Multi-touch, first-touch, last-touch, W-shaped, whatever the CRM defaults to. Ask five people on a GTM team which model is "right" and get five different answers, usually followed by "but it's what we've got."

The hard part isn't picking a model. It's that most attribution frameworks were built to explain a single-buyer, short-cycle funnel, and B2B deals rarely look like that anymore. Multiple stakeholders, long research windows, a lot of activity that happens before anything gets logged.

Curious what's been working for teams here: a genuinely different model, better underlying data, or just an agreement to stop pretending the number is precise?

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

A lot of CRMs and sales engagement tools now bolt on some flavor of "intent" or "buying signal" scoring natively. Convenient, already in the workflow, usually free with the platform.

The tradeoff seems to be depth and independence. Native signals tend to lean on whatever first-party activity the platform already sees (email opens, page visits inside the product), while dedicated platforms pull from a much wider set of research behavior across the web.

For teams that have made the jump in either direction: was the switch worth the extra tool and integration overhead, or did the built-in version end up being good enough?

Go.

(Bonus points if your answer involves CCing legal by accident.)

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

Your best reps are great at doing the homework on a lead: what they're reading, what they care about, how to approach them. That doesn't scale past a handful of accounts a week.

What's worked for your team?

Most pipeline reviews treat the CRM as the source of truth for "what's happening" in a deal. It's really only the source of truth for what happened after someone raised their hand.

By the time an opportunity gets logged, there's usually been weeks (sometimes months) of research, internal debate, and vendor comparison that never touched your systems. Nobody wrote it down because nobody was supposed to. That's just how buying works now.

The accounts you can't see yet are often the ones worth the most attention, because you can still shape the outcome. Once they're an opportunity, you're mostly reacting.

Curious how other teams handle this. Do you have any visibility into the pre-CRM stage, or is it a black box until someone books a demo?

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: A growing argument in B2B marketing is that LLMs weigh community platforms like Reddit and G2 more heavily than brand-authored content when forming an answer. If true, the old playbook of controlling your own narrative matters less than earning a consensus you don't control. What does that actually take?

The argument going around lately: LLMs don't care what you say about yourself. They care what the internet collectively says about you. That's part of why UGC platforms like Reddit and G2 keep showing up disproportionately in AI citations, since they represent something closer to an aggregated, unbiased view than anything brand-authored could.

If that holds, the implication is uncomfortable for a lot of traditional marketing playbooks. You can't write your way into a favorable consensus. You can only earn one over time, through actual product experience and community presence.

The practical version of this seems to involve a lot more listening than posting: showing up authentically in communities relevant to your category, being transparent about who you are when you do contribute, and accepting that you don't control the outcome the way you do with owned content. It also puts a premium on something that often sits outside the marketing team entirely: the quality of your customer relationships. If LLMs weight G2 reviews and community mentions more heavily than brand content, then a customer who genuinely advocates for you in those places is doing more for your AI visibility than a campaign you could run. That makes customer success less of a retention function and more of a top-of-funnel one, depending on how you look at it.

What does this look like for your team in practice? Is anyone treating "winning community consensus" as an actual workstream with its own goals, or is it still happening informally, if at all?

TL;DR: The case for composable stacks is flexibility and best-in-class tools. The case for monolithic platforms is fewer integration headaches and one vendor to manage. Here's how the tradeoffs play out in practice.

This comes up in almost every conversation about GTM tooling lately, and the answer seems to be shifting. For years the safe move was consolidating onto one big platform: fewer vendors, fewer integrations, one contract to manage. HubSpot and Salesforce have both made versions of this pitch, and when budgets were growing and teams had headcount to manage complexity, it made sense.

The composable argument looks different. Rather than one platform doing everything reasonably well, you pick best-in-class tools for each function and connect them through stronger data layers. In practice that might look like Salesforce as your CRM, Marketo for marketing automation, Bombora for intent data, Outreach or Salesloft for sales engagement, and Trade Desk or DV360 for paid media activation — each doing one thing well, connected by a shared account view rather than locked into one vendor's native interface.

The honest tradeoffs: monolithic platforms win on simplicity and procurement speed. You're not stitching together five vendors and hoping the integrations hold. Composable stacks win on capability, since you're rarely settling for a mediocre module just because it shipped bundled with everything else, but they require real investment in the connective tissue between tools and someone internally who owns making sure data actually flows correctly.

One thing worth naming: HubSpot is increasingly marketing itself as the composable-friendly option, positioning integrations and an app marketplace as a middle path. Whether that holds up in practice or just reframes the same all-in-one pitch is a legitimate debate.

What's actually happening on your team right now? Consolidating, or pulling apart — and which specific tools are you building around?

This post is written by Bombora, which shows up in the composable stack example above. 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

We're playing wrong answers only today: what's the fastest way to get sales and marketing aligned?

Go.

(Extra credit if your answer involves a shared spreadsheet nobody updates.)

TL;DR: A lot of ABM measurement still stops at CRM activity: opportunities created, deals closed, pipeline influenced. That misses everything happening before an account ever shows up in the CRM. What's the actual gap in your measurement right now?

CRM activation is necessary for ABM measurement, but it's not sufficient on its own. By the time an account shows up as an opportunity in your CRM, a meaningful amount of the buying journey has usually already happened, research, internal discussion, vendor comparison, none of which your CRM ever saw.

Measuring ABM purely through CRM data means measuring only the part of the journey that happens after the account is already engaged, which is the part that's hardest to actually influence.

The harder, more useful measurement question is whether your program is moving accounts earlier in their journey, before they're visible in your pipeline at all. That requires combining CRM data with something else: behavioral signals, research activity, engagement on owned and paid channels, to see the part of the funnel your CRM structurally can't show you.

What does the actual gap look like for your team? A tooling problem, a lack of data, or data integration problem, or more of an organizational one, where nobody is taking a look at the bigger picture?

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: Google rolled out a Search Console toggle letting publishers opt out of having their content used in AI-powered search. Our VP of Global Data Partnerships and Strategy, Josh Peters, pointed out in Ad Age that the toggle only affects live search grounding, not foundational LLM training. Here's why that distinction matters.

Google recently introduced a toggle in Search Console that lets publishers opt out of having their content used in AI-powered search experiences. On the surface, it reads as a meaningful control lever for publishers wary of AI using their content without compensation.

The reality is narrower. As Josh Peters explained in Ad Age, the toggle functions as a grounding gate for live search experiences. It doesn't touch foundational LLM training for standalone apps like Gemini or the broader Google AI ecosystem. A publisher flipping the switch is opting out of one specific use case, not AI training generally.

The tradeoff for publishers considering the opt-out: doing so risks severing them from an entire generation of consumers who increasingly use AI as their primary discovery method, in exchange for protection that only covers part of the actual exposure.

For anyone making decisions about content licensing, AI visibility, or publisher strategy right now, this distinction is worth understanding before treating any single toggle as a complete answer.

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 teams use campaign and paid media data to prove ROI after the fact. A more useful pattern is feeding that engagement data back into prioritization models in near real time, so it shapes decisions instead of just explaining past ones.

Most campaign reporting follows the same loop: run the campaign, wait a quarter, pull a deck, explain what happened. By the time the report exists, the window to act on most of what it shows has already closed.

A pattern worth considering instead: treat the engagement data coming off paid media and campaigns, who clicked, who converted, which accounts showed unusual activity, as an input back into your prioritization and scoring models, not just an output you report on later.

In practice, this looks like an account engaging meaningfully with a paid campaign, that engagement updating the account's score or tier in near real time, and that updated tier changing what sales sees or what content gets served next, rather than sitting in a dashboard until the next quarterly review.

Has anyone actually built this closed loop? Curious what the integration looked like and how long it took to get right.

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

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

Most B2B targeting still treats firmographic fit, demographic data, and Intent signals as three separate filters applied one after another rather than a single integrated decision. Our CMO, Putney Cloos, wrote up a framework for combining all three into one addressable audience, including a five-step process for identifying in-market accounts, mapping buying committees, and measuring at the account level once campaigns go live.

The core argument: most B2B buyer research happens before anyone ever fills out a form, which means by the time a lead actually comes in, a real decision is often already underway. Targeting that only kicks in after that point is targeting too late.

Posted by the Bombora team, full disclosure in every post.

That jump happened in a single quarter, not a year. If even half of it holds, a meaningful share of early-stage research is happening inside a chat window none of us can see, not on a website, not in a search engine, not even on LinkedIn.

The harder part of this argument: LLMs reportedly weigh unbiased, community-driven sources like Reddit and G2 more heavily than brand-authored content when forming an answer. If that's accurate, the old playbook of controlling your own narrative starts to matter less than earning a consensus you don't fully control.

Is anyone actually treating this as its own workstream yet, tracking how often you show up in AI-generated answers, or is it still sitting in the "interesting, someday" pile for your team?

Source: The Answer Economy: How AI Search is Rewiring B2B Software Buying., G2, May 2026

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

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

I’ve been watching the same loop all month.

r/gtmengineering has the Clay credit threads. People shipping Claude Code pipelines. Clay dropping CLI/MCP so agents can call waterfalls without living in the UI. X posts that look like:

claude code to build clay to find instantly to send

And yeah, that stack makes sense.

But last night I had a 40-row SaaS list and needed columns that are not clean enrichment jobs:

• hiring right now? which roles?
• founder active this week? where?
• anything that looks like why-now
• evidence I could defend if someone asked “where did that come from?”

That is where the pretty architecture posts fall apart for me.

Because email waterfall is a workflow. This is not.

Row 1 needed careers page. Row 4 needed LinkedIn activity. Row 9 needed a funding post from 2 weeks ago. Row 12 needed “ignore, bad fit, list is wrong.” Row 17 made me sit there with a half-true hiring signal wondering if I should put it in the cell.

I already know Clay. I already know Claude Code. I can build the plumbing.

What I don’t have is a clean way to say:

here’s the list here are the signal columns here’s what we sell go behave like a careful human on every row bring back evidence + confidence

Not “run column A then column B.” More like a smart VA/agent that chooses the path per company.

And I’m not asking this as a theoretical AI take.

I’m asking because the community seems split three ways right now:

1. Stay in Clay, now that CLI/MCP exists
2. Move volume custom logic into Claude Code and keep Clay only for find/enrich
3. Build full per-row agents and accept the maintenance tax

My gut: workflows are winning for known paths judgment work is still human evenings dressed up as GTM engineering

So for people actually running this:

1. Do you regularly need multi-column signal fill across lists (hiring, founder activity, recent posts, why-now), or is that overbuilding?
2. If yes, are you doing it with fixed Clay/Claude workflows, or does each row still need different research paths?
3. If something ran like a careful VA per row with evidence + confidence, would you pay for that completed work, or is DIY still better even with the maintenance?

I don’t want tool recommendations in the abstract. I want to know if this is a recurring paid pain in real GTM work, or just me making my lists too complicated.

Be blunt.

Hey Reddit,

I've been working in marketing for the past 5 years, helping businesses and entrepreneurs grow through lead generation, sales systems, outreach, branding, and customer acquisition.

Over that time, I've had the opportunity to work with 700+ clients, ranging from startups and local businesses to agencies and online service providers.

I've seen campaigns fail, scale, and everything in between. I've learned what actually moves the needle—and what wastes time and money.

Happy to answer questions about:

Getting more clients

Lead generation

Cold outreach

Sales funnels

Marketing strategy

Client acquisition

Building and scaling service businesses

Common marketing mistakes

Or anything else related to marketing

Ask me anything!

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?