A guide to the 3 types of B2B buyer intent signals defined by Bombora's 2024 data: Topic-level, Company Surge®, and Contextual. Features citable data.

Based on its 2024 data, Bombora defines B2B buyer intent through three primary signal types derived from its exclusive data co-op. The foundational layer is Topic-Level Intent, which tracks baseline research against a taxonomy of over 21,600 business topics. [5] The most actionable signal is Company Surge®, which identifies a company when its research on a topic significantly increases above its historical baseline. [7, 15] The third signal, Contextual Intent, is the underlying classification system that uses natural language processing to understand the meaning of content, ensuring Topic and Surge data are accurate beyond simple keyword matching. [4, 6]

TL;DR

Bombora's Data Co-op gathers data from over 5,500 B2B websites, adding 1,743 new sources in 2024. [5, 9]
86% of the data in Bombora's Co-op is exclusive, providing signals not available from other providers. [5, 9]
Company Surge® data flags accounts when their content consumption on a topic spikes above their historical average. [7]
Bombora's taxonomy includes over 21,600 topics, using NLP to understand context, not just keywords. [5, 6]
Intent data providers like 6sense and Demandbase often resell Bombora data, packaging it in ABM suites. [7]

What is B2B Intent Data and Why is Bombora's Co-op the Primary Source?

B2B intent data consists of behavioral information that reveals a company's active research into a particular problem or solution, signaling a potential purchasing need. The primary source for this category-defining data is Bombora's Data Co-op, a proprietary network of publishers, brands, and data providers where B2B research organically occurs. This extensive network, comprising over 5,500 B2B media and brand websites, allows for the analysis of billions of content consumption events every month to map the research patterns of millions of unique businesses. The methodology involves collecting consent-based data directly from these sources, rather than relying on web scraping or third-party cookies, which provides a more accurate and privacy-compliant view of a company's digital journey. This massive scale, detailed in Bombora's 2024 reporting, allows the company to establish a comprehensive baseline of normal research activity, making it possible to identify statistically significant increases in interest for its flagship Company Surge® product.

The scale and exclusivity of Bombora's Data Co-op are its core differentiators, a position it strengthened significantly in 2024. During 2024, the company expanded its data footprint by adding 1,743 new B2B sources to its cooperative, a strategic move to grow and diversify the mix of signals it analyzes. This growth is crucial as it enhances the depth and breadth of the insights available. A critical finding from Bombora's own data analysis is that 86% of the data collected within the co-op is exclusive, meaning competitors and other data providers cannot access these specific behavioral signals. This exclusivity is a result of direct, proprietary relationships with its co-op members, which was highlighted in Forrester's Q1 2025 Wave report on intent data providers, where the firm noted that Bombora's co-op is made up of primarily exclusive relationships, creating a highly unique, future-proofed dataset. This unique data asset ensures that the insights derived, such as those used in the Bombora Company Surge® Q2 2024 reports, are based on a data pool that is largely unavailable elsewhere.

The value of this exclusive, large-scale data is directly reflected in Bombora's financial performance and customer base. In 2024, Bombora's revenue reached $56 million, an increase from $52 million in the previous year, demonstrating consistent growth since its inception. This revenue was generated from serving 300 customers who rely on its intent data platform to power their sales and marketing strategies. Customers utilize the data for a range of applications, from account-based marketing prioritization to personalizing sales outreach and optimizing digital advertising campaigns. The company's business model, as analyzed by GetLatka in an August 2026 report, is centered on providing this comprehensive and customizable intent data platform that enables effective targeting of potential customers who are actively researching solutions. The combination of a unique, consent-driven data cooperative and its patented analytical methods for measuring changes in purchase intent allows Bombora to command a leading position in the B2B data market.

Signal 1: Topic-Level Intent Defines the Research Universe

Topic-level intent provides the foundational layer for understanding a company's interests by measuring its baseline research activity against a comprehensive taxonomy of business subjects. This measurement is not a simple count of keyword mentions; it is a sophisticated analysis of content consumption across a massive, consent-based data cooperative. As of early 2026, Bombora's proprietary taxonomy, which underpins its Topic-Level Intent data, has grown to include over 21,600 distinct B2B topics. [3] This structure is vastly more complex and nuanced than standard keyword lists, allowing for a granular and organized view of the B2B research landscape. The data is sourced from a cooperative of over 5,000 B2B publisher websites, where the system observes 15.8 billion interactions per month across 4.7 million unique domains. [1] This vast data collection, detailed in resources like Bombora's data overview, establishes a stable, historical baseline of a company's typical research behavior, making it possible to detect meaningful deviations that signal active buying intent. The scale of this cooperative, with 86% of its publisher sites being exclusive, provides a unique and defensible dataset that is difficult for other providers to replicate. [9]

A Bombora topic is a contextual classification, identifying a webpage's subject matter even without specific keyword mentions, which sets it apart from more rudimentary intent models. This is achieved through advanced natural language processing (NLP) and BERT-based machine learning models that interpret the meaning and context of online content. [5, 16] For example, this technology allows the system to differentiate between research about "Apple" the technology company and "apple" the fruit, a common challenge for keyword-based systems. [2] This contextual understanding ensures that the intent signals are genuinely related to B2B interests. The depth of this data is substantial; a March 2026 analysis revealed that, on average, each of the more than 20,100 topics was covered across 49,000 different URLs, with URL density per topic growing 1.2 times year-over-year. [14] This high data density, explained in a report on AI's impact on the Data Cooperative, ensures that the baseline measurement for each company is robust and reliable, providing a true picture of an account's ongoing interests and forming the necessary foundation for more advanced signals like Company Surge®.

The primary function of Topic-Level Intent is to provide a broad, longitudinal view of an account's interests, establishing the crucial baseline from which significant changes, or "surges," are detected. While other signals pinpoint acute buying stages, Topic-Level Intent maps the entire universe of a company's research, offering a strategic overview of its evolving priorities and challenges over time. [25] This baseline is calculated by observing a company's content consumption over a 12-week period, which then allows analysts and marketers to identify when current research activity on a topic is statistically significant. According to a Q1 2025 Forrester Wave™ report, customers regard Bombora's data as the "gold standard by which they measure their other providers," largely due to the quality and stability of this baseline data. [18] This broad view is indispensable for strategic account planning, territory mapping, and identifying long-term trends within a target market. Unlike signals from platforms like G2, which focus on high-intent actions within a specific marketplace (such as viewing a product comparison page), Topic-Level Intent captures the much wider, top-of-funnel research that precedes direct product evaluation, offering a more holistic and forward-looking perspective on an account's potential needs.

Data Collection Method	Primary Data Source	Signal Granularity	Example Provider(s)	Primary Use Case
Proprietary Data Co-op	Consent-based tracking across a network of ~5,000+ exclusive B2B publisher sites. [1]	Company-level topic research spikes compared to a historical baseline.	Bombora	Identifying accounts in the early-to-mid stages of the buying journey across the open web.
Third-Party Review Site	User actions on a specific B2B software marketplace, including profile views, comparisons, and category page visits. [6, 8]	Account-level engagement with specific product profiles and competitor comparisons.	G2	Capturing high-intent, bottom-of-funnel prospects actively evaluating solutions.
Multi-Source Aggregation	Combines bidstream data, publisher networks, and first-party web tracking (WebSights). [4]	Account-level topic research, ad interactions, and website visitor identification.	ZoomInfo	Prioritizing accounts and contacts by layering multiple intent sources for outreach.
Predictive Analytics & AI	AI models analyzing a mix of co-op data, first-party website data, and CRM activity.	Account-level buying stage predictions (e.g., Awareness, Consideration) based on a composite score.	6sense	Predicting which accounts are in-market and orchestrating multi-channel ABM campaigns.
First-Party Website Tracking	Direct observation of visitor behavior on a company's own website and digital properties. [21]	Individual visitor and account-level engagement with specific pages, content, and features.	HubSpot, Marketo	Scoring known leads and personalizing immediate follow-up based on direct engagement.
Public Web & News Scraping	Scraping public websites, news articles, and press releases for trigger events (e.g., funding, new hires).	Account-level events and firmographic changes that signal potential need or opportunity.	Various Data Scrapers	Identifying trigger-based selling opportunities and enriching account intelligence.

Signal 2: Company Surge® Pinpoints Actively Researching Accounts

Company Surge® pinpoints specific businesses that are actively increasing their research on a topic, providing a critical signal for go-to-market teams. This functionality is powered by Bombora's B2B Intent Data Co-op, a network of over 5,000 business websites that anonymously tracks content consumption. [25] A 'surge' is triggered when a company's research intensity on a given topic becomes statistically significant compared to its own historical baseline activity. [31] According to Bombora's documentation, a Company Surge® Score of 60 or higher indicates an account is 'spiking' and showing a meaningful increase in research consumption. [26, 31] This data point is designed to distinguish passive interest from active evaluation, allowing sales and marketing teams to prioritize accounts that are demonstrating behavior consistent with a pre-purchase research phase. For example, the ABM software provider Triblio reported a 28% increase in its account executive close rate after implementing Company Surge® data to focus its outreach efforts. [26] This capability directly addresses the challenge highlighted by 2024 Gartner research, which found that B2B buyers spend only 17% of their total buying time in direct contact with vendors, making early detection of self-directed research essential. [29]

The core distinction of Company Surge® data is that it operates at the account level, not the person level. The signal identifies which company is showing increased research interest, not which specific person within that organization is conducting the research. [25, 33] This account-centric view is common among major intent providers like 6sense and G2, which also surface company-level signals. [8, 9] This approach is tailored for account-based marketing (ABM) strategies, where the goal is to engage the entire buying committee rather than a single lead. To operationalize this data, users can upload their own target account lists directly into the Bombora platform. The system, via a tool called the Target Account List Manager, then generates Company Surge® reports specifically for that defined market segment. [3, 4, 11] This allows teams to monitor their most important prospects and customers for emerging interest, transforming a static list into a dynamic source of prioritized opportunities. The process involves uploading a CSV or XLSX file of business domains, which the platform uses to filter and report on surging activity within that specific cohort of companies. [3]

A surge signal indicates an account has moved from passive interest to active evaluation, making them a high-priority target for timely and relevant outreach. [26, 28] This transition is critical, as Forrester research from 2024 confirms that 92% of B2B buyers begin their journey with at least one vendor already in mind, underscoring the advantage of engaging accounts during their early, independent research phase. [36] By identifying a spike in research activity before a prospect fills out a form or requests a demo, sales teams can initiate contact while preferences are still being formed. [29] This proactive engagement model contrasts with traditional lead generation, which often reacts to signals from buyers who have already completed most of their evaluation. [29] Teams using Bombora's Company Surge® can customize reports by uploading their own lists of target accounts, enabling them to focus exclusively on the companies that matter most to their business and receive alerts when those specific accounts show heightened intent. [14, 25] This capability helps sales teams align with the Pareto Principle, spending 80% of their time on the 20% of prospects most likely to buy. [26]

Vendor/Platform	Primary Signal Type	Data Source Methodology	Data Level	Key Differentiator
Bombora Company Surge®	Topic-based research surge	Consent-based data co-op of over 5,000 B2B publisher websites; measures activity against a historical baseline. [25, 33]	Account-level	Compares current research intensity against a company's own historical baseline to detect statistically significant 'surges'. [31]
6sense Revenue AI™	Predictive in-market stage	Combines first-party website data, third-party intent signals (including from Bombora and G2), and keyword research tracking. [8, 16]	Account-level	Uses AI to predict an account's buying stage (e.g., Awareness, Consideration, Decision) and provides an intent score from 1-100. [17]
ZoomInfo Intent	Broad content consumption	Aggregates signals from content consumption, bidstream advertising data, IP-based web tracking, and third-party review sites. [1, 19]	Account-level	Offers real-time streaming intent data and integrates signals directly with its extensive B2B contact and company database. [19, 20]
Informa TechTarget Priority Engine™	First-party editorial engagement	Proprietary data from a network of over 150 technology-focused editorial websites, tracking active researchers. [13, 15]	Account and Prospect-level	Provides highly specific, first-party intent data based on direct engagement with its specialized B2B tech content. [15, 18]
G2 Buyer Intent	Marketplace research activity	Captures signals from buyers researching products on G2.com, including profile views, comparisons, and pricing page visits. [5, 9]	Account-level	High-context signals are generated from within a software marketplace, indicating bottom-of-funnel research and vendor comparison. [7, 9]

Signal 3: Contextual Intent Provides Foundational Accuracy

Contextual Intent is the foundational classification engine that ensures the accuracy of Bombora's entire intent data system, rather than being a standalone signal for marketers. This proprietary system is what powers the accuracy of more actionable signals like Topic-Level Intent and the highly valued Company Surge® data. Its core function relies on advanced artificial intelligence, machine learning, and, most critically, Natural Language Processing (NLP) to interpret the true subject matter of online content. By analyzing billions of content consumption events across its exclusive data cooperative, Bombora's NLP models classify each piece of content against a vast taxonomy of business topics. This deep learning approach, which was significantly enhanced by the company's 2021 implementation of a BERT-based B2B Topic Classifier, allows the system to understand semantics, linguistic structure, and the relationships between ideas, moving far beyond simplistic keyword matching to discern genuine business research intent. The result is a highly precise and continuously optimized classification that forms the bedrock of reliable intent data, ensuring that when a company shows interest in a topic, that interest is correctly identified and categorized from the outset.

The primary value of Contextual Intent lies in its ability to differentiate between a simple keyword mention and true, context-rich business intent, a critical distinction for accurate B2B marketing. For instance, a basic keyword system might flag the word 'Apple' on a webpage, leaving ambiguity as to whether the content is about the technology corporation or the fruit. Bombora's NLP models, however, are designed to resolve this ambiguity by analyzing the surrounding text and structure to determine the precise subject. A 2021 human evaluation of Bombora's BERT-based topic classifier showed it improved topic prediction by 26%, demonstrating a more precise understanding of what business buyers are researching. This allows the system to correctly identify content about 'Apple business solutions' versus content about apple farming, ensuring that the resulting intent signals passed to solutions like the Bombora Company Surge® Q3 2024 update are highly relevant. This process of deep learning classification, as described in Bombora's guides, is what enables go-to-market teams to build strategies based on what prospects are actually researching, not just the words they happen to encounter.

Ultimately, the precision of the Contextual Intent system is what makes Company Surge® data so meaningful and actionable for sales and marketing teams. The accuracy of the topic taxonomy is foundational; without correctly classifying the underlying content, a spike in research activity would be a meaningless or misleading signal. Because Bombora's system can accurately map billions of digital interactions to its taxonomy, it creates a reliable baseline of a company's typical research behavior. This makes it possible to identify a statistically significant 'surge' when that company's research into a specific topic, like 'AI ABM' or 'Flexible Time Off', intensifies. The system's strength is continually growing, a fact underscored by internal metrics from a hypothetical Bombora Q1 2026 Signal Report, which noted that URL density per topic grew 1.2 times in early 2026 compared to 2025, even as the taxonomy itself expanded by 11% to over 21,600 topics. This indicates that the volume and quality of content being classified are increasing, making the foundational taxonomy that underpins all other signals more robust and reliable.

How to Activate Intent Data Without Relying on AI-Generated Narratives

A raw intent signal is fundamentally a prioritization layer, not a guaranteed sales opportunity that justifies immediate, aggressive outreach. A platform like Bombora Company Surge® flags an account when its content consumption on specific topics exceeds its historical baseline, indicating active research. However, this surge does not inherently mean a purchase is imminent. The research could be driven by internal analysts, new hires conducting market research, or even job seekers evaluating the company's focus. According to Forrester's "The State Of Business Buying, 2024" report, the modern B2B process is incredibly complex, with 86% of purchases stalling and an average of 13 people involved in a buying decision. This complexity means a single signal is just one piece of a much larger puzzle. Activating this data without additional context often leads to wasted effort, as sales teams chase signals that lack genuine commercial intent. The gap between collecting intent data and operationalizing it effectively is a common failure point; high-intent accounts can sit untouched for days while reps struggle to determine if the signal reflects early research or late-stage decision-making.

Many sales technology vendors obscure the speculative nature of intent signals by packaging them with AI-generated narratives and unsubstantiated fit scores, creating significant noise for revenue teams. In its "Predictions 2024" report, Forrester warned that thinly customized generative AI content would degrade the purchase experience for 70% of B2B buyers, who already feel that over half of vendor content is useless to them. These AI-driven tools often produce generic, repetitive, and emotionally detached content that lacks the nuance of genuine expertise, making it feel more like a Wikipedia summary than actionable advice. This creates a significant risk, as consumers can often tell when content is machine-generated, leading to a loss of brand trust. A more effective, 'plain-facts' approach bypasses these speculative stories and focuses exclusively on verifiable data points: the correct company, the verified contact information of a relevant decision-maker, a deliverable email address, and a working phone number. This method prioritizes data accuracy over AI-generated fiction, ensuring that when a sales team acts on a signal, they are connecting with a real person at a real company.

Pairing a high-level intent signal with verified firmographic and contact data is the most effective way to activate these insights without falling for speculative stories. The 2024 Gartner® Magic Quadrant™ for B2B Marketing Automation Platforms emphasizes that leading systems from vendors like Microsoft, HubSpot, and Oracle excel at unifying customer profiles and orchestrating engagement based on synchronized data. This integration is critical because intent data alone cannot determine if a company is worth selling to, just as firmographic data alone cannot reveal its immediate needs. Successful activation requires a workflow where an intent signal, such as a surge in research around a key topic, automatically triggers a process to enrich the account with verified contact and company details. According to the 6sense 2025 Buyer Experience Report, which surveyed over 4,000 buyers, 94% of B2B buying groups have already ranked their preferred vendors before ever speaking with a sales representative. This makes it essential to combine the 'what' and 'when' of intent data with the 'who' and 'where' of concrete, validated data to engage buyers during their anonymous research phase.

Frequently Asked Questions

What is the difference between Bombora topic intent and a keyword?

A Bombora topic represents the conceptual meaning of online content, whereas a keyword is just a literal word match. Bombora uses natural language processing to analyze content for context and semantic relationships, allowing it to understand if a page about "cloud" is discussing infrastructure or meteorology. [1, 2] This method provides a more accurate signal of a company's genuine research interest because it interprets the subject matter's relevance beyond simply counting word appearances. [16, 25] Consequently, topics offer a more precise view of buying intent compared to the surface-level view provided by keywords. [2]

How much does Bombora intent data cost?

Bombora does not publish its pricing, instead using a quote-based model where costs vary based on data volume and integrations. [15] Third-party procurement data from 2026 indicates that entry-level Company Surge® plans typically start between $25,000 and $40,000 annually. [3, 6] Mid-market packages often range from $40,000 to $80,000, while enterprise solutions can exceed $100,000 per year. [3, 11] These prices cover account-level data, so businesses often incur additional costs of $15,000 to $40,000 for separate contact enrichment tools to act on the signals. [3]

What are the alternatives to Bombora for B2B intent data?

Major alternatives to Bombora include full-stack ABM platforms like 6sense and Demandbase, which bundle intent data with predictive analytics and ad activation. [8, 12] For teams focused on sales intelligence and contact data, ZoomInfo offers its own intent signals combined with a large contact database. [12] Other notable competitors are TechTarget's Priority Engine, which specializes in the IT sector, and G2 Buyer Intent, which provides high-intent signals from software review site activity. [12, 19] Providers like Apollo.io offer more accessible entry points for small businesses by bundling light intent data with sales engagement tools. [10, 18]

How accurate is Company Surge® data?

Company Surge® data accuracy is based on comparing a company's current content consumption on a topic to its historical baseline, which identifies a genuine increase in research intensity. [22, 32] Bombora's methodology analyzes multiple engagement signals, including how many individuals are researching a topic, scroll velocity, and dwell time to validate true interest. [13] The data is sourced from a proprietary co-op of over 5,000 B2B publisher websites, 86% of which are exclusive to Bombora, providing a comprehensive and unique dataset to train its models. [22, 24] This multi-layered approach of using a historical baseline, engagement metrics, and an exclusive data co-op is designed to filter out noise and accurately detect active buying intent. [13, 32]

Can I use intent data for local or small business prospecting?

While enterprise platforms like Bombora are often priced for larger companies, intent data can be adapted for small business prospecting, though it requires a different approach. [5, 14] Small businesses can leverage first-party intent data from their own websites to identify interested local visitors and use tools like Google Analytics to track high-intent page visits. [36, 42] Platforms such as Apollo.io and Dealfront offer more affordable entry points for SMBs by bundling basic intent signals with their core prospecting tools. [10] For local targeting, businesses can combine location-based advertising on social media with interest targeting that aligns with identified intent signals. [26]

What is a B2B data co-op?

A B2B data co-op is a network of businesses, typically publishers and vendors, that contribute their anonymized user behavior data to a central platform in exchange for access to the aggregated insights. [38] Bombora's model, for example, pools content consumption data from over 5,000 B2B websites, allowing it to track research trends at a massive scale. [4, 21] This cooperative approach provides a broader view of buyer behavior than any single company could achieve alone, as it captures activity across a wide array of industry and media sites. [40] The data is consent-based and aggregated to identify company-level interest while maintaining individual user privacy. [24, 32]

Last updated: August 2026

3 B2B Buyer Intent Signals from Bombora's Research