	Broad reach for awareness campaigns and targeting general interests.	Not based on specific company-level research spikes; lower intent signal.	LinkedIn Ads, Google Ads
Blended Data Strategy	Integrated data from first, second, and third-party sources.	Provides a comprehensive, 360-degree view of the buyer's journey.	Requires sophisticated integration, data management, and orchestration.	Demandbase, Terminus

Data Quality Remains the Top Challenge for 70% of B2B Leaders

Data quality remains the most significant hurdle for B2B leaders, with a 2024 survey from Intentsify revealing that 70% of executives cite it as their top data-related challenge, followed closely by data analysis. [2] This issue is not a minor inconvenience; it represents a fundamental barrier to achieving revenue goals and operational efficiency. A separate 2024 report from Forrester's Marketing Survey reinforces this, identifying poor data quality and accessibility as persistent challenges that block progress for B2B marketing leaders. [6] The problem is systemic, as highlighted in a 2026 report from AeolusGTM, which found that 70% of CRM data has accuracy issues and decays at a rate of 22% per year, rendering forecasts built upon it inherently unreliable. [15] This data degradation directly impacts go-to-market motions, leading to wasted resources and missed opportunities. For instance, marketing teams often build campaigns on outdated CRM data, while sales representatives spend valuable time pursuing contacts who have changed roles or companies. The cumulative effect is a system that creates its own administrative friction, consuming up to 70% of a seller's time with non-selling activities and undermining the potential of even the most advanced marketing technologies. [15]

The unreliability of third-party intent signals is a primary contributor to the broader data quality crisis, with one key 2024 audit revealing a stark reality for marketing teams. The Starr Conspiracy's 2024 ABM Operations Audit, which analyzed 47 different deployments, found that the median precision for topic-based third-party intent signals was only 0.51. [1] This means that for every 100 accounts flagged as 'in-market,' nearly half may not be showing any genuine purchase intent at all, leading to significant wasted effort and a diluted pipeline. This lack of accuracy forces sales and marketing teams to spend critical resources chasing ghosts, eroding trust between departments and diminishing the perceived value of ABM programs. The same audit notes that this precision was determined by using first-pass validation against CRM activity within a 30-day window as the standard for comparison. [1] This methodology highlights the disconnect between the signals provided by vendors and the verifiable actions taken by prospects, a gap that has profound consequences for resource allocation and campaign effectiveness. The low precision rate directly contributes to sales team fatigue and skepticism, making it harder to gain adoption for intent-driven strategies.

Downstream metrics confirm the widespread problem of signal inaccuracy, with a majority of practitioners struggling to validate intent flags with concrete buyer activity. A 2024 Intent Data Practitioner Report found that 62% of intent data buyers see fewer than 70% of their flagged accounts show any corroborating activity within their CRM or marketing automation systems within 30 days of receiving the signal. [1] This corroborating activity, such as a website visit, content download, or sales engagement, is the first proof point that an intent signal is legitimate. The absence of it for a large portion of flagged accounts suggests that go-to-market teams are often acting on noise rather than true intent. Consequently, it is no surprise that data quality has become the most critical factor for B2B marketers when choosing a vendor. In fact, a separate 2024 survey showed that 67% of B2B marketers prioritize data quality above all other attributes when selecting an intent data provider, according to a report from Intentsify titled The State of Intent Data 2024. [2] This focus reflects a market maturing beyond the initial hype of intent data and moving toward a more discerning, results-oriented approach that demands transparency in data sourcing and verifiable accuracy from providers like Bombora and its Company Surge® product. [22]

The Limits of Account-Level Intent: Who Is Actually Researching?

Intent data platforms like Bombora primarily provide account-level signals, which identify the companies showing heightened research activity but deliberately conceal which specific individuals are responsible. Products such as Bombora's Company Surge®, which became a native signal within HubSpot in August 2026, track online behavior by aggregating activity from IP addresses and cookie pools across a cooperative of over 5,500 publisher websites. [3, 8, 9] This methodology is designed to be compliant with privacy regulations like GDPR by reporting which company is surging on one of its 20,100+ topics, not who is doing the research. [3] While this approach is powerful for identifying and prioritizing target accounts early in their buying journey, it inherently creates an information gap. Marketing and sales teams learn that "Company A is showing intent for marketing automation," but they cannot know if the research is being conducted by a C-level executive, a team of end-users, or a single intern writing a report. [1, 2] This distinction is critical, as the identity of the researcher provides the necessary context for any meaningful sales engagement.

The account-level nature of most intent data creates a significant 'last-mile' problem for sales teams, who are left to manually identify the actual buyers. Upon receiving a notification that an account is surging, a sales development representative must then begin the difficult work of navigating the target company's structure to locate the relevant stakeholders. This challenge is compounded by the growing size of B2B buying committees; Forrester's 2024 "The State of Business Buying" report found that the average purchase now involves 13 stakeholders, while Gartner's research places the number for complex solutions between six and ten decision-makers. [6] For enterprise deals, this number can swell to 20 or more individuals from departments as varied as IT, finance, legal, and operations. [14] This manual discovery process, which involves cross-referencing tools like ZoomInfo or Apollo, introduces significant delays and inefficiencies, turning a real-time intent signal into a multi-day research project before a single personalized email can be sent. [3, 9]

Large-scale intent data systems are structurally misaligned for go-to-market motions targeting small and local businesses. The data collection model, which relies on a vast network of B2B publisher websites, is not designed to capture meaningful signals from business owners like independent plumbers, salon operators, or local agencies. These proprietors are far less likely to consume content within the specific publisher ecosystems that vendors like Bombora monitor. [3] Their research journey is more likely to occur on broad search engines, local review sites, or community forums, which fall outside the typical intent data cooperative. This creates a significant data gap for companies whose ideal customer profile is a main-street business owner rather than a large enterprise. While account-level intent data is effective for prioritizing and targeting large B2B organizations, its fundamental architecture makes it an unsuitable tool for identifying when a local business owner is actively seeking a new product or service, rendering it ineffective for go-to-market strategies focused on the SMB sector.

Frequently Asked Questions

What is a good benchmark for B2B intent data accuracy?

A good benchmark for B2B intent data accuracy is a 60-75% success rate in predicting buying behavior. [18] However, accuracy is more often measured by performance lift, such as the 21.3% conversion rate for intent-prioritized accounts versus 8.4% for those not prioritized. [20] A 2024 audit found a median precision of 0.51 for third-party intent signals, meaning about half of flagged accounts showed confirming activity within 30 days. [20] Ultimately, quality depends on the provider's data sources and methodology, as 70% of B2B leaders cite data quality as their top challenge. [34]

How much does Bombora intent data cost?

Bombora's intent data pricing typically starts at $25,000 to $30,000 per year for a basic plan. [1, 5] Mid-market packages range from approximately $50,000 to $100,000 annually, while enterprise-level solutions can exceed $100,000 to $300,000. [2, 9] The final cost is quote-based and depends on variables like the number of topics tracked, data refresh frequency, and the level of integration required. [2, 4] Most companies require 20-50 topics to cover their ideal customer profile, which significantly influences the total price. [2]

What is the difference between intent data and predictive analytics?

The primary difference is that intent data shows what a company is researching right now, while predictive analytics forecasts what a company is likely to do next. [15] Intent data consists of present-day behavioral signals, such as content consumption on specific topics, flagging an account as actively interested. [32] Predictive analytics uses machine learning to analyze historical data, first-party signals, and third-party intent data to identify patterns and predict which accounts are most likely to enter a buying cycle before they even begin their research. [13, 32] In essence, intent data captures current demand, whereas predictive analytics aims to identify future demand. [28]

How many topics does Bombora track for intent signals?

As of June 2026, Bombora tracks a total of 21,632 topics within its B2B taxonomy. [7] This taxonomy is continuously updated, with a March 2025 release bringing the total to over 17,000 topics and other releases in 2025 and 2026 adding thousands more. [3, 7] The taxonomy includes a wide range of business concepts and specific company or product names, allowing for granular tracking of research behavior. [3] This extensive topic list is a core component of its Company Surge® solution, which identifies when businesses show heightened interest in specific subjects. [3]

What is a B2B data cooperative?

A B2B data cooperative is a network of business-focused websites, such as publishers and vendors, that pool their anonymized visitor data to gain broader market insights. [17, 21] Bombora operates the largest B2B data co-op, with nearly 6,000 member sites. [27] These members place a tag on their websites, which allows Bombora to monitor billions of content consumption events and identify which companies are researching specific topics. [17, 29] In exchange for contributing their data, members receive access to the aggregated intent insights, creating a shared, consent-based view of B2B research activity. [21, 29]

Last updated: September 2026

How Many Intent Signals Show B2B Purchase Intent?