B2B In-Market Buyer Benchmarks 2024
Only 15% of B2B buyers are in-market at any time. This post outlines 2024 intent data benchmarks from Bombora, Forrester, and Gartner for marketers.
According to Bombora's 2024 benchmarks, only about 15% of B2B companies are actively in-market for a given solution at any time. This is identified using their Company Surge® methodology, which tracks content consumption from a co-op of over 5,000 B2B publisher websites. A surge is detected when a company's research on specific topics spikes significantly above its historical 12-week baseline.
TL;DR
- Only 15% of B2B companies are actively in-market at any given time, according to Bombora's Company Surge® data.
- The average B2B buying committee now involves 13 internal stakeholders, according to Forrester's 2024 State of Business Buying report.
- Bombora's intent signals are sourced from a data co-op of over 5,000 B2B publisher sites, monitoring 16 billion monthly content consumption events.
- The B2B buyer intent data market is valued at $4.49 billion in 2026 and is projected to grow to $20.89 billion by 2035.
- Forrester's 2025 research shows 85% of B2B buyers purchase from a vendor that was already on their consideration list before formal evaluation began.
What Percentage of B2B Buyers Are In-Market?
Intent data from Bombora's 2024 benchmarks indicates that only 15% of B2B companies are actively in-market for any specific solution at a given time. [20] This figure is derived from the company's proprietary Company Surge® methodology, which identifies accounts demonstrating a significant increase in research activity. [15, 20] The system monitors content consumption behavior from a massive data cooperative of over 5,000 B2B publisher websites, tracking billions of interactions monthly. [6, 20] An account is flagged as "in-market" when its research on specific topics, classified across a taxonomy of over 25,300 options, spikes dramatically compared to its historical 12-week baseline. [5, 23] This spike is quantified as a Company Surge® score; a score of 60 or higher signifies a statistically meaningful increase in content consumption, indicating that the business has moved from passive interest to active research. [15, 19] This data-driven approach allows marketing and sales teams to focus resources on the accounts showing the strongest propensity to buy, rather than treating all prospects as equally ready.
This 15% benchmark presents a notable contrast to the widely cited 95:5 rule, a concept popularized by Professor John Dawes of the Ehrenberg-Bass Institute for Marketing Science. [2, 3] The 95:5 rule posits that only 5% of B2B buyers are actively seeking to make a purchase at any one time, while the other 95% are not currently in a buying cycle. [3, 4, 7] This rule is often presented as a heuristic based on long B2B purchase cycles; for example, if a company replaces a core service provider once every five years, only 20% of the market is potentially active in a given year, which translates to 5% in any single quarter. [3, 10] The difference between Bombora's 15% and the Ehrenberg-Bass Institute's 5% can be attributed to differing measurement methodologies. The 95:5 rule is a general framework for market dynamics and purchase frequency, whereas Bombora's Company Surge® specifically measures active research behavior, capturing a broader set of signals that precede a formal buying decision. This means Bombora identifies companies in earlier research phases, expanding the definition of "in-market" beyond just those ready for immediate purchase.
The remaining 85% of businesses, while not considered actively in-market, are not entirely dormant. [20] These companies are engaged in baseline-level research, representing their standard, ongoing consumption of business content. According to Bombora's methodology, this activity does not constitute a significant deviation from their normal behavior and therefore does not trigger a high Company Surge® score. [20] For instance, an account with a score below 60 is not showing a statistically significant increase in research intensity compared to its recent history. [19] Over-targeting this 85% segment with aggressive, bottom-funnel sales messaging can be counterproductive, potentially leading to prospect fatigue and wasted resources on accounts that are not in an active buying stage. [20] Instead, the 95:5 rule suggests that this larger group should be engaged with long-term brand-building and nurturing strategies, building the trust and mental availability required to be shortlisted when they eventually do enter an active buying cycle. [3, 7] Therefore, a balanced strategy involves using intent data to precisely target the 15% for immediate sales activation while deploying broader brand marketing to influence the 85% over the long term.
How Intent Data Identifies In-Market Accounts
Bombora's Company Surge® methodology identifies in-market accounts by tracking content consumption across a massive, consent-based data cooperative. This cooperative consists of over 5,000 B2B publisher and brand websites, creating a vast and exclusive dataset where 86% of the signals are shared solely with Bombora. [32] The system monitors billions of monthly content consumption events, analyzing research patterns across more than 25,300 distinct B2B topics. [1] This proprietary data collection network, built over a decade, allows the platform to observe the early research activities of nearly 4 million unique domains each month. [4, 7] By leveraging direct relationships with these publishers via a proprietary tag, Bombora captures detailed engagement data that goes beyond simple keyword mentions, including interactions with gated content, downloads, and webinar registrations. [32] This direct-source model, which Forrester has described as creating a "highly unique, future-proofed dataset," provides the foundational data needed to distinguish genuine buying intent from routine online browsing. [31, 33]
An account is flagged as 'in-market' only when its research activity on specific topics significantly exceeds its own historical baseline, a model designed to filter out signal noise. The Company Surge® system establishes a 12-week historical baseline of content consumption for each company-topic pair. [1, 28] A 'surge' is then identified when that company's research intensity, measured by the frequency and depth of engagement, spikes above this established norm over a three-week period. [4, 28] A score from 0 to 100 is assigned, and a score of 60 or higher indicates a statistically significant increase in research, flagging the account as actively in-market. [26, 30] This baseline-relative scoring is crucial because it contextualizes activity; a company that routinely browses a topic is not flagged unless its behavior changes demonstrably. This focus on behavioral change is a key differentiator from methods that simply track raw activity volume. In The Forrester Wave™: Intent Data Providers for B2B, Q1 2025, analysts recognized this approach, calling Bombora "the gold standard for account-level intent data feeds" and noting that customers use its data quality as the benchmark for other providers. [31, 33]
The broader intent data market includes various methodologies, primarily categorized as first-party, second-party, and third-party data collection. First-party intent data is derived from a company's own digital properties, such as website visits and form fills, offering high accuracy but limited reach. [12, 24] Third-party intent data, the category where Bombora is a leader, is aggregated from external publisher networks to spot research activity before a buyer ever visits a company's site. [9, 10] Many comprehensive Account-Based Marketing (ABM) platforms, such as 6sense Revenue AI for Marketing and Sales and Demandbase One, combine both types. These platforms often license third-party data from Bombora and integrate it with their own first-party tracking and predictive analytics. [2, 6] For instance, the 6sense platform, updated in 2024, uses its 6AI engine to analyze trillions of signals from its network, including Bombora data, to predict which accounts are in a buying cycle. [15] Similarly, Demandbase One leverages its own bidstream data and integrations with providers like Bombora and G2 to create a holistic view of account activity. [14, 20]
| Vendor / Platform | Primary Data Source | Core Methodology | Signal Level | Common Use Case |
|---|---|---|---|---|
| Bombora Company Surge® | Third-Party (Proprietary Data Co-op of 5,000+ B2B sites) | Measures content consumption on a topic against a company's 12-week historical baseline to detect a 'Surge'. [1] | Account-Level | Identifying new in-market accounts and feeding raw intent signals into other marketing or sales platforms. [14] |
| 6sense Revenue AI | First-Party & Third-Party (Own network, Bombora, G2, etc.) | Uses AI and predictive models to score accounts and identify their buying stage based on a blend of intent signals. [6, 15] | Account-Level | Predicting which target accounts are in-market and orchestrating multi-channel ABM campaigns. [19] |
| Demandbase One | First-Party & Third-Party (Own bidstream data, Bombora, G2) | Combines direct bidstream keyword data with first-party signals and patented ID technology for account and buying group identification. [14, 37] | Account & Person-Level (via Person-Based Intent) | Activating intent signals directly within a native B2B advertising platform (DSP) and sales intelligence workflows. [14] |
| G2 Buyer Intent | Second-Party (Activity on G2.com) | Tracks which companies are viewing product profiles, running comparisons, and reading reviews within a specific software category. [2] | Account-Level | Identifying bottom-of-funnel accounts actively comparing a company's product against named competitors. [14] |
| ZoomInfo (with Intent) | First-Party & Third-Party (Own network, contact data, Bombora license) | Bundles third-party topic-based intent signals with its extensive B2B contact and company database. [2, 8] | Account-Level | Enriching target account lists with contact data for sales outreach once intent signals are detected. [2] |
| Dealfront (formerly Leadfeeder) | First-Party (Website Visitor ID) | Identifies the companies visiting a user's website, even if they don't fill out a form, by matching IP addresses to company profiles. [8] | Account-Level | Converting anonymous website traffic into a list of target accounts for sales follow-up or retargeting. [8] |
In-Market Buyer Benchmarks by Industry
The percentage of B2B companies actively in-market for a solution varies significantly from the 15% average, driven largely by disparate purchase cycles and market dynamics across industries. Research shows that average B2B sales cycles can range from as short as 70 days in retail to as long as 162 days for non-profits, with a blended average of approximately 118 days. For example, a 2026 analysis by Focus Digital found the average software sales cycle is around 90 days, while manufacturing cycles are considerably longer at 130 days. This variance directly impacts the window of opportunity when a company is likely to be receptive to outreach. Faster cycles in sectors like technology and business services mean a higher percentage of accounts may enter an active buying journey within a given quarter, whereas the longer, more capital-intensive procurement processes in manufacturing or government sectors result in a smaller portion of the total addressable market being in-market at any one time. Understanding these industry-specific cadences is the first step for marketers to set realistic pipeline goals and properly calibrate their intent-driven strategies.
Bombora provides industry-specific benchmarks by tracking research activity across its proprietary taxonomy of tens of thousands of B2B subjects, which are then grouped into relevant topic clusters. Instead of relying on simple keyword matching, the Company Surge® platform uses natural language processing to analyze the context of the content being consumed across its data co-op of over 5,000 B2B publisher websites. This allows for a nuanced understanding of buyer intent; for instance, the system can differentiate between research on 'cloud infrastructure' versus 'cloud meteorology'. To tailor this to a specific vertical, a user would define a cluster of topics pertinent to their industry, such as grouping 'cybersecurity compliance' and 'IAM solutions' for the IT security space. A 'surge' is then identified when a company's content consumption on these specific topics spikes significantly above its historical 12-week baseline, indicating a genuine shift into an active research phase and providing a strong, industry-contextualized buying signal.
Accessing these granular benchmarks requires marketers to segment intent data reports by industry to accurately compare surge activity against the overall 15% average. Within a platform like Bombora's Company Surge®, a user can filter account lists by industry to isolate the companies showing heightened intent within a specific vertical. A statistically significant surge is typically flagged when an account's research intensity on a topic reaches a score of 60 or higher compared to its normal baseline activity. However, industry is not the only factor; an Optifai 2026 pipeline study of 939 B2B companies confirmed that deal size is a powerful predictor of cycle length, with enterprise deals over $100,000 ACV taking 90 to 180-plus days to close. Therefore, the most sophisticated analysis involves layering industry segmentation with other firmographics like company size and data on deal complexity to build a highly accurate model of which accounts are truly in-market and how long their buying journey is likely to take.
| Industry Vertical | Average Sales Cycle (Days) | Typical In-Market Percentage | Example Intent Topic Clusters |
|---|---|---|---|
| Software / Technology | 90-121 | Higher than average | "Cloud Migration", "AI Platforms", "Data Warehousing" |
| Financial Services | 98 | Slightly above average | "Digital Transformation", "Risk Management Solutions", "Fintech APIs" |
| Consulting & Business Services | 103 | Higher than average | "Lead Generation Services", "CRM Implementation", "Go-to-Market Strategy" |
| Healthcare | 125 | Below average | "EHR Systems", "Telehealth Platforms", "HIPAA Compliance Software" |
| Manufacturing | 130 | Below average | "Supply Chain Optimization", "Industrial IoT", "Predictive Maintenance" |
| Energy | 155 | Significantly below average | "Renewable Energy Management", "Grid Modernization", "Energy Trading and Risk Management" |
How Company Size Impacts Buying Behavior
The size of a B2B buying committee expands directly with the size of the company and the value of the deal, a trend that fundamentally reshapes modern sales and marketing strategy. According to Forrester's 2024 State of Business Buying report, the average purchase decision now involves 13 different stakeholders. This marks a significant increase in complexity, transforming B2B sales from a one-to-one conversation into a one-to-many consensus-building exercise. These buying groups are not monolithic; they are cross-functional, with 89% of purchases involving two or more departments. This group is a fluid network of roles, including economic buyers focused on ROI, technical evaluators scrutinizing integrations and security, end-users concerned with daily usability, and executive sponsors who hold the ultimate budget authority. For sellers, this means a single value proposition is no longer sufficient. Instead, a portfolio of tailored messages is required to address the distinct priorities and pain points of each persona within the account, from the CFO analyzing total cost of ownership to the IT manager assessing compliance risks. The failure to navigate this internal web of influence is a primary reason that, according to Forrester's 2024 data, a staggering 86% of B2B purchase processes stall before a decision is made.
Deal value and company size are the primary drivers of buying committee expansion, creating distinct tiers of complexity for revenue teams to navigate. Research from The Starr Conspiracy's 2024 B2B buying journey benchmarks shows that smaller transactions, specifically those under $50,000, typically involve a more contained group of three to four people. However, as the financial commitment grows, so does the oversight. For high-value strategic purchases over $1 million, the committee swells to an average of nine to twelve stakeholders. This larger group often includes additional layers of scrutiny from procurement, legal, and senior executives. Furthermore, a 2026 poll of 50 buyers by Influ2 revealed a clear correlation with company headcount, noting that buying groups of 10 or more people were found exclusively in firms with over 1,000 employees. This data, while from a small sample, provides a directional hint that enterprise-level organizations inherently operate with more intricate and layered decision-making frameworks. This structural reality means that selling to a large corporation is fundamentally an exercise in multi-threading, requiring sales teams to build relationships and secure buy-in across a wide and diverse set of roles to prevent the deal from collapsing under the weight of internal bureaucracy.
The inherent complexity of large, multi-stakeholder buying groups makes account-level intent data more critical than ever for effective enterprise selling. When a purchase decision involves a committee of 10 or more people, tracking the behavior of a single individual provides an incomplete and often misleading signal. A lone marketing manager downloading a whitepaper is a weak indicator of a company-wide initiative, but when multiple individuals from the same organization begin researching related topics simultaneously, it signals a coordinated buying motion. This is where platforms that provide account-level insights, such as Bombora's Company Surge®, become indispensable. This methodology works by monitoring the content consumption of millions of B2B organizations across a cooperative of over 5,000 publisher websites. A "surge" is identified when a company's research activity on specific topics shows a statistically significant increase compared to its historical 12-week baseline. This approach effectively surfaces the collective interest of the entire buying group, allowing revenue teams to prioritize accounts that are genuinely in-market and tailor their outreach based on the specific themes the account is actively researching.
The Rise of AI in the B2B Buyer Journey
The B2B buyer's journey now begins within a chat window, fundamentally reshaping how vendors are discovered and evaluated. According to a pivotal March 2026 report from G2, "The Answer Economy: How AI Search Is Rewriting B2B Software Buying," 51% of B2B software buyers now start their research process using an AI chatbot more frequently than Google. This marks a significant behavioral shift, as these tools are no longer just for peripheral information gathering but have become the primary starting point for vendor selection. The same study, which was based on a survey of 1,076 B2B software buyers and decision-makers, found that 71% of all buyers now depend on AI chatbots at some point during their software research. This trend is further validated by Forrester's 2026 Buyers' Journey Survey, which noted that 94% of B2B buyers utilized AI during their most recent purchase, an increase from 89% in the previous year's survey. This migration from traditional search to AI-driven answer engines means the initial vendor shortlist is often created before a buyer ever visits a company's website, compressing the discovery phase and prioritizing vendors who are most visible within these new platforms.
AI's influence extends far beyond initial discovery, actively altering final purchasing decisions and creating opportunities for new vendors to enter the consideration set. The same March 2026 G2 report revealed that a remarkable 69% of buyers chose a different software vendor than they had originally planned specifically because of guidance received from an AI chatbot. This demonstrates that AI is not merely a passive research assistant but an active influencer in the decision-making process. Furthermore, the study found that one-third of these buyers purchased from a vendor they had never even heard of before the AI chatbot recommended them, highlighting the technology's power to disrupt established brand recognition and market share. This is corroborated by a Semrush survey from July 2026, which found that 83% of B2B buyers said AI influenced their final vendor decision, with 32% stating it had a major influence. The implication is clear: AI recommendations are now a powerful force for shaping buyer perception and can override pre-existing biases, making it a critical channel for both incumbent and challenger brands.
Despite the rise of AI for initial discovery, high-intent research and validation still frequently lead buyers to authoritative publisher and vendor websites, a behavior that underpins the value of intent data methodologies. While some research from Forrester in February 2026 indicated that B2B companies are reporting traffic declines of 10-40% as buyers migrate research to AI engines, this doesn't capture the whole picture. Studies analyzing AI referral traffic, which is traffic from users clicking links within AI chat responses, show these visitors often have higher intent. For instance, a 2025 analysis by Previsible found that AI-referred visitors converted at 4.4 times the rate of organic search visitors. This suggests that while AI handles broad, top-of-funnel queries, buyers click through to specialized 'destination' sites for deep, validating research. This is precisely the type of high-value activity that Bombora's Company Surge® data is designed to capture, tracking spikes in content consumption across its co-op of over 5,000 B2B publisher websites. Therefore, even as AI changes the starting point of the journey, the signals of genuine, in-market buying intent remain strongest on the trusted publisher sites where final evaluations occur.
From Intent Signals to Actionable Leads
Intent data platforms excel at identifying which company is showing in-market signals, but they inherently fail to pinpoint who the specific buyers are within that organization. This creates a significant gap for sales outreach, as a raw signal of company-level interest is not an actionable lead. For example, a Bombora Company Surge® report might flag a business for researching "cloud security," but it does not reveal if the research is driven by a CISO building a business case, a procurement manager comparing vendors, or an IT analyst conducting exploratory research. This ambiguity is a core challenge; a recent DemandScience report from late 2025, surveying 750 senior B2B marketers, found that 87% believe their marketing investments generate unreliable or inflated intent signals. The same study noted only 26% of these signals ultimately convert into qualified opportunities, highlighting a major disconnect between a detected surge and a sales-ready contact. This forces sales development representatives to engage in time-consuming manual prospecting within the target account, diluting the efficiency gains that intent data promises and often leading them to engage the wrong stakeholders or entire buying committees that have already made up their minds.
The gap between account-level signals and contact-level action directly contributes to a significant return on investment problem for many B2B marketing teams. While adoption is widespread, performance is inconsistent. According to 2026 data from DemandScience and Autobound, a striking 91% of B2B marketers now use intent data for critical functions like account prioritization. However, a mere 24% of those same marketers report achieving exceptional ROI from their investment. This disparity underscores that simply having the data is not enough; the value is lost in translation when sales teams cannot effectively act on it. The challenge is often operational, as raw intent signals are frequently siloed within marketing platforms, disconnected from the daily workflows of sales representatives who need context to act. Without clear guidance on who to contact and what specific pain points their research indicates, sales teams often default back to less efficient methods like cold outreach, even when intent data is available. This failure to bridge the data-to-action gap means that while intent data successfully identifies potential demand, many organizations lack the activation processes required to convert that potential into revenue.
For businesses targeting the local and small business (SMB) segment, the limitations of broad, topic-based intent data are even more pronounced. The complex, multi-stakeholder buying committees that providers like Bombora or 6sense are designed to detect often do not exist in companies with fewer than 50 employees. In a local plumbing company, a dental office, or a small accounting firm, the buying "committee" is typically just the owner. Therefore, knowing that an anonymous business in a specific zip code is researching "local SEO services" is far less valuable than having the verified name, email, and direct phone number of the owner of that business. The critical need in the SMB space is not for inferential signals but for direct, actionable contact information. Traditional intent data, which focuses on aggregating content consumption patterns to identify topical interest, is an inefficient tool for this market. Sales cycles are shorter, decisions are made by one or two people, and the key to engagement is establishing a direct line of communication with the ultimate decision-maker, making high-quality, verified contact data the most crucial asset for any go-to-market team focused on this segment.
Keendai directly addresses the critical data-to-action gap for B2B companies that sell to the SMB and local business market. While large-scale data providers like ZoomInfo or Apollo have built extensive databases, their resolution and accuracy can be limited when it comes to the fragmented and constantly changing world of small, privately-owned businesses. These platforms are often optimized for identifying contacts within larger, more structured corporate hierarchies, leaving a significant void in coverage for the main street business segment. Keendai bridges this divide by focusing specifically on providing verified, direct contact information for the owners and key decision-makers at these local enterprises. This approach effectively bypasses the ambiguity of traditional intent signals, which only identify the 'which' (the company), and instead delivers the 'who' (the owner). By providing this direct, actionable intelligence, Keendai transforms a vague signal of potential interest into a concrete sales lead, enabling outreach teams to connect immediately with the person who holds the purchasing power. This closes the loop that generic intent data leaves open, providing a clear and efficient pathway to revenue for businesses targeting the vast but underserved SMB sector.
Related reading
- see our 11 tactics for abm success at every funnel stage analysis
- see our 12 tips for selling to the c suite analysis
- see our 2024 b2b intent data benchmarks analysis
- see our ai in sales salesforce data productivity analysis
Frequently Asked Questions
What is the 95:5 rule in B2B marketing?
The 95:5 rule states that only about 5% of B2B buyers are actively in-market for a product at any given time, while the other 95% are not. [1, 13] This concept was developed by Professor John Dawes of the Ehrenberg-Bass Institute to guide marketing strategy. [11, 12] Because most potential customers are not ready to buy, the rule suggests that marketers should focus on building brand awareness with the 95% so their company is remembered when those buyers eventually enter the market. [23] This balances long-term brand building with short-term lead generation efforts aimed at the 5% of active buyers. [1]
How does Bombora calculate a Company Surge score?
A Company Surge® score measures a company's research intensity on a specific topic by comparing its recent content consumption to its own historical baseline. [2, 7] Bombora establishes a normal research pattern for a company over the previous 12 weeks, which serves as its baseline. [7] A 'surge' is detected when a company's research activity on a topic becomes significantly higher than this baseline, resulting in a score from 0 to 100. [2, 25] A score of 60 or higher indicates a statistically significant spike in interest, signaling that the account is actively engaged in a buying journey. [20, 24]
How many people are in a typical B2B buying committee?
A typical B2B buying committee for a complex solution involves between 6 and 10 stakeholders, according to research from Gartner. [8, 18] However, recent 2024 data from Forrester suggests this number is growing, putting the average at 13 people involved in a purchase decision. [3, 8, 21] The exact size often depends on the deal's value, with committees for deals over $1 million involving 9 to 12 people across departments like finance, legal, and IT. [5] This group size means vendors must build consensus across multiple roles and priorities to win a deal. [8]
What is the difference between first-party and third-party intent data?
First-party intent data is information you collect directly from your own digital properties, such as your website, CRM, or product analytics. [9, 14, 16] This data, which includes actions like pricing page visits or content downloads, is highly accurate but limited to audiences already familiar with your brand. [16, 17] In contrast, third-party intent data is aggregated by external providers from a network of publisher websites, showing which companies are researching specific topics across the internet. [9, 15] Third-party data provides a broader, earlier view of market interest, helping you discover accounts before they have visited your site. [15]
How is AI changing B2B buyer research habits?
AI is fundamentally changing B2B research by allowing buyers to complete more of the journey independently before ever contacting a vendor. [10, 22] Buyers now use generative AI tools to build initial vendor shortlists, compare solutions, and synthesize complex information, which accelerates the early research phase. [4, 6] According to 2025 research from Forrester, generative AI is now considered a more meaningful information source than vendor websites for many buyers. [4] As a result, buyers arrive at the first sales call more informed and with pre-formed opinions, shifting the conversation from discovery to validation. [10, 22]
What is a B2B data co-op?
A B2B data co-op is a partnership where multiple publishers, brands, and B2B websites contribute their anonymized user engagement data to a central platform. [27, 29] This pooled data provides a comprehensive view of content consumption and research trends across the internet. [27] For example, Bombora's data co-op consists of thousands of B2B publisher sites, and it uses this collective, consent-based data to identify which companies are researching specific topics. [28, 29] This allows members to understand buyer intent on a much larger scale than they could by only analyzing traffic on their own websites. [30]
Last updated: October 2026