What is Bombora intent data?

Bombora's Data Co-op is a network of 5,500+ B2B publisher websites — analyst sites, trade publications, technology media destinations — that have agreed to share anonymized reader-behavior data with Bombora in exchange for access to the co-op's aggregated intelligence. Bombora places a proprietary tag on each participating publisher's pages. Every time a user from a business domain reads a piece of content, that interaction is captured, consented under the IAB Transparency and Consent Framework v2.2, and sent to Bombora.

Bombora's NLP models — trained on over a decade of B2B content signals using large language model techniques and named entity recognition — read the full text of each consumed page and map it to one of 17,000+ B2B topic categories. This is semantic analysis, not keyword matching: a page about 'protecting cloud workloads' and a page about 'cloud security posture management' both map correctly to the same intent topic even if they share no keywords.

For every company in Bombora's identity graph (2.8 million businesses), the platform establishes a baseline: how much content does this account typically consume on each topic over a 12-week window? The weekly Company Surge score then compares the past 3 weeks of consumption to that baseline, weighing four factors — number of topic interactions, number of unique users, topic-relevance weight of the content, and content engagement depth (scroll velocity, dwell time). A score of 50 equals average activity; 60+ signals a statistically significant surge; 80+ is a strong in-market indicator.

Most third-party intent data comes from the 'bidstream' — real-time ad-auction signals that capture page URLs but not page content, and that rely on third-party cookies for identity. Bidstream data is broad but noisy, and it is degrading as third-party cookies are deprecated and ad fraud filters tighten.

Bombora's co-op model is structurally different: it collects intent signals directly from publisher pages (not the ad auction), reads page content semantically, and resolves identities through a patented deterministic + behavioral + IP-to-company fusion. All data is collected with explicit publisher consent under IAB TCF v2.2, making it durable in a post-cookie environment. Forrester cited Bombora's approach as 'the gold standard for account-level intent data feeds' in its Q1 2025 Wave report, and 86% of co-op sites share data exclusively with Bombora — meaning these signals cannot be fully replicated by competitors.

For practitioners choosing between intent providers, the most common pattern is to use Bombora for broad top-of-funnel account discovery, then layer in second-party sources — G2 Buyer Intent for software-evaluation signals, TechTarget Priority Engine for enterprise IT media signals — for higher-confidence evaluation-stage triggers. An independent precision test by Brixon Group placed Bombora at 81% accuracy, compared to 87% for G2 Buyer Intent and 92% for Echobot in that same study. The trade-off is breadth: Bombora's 17,000+ topic taxonomy and 5,500+ co-op sites provide coverage that narrow second-party sources cannot match.

Customer case studies reported by Bombora show material improvements in pipeline efficiency across industries. A premier cybersecurity organization achieved a 33% improvement in target-to-booked-meeting conversion and a 15x industry benchmark for meeting conversion rate after deploying Company Surge alongside display ads and BDR sequences — in addition to a 2.8x display ad click-through benchmark and a 122% increase in BDR email open rates. Siemens Digital Industries reported that filtering outreach through intent data lifted sales acceptance of MQLs from 1% to 90% and cut telequalification costs per sales-accepted lead by 99%.

Bombora's Forrester-commissioned Total Economic Impact study found a 342% ROI from Bombora deployment among composite customers. The Forrester Wave Q1 2025 report confirmed Bombora's Leader position, noting that customers use its data 'as the standard by which they measure their other providers.'

That said, intent data is an input to pipeline efficiency, not a guarantee of pipeline. ROI depends heavily on how quickly teams act on surging accounts (standard weekly refresh means signals can be 7 days old by the time they arrive), whether account-level signals are combined with person-level contact enrichment, and whether ICP definitions align tightly with the accounts Bombora surfaces.

Bombora's most significant structural limitation is that it operates at the company level, not the person level. A surge signal tells you that 'Acme Corp' is researching 'revenue intelligence' — it does not reveal which individuals on the buying committee are doing the research, what their titles are, or how far along their evaluation has progressed. Acting on Bombora signals requires a separate contact-enrichment tool (ZoomInfo, Apollo, Lusha) to identify the right people, which adds $10,000–$50,000 per year to the total stack cost.

The standard data refresh is weekly, which creates a timing risk. In compressed buying cycles where committees evaluate vendors over days rather than weeks, a signal can arrive after the peak research window has already closed. The Plus tier offers daily refresh at additional cost.

Coverage also varies by industry and geography. Bombora's co-op is weighted toward North American B2B media in English, so signal quality for niche verticals or non-English-speaking markets is thinner. Teams selling into those segments frequently layer Bombora with G2 Buyer Intent (software review signals) or TechTarget Priority Engine (enterprise IT media signals) to compensate. Finally, at 81% precision in independent testing, roughly 1 in 5 high-scoring accounts may not have genuine near-term purchase intent — which is why practitioners combine surge data with fit scoring and additional signals rather than treating a score above 60 alone as an automatic trigger.

Bombora integrates with more than 100 platforms spanning CRM, marketing automation, ABM, digital advertising, and data warehouses. Native connectors exist for Salesforce, HubSpot, Marketo, Pardot, LinkedIn Campaign Manager, 6sense, RollWorks, and Adobe Experience Platform, among others. The typical activation pattern is: surge scores surface as a field on CRM account records, trigger lead-score updates in marketing automation, and fire sales tasks or sequence enrollments automatically above a configurable threshold.

For paid media, Bombora powers audience segments on LinkedIn and programmatic DSPs — so a company surging on a target topic can be added to an ABM audience list automatically without manual list uploads. For data science teams, a Company Surge API delivers raw scores for custom scoring models and warehouse integration.

Pricing is customized by number of topics tracked, volume of surging accounts delivered, and integration tier. Market-reported starting prices are approximately $30,000 per year for a basic data feed, with mid-market deployments running $50,000–$100,000 and enterprise contracts exceeding $200,000 annually. The total realistic stack cost — including contact enrichment, marketing automation, and RevOps resources — typically runs $73,000–$200,000+ per year.

Bombora tells you which companies are in-market. The next problem is moving fast enough to matter: researching the surging account, finding the right contacts, drafting a relevant message, and following up — all within the narrow window before the buying committee shortlists vendors. That workflow is where signal-based selling breaks down in practice; teams have the data but lack the execution speed.

Komo is built to close that gap. When a Company Surge signal fires for an account in your CRM, Komo researches the account and contact, drafts outreach personalized to the surge topic, and queues it for your review — with a human approving every send that matters. The intent signal provides the timing; Komo provides the account research and the first draft.

The result is the responsiveness of a well-staffed RevOps team without the manual overhead — and without the compliance and deliverability risks of a fully automated send. Intent data is only as valuable as the speed and quality of the action taken on it; Komo is the execution layer between the signal and the conversation.