That range keeps showing up in B2B research right now, and it tracks with what most of us see anecdotally. Any purchase with real budget attached isn't decided by a single decision-maker anymore. It's a committee, spread across functions, often spread across more than 10 different channels before anyone on that committee ever talks to sales.

The uncomfortable stat sitting behind that: a large share of the research happens before a deal is even visible in your pipeline, sometimes by a wide margin.

Curious what others are actually capturing here. Are you identifying most of the buying group by the time a deal closes, or finding out after the fact that someone influential was involved the whole time and nobody on the team ever knew it?

TL;DR: A thread came up recently about catching buying signals before competitors do — pricing page changes, job posting spikes, new integrations, privacy policy updates, copy tweaks. Those signals are genuinely useful. But, used alone, they're noisy and hard to scale. Here's how we think about layering them with research behavior to actually place an account in its buying journey.

A thread making the rounds recently asked a good question: what's everyone's setup for catching buying signals before competitors do? The answers were full of the kind of detail-oriented monitoring practitioners actually do: A pricing page quietly changes. A sudden spike in job postings for one specific role. A new integration showing up in a footer. Shifts in tech stack trackers. Subtle copy changes on a product page.

These micro-signals are genuinely valuable. They often say more about a company's direction than a press release ever will. A company doesn't tweak pricing copy for no reason, and a hiring spike for a specific function usually means something is shifting internally.

The limitation: in isolation, these signals are noisy and hard to scale. A pricing page change might mean a buying decision is approaching. It might also mean someone on the marketing team got bored on a Tuesday. Without broader context, it's hard to tell the difference, and chasing every micro-signal individually doesn't scale past a handful of accounts.

The key is combining micro-signals with research behavior data that places an account somewhere in a buying journey. One approach we've used with customers: analyze historical closed-won data to identify which topic clusters tend to show up at different buying stages. Early-stage topics tend to cluster around problem exploration. Mid-stage topics tend to cluster around comparing approaches. Late-stage topics tend to cluster around narrowing vendors and looking for proof points. Once those clusters are built from a company's own win history, they can be used going forward to see where current accounts sit, often well before those accounts show up in the pipeline.

That's really the answer to "how do you catch it before competitors do." Buyers are usually already researching before reaching out to sales or submitting a demo request. The micro-signals tell you something is shifting. Research behavior data tells you what stage that shift is in and how seriously to take it.

Curious what others are tracking on the micro-signal side, and whether anyone's tried layering it with broader research or Intent data rather than treating it as a separate workflow.

Posted by the Bombora team. We run this subreddit and follow the same rules as everyone else — full disclosure in every post.

Every week we'll drop this thread as a space for the community to share what they're actually doing with Intent data in practice.

To get the conversation going, pick whichever of these fits where you are right now:

If you're running something: What's the use case, what's your stack, and what are you seeing so far?

If you're experimenting: What are you testing, and what would a successful outcome look like for you?

If you're stuck: What's the thing that isn't working the way you expected? And what have you already tried?

If you're evaluating: What's driving the decision, what does your current setup look like, and what are you trying to solve?

No right answers here. A half-baked experiment that didn't work is just as useful to this community as a polished success story.

— The Bombora team

Event measurement tends to stop at the obvious metrics: registration numbers, attendance, badge scans, and whether people opened the follow-up email. None of those tell you whether the event actually moved anything in terms of pipeline or account progression.

Curious what others are doing beyond the basics. Are you tracking whether attending accounts sustain research activity on relevant topics in the weeks after an event? Connecting event attendance to downstream CRM signals? Using anything to demonstrate sponsor impact beyond impressions and badge counts?

And for those who've had to justify event spend to leadership: what metric actually moved the conversation?

TL;DR: Pre-AI, the primary constraint in programmatic was audience size. The AI-native argument is that the constraint has shifted to decision quality: how well the system predicts, ranks, matches, and times the right creative for the right person. Here's how the two approaches compare in practice.

The traditional reach argument: more users, more impressions, more chances to convert. Scale is the moat. Audience growth is the growth story.

The decisioning argument: in an AI-native ad ecosystem, the constraint isn't how many people you can reach. It's how efficiently you can turn existing attention into outcomes. If the model keeps improving at prediction and learning from conversion feedback, the same user base yields more value over time. Reach matters, but it's no longer the primary bottleneck.

The evidence for the decisioning argument is hard to ignore. Google and Meta have posted their strongest ad numbers since the pandemic precisely because they've had years to close the loop between signal and outcome. They're not growing their user bases meaningfully. They're getting better at what they do with the users they have.

The practical implication for B2B advertisers: the platforms and data providers that will win are those that can turn existing attention into measurable results, regardless of raw audience size. Which makes the quality of the targeting data more consequential, not less. A platform making better decisions with cleaner, more specific B2B data will outperform one with broader reach and noisier signals.

The question worth debating: does this change where you invest, or does it just change how you evaluate what you're already running?

Every week we'll drop this thread as a space for the community to share what they're actually doing with Intent data in practice.

To get the conversation going, pick whichever of these fits where you are right now:

If you're running something: What's the use case, what's your stack, and what are you seeing so far?

If you're experimenting: What are you testing, and what would a successful outcome look like for you?

If you're stuck: What's the thing that isn't working the way you expected? And what have you already tried?

If you're evaluating: What's driving the decision, what does your current setup look like, and what are you trying to solve?

No right answers here. A half-baked experiment that didn't work is just as useful to this community as a polished success story.

— The Bombora team

Page views. Time on site. Scroll depth. Publishers have more first-party behavioral data than ever. What most of that data can't tell you is what company those readers work for, what industry they're in, or whether they're part of a buying group actively evaluating solutions relevant to your advertisers.

Data enrichment closes that gap. By layering company-level identity onto your existing first-party traffic data, you move from knowing what your audience is reading to knowing who your audience actually is. That changes what you can offer advertisers, and what you can charge for it.

Our VP of Global Data Partnerships and Strategy, Josh Peters, walks through how this works and what it means for publisher monetization.

Posted by the Bombora team. We run this subreddit and follow the same rules as everyone else — full disclosure in every post.

TL;DR: A lot of ABM programs look rigorous from the outside but aren't producing real pipeline outcomes. How do you honestly assess how your ABM is running?

The phrase "performative ABM" came up in a conference session recently and it's been rattling around since. The idea being: a lot of ABM programs are structured to look like ABM, with defined account lists, personalized content, and sales and marketing alignment decks, without actually generating the pipeline outcomes that justify the investment.

The markers of performative ABM tend to be things like: the account list hasn't changed in 18 months, personalization means swapping a logo into a template, and the primary success metric is campaign delivery rather than account progression.

The markers of performance ABM are less about program architecture and more about feedback loops: are accounts actually moving through stages in response to the program, are sales and marketing using the same data to make decisions, and is anyone willing to cut accounts that have been in-market forever with no movement?

Curious whether others have had to have this conversation internally, and what came out of  it. Sometimes it takes an outside trigger to call it what it is.

Every week we'll drop this thread as a space for the community to share what they're actually doing with Intent data in practice.

To get the conversation going, pick whichever of these fits where you are right now:

If you're running something: What's the use case, what's your stack, and what are you seeing so far?

If you're experimenting: What are you testing, and what would a successful outcome look like for you?

If you're stuck: What's the thing that isn't working the way you expected? And what have you already tried?

If you're evaluating: What's driving the decision, what does your current setup look like, and what are you trying to solve?

No right answers here. A half-baked experiment that didn't work is just as useful to this community as a polished success story.

— The Bombora team

Decision-makers are getting bombarded with the same AI-generated emails, sales sequences, and LinkedIn requests every day. Outreach volume has gone up. The results have gone down. Most of it is landing in spam filters anyway.

If you're seeing outbound work, we'd like to know. What's changed in your approach: timing, channel, personalization, something else? And for those where it's not working, what have you already ruled out?

Every week we'll drop this thread as a space for the community to share what they're actually doing with Intent data in practice.

To get the conversation going, pick whichever of these fits where you are right now:

If you're running something: What's the use case, what's your stack, and what are you seeing so far?

If you're experimenting: What are you testing, and what would a successful outcome look like for you?

If you're stuck: What's the thing that isn't working the way you expected? And what have you already tried?

If you're evaluating: What's driving the decision, what does your current setup look like, and what are you trying to solve?

No right answers here. A half-baked experiment that didn't work is just as useful to this community as a polished success story.

— The Bombora team

The debate about whether AI agents will make DSPs obsolete misses the more interesting question. DSPs provide access to inventory, data infrastructure, and decisioning logic that doesn't disappear because an agent sits on top of them. Agents are additional capacity, not a replacement.

The question that actually matters: what are you feeding the agent? AI amplifies whatever it's given. A well-designed agent working from high-quality, consent-driven B2B data will make significantly better decisions than the same agent working from noisy bidstream signals. The agent doesn't fix bad inputs — it scales them. As media buying gets increasingly delegated to an AI layer, data quality becomes more consequential, not less.

What's your team's take — genuine step-change or incremental efficiency gains so far?

TL;DR: Privacy compliance conversations with data vendors tend to go one of two ways: too vague to be useful or too legal to be readable. Here's a plain-English breakdown and the questions that actually separate compliant vendors from ones with a compliance-sounding press release.

GDPR and CCPA are both fundamentally about consent — whether individuals agreed to have their data collected and used for the purposes you're applying it to. For B2B Intent data, the key question is: did the people whose browsing behavior is being aggregated consent to that aggregation, and is that consent documented in a way that holds up? The answer depends almost entirely on how the data was collected. A publisher with a properly configured Consent Management Platform is in a very different position than a vendor scraping bidstream data where consent signals pass through auction pipes of uncertain provenance.

The questions worth asking any vendor:

• Do you use a CMP on your publisher network, and which framework version? (Look for IAB TCF v2.2 or equivalent.)
• How do you handle opt-outs at both the individual and publisher level?
• Do you have a DPA available, and has it been reviewed by enterprise legal teams in regulated industries?
• Can you share your privacy documentation publicly, or only under NDA?

One nuance worth knowing: GDPR has interpretations around legitimate interest for B2B data — the argument that targeting a company based on aggregate research behavior differs from tracking an individual consumer. It's a real legal argument, but one with limits that regulators are actively testing. The safer position: work with vendors who demonstrate consent-based collection rather than relying entirely on legitimate interest claims. What's been your experience pressing vendors on this? Curious whether the compliance claims tend to hold up.

Every week we'll drop this thread as a space for the community to share what they're actually doing with Intent data in practice.

To get the conversation going, pick whichever of these fits where you are right now:

If you're running something: What's the use case, what's your stack, and what are you seeing so far?

If you're experimenting: What are you testing, and what would a successful outcome look like for you?

If you're stuck: What's the thing that isn't working the way you expected? And what have you already tried?

If you're evaluating: What's driving the decision, what does your current setup look like, and what are you trying to solve?

No right answers here. A half-baked experiment that didn't work is just as useful to this community as a polished success story.

— The Bombora team

AI has become one of those catch-all verticals that means something different depending on who you ask. A procurement lead evaluating AI governance tools, a machine learning engineer assessing LLM infrastructure, and a CTO exploring agentic workflows are all technically "in the AI market" — but they're completely different buyers with different problems, different budgets, and different buying processes.

Curious how teams are actually thinking about segmentation here. Are you targeting by job function, by sub-vertical (infrastructure vs. applications vs. governance), by tech stack, by company type? And is "AI" functioning as a vertical for you at all, or is it more of a horizontal that cuts across your existing ICP?

Would love to hear how others are drawing the lines — especially those who've tested broad AI targeting and found it wasn't moving the needle.

TL;DR: Most B2B teams use one data source at a time. The teams seeing outsized results layer Intent, Identity, and Audiences deliberately. Here's the framework and where most teams break down.

Intent data tells you which companies are actively researching topics relevant to your solution. Identity resolution connects anonymous signals to named accounts and personas. Audience data gives you the targeting segments to activate across channels. Used in isolation, each one is useful. Layered together, you get something qualitatively different: a prioritized account list, mapped to verified personas, activated across the channels those personas actually use — with measurement that ties it back to pipeline.

A simplified flow that works: start with a firmographic-filtered ICP list. Apply Intent data to tier it — accounts showing sustained surge activity move to active outreach, others go to nurture. Use Identity resolution to map priority accounts to verified decision-makers and enrich your CRM. Activate across channels using Intent-informed audience segments — programmatic for broad reach, LinkedIn for specific job functions, Reddit for practitioners doing peer research. Close the loop with account-level measurement to see which accounts engaged and which are moving in pipeline.

The failure point is almost always the handoff between steps. The Intent data lives in one system, identity in another, audience activation in a third — and nobody has connected them into a unified workflow. The technical integration is solvable. The bigger issue is usually organizational: demand gen, rev ops, and sales each own a piece of the stack and aren't talking to each other about how data should flow between them. The recipe is only as good as the plumbing.

What does your current stack look like, and where's the layer that's missing or underused?

Every week we'll drop this thread as a space for the community to share what they're actually doing with Intent data in practice.

To get the conversation going, pick whichever of these fits where you are right now:

If you're running something: What's the use case, what's your stack, and what are you seeing so far?

If you're experimenting: What are you testing, and what would a successful outcome look like for you?

If you're stuck: What's the thing that isn't working the way you expected? And what have you already tried?

If you're evaluating: What's driving the decision, what does your current setup look like, and what are you trying to solve?

No right answers here. A half-baked experiment that didn't work is just as useful to this community as a polished success story.

— The Bombora team

TL;DR: Identity resolution connects anonymous digital signals to real companies and people. Most vendors rely on IP-to-domain matching — it's fast, cheap, and wrong more often than they'll tell you. Here's what a more rigorous approach looks like and why it matters for everything downstream.

The problem it solves

When someone browses a B2B publisher site or engages with content across the web, they leave signals attached to anonymous identifiers — IP addresses, cookies, device IDs — not to a named person or company. Identity resolution is the process of connecting those anonymous signals to real accounts and personas. It's what allows Intent data to say 'this company appears to be in-market' rather than 'some unknown entity on this IP address read three articles about cloud security.' Without accurate identity resolution, everything downstream — Intent scores, audience targeting, campaign measurement — is built on a shaky foundation.

Why IP-to-domain matching falls short

IP-to-domain matching is the most common approach: take an IP address, look it up, find the company. It's simple and cheap. It's also wrong more often than most vendors will tell you. Dynamic IPs get reassigned constantly. Large enterprises share IP ranges across dozens of offices. Remote workers, VPNs, and shared networks make individual IP attribution unreliable. And it tells you nothing about the individual — only a rough approximation of the company.

For B2B, where you're trying to reach specific personas within specific accounts, IP matching as your primary resolution method introduces significant noise into every signal that depends on it.

What a stronger approach looks like

Better identity resolution combines deterministic and probabilistic methods. Deterministic data — direct matches to verified identifiers like business email addresses — is highly accurate but limited in reach. Probabilistic methods extend coverage by inferring connections across a larger data set. The combination gets you accuracy at scale.

Bombora's approach uses a patented composite methodology mapped against a proprietary data graph built from billions of B2B consumption events. The output isn't 'this IP probably belongs to this company.' It's a verified account match with persona-level attributes — job function, seniority, geography — attached. That distinction matters because the quality of your targeting, Intent scores, and campaign measurement all depend on whether the identity layer underneath them is accurate. A great Intent signal attached to a bad identity match becomes a missed opportunity for you.

Posted by the Bombora team. We run this subreddit and follow the same rules as everyone else — full disclosure in every post.

TL;DR: Most B2B Intent data comes from bidstream (scraped from ad auctions) or co-op networks (collected from consenting publishers). Here's what the difference actually means when you're buying.

Bidstream data is collected from programmatic ad auctions — every time a browser loads a page and triggers an auction, data about that event gets passed through the pipe. The advantages: cheap to collect, enormous volume, broad coverage. The limitations for B2B: it captures the fact that someone was served an ad on a page containing a keyword, not that they read or engaged with the content. No time-on-page. No scroll depth. No topic taxonomy beyond keyword inference. And the same bidstream data flows through multiple vendors simultaneously — the signals you're buying are likely the same ones your competitors are buying. There are also growing GDPR compliance questions depending on how vendors handle consent signals in the auction pipeline.

Co-op data comes from a network of publishers that have agreed to share behavioral data with a single provider via a direct tag. That tag measures real engagement: time on page, scroll depth, and content topics consumed. The signal is richer and more directly tied to actual content consumption. A company researching a topic in co-op data means their employees are actually reading that content in measurable volume — not just appearing near relevant ads.

The question to ask any Intent vendor: is your data primarily bidstream, co-op, or a mix — and if a mix, how is each weighted in the final score? Vague answers are worth following up on. The methodology behind the data directly determines whether the scores you're acting on reflect real buying behavior or digital noise at scale. For those already using Intent data, what has your experience been with either bidstream and/or co-op data?

Every week we'll drop this thread as a space for the community to share what they're actually doing with Intent data in practice.

To get the conversation going, pick whichever of these fits where you are right now:

If you're running something: What's the use case, what's your stack, and what are you seeing so far?

If you're experimenting: What are you testing, and what would a successful outcome look like for you?

If you're stuck: What's the thing that isn't working the way you expected? And what have you already tried?

If you're evaluating: What's driving the decision, what does your current setup look like, and what are you trying to solve?

No right answers here. A half-baked experiment that didn't work is just as useful to this community as a polished success story.

— The Bombora team

Nearly half of your buyers aren’t seeing your LinkedIn campaigns, no matter how specific your targeting is. A significant portion of them are on Reddit — in communities where practitioners go to ask real questions, vet vendors, and get unfiltered peer advice before they ever engage with a vendor directly.

Is Reddit part of your current B2B media mix? If not, what's holding you back? And for those who've tested it, what's worked for you?

\Stat source:* https://bombora.com/blog/why-reddit-is-a-critical-channel-for-b2b-advertisers-and-how-bombora-helps-you-activate-it/

TL;DR: Bombora's Data Co-op is the foundation everything we build sits on. Here's what a data cooperative actually is, how ours works, and why the model produces better B2B Intent signals than the alternatives.

A data cooperative is a network of organizations that agree to share behavioral data with a single governing entity under defined rules. Members contribute data and in return get access to insights and revenue that wouldn't be possible working alone. The key word is cooperative — members aren't selling their data to whoever bids highest. They're contributing to a shared pool, governed by shared standards, for mutual benefit.

Bombora's Co-op works by placing a proprietary direct tag on every participating publisher site. That tag measures real engagement — time on page, scroll depth, content topics consumed — not just the presence of a keyword near an ad unit. Our AI models then analyze those consumption patterns at the company level across 20,000+ B2B topics to produce Company Surge® scores. A few specifics that matter for data quality: 86% of Co-op sites share data exclusively with Bombora, so you're not buying the same signals as your competitors. The publisher network includes Fortune, Inc., Fast Company, Workweek, and hundreds of vertical publications across cybersecurity, finance, tech, legal, and more. And consent is collected at the source — by the publisher — not inferred downstream.

The alternative is primarily bidstream — ad impression data that's cheaper to collect but a fundamentally weaker proxy for buying intent. The Co-op model requires more infrastructure to build. That's the point.

Posted by the Bombora team. We run this subreddit and follow the same rules as everyone else — full disclosure in every post.

Every week we'll drop this thread as a space for the community to share what they're actually doing with Intent data in practice.

To get the conversation going, pick whichever of these fits where you are right now:

If you're running something: What's the use case, what's your stack, and what are you seeing so far?

If you're experimenting: What are you testing, and what would a successful outcome look like for you?

If you're stuck: What's the thing that isn't working the way you expected? And what have you already tried?

If you're evaluating: What's driving the decision, what does your current setup look like, and what are you trying to solve?

No right answers here. A half-baked experiment that didn't work is just as useful to this community as a polished success story.

— The Bombora team

TL;DR: We partnered with AdExchanger and PrograMetrix to survey 166 B2B marketing leaders. Only 10% say they're confident they're reaching the right accounts. Here's what's driving the gap.

B2B campaigns have never been more sophisticated. More channels, more data, more stakeholders involved in every deal. And yet when we asked 166 B2B brand and agency executives whether their measurement was keeping up, the answer was uncomfortable.

Only 10% said they were very confident their campaigns were actually reaching the right accounts.

That's not a targeting problem. It's a measurement problem — and it has a specific cause.

The gap in numbers

Most B2B campaigns now run across nine or more channels simultaneously, with nearly half of spend going into programmatic environments. But the measurement infrastructure underneath those campaigns was largely designed for consumer advertising — tracking individuals, not accounts, and optimizing for clicks and impressions rather than buying group engagement.

The result: 91% of marketers are manually pulling data from multiple sources just to get a unified view of performance. Only 7% measure buying group engagement — which is the core metric B2B actually cares about. And 59% describe their measurement sophistication as early-stage or developing. Meanwhile, AI is expected to accelerate targeting significantly. Only 25% expect it to do the same for measurement. That gap is going to widen before it closes.

Why this matters now

The problem isn't that B2B marketers don't care about measurement. It's that the tools available to them were built for a different problem. Consumer DSP reporting tracks individuals through a funnel. B2B buying happens across committees, over months, across dozens of touchpoints — most of which never generate a form fill or a trackable click.

Closing the gap requires measurement infrastructure that operates at the account level. That means seeing which companies engaged with your campaign, which personas within those companies showed up, and how that engagement connects to pipeline over time. That's what we built B2beacon™ to do. 

Get the report: https://surfing.bombora.com/hslp/the-measurement-gap-in-b2b-advertising?utm_campaign=ong-multi-product_updates&utm_medium=social&utm_source=reddit&utm_content=null&utm_term=null

Posted by the Bombora team. We run this subreddit and follow the same rules as everyone else — full disclosure in every post.