A comparison of Bombora's Company Surge® third-party data and first-party intent signals for B2B go-to-market teams in 2025. Learn the differences.

In 2025, the primary difference is that Bombora's Company Surge® identifies account-level interest spikes across a data cooperative of over 5,000 websites, indicating top-of-funnel research. [14, 15] First-party intent data, collected from a company's own website and CRM, tracks specific user actions, signaling higher, bottom-of-funnel purchase intent. [6, 9] A Forrester study found that layering both data types is optimal, using third-party signals for broad discovery and first-party signals for timing and personalization. [8, 22]

TL;DR

Bombora's Company Surge® data is sourced from a cooperative of over 5,000 B2B publisher websites, with 86% of signals being exclusive. [2, 14, 16]
First-party intent data, from owned digital properties, can achieve 90-95% precision, while third-party data accuracy ranges from 65-85%. [6]
A joint study by Bombora and Forrester found that companies using intent data see an 18% higher conversion rate and a 30% faster sales cycle. [9]
Leading third-party B2B intent data providers in 2025 include Bombora, ZoomInfo, 6sense, and Demandbase. [1, 11]
Bombora's model identifies surging accounts, not specific people, a key limitation for sales activation that requires further enrichment. [15]

What is Bombora Company Surge®? A Look at the Co-op Model

Bombora's Company Surge® data originates from a vast, consent-driven Data Cooperative, which forms the bedrock of its account-level intelligence. This co-op is a network of more than 5,000 B2B publisher and brand websites, including prominent names like Fortune and Bloomberg, that contribute anonymized content consumption data. [14, 19] The scale of this operation is substantial, capturing over 16.3 billion interactions each month from millions of unique business domains. [14] This model provides a panoramic view of B2B research behavior that is not confined to a single publisher's ecosystem. A critical differentiator detailed in Bombora's 2025 documentation is that 86% of these data signals are shared exclusively with Bombora, meaning competitors do not have access to the same raw inputs. [6] This exclusive access, governed by privacy-first protocols compliant with GDPR and CCPA, allows the platform to build a uniquely comprehensive and durable dataset for identifying top-of-funnel research trends across the B2B web, as explained in their privacy compliance resources. [11, 15] This structure ensures that the foundational data is both broad and proprietary, feeding the models that generate intent scores.

Company Surge® identifies accounts with statistically significant research interest by comparing recent content consumption to a historical baseline. [5] Specifically, Bombora's patented methodology, updated for 2025, analyzes an organization's research activity on a given topic over the most recent three weeks against its typical activity over the previous 12-week period. [3, 6] This analysis is performed across an extensive and evolving B2B taxonomy, which by mid-2026 had grown to include over 21,600 distinct topics. [21, 28] The output of this analysis is a Company Surge® Score, a numerical value from 0 to 100. According to the Company Surge User Guide from January 2023, a score of 60 or higher indicates that a company is 'spiking' or actively researching a topic more intensely than its historical norm. [4] It is crucial to understand that this score flags interest at the company or account level; it does not identify the specific individuals conducting the research, thereby providing an anonymized signal of buying intent that respects privacy while delivering actionable intelligence for sales and marketing teams. [11, 23]

The technical foundation of Company Surge® relies on a proprietary JavaScript tag deployed across its cooperative of B2B websites, which captures detailed engagement data in a privacy-compliant manner. [7, 16] This lightweight, asynchronous tag monitors user interactions with both open and gated content, collecting not just page views but also nuanced engagement metrics like dwell time, scroll depth, and scroll velocity. [7, 18] Once collected, this raw event data, which includes unique cookie IDs and IP addresses, is anonymized and aggregated at the company domain level. [11] From there, Bombora applies a sophisticated suite of patented AI models, including natural language processing (NLP) and machine learning, which have been trained for over a decade. [6, 18] These models analyze the contextual meaning of the content being consumed to assign it to the correct topics within Bombora's taxonomy and calculate the Surge Score. This process, which is detailed in documents like the Visitor Insights User Guide, transforms billions of anonymous behavioral signals into structured, account-level intent data. [7, 24]

First-Party Intent Signals: The High-Fidelity Data You Own

First-party intent data is collected directly from a company's owned digital assets, providing an unadulterated view of how prospects interact with the brand. [6, 25] These high-fidelity signals originate from sources like website analytics, customer relationship management (CRM) systems, mobile app usage, content downloads, and email engagement patterns. [3, 7] Because the company controls the collection and governance, this data is considered the gold standard for accuracy and relevance. [3] The strategic importance of this data has surged; a 2024 study by IAB/BWG Strategy involving over 500 advertising and data decision-makers found that 71% of brands are actively growing their first-party datasets, a figure that nearly doubled from just two years prior. [12] This shift is a direct response to increasing privacy regulations and the unreliability of external data sources. [25] According to a 2024 Forrester Consulting study, successfully incorporating first-party behavioral data into marketing strategies can reduce customer acquisition costs by as much as 83% and increase conversions by 73%, demonstrating a clear return on investment for building a robust internal data practice. [12]

The primary advantage of first-party signals is their exceptional accuracy, as they reflect actual, observed user engagement with your brand, eliminating the inference and modeling required for third-party data. [6, 28] This direct sourcing avoids the data decay and panel-sampling errors that can reduce the reliability of third-party signals by an estimated 30-50%. [1] While no data is perfect without proper governance, first-party information provides a much clearer provenance, as the direct relationship between the customer and the brand is known. [14, 27] Research from Forrester Consulting in 2024 confirms the tangible benefits, showing that businesses leveraging first-party behavioral data see a 72% improvement in ROI and a 78% increase in customer satisfaction. [7, 12] This precision allows marketing and sales teams to act with greater confidence. For instance, in its 2026 Marketing Data Report, Supermetrics noted that despite the clear advantages, 52% of marketing teams still do not own their data strategy, highlighting a significant gap between recognizing the value of first-party data and operationalizing it effectively. [12]

High-intent first-party signals are specific, bottom-of-the-funnel actions that indicate a prospect is moving from research to active purchase consideration. [10] Examples include repeated visits to a pricing or product comparison page, downloading a case study, requesting a demonstration, or using a solutions-configurator tool on the website. [5, 6] These actions are far more predictive of immediate sales potential than top-of-funnel activities like reading a blog post. [15] However, the significant limitation of first-party data is its restricted scope; it only provides deep insights into the accounts that are already aware of and interacting with your brand. [5, 6] This means it cannot reveal in-market buyers who have not yet landed on your digital properties, creating a blind spot for a potentially large portion of the total addressable market. [6] As noted in a May 2024 Forrester report, modern B2B buying journeys are increasingly self-directed, with buyers conducting extensive research across many channels before ever engaging a vendor directly, making it critical to combine the depth of first-party signals with the breadth of third-party discovery data. [29]

Comparison: Bombora Company Surge® vs. First-Party Signals

The fundamental difference between Bombora's Company Surge® and first-party signals in 2025 lies in the signal's origin and the visibility it provides. Bombora generates its insights from a third-party data cooperative, a proprietary network of over 5,500 B2B publisher and brand websites that share anonymized content consumption data. This extensive reach allows Bombora to detect when a specific company shows an abnormal spike in research on any of its 21,600+ topics, indicating early, top-of-funnel interest before that company has ever visited your properties. In contrast, first-party intent data is sourced entirely from your owned digital ecosystem, including your website analytics, CRM, and product engagement tools. These signals, such as pricing page visits, demo requests, or repeat content downloads, are generated by users directly interacting with your brand. While this limits your view to audiences already aware of you, the data's direct nature makes it an extremely reliable and privacy-compliant indicator of interest. A February 2025 Forrester Wave™ report recognized Bombora as a leader, calling its co-op a "highly unique, future-proofed dataset" for this broad, account-level discovery.

This distinction in data source directly impacts the specificity and the corresponding buyer stage each signal type addresses most effectively. Company Surge® data is account-centric and anonymous; it identifies that a particular company is researching a topic but not which specific individual is doing the research. This makes it exceptionally powerful for top-of-funnel (ToFu) and middle-of-funnel (MoFu) strategies, such as account discovery, territory planning, and broad digital advertising to net-new prospects. Conversely, first-party data, gathered from platforms like your marketing automation system or website, can be tied to known, user-level actions. When a prospect fills out a form, attends a webinar, or has multiple interactions tracked in your CRM, you gain a granular view of their specific interests and pain points. This user-level specificity is ideal for identifying bottom-of-funnel (BoFu) purchase readiness, enabling highly personalized follow-up and sales nurturing. According to a 2025 report from 6sense, with 94% of B2B buying groups ranking vendors before sales contact, leveraging both broad account signals and specific user actions is critical to influencing decisions at every stage.

The actionability of these two data types presents a clear operational divergence for sales and marketing teams. A signal from Bombora Company Surge® indicates an account is in-market, which is a powerful starting point for outreach. However, it requires a subsequent step: sales development representatives must still perform discovery within the surging account to identify the relevant members of the buying committee and understand their specific context before engaging. The signal provides the 'where' but not the 'who'. In stark contrast, a strong first-party signal, such as a known contact from a target account viewing your pricing page three times in a week, is immediately actionable. The sales team knows exactly who is showing interest and can initiate a timely, context-aware conversation tailored to that user's observed behavior. This direct line from signal to action significantly shortens the time to engagement for high-intent leads. A 2026 report from DemandScience noted that while 91% of B2B marketers use intent data, many struggle with unreliable signals, highlighting the value of clear, actionable first-party indicators that convert to qualified opportunities. Ultimately, layering both data types allows teams to use Bombora for broad discovery and first-party signals to pinpoint the exact moment for sales to strike.

Attribute	Bombora Company Surge®	First-Party Signals	Primary Funnel Stage
Data Source	Third-party data cooperative of 5,500+ B2B websites.	Owned digital properties (website, CRM, product analytics).	Bombora: Top-of-Funnel (ToFu); First-Party: Bottom-of-Funnel (BoFu).
Data Granularity	Account-level (identifies the company, not the individual).	User-level (can track known individuals and their specific actions).	Bombora: Account Discovery; First-Party: Lead Conversion.
Signal Type	Anonymized research spikes on specific topics across the web.	Direct user engagement (e.g., page views, form fills, demo requests).	Bombora: Broad Interest; First-Party: Specific Intent.
Anonymity	Anonymous at the user level, identifies the company.	Often tied to known contacts or identifiable users in a CRM/MAP.	Bombora: Prospecting; First-Party: Nurturing.
Actionability	Requires sales reps to identify the right contact within the account.	Often immediately actionable by sales for a specific, known user.	Bombora: Prioritization; First-Party: Engagement.
Primary Goal	Identify net-new, in-market accounts that are not yet on your radar.	Deepen engagement and convert leads already within your ecosystem.	Bombora: Pipeline Expansion; First-Party: Pipeline Acceleration.

The Activation Gap: Why Third-Party Intent Fails for Local SMB

Third-party intent models from providers like Bombora and ZoomInfo are fundamentally misaligned with the realities of local small-to-medium businesses (SMBs). The core mechanism of platforms like Bombora's Company Surge® relies on aggregating content consumption from a B2B data cooperative of over 5,000 publisher websites to identify when a company shows a statistically significant spike in research around specific topics. This model works by using reverse IP lookups to match online activity to a corporate domain, a process that is highly effective for mid-market and enterprise companies with dedicated corporate networks and static IP addresses. However, this IP-to-company mapping architecture breaks down for the vast majority of local businesses. A local plumber, salon, or restaurant owner does not operate on a corporate network; their online research happens from a residential IP address or a dynamic mobile IP, making it nearly impossible to resolve their activity back to a specific business entity. As a result, a 2026 analysis noted that the accuracy of major data providers like ZoomInfo often declines for smaller businesses compared to their enterprise counterparts. This creates a structural blind spot, as the very methodology designed for large-scale B2B prospecting is incapable of capturing the digital buying signals of non-corporate, hyper-local entities.

Most local businesses fail to generate the specific type of digital activity that registers as a 'surge' signal within large-scale third-party intent systems. A surge is defined by a significant increase in content consumption compared to a historical baseline, often tracked over several weeks. This model presupposes a certain volume and pattern of research, typically involving multiple stakeholders at a company reading whitepapers, visiting vendor comparison sites, and engaging with trade publications, all of which are part of the data cooperative. A local business owner's purchasing journey is fundamentally different; they are more likely to perform a few direct 'near me' searches, consult online reviews, and visit a competitor's website. According to a survey of 180 local businesses, their most important lead sources are organic search (31%), local listings and maps (28%), and paid search (27%), channels that are largely outside the purview of B2B intent data cooperatives. The research footprint of a single business owner looking for a new scheduling software or local marketing service is too small and sporadic to trigger a surge score from platforms like Bombora's Q3 2024 Company Surge®, which are calibrated to detect account-level, not individual-level, research trends across a corporate hierarchy.

For prospecting into local SMBs, a straightforward, verified lead is significantly more effective than a vague, high-cost intent signal. The primary output from a platform like Bombora is the name of a company showing interest, with no contact data included; activating that signal requires separate, costly integrations with other data vendors to identify and enrich contacts at the surging account. This multi-step process is inefficient for the SMB market, where the target is often a single owner or manager. A 2026 report highlighted that 61% of marketers cite generating quality leads as their top challenge, emphasizing the need for accuracy over abstract signals. Instead of a low-confidence surge score, a more actionable asset for local outreach is a simple lead from a public business directory, containing a named owner, a verified email, and a direct phone number. This represents a structural capability gap for large data providers, whose models are built for enterprise-scale firmographics and are not designed to resolve and verify named contacts at the hyper-local level. The economics also favor direct sourcing; leads scraped from public sources like Google Maps can cost as little as $0.01 per record, whereas a basic Bombora license starts around $30,000 annually.

Quantifying the Impact: Conversion Lift & ROI by Signal Type

Organizations leveraging third-party intent data realize a substantial and quantifiable return on investment, primarily through accelerated sales velocity and improved marketing efficiency. A 2024 Total Economic Impact study commissioned by Bombora and conducted by Forrester Consulting provides a clear financial benchmark, revealing that a composite organization achieved a 342% ROI over three years from its investment in Bombora's Company Surge® data. This financial gain was not isolated; the study also quantified a 30% increase in sales velocity, directly attributing this acceleration to the ability to prioritize accounts demonstrating active research behavior. The core mechanism for this impact is the strategic reallocation of resources. Instead of pursuing a broad, undifferentiated market, sales and marketing teams can focus their efforts exclusively on accounts that are already in-market for their solutions. A separate case study involving a global financial services firm further validates this, showing that using Company Surge® to identify high-intent prospects led to a conversion rate four times higher than that of cold leads, demonstrating the immense value of timing and relevance in B2B engagement. These results underscore a fundamental shift from static, firmographic-based targeting to a dynamic model where real-time behavioral signals dictate outreach priorities.

The precision of first-party data, gathered directly from a company's owned digital properties, provides an unmatched level of accuracy that translates directly into higher-quality leads and more effective personalization. While third-party data provides invaluable scale for top-of-funnel discovery, its accuracy is inherently lower due to its aggregated and modeled nature. Industry analysis from 2026 suggests that high-quality first-party data, sourced from website interactions and CRM systems, can achieve 90-95% accuracy, as it reflects directly observed, consensual user engagement. In contrast, typical third-party data accuracy hovers between 65% and 85%. This accuracy gap has profound implications for bottom-of-funnel conversion. According to a 2024 report, 75% of marketers recognize the high value of real-time first-party behavioral data, yet only 47% have systematic processes to collect it, highlighting a significant operational disconnect. Closing this gap is critical, as a 2026 analysis by DemandScience indicates that leads generated from accounts showing validated high intent can yield a 2-3x higher conversion rate than other leads. This lift is a direct result of engaging prospects with hyper-relevant messaging based on specific actions they have taken, such as downloading a whitepaper or visiting a pricing page.

Integrating both first-party and third-party intent signals allows organizations to optimize the entire sales cycle, leading to measurable reductions in the time required to close deals and a significant lift in overall win rates. Third-party data, like that from Bombora's Data Co-op, excels at identifying net-new accounts in the early research phase, enabling marketing teams to engage prospects before they have even made direct contact. This early engagement is crucial for shaping consideration. Once a target account visits a company's website or interacts with its content, first-party data collection takes over, providing the granular insights needed for sales to act decisively. According to a 2026 B2B sales performance report from MarketsandMarkets, this synchronized approach can shorten the average sales cycle by 30-40%. This reduction occurs because sales teams are no longer wasting cycles on cold outreach; instead, they are engaging prospects who are already problem-aware and solution-seeking. Further analysis from 2026 reinforces this, showing an expected sales cycle reduction of 20-40% when targeting is guided by intent signals, a metric that compounds financial benefits over time. A Bombora case study with a financial services client demonstrated this principle in action, where layering intent data onto their lead scoring model transformed their pipeline and directly contributed to an $800 million pipeline increase within three months.

Signal Type	Primary Use Case	Data Accuracy Benchmark	Expected Conversion Lift	Key Vendors / Platforms (2025)
First-Party Website Behavior	Bottom-of-funnel conversion, personalization, lead scoring	90-95%	2-3x vs. non-intent leads	HubSpot, Marketo, Salesforce Pardot
First-Party CRM/MAP Data	Upsell, cross-sell, renewal, account prioritization	~90%	40-60% win rate on expansion deals	Salesforce, Microsoft Dynamics 365, Gainsight
Third-Party Topic-Level Intent	Top-of-funnel discovery, account-based advertising, market segmentation	70-85%	4x higher than cold leads	Bombora (Company Surge®), 6sense, Demandbase
Third-Party Bidstream Data	Broad audience targeting, programmatic advertising	65-75%	Varies; used for reach	The Trade Desk, StackAdapt, MediaMath
Account-Based Ad Engagement	Mid-funnel nurturing, gauging account-wide interest	85-90% (for engagement metrics)	1.5-2x lift in opportunity creation	LinkedIn Ads, Terminus, RollWorks
G2/Review Site Intent	High-intent, competitor comparison, bottom-of-funnel targeting	~95% (for active researchers)	3-5x vs. non-intent leads	G2, TrustRadius, Capterra

Building a Blended GTM Strategy for 2025

A blended go-to-market strategy for 2025 must begin with broad account discovery, using third-party intent data to identify net-new prospects and analyze market-level demand. Platforms like Bombora's Company Surge® are designed for this top-of-funnel purpose, monitoring research activity across a cooperative of over 5,000 B2B websites to flag accounts showing increased interest in specific topics. [10, 12] This approach allows revenue teams to move beyond static firmographic lists and build target account lists based on active, timely research behaviors. [9] For example, a spike in an account's research on topics related to "cloud data migration" or "cybersecurity compliance" signals an emerging need before the buyer makes direct contact. According to a July 2025 Forrester report, applying intent analytics this way helps organizations understand buying cycle stages for smarter engagement, rather than limiting its use to only identifying late-stage, in-market buyers. [4] By using these third-party signals to power top-of-funnel advertising and content syndication, marketing teams can efficiently allocate budget toward accounts that are demonstrating verifiable interest, increasing the return on advertising investment and filling the pipeline with prospects who are already in an education phase. [10]

The next strategic layer involves enriching top-of-funnel insights with high-fidelity, first-party intent signals to prioritize outreach and personalize engagement. While third-party data identifies which companies are researching, first-party data reveals who at that company is engaging directly with your brand and how they are doing it. As privacy regulations tighten, a 2025 analysis of lead generation trends highlights that combining first-party behavioral data, such as website visits and content downloads, with third-party signals provides a more complete and resilient view of buying behavior. [7] An effective 2025 playbook involves using third-party surge data to identify a universe of active accounts and then using first-party signals, like a key contact visiting your pricing page or downloading a technical whitepaper, to score and escalate that account for sales development outreach. [20, 24] This blended model prevents sales teams from wasting resources on accounts that are only conducting passive, top-of-funnel research while ensuring they never miss an opportunity to engage a prospect who has moved from the broader market into active evaluation of your specific solution. This methodology turns raw intent into a clear, actionable workflow for revenue teams. [18]

For go-to-market motions targeting local and small-to-medium business (SMB) segments, a blended strategy must account for the inherent sparseness of third-party intent data and supplement it with plain-facts data providers. While enterprise-level research activity is readily captured by platforms like Bombora, signals from smaller, local companies are often less visible across the B2B web. [6] In these cases, leading with intent data alone can result in a limited addressable market and missed opportunities. The optimal approach is to use a specialized B2B data provider, such as Data Axle or Lead411, to acquire curated lists of SMB leads with verified contact details within a specific geographic or industry niche. [5, 15] These platforms provide the foundational firmographic and contact information needed for initial outreach. From there, go-to-market teams can layer on any available first-party engagement signals, such as email opens or website visits generated from initial cold outreach, to prioritize follow-up and nurture nascent interest. This ensures that even in data-sparse segments, sales and marketing efforts remain targeted and efficient, focusing resources on verified entities rather than chasing faint or nonexistent third-party signals. [28]

To effectively execute a blended data strategy in 2025, organizations must adopt flexible, self-serve data tools that avoid long-term, rigid contractual lock-ins. The B2B data landscape is evolving rapidly, with new signal types and providers emerging continuously; a Forrester report from Q2 2023 noted the importance of mixing providers to suit diverse use cases and data needs. [2] An annual, all-in contract with a single provider can hinder a company's ability to adapt its strategy as it learns which signal combinations, such as Bombora's surge topics paired with G2 review activity, actually drive pipeline. Modern buyers expect frictionless, digital-first experiences, and a 2026 Gartner analysis predicted that 80% of B2B sales interactions will occur in digital channels by 2025. [30] This shift necessitates tools that empower marketing and sales teams to access and activate data themselves. [21] Self-service platforms that offer credit-based or monthly subscription models allow teams to test different data sources, scale consumption up or down based on campaign needs, and integrate insights directly into their CRM or marketing automation platforms without a lengthy implementation cycle, ensuring the GTM strategy remains agile and responsive to market feedback. [17]

Frequently Asked Questions

What is the main difference between Bombora and first-party intent data?

The main difference is the source and what it reveals about the buyer's journey. Bombora's Company Surge® data is third-party information, aggregated from a cooperative of over 5,000 B2B websites to show which companies are researching specific topics, indicating top-of-funnel interest. [1, 2] First-party intent data comes from your own digital properties, like your website or CRM, tracking direct interactions such as pricing page visits or demo requests. [9] This makes first-party signals a more reliable indicator of bottom-of-funnel purchase intent because they capture engagement with your specific brand. [15]

How accurate is Bombora's Company Surge® data?

Bombora's accuracy comes from its data source and methodology, which avoids less reliable bidstream data. [2] It uses a cooperative of over 5,000 B2B publisher websites, where 86% of the data is exclusive to Bombora, to monitor content consumption and establish a historical baseline for millions of companies. [12, 24] A "surge" is only identified when a company's research activity on a topic significantly increases over its normal behavior, a process refined by AI models trained for over ten years to filter out noise and understand context. [7, 12] This consent-based, co-op model provides a clearer picture of genuine account-level interest than many other sources. [2, 13]

Can you see which individuals are researching topics with Bombora?

No, Bombora's Company Surge® data identifies interested accounts, not specific individuals. The data is aggregated at the company level to show that a particular business is surging in research on a topic, but it does not reveal who within that company is conducting the research. [1, 8] This is a result of its privacy-compliant design, which adheres to regulations like GDPR and CCPA by grouping anonymous, consent-based signals. [2, 6] To identify the specific people, teams must use the account-level signal from Bombora to guide their own outreach or layer it with first-party data capture tools. [1]

Is third-party intent data effective for targeting small businesses?

Third-party intent data can be less effective for targeting small businesses compared to enterprises. The challenge arises because data co-ops, including Bombora's, may have lower signal volume from smaller companies, making it harder to detect a meaningful research "surge". [6] Many intent providers rely on matching IP addresses to identify companies, an approach that often yields a higher rate of false positives for smaller or remote-first businesses. [6] While not impossible, the data is generally more reliable for identifying interest from larger, more digitally visible organizations, creating a potential activation gap for those targeting local or small businesses.

What are the best tools for capturing first-party intent signals?

The best tools for capturing first-party intent signals are platforms that track user behavior directly on your own digital properties. Customer Data Platforms (CDPs) like 6sense and Demandbase are excellent for unifying data from your website and CRM to identify in-market accounts. [4, 9] Website visitor identification tools such as Lead Forensics can de-anonymize traffic, showing you which companies are browsing your pages. [4, 17] Ultimately, even standard marketing automation platforms and analytics tools like Google Analytics are crucial for tracking high-intent actions like pricing page views and demo requests. [11]

How does Bombora's data co-op work?

Bombora's data co-op is a network of over 5,000 B2B websites, including publishers, vendors, and analysts, that agree to share anonymous visitor data. [2] These members place a proprietary Bombora tag on their sites, which collects consent-based data on content consumption across more than 21,000 topics. [10, 12] Bombora's system then aggregates these billions of monthly interactions, maps them to specific companies, and compares recent activity to a 12-week historical baseline to identify a "Company Surge®". [12] This model creates a powerful, shared view of B2B research trends, with 86% of the signals being exclusive to Bombora's network. [12]

Last updated: October 2026

Intent Data 2025: First-Party Signals vs. Bombora Surge