But honestly somewhere in the last stretch, the self-imposed Sunday deadlines have started outrunning the pace in which I can get inspired, learn new, meaningful things, draft something worth reading, and so on.

I started Looped In to write about the things that genuinely light me up, have helped me grow, could help others grow. A place to share cool shit I’m seeing, or building, or consuming, etc. And our whole space is shifting so fast right now that what I actually want to do is disappear into deep research mode for a minute and come up for air when I can pack a punch in your inbox again. But I need a minute to be able to do that effectively.

So here’s what changing:

The paywall is now off. Looped In is free from here on out.

I’m trading the Sunday deadline for a simpler, truer rule: I’ll write every time inspiration strikes. That might be twice a week, or once a month, or somewhere in between when I’ve got my head down in the research and I’m not willing to hand you something half-baked just to hit a date. Fewer, but better posts is the goal.

To everyone who’s been a paying subscriber, some of you for years now: I see you, and I’m forever grateful. You back this before I even knew what this was, and that means the absolute world to me. To readers who’ve been so loyal and read nearly every edition, you’re the real MVPs. I would have never kept going without your support.

So, same newsletter, same insane obsession with getting the next generation of demand gen frameworks codified and vetted so we can all grow together, just maybe delivered a little more randomly to your inboxes going forward.

Thanks for being looped in. I’ll see you when the next one’s ready.
Kaylee ✌️

Back in November 2023 I wrote a post calling paid media the anti-hero of the marketing equation. The argument was simple. A CFO agrees to put $1 into marketing and expects at least $4 back, no caveats. Paid got the biggest slice of variable spend for one reason. It looked the most trackable. Not always because it worked the best, but it fit on the spreadsheet cleanly.

You know the old joke. A man is crawling around under a streetlight at night. A cop asks what he lost. His keys. Did you drop them here? No, he says, over in the park. So why look here? Because this is where the light is. That is the whole history of a marketing budget. We fund what the light happens to touch.

In the 2023 article I had said the software attribution we were basing those ROI commitments on was biased, flawed, and limited. It hadn’t innovated in five years while buying behavior changed completely. Communities, peer networks, social selling, privacy changes, algorithms nobody controls.

My call was that people would wake up to the fact that building an audience creates optionality, and owned media would get its moment.

I was right about the destination.
I was early on the timing.

And I completely underestimated how the industry would fumble the landing.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday.

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Where the market landed in 2026

Let me set the scene with numbers, because you know that’s the only way I know how to do this.

Paid is structurally and creatively exhausted and the expense is just the symptom that people notice first. Between January 2025 and January 2026, paid search click share roughly doubled while classic organic click share fell by as much as 23 percentage points across major verticals. Advertisers are paying more to hold the same ground. Every dollar produces output until the second you stop, and then it produces nothing. No asset left behind, nothing that compounds. When the spend ends, the results do too.

The organic side though does the opposite. Content cost per lead drops as the library grows. Paid cost per lead climbs as competition and fatigue pile up.

So the market did the rational thing and sprinted for the exits. The “create demand, don’t just capture it” argument went mainstream. Amanda Natividad’s zero-click content became the default frame. Adam Robinson turned a founder LinkedIn account into a reported $5M ARR in 13 months with no paid ads, and made every founder in B2B want the same thing. Then AI slop showed up and handed the organic crowd their closing argument. Publishing AI-generated content is now a fast track to getting penalized in search, and half of consumers say they’d rather buy from brands that keep generative AI out of their content.

Every one of those takes is correct. That’s exactly the problem.

When a thesis goes from contrarian to consensus, being right about it stops being worth anything. “Organic is winning, paid is dying” is a cliche nearly everyone is selling on LinkedIn now.

Saying the obvious out loud…

The reason organic is still underfunded relative to the hype comes down to one thing. Your CFO still can’t see it. Your leadership believes in it fine. Belief was never the question.

The blocker was visibility all along. That was true for paid in 2023 and it’s true for organic in 2026, except now it’s worse, because organic is even harder to trace. We fled paid partly because attribution was broken, and we ran straight into a channel where attribution is broken in every direction at once. Love that for us.

I’m not going to leave you with a diagnosis and no prescription. You know me better than that. Here’s how I’m building against this.

1. Organic is a motion, not a personality

The whole market is treating organic like a charisma contest. A pocket where people are running to pressure test their “taste”. Find a loud, likeable founder, point them at LinkedIn, and cross your fingers.

There are a handful of founders that are absolutely crushing founder brand, but for every one of those there are thousands of founders posting into the void, burning out on the content treadmill, looking to their marketing team for the answers of why it’s not working for them.

Organic that produces pipeline has inputs, plays, a cadence, a feedback loop, and a reporting layer, same as any paid program you’ve ever run. We built dashboards for paid, but expect organic to kinda run on vibes.

So stop asking “who’s our best poster” and start asking “what’s our organic play.” A play has a signal that triggers it, an audience it targets, a message it tests, and a destination it drives toward. If you can’t describe your organic motion the way you’d describe a campaign, you don’t have a motion yet.

2. Your CFO still can’t see organic working

Software attribution badly undercounts organic, and it does it in a specific, mechanical way. Someone hears about you on a podcast, in a Slack group, or in a LinkedIn comment, then types your name into a browser to find you. By the time they land on your site, the referral data is already gone. GA4 can’t see where that visit came from, so it files it under “Direct.” SparkToro’s research found that a large share of what tools label direct traffic is actually dark social that lost its referrer on the way in.

There’s a deeper reason the click can’t see it. Only about 5% of your buyers are in-market at any given moment. The other 95% are people you’re warming up for a purchase that’s months or quarters out. Attribution measures the click that happens this week. The organic work that matters most is building memory in people who won’t convert until long after this quarter’s report resets. You’re asking a tool built to count this week’s conversions to prove the value of next year’s pipeline, but it can’t.

Here’s what that does in a room. Your VP of Sales trusts the number on the Salesforce report. Your CFO trusts the number on the Salesforce report. And that report, fed by software attribution alone, tells them the channel creating demand contributed a rounding error. So it gets a rounding error’s worth of budget. Because the system of record is telling them a confident lie in a format they trust, sadly.

So stop trying to win the argument with a slide about how dark social works. Nobody funds a channel because they understand it philosophically. They fund it because they can see it on a report they already trust. Your job isn’t to convince them organic matters. Your job is to put organic on the report in a number they believe.

3. Three proofs a CFO will trust

The popular answer to everything I just described is self-reported attribution. Add a “how did you hear about us” field, ask the buyer directly, count what the software missed. I used to believe that was the fix, but I don’t anymore.

Here’s why. Dreamdata put that exact field on 100 demo signups and checked the answers against their tracked data. Thirty percent skipped it. Of the ones who answered, only about half said anything usable, and most of that was “Google” or “word of mouth.” When they matched the responses back to real account journeys, what people reported as their first touch routinely wasn’t. Then they deleted the field.

The failure is baked into the method. People name the thing they remember, not the thing that moved them. One person answers for a whole buying committee. And at best it names a channel, never the specific post or page or play, so it can’t tell you what to double down on.

So what do you build instead. Three things, in order of how much a CFO will trust them.

Run a holdout. This is the one nobody wants to do and the only one that actually proves cause. Pick a region or a segment, go dark on a channel or hold back a play, and measure the difference against a matched control. When Dropbox ran month-long blackouts against international control markets, they found paid search was mostly capturing demand that organic had already created. Cut it, and the traffic walked right back in through organic. On the strength of those experiments they moved roughly $25M of spend and reported a 53% jump in blended LTV to CAC. Those are their own numbers, not independently audited, but the directionality is the point. The same result keeps showing up in geo holdout tests, where branded paid search often shows zero incremental lift because those conversions were coming through organic anyway. A holdout is the closest thing we have to a receipt.

Watch the leading indicators. Demand creation doesn’t show up as a click. It shows up as more people searching your name and more of them typing your URL straight into the browser. Branded search volume and direct traffic are the fingerprints organic leaves behind. The sharpest version of this is Share of Search, your brand’s search volume as a percentage of your whole category’s. Les Binet’s work found it leads market share by six to twenty-four months, and you can pull it from Google Trends for free. It measures what your buyers actually do vs what they tell a form when they’re in a hurry to submit a form.

Report the cohort gap. This is the number that I’ve found helps end the budget argument. Take the accounts that engaged with your organic motion and the accounts that didn’t, and put their outcomes side by side: win rate, sales cycle length, average deal size, pipeline conversion. You’re not claiming a single post closed a deal. Instead this is showing the accounts touched by the motion close more, faster, bigger, maybe stay longer, etc. And if you can get your CFO on board with this, you’ll also start moving the narrative above one-off ROI tracking and instead to a more holistic approach to measurement. Which if you’re in the enterprise space will be more critical than if you’re in the PLG space for example.

One warning while everyone’s reaching for a shiny fix. Marketing mix modeling is having a moment, and for most B2B it’s the wrong tool. Recast’s own co-founder wrote the piece on why it rarely works for B2B. You close too few deals, your deal sizes swing by orders of magnitude, and your sales cycle runs too long to ever validate the model against reality. It’s built for companies doing thousands of transactions a month. If that’s not you, one clean holdout and one honest cohort report will get you further than a model you can’t check.

The reporting layer is the strategy. I mean that literally. In 2026, the highest-return thing a demand gen leader can build is the instrumentation that proves the content worked, in a form your CFO already trusts. Everyone can post. Almost nobody can prove it. Proof is the moat now, and building it is systems work.

4. Build it so it survives the founder

Founder-led content has a failure mode nobody puts on the highlight reel. The founder. The month they burn out. The quarter they get pulled into fundraising. The day they leave. If your entire demand engine lives in one person’s LinkedIn account, you don’t have a motion. You have a single point of failure with a personal brand.

This is the whole reason DemandLoops runs on a build first, hire second model. You build the system, the plays, the data inputs, the reporting, so the motion is a company asset and not a person’s mood. Then you put people on it.

The founder brand can be the spark. It can’t be the entire fire, or you’re one burnout away from a major pipeline cliff. Document the play. Instrument the play. Then the founder becomes the best contributor to the motion instead of the load-bearing wall holding it all up.

The pushback I’d have if I were reading this

“This is enterprise-grade instrumentation. We’re a 15-person team. We don’t have the ops muscle to stand this up.”

Fair. Let me be direct. You don’t need a RevOps team or a six-figure attribution platform to start. You need three things you already have access to. A branded-search line pulled from Google Trends in your weekly report. One cohort report that compares win rate and deal size for accounts that touched your organic motion against the ones that didn’t. And the discipline to run one real holdout a quarter instead of guessing.

What I got wrong, and why I’m telling you

In 2023 I told you paid was cracking and audience was the future. That aged well.

But I really thought the blocker was belief and that was wrong. It was the measurement gap all along. The industry is now standing on the right side of the argument I made three years ago, holding the wrong tools, wondering why an obviously-correct organic strategy is still scrapping for budget.

Everyone’s running to organic; I can see it in my feed, in the newsletters I subscribe to, in the clients I work with, and the peers I talk to. But organic is hard. Almost no one can prove it, scale it, or keep it alive.

The keys were never under the streetlight. They were out in the park the whole time, in the dark, where the light never reached. The teams that can start winning mindshare, and eventually wallet share have stopped crawling around under the lamp and went to get a flashlight. The flashlight is a holdout, a branded-search line, a cohort report. Unglamorous, but it works.

I’ve been in the weeds on exactly this with the teams I work with, and the reporting layer is the hardest and most valuable thing to get right. If you’re wrestling with it, hit reply and tell me where it’s breaking. Would love to be a phone a friend as you figure it out.

See ya next week,
Kaylee ✌

Instead I’m replaying the most-read issue of Looped In this year. Partly because I want the week off. But also because a bunch of you subscribed after March and never saw it, and it’s the one post I still get replies about.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday.

This week’s replay 📼

Every Demand Gen Use Case I’m Running in Claude Right Now

This one outperformed everything else I’ve published this year.

The TL;DR is 11 workflows I run in Claude: discovery call research briefs, the weekly HubSpot to Salesforce data pulls, personalized ABM copy by industry and persona, and eight more. Plus the order I’d build them in if you’re starting from zero. The boring stuff comes first, and I stand by that.

If you read it back in March, read it again and actually build one this time. If you’re newer here, start with this post. It’s the fastest way to understand how I think about AI inside demand gen work.

I spent 4 hours last week training a client’s marketing team on Claude. Not the “here’s how to write a blog post with AI” kind of training. The kind where we built a shared marketing brain (very similar to the one I shared here last week), loaded it with ICP definitions, competitive battle cards, messaging guidelines, and editorial standards, and then showed the team how to use it to augment the parts of their job that are most repetitive and could be 80% augmented with this new brain.

By the end of the session, we had talked through additional potential use cases, and it felt like it was finally starting to click for a room full of people who had been using Claude to “help me rewrite this email.”

I’ve been playing and building in Claude Code and Claude Cowork for a few months now. I keep finding new use cases that save me time, help me think differently, iterate on concepts faster. And like I shared on LinkedIn earlier this week, I feel like I’m spending every waking moment possible in this new stack, yet still feel more behind in my craft than I ever have. So I’ll say this, if you’re building, exploring, testing in a new tool this week, you’re right where you’re supposed to be. There’s so much hype, especially on LinkedIn these days, and these tools are shipping new models, functionality, and features faster than ever. And that combined makes all of us feel like we’re falling behind. But as long as we keep building, and sharing what we’re learning, I think we’ll all turn out just fine.

So, let me open up a bit about what I’ve been testing and learning.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday.

Why Most Marketers Are Getting 10% of What Claude Can Do

Most demand gen teams I talk to are using AI for two things: writing first drafts of copy, and summarizing meetings. That’s fine. But you’re leaving a lot on the table.

The gap between “I use Claude” and “Claude runs half my workflows” really comes down to context.

I built what I call a “marketing brain.” It’s a set of markdown files that load automatically at the start of every Claude session: who I am, who my clients are, how I write, how I run campaigns, what tools I use, my ICP definitions, my messaging house. Claude reads all of it before I type a single word. That means when I ask it to do something, it already knows my business. It’s not starting from zero every time.

(I wrote about the marketing brain concept last week, so I won’t belabor it. If you missed it, go read that one first.)

Here are the 11 use cases I’m running (or building/finessing) right now. Some of these save me 30 minutes a week. A couple of them eliminated entire workstreams.

1. Discovery Call Research Briefs

Before every discovery call with a potential client, Claude pulls together a research brief. Company background, the prospect’s LinkedIn activity, any public talks or posts they’ve done, likely pain points based on role and company stage.

Last week I had a call with a Head of Demand Gen at a customer experience platform. Claude surfaced that she’d spoken publicly about whether marketing attribution is broken, that she was actively hiring a Demand Gen Manager and ABM Manager (suggesting the engine is early-stage), and flagged that she likely was the budget holder. So I should position DemandLoops as complementary to her hiring plan, not competitive with it.

90 seconds. That used to be 20-30 minutes of LinkedIn stalking and Googling.

2. Weekly Data Pulls from HubSpot + Salesforce

I’m embedded in a client right now where the HubSpot-to-Salesforce integration is... let’s just say it’s a project.

Every week, Claude pulls data from HubSpot, runs VLOOKUPs against Salesforce records, cleans up naming conventions, deduplicates contacts, and flags anything that looks off.

Not glamorous. But this used to eat 3-4 hours a week, and if you skip it, we had no idea what to go optimize for pipeline.

3. CRM Property Mapping Across 5 Systems

Same client. They run HubSpot, Salesforce, Vitally, NetSuite, and PandaDoc. Five systems. Trying to figure out which property maps to what across all of them in a spreadsheet made me want to quit consulting. (I’m being dramatic. But only slightly.)

I built a Claude assistant that takes the property lists from each system and creates a unified mapping doc. Markdown file for quick reference, structured spreadsheet for the full picture. Now when someone asks “where does this data live?” I can answer in seconds instead of opening five admin panels.

This is one of those use cases where Claude is serving as a bandaid solution. Eventually these systems will all be cleaned up, or replaced entirely, synced to the data warehouse, and integrated appropriately, but for now while we’re in the messy middle, post M&A (we’ve all been there), this Claude task is doing some heavy lifting.

4. Competitive Intel, Weekly

I have a competitive intel workflow that runs every week. Claude pulls from competitor websites, checks their ad libraries on Meta and LinkedIn, and flags what changed: messaging shifts, new product positioning, campaign themes, creative formats they’re testing.

The output is a structured report. What changed, what it probably means, whether we need to respond. Typically, quarterly competitive reviews are already stale by the time they ship. This approach keeps you within a week-ish of what competitors are doing.

5. Personalized ABM Ad Copy and Landing Pages

For one client’s ABM program, we’re building hundreds of individualized ads and landing pages. And I mean individualized. Not “Hi {Company Name}” personalization. Actually different messaging by industry, company size, and persona.

Claude generates the copy variations using our ICP definitions and messaging house as the foundation. We pipe account data through Clay for enrichment. The output is account-specific ad copy and landing page content.

6. Lead Scoring and Account Tiering

I’m building what I’m calling an enterprise appetite scoring matrix for a client. Six components for now: company size signals, tech stack indicators, buying intent, engagement depth, organizational complexity, and budget authority signals. I’ll also add in their GTM Alpha. Claude will weigh each one and assign a tier.

The part I find most useful is what I’ll call “synthetic attributes” for now. Data points that don’t actually exist in your CRM but can be inferred from combinations of other fields. For example: you might not have a “budget authority” field, but you can infer it from title seniority + company size + the presence of a procurement process. Claude is surprisingly good at this kind of inference when you give it a clear framework to work within.

7. Salesforce Flow Documentation

If you’ve ever inherited a Salesforce instance with 40+ automation flows and zero documentation, you know this pain.

Claude analyzes the flows, documents what each one does, flags redundancies, and identifies which ones are actually firing vs. sitting dormant. What would have been a two-week documentation project took about three hours. My output for the first run was far from perfect, but probably 60-70% there.

8. Campaign Consistency Checks

Messaging drift is real. Especially when you have three or four people writing copy across email, ads, landing pages, and social.

I built a workflow where Claude checks any new piece of copy against a client’s campaign strategy doc and messaging house before I launch it. Flags anything off-brand, off-message, or inconsistent with what they’ve already published. Takes about 10 seconds. Replaces what used to be a “can you review this” Slack thread that took a day to resolve.

9. Newsletter Topic Development

I use Claude to help me develop newsletter topics, but probably not in the way you’d expect. I don’t ask it to “give me 10 newsletter ideas.”

Instead, I have it pull from my meeting notes (via Granola), my Slack conversations, and current industry trends, then find the intersections. Where does my lived experience this week overlap with what the market is talking about?

This newsletter is a good example. Claude surfaced that the intersection of “I just trained a client team on AI workflows” and “the industry is obsessed with AI in marketing but nobody’s sharing specific use cases” was a strong topic. The pattern-matching was collaborative. The writing is mine.

10. Automated Content Maintenance

For clients with large content libraries, I’m building a series of Claude agents that pull existing site content, find what needs updating (outdated stats, broken cross-links, FAQ gaps), make the changes, and push them back for approval.

Nobody wants to do this work. I’m finding it sits undone for months. But it compounds. Outdated stats kill credibility. Broken links hurt SEO. FAQ pages that don’t reflect the current product confuse prospects. Claude is great for this grunt work.

11. Meeting Prep and Follow-Up

Every morning, Claude pulls my calendar, cross-references it with my meeting notes from prior conversations with the same people, and gives me a prep brief. After meetings, it processes the transcript and drafts follow-up emails, action items, and internal notes for my team.

The follow-up emails are where the time savings really add up. Claude knows my voice, knows the client context, and knows what was discussed. The drafts need light editing, not full rewrites. I was spending 15-20 minutes per follow-up before. Now it’s 2-3 minutes of editing.

How I’d Prioritize If You’re Starting From Scratch

If you’re looking at this list and wondering where to start, here’s the honest answer: start with the boring stuff.

Data pulls. Research briefs. Competitive monitoring. Copy QA. The work you do every single week that requires knowing your business well but follows a predictable pattern.

After that, move to the structured creative work. ABM copy, account scoring, content ideation. These still need your judgment. But Claude handles the 80% that’s pattern-matching, and you focus on the 20% that requires taste. Claude has very little natural taste.

Last priority: the one-off projects like documentation.

The through-line across everything I listed: the marketing brain is the multiplier. Every single use case works dramatically better because Claude already knows my clients, my ICPs, my voice, and my tech stack before I ask it to do anything. Without that context layer, you’re prompting from scratch every time.

Two follow-ups if you want to keep going: 3 AI-Native Demand Gen Plays You’re Not Running and My most-used Claude Skill.

I’ll be back next Sunday with fresh content. Hope you’ve enjoyed the long weekend. Demand gen will, once again, survive without you for a weekend.

See ya next week,
Kaylee ✌

That assumption is coming apart, and the data this quarter is hard to argue with.

SparkToro’s 2026 study found that 68% of U.S. Google searches now end without a click. Two years ago that number was 60%. Search Engine Land’s breakdown of the same data calls it the steepest two-year jump since anyone started measuring it, driven mostly by AI Overviews, which cut click-through rates by nearly 60% when they show up.

At the same time, new G2 research found that 51% of B2B software buyers now start their research inside an AI chatbot. In April of last year that was 29%. G2’s own writeup puts it plainly: 71% of buyers now lean on AI chatbots for software research, and a majority say the research feels more productive than a regular search.

Most of the coverage I’ve seen on this lands in SEO circles. Practitioners arguing about schema markup and whether AEO is a real acronym. That conversation matters, sure, but it skips the people who are about to feel this first and explain it worst: demand gen operators who built a pipeline number on top of organic inbound.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday. If this was forwarded to you by a friend, firstly, thank them, and then subscribe below. See you around!

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Why this hits demand gen harder than it hits SEO

An SEO person loses rankings and traffic. That’s painful, but it’s legible. You can see it in the dashboard, you can point at it, you can build a recovery plan.

A demand gen operator loses something quieter. You lose the first touch, and you lose it invisibly.

Walk the path. A buyer at one of your target accounts has a problem. A year ago they Googled it, clicked your blog post, maybe grabbed a guide, and entered your world as a trackable contact. Now they open ChatGPT, Perplexity, or Claude, describe the problem in a sentence, and get an answer that may or may not include your name. They read three vendor comparisons that were synthesized for them. They form a shortlist. None of that touches a property you own. None of it fires a pixel.

By the time they do hit your site, they’re typing your brand name into the search bar or going direct. So your attribution model, which is almost always weighted toward last touch or first trackable touch, credits branded search or direct. The research that actually moved them, the work that put you on the list, happened in a room you weren’t allowed into (at least not yet anyway).

Onto more gruesome realizations…When that buyer converts, your dashboard will say direct or branded. It will not say “the comparison content we seeded eight months ago that an AI model later quoted.” So the channel doing the heavy lifting at the top will look like it’s contributing nothing, and the channel catching the demand at the bottom will look like a hero.

I watched a version of this play out with a client this spring. Non-branded organic clicks down quarter over quarter, branded search up, direct up, and a sales team reporting that more discovery calls were opening with “yeah, I’d already looked into you.” The funnel looked like it was shrinking at the top and getting stronger at the bottom. But we know it wasn’t. The top had moved somewhere the analytics couldn’t follow.

If you manage to a pipeline number, this is the trap. The motion that builds demand is going darker, faster, in your reporting at the exact moment you need to defend its budget.

The dark funnel was already a problem. Now it’s basically the default.

We’ve talked about dark social and the dark funnel for years. The podcast someone heard, the Slack community recommendation, the LinkedIn post that did the convincing, all the influence that happens off-platform and shows up as “direct” later. Most teams treated it as a known gap. A rounding error you acknowledged and moved past.

AI research takes that gap and moves it to the center of the buying process. The single most important stage, the part where a buyer decides who’s even in the running, is now the least trackable stage. The dark funnel stopped being the edges. It’s the middle now.

I’m pretty sure this changes our jobs, y’all. For a long time the demand gen mandate was capture: get in front of in-market buyers and pull them into a trackable path. Capture still matters. But capture assumes the buyer ends up somewhere you can measure. When the research happens inside a model, you can’t capture what you were never present for. You have to be in the answer itself.

The four moves to make before Q3

None of this is a reason to panic, and none of it means SEO is dead. LinkedIn posts are always trying to claim something else in our toolkit is dead…this isn’t that. It means the inbound model needs a second engine bolted onto it, and the demand gen team should own that engine rather than tossing it over the fence to SEO. Or shoot, maybe you are SEO, but either way.

Here’s the play I’m running with clients right now.

1. Audit whether you exist in the answers. Before you change anything, find out where you stand. Take your fifteen highest-intent buying questions, the ones a prospect asks right before a shortlist forms, and run them through ChatGPT, Perplexity, Gemini, and Google’s AI Overviews. Write down who gets named, who gets cited, and where you don’t appear at all. This is your new rank tracking. Do it as a recurring monthly check because the answers will shift.

2. Write content a model can quote, not just a human can skim. Answer engines pull from content that states a claim cleanly, backs it with a number, and attributes it to a source. That means clear definitions near the top of the page, real data with citations, direct answers to direct questions, and structure a machine can parse. The fluffy, keyword-stuffed SEO post that ranked in 2021 is the worst possible input for an AI answer. Your best original research and your sharpest point-of-view content are the best. This is a reason to spend on primary data and strong opinions, not generic volume.

3. Re-instrument attribution around self-reported and branded signals. Stop pretending your multi-touch model sees the whole journey. It doesn’t, and it’s about to see less. Add a “how did you first hear about us” field to your demo and contact forms, and report on it. Track branded search volume and direct traffic as demand indicators because a rise in both is often the visible shadow of invisible upstream work. When you present pipeline to leadership, name the dark funnel explicitly so a branded-search spike gets read as demand you created.

4. Build presence where the research actually happens. If buyers are forming opinions in AI chats, communities, podcasts, and peer conversations, that’s where your demand creation budget belongs. Review sites and third-party comparison content feed the models, so your G2 and Capterra presence is now an AEO input, not just a sales-stage proof point. Founder and executive presence on LinkedIn shapes the conversations that models later summarize. Original research gets cited and quoted in ways a product page never will. This is signal-driven demand gen in the truest sense: you’re planting the inputs that the machines, and the humans, pull from later.

The takeaway I’d write

The buyer’s research moved into rooms you can’t measure and can’t gate. What you can do is be present in those rooms, feed the systems that summarize you, and fix your reporting so the work doesn’t look like it’s failing when it’s actually doing its job.

If you do one thing this week, do the audit in move one. It’ll take you an afternoon, and it will tell you more about your real top-of-funnel position than your rankings report has.

Here’s to growth,
Kaylee ✌

In late May, Exit Five published a newsletter with the hook "Most marketing teams don't have a strategy problem. They have an operating system problem." The newsletter was promoting a podcast episode with the team at Tenon (a marketing automation platform built on ServiceNow, $8M Series A, positioning itself as the marketing operating system). The Tenon team's pitch on the episode: marketing teams whose strategy looks sound but whose execution doesn't, because campaigns aren't coordinated, the team isn't aligned, and focus drifts. The fix, they argue, is a platform that organizes the work.

I’ve spent the last decade+ inside exactly those teams. $20M - $80M ARR B2B SaaS, different industries, differing personas, degrees of variance in tech stack, but all share a few common threads of challenges. Most tried to buy tools/tech to fix it…to no avail.

My TL;DR: The operating system isn’t software alone. It’s backed by a posture.

We’ve seen this before

In the 1980s, every American automaker watched Toyota eat their lunch and concluded the answer was Toyota’s equipment. They bought the robotic arms, imported the just-in-time inventory systems, retrained the engineers, rebuilt the assembly lines. They were still getting destroyed by Toyota a decade later.

The reason is documented in every business school case study about the period. Toyota wasn’t winning because of the equipment. Toyota was winning because of how they thought about the equipment. The Toyota Production System wasn’t a stack. It was a posture: about who made decisions on the line, about what counted as a defect, about who had the right to stop production, about what a problem was for. The companies that imported the tools without the posture stayed broken. The ones that imported the posture (and built the tools around it) caught up.

There’s a phrase for what the laggards bought. Form without substance. The B2B version of this hit in the 2010s, when every mid-market company started importing Google’s open-plan office, free snacks, and ping-pong tables, then wondering why their teams didn’t suddenly produce Google-quality work. They copied the visible signals of how a great company looks, but didn’t import the hiring bar, the strategic clarity, or the autonomy that made any of those signals work in the first place.

The “marketing operating system” companies are selling ping-pong tables without the posture to support it.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday.

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What an operating system should be

If software is the plumbing, the operating system is the architecture in this scenario. The set of decisions about who lives where, who has water, who shuts off the valve, and what happens when the basement floods.

For a marketing team, four things make up a well-oiled operating system.

One: role definition. What this function is for. Most teams can’t write it in under 100 words, which is the first sign they don’t have an OS.

Two: scope contract. What this function owns, what it influences, what it stays out of. Where the lines are. When sales or product or RevOps walk across the line, what happens next.

Three: skill stack. What the people on the team need to be able to do. The capability map: the math, the writing, the systems thinking, the political muscle.

Four: decision rights. Who decides what, with what evidence, at what cadence. When you change a campaign, who signs off. When you kill a channel, who signs off. When a CEO says “let’s replace HubSpot with AI in 40 days,” who has the authority to push back.

A team with all four has an operating system. A team with three or fewer probably has a tech stack and some hope.

Finding 1: Role definition is a hiring problem usually masked as a strategy problem

A GTM engineer started at one of my clients sixty days ago. Smart hire, real technical skill, exactly the role the company needed. His mandate, in the words of the CEO who hired him, was “generate pipeline.”

Two months in, he’s running outbound sequences like an SDR.

This isn’t his fault. The mandate was the problem. When the only thing you ask a GTME to do is generate pipeline, you’ve defined the role at the wrong altitude. The point of a GTME is to compound the entire revenue function. To fix conversion rates, build infrastructure, plug leaks, automate handoffs. None of that satisfies a “pipeline generation” mandate on the first measurement cycle. So he defaulted to the only motion that did: outbound. He’s the most expensive SDR in the building.

Buying him a better tool wouldn’t have fixed this. Plugging him into marketing operating software wouldn’t have fixed this. Nothing in the tech stack fixes a role aimed at the wrong altitude.

The role definition is the OS. The tool is the plumbing.

Finding 2: Scope contract is what makes the function defensible

Try this. Pull up some recent internal marketing decks. Find the slide where someone wrote down what demand gen (or whatever your department is) is for. This function exists because [X], it owns [Y], it produces [Z], and the company stops being able to do [W] if it stops.

If that slide doesn’t exist, you don’t have a scope contract. That’s the OS problem.

What you have instead is a budget line and a set of KPIs. Both are revisable. Both will be revised. The function survives whoever’s holding the budget; it doesn’t survive someone questioning whether the function should exist at all. The first time a CEO or a board member asks what the function produces and you can’t point to a concrete answer, the absence becomes the answer.

This is happening more in 2026 than it did in 2024. Boards are tighter, CEOs are reading more AI think pieces, and the question “do we still need this function” is being asked of marketing more often than of any other GTM team. A scope contract won’t save the function from a CEO who’s already decided to cut it. But it will save it from the CEO who’s only asking because nobody has put the answer in front of them. Most of these conversations are the second kind.

A scope contract is that answer. Not a JD or an OKR, but a written statement of what the function is for, what it owns, what it produces, and what stops if it stops. It’s amazing how simple it sounds, but still true that a ton of orgs don’t know the answer until it’s written (I’ve used the same template across every fractional engagement I’ve run that lacks this clarity. It walks through role overview, what’s in scope, and what’s explicitly out of scope, for ABM, growth, field, partner, and lifecycle. Copy it.)

Most marketing functions don’t have one. That is the OS problem.

Finding 3: Skill stack is the gap nobody is willing to name

DemandLoops (the frac b2b markting biz I run) has worked with plenty of companies that have no marketing operations owner. Not in the sense that the role is vacant, but more so in the sense that nobody on the team has been hired with the technical skill to own the role.

The pattern goes like this…

Salesforce breaks in a way that affects attribution.

The marketer in the meeting (smart, capable, not a MOPs person) raises her hand to figure it out.

She spends three weeks learning what should have taken a specialist maybe a few hours.

The dashboard ships, two weeks late, with an undisclosed flaw.

Three months later, the board deck cites numbers that are quietly wrong.

Multiply that by every function that’s facing similar gaps. That’s the OS problem at the skill-stack layer. A platform doesn’t fix this. Skill stack work is the least glamorous part of building an operating system. It’s also the only part though that compounds (especially when those human-skills can now become agent-skills).

Finding 4: Decision rights separate operators from spectators

Try a different test. Think about the last three significant marketing decisions your company made. A tool getting replaced, a channel getting killed, a budget reallocated mid-quarter. For each one, ask two questions: who was authorized to make the decision, and who was authorized to push back on it.

If the answer to either question is “nobody knew” or “leadership just decided,” you don’t have decision rights. Decisions are happening to the function rather than through it.

Strategy questions tend to get debated. Operational questions get a memo. But the in-between decisions, the ones that shape what the function actually does day to day, get made unilaterally because nobody specified who was supposed to make them. By the time you find out, the work has changed and the team that has to absorb it had no channel to weigh in before it happened.

That’s the decision rights gap. The gap isn’t “should we make this change,” but really “who is allowed to push back on a change after it’s been decided.” When the answer is “nobody,” you don’t have an operating system. You’ve found yourself in a top-down scenario where leadership oftentimes makes every decision, and you execute against whatever it is.

The difference between a company that absorbs a hard decision well and one that doesn’t is whether the operating system gave someone the right to push back before the decision was final. If it did, the conversation happens upstream. If it didn’t, the conversation happens downstream, after the cost is already real.

That’s not a tool. That’s a posture.

The test

Kieran Flanagan published a piece last week applying Satya Nadella’s framing of digital sovereignty to AI strategy. The test he names is one I’m adapting for client work going forward, because it maps almost perfectly to the operating system question.

Can you swap your entire tech stack tomorrow and still keep your competitive advantage?

If yes, you have an operating system. The stack is plumbing. Useful, but interchangeable. Your advantage lives in how you think, how you scope, how you decide.

If no, you don’t have an operating system. You have a tool you’ve grown to depend on, in a posture you never built. The day a competitor buys the same tool, your advantage disappears.

Most platforms in this category would fail this test for most of the teams buying them. The products themselves aren’t inherently bad, but the buyers don’t have an OS to plug them into. You can’t get sovereignty from a vendor. You build it, or you don’t have it.

The objection I’d have if I were reading this

Fair pushback: “But platforms still matter, right? You’re not telling people to stop buying software.”

I’m not. Platforms are great. Plumbing matters. The Toyota analogy isn’t “don’t buy robotic arms.” It’s “buy the robotic arms in service of the posture, not as a substitute for it.”

The way to know which you’re doing is to look at the order of the decisions. If your team picked the platform first and is now trying to design the posture around what the platform supports, you’re buying ping-pong tables. If your team wrote down the role definition, scope contract, skill stack, and decision rights first, and then picked the platform that fits, you have an OS and the right plumbing.

Most teams I work with picked the platform first. That’s the OS problem, not the platform problem.

Own the methodology. Rent the tools.

Kieran’s frame for AI strategy was “Own the layer. Rent the model.” The same logic applies to the operating system question.

Own the methodology. Rent the tools.

Your methodology is the four things. Role definition, scope contract, skill stack, decision rights. That’s the asset that compounds. It survives every platform migration and every leadership change. It is, to borrow Kieran’s framing, your sovereignty.

Your tools are interchangeable. Salesforce becomes whatever replaces Salesforce. Marketo becomes whatever replaces Marketo. None of that should affect what your function is for, the skills it houses, or the why behind its operation.

The companies that win this leg of the race are the ones who built the posture, and then plugged tools into it. We’re entering into the era of everyone being a builder after all.

See ya next week! ✌️
Kaylee

Introducing the team, the new sprint model, and the chapter I’ve been waiting to announce.

DemandLoops has a new chapter, and as of this week, it’s live.

Here’s the part I’ve been waiting to say out loud: I’m no longer running this alone.

I just spent three days in Portland, Maine with Mary Keough and Olivia Wicks at our first-ever DemandLoops off-site. The agenda was intentionally loose. We went up there to talk about what fuels us, what drives us, what we’re continuously curious to explore and deliver against. Big campfire-type questions, the kind you never make time for between client deliverables.

What those conversations actually produced was clarity. On our messaging, our positioning, our offers, the direction of the business. And on something bigger that I’d been circling for months without naming: DemandLoops has outgrown being a solo venture. It’s becoming a proper company. Which is crazy to say out loud.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday.

From Solo Venture to Company

If you’ve followed me or DemandLoops for a while, you know this started out as a solo venture in 2023. I built it for myself as a way to find more creative ways to partner with multiple companies at a time instead of holding one in-house seat. I’ve always been a demand gen operator for B2B SaaS, and I’ve spent the last three years doing that work fractionally, embedded inside companies as their demand gen leader without the full-time badge.

The solo chapter was good to me. It sharpened how I think about engagement, scoping, and what clients actually need versus what agencies typically sell them.

But somewhere after the first year really, the work outgrew one person. And the answer to that turned out to be sitting right in front of me.

I am just beside myself that I get to work with two absolute bad*ss women and build DemandLoops 2.0 together.

Meet the Team

Olivia Wicks spent two and a half years inside HubSpot, building the systems most marketers only ever use from the outside. She knows what a marketing engine looks like from the inside of the machine, at a scale most of us only read case studies about.

Mary Keough ran demand gen at CoLab and built a 31k-person audience by writing about the work, in public. She’s the rare operator who can both run the program and articulate exactly why it works.

And me: I owned a $14M demand gen budget at Campaign Monitor, and built 0-1 DG programs for Chili Piper and brightwheel, before going fractional, carrying the pipeline number and answering to the board for it.

That’s the through-line of this team. Every DemandLoops operator has held the in-house seat, accountable to a CMO, a board, and a sales team that wanted to know where the pipeline was. When you work with us, the operator on your account has seen the movie.

What DemandLoops Is Now

Fractional demand gen for B2B SaaS, run in sprints.

You bring us the objective. Land more target accounts. Give us visibility into what’s working/what’s not. Cover a parental leave. Deliver more qualified pipeline. We design a sprint for you, and we run the sprint that gets you there.

Every sprint is scoped to an outcome and has a finish line. You know what you’re getting, when it ships, and what you own at the end. The finish line is the feature: when an engagement has a defined end, the only way we win is by actually finishing.

Why We Rebuilt the Model

Two convictions came out of Portland alongside the team news.

Your first six weeks shouldn’t be a waiting room

The standard agency model charges a full retainer through a 4 to 6 week “onboarding period” where nothing ships. I lived this from the client side at every in-house gig. I sat through the discovery calls and watched six weeks of a six-figure engagement produce a slide titled “Our Understanding of Your Business.” 🤢

So we killed the waiting room. We’re in your systems from the day the contract is signed, auditing and fixing at the same time. Broken lifecycle stages, UTM chaos, paid budget bleeding into already-converted accounts: when we find it, we fix it, and it goes in the report as “found and resolved” rather than “recommended for Phase 2.”

Up to speed in 2 to 4 weeks. Executing against sprint goals immediately after.

We build to leave

Sprints end on purpose. Everything we build ships with documentation, reporting infrastructure that lives in your CRM, and a clear handoff designed for the team that comes in after us.

Most agencies architect for dependency. The dashboards live in their tools, the institutional knowledge lives in their heads, and leaving them means starting over. We’re betting the other way. The best outcome of a DemandLoops engagement is a foundation strong enough that you can hire a full-time leader into it. Build first, hire second has been our philosophy from the start, and the sprint model takes it to its logical end.

How to Work With Us

*Every engagement starts with the first one.

The Foundations Audit. We get access to your systems and run a full analysis across ops, pipeline, paid, and content. And because we audit and fix simultaneously, you get more than a readout: you get the results, the list of changes we already made, and a recommendation for your first sprint.

Sprints. ABM, paid media, marketing ops infrastructure, website, parental leave coverage. Scoped to an outcome, run by an operator who’s done it in-house.

The new site walks through all of it: demandloops.com.

What Changes for Looped In

More material, frankly. Sprints generate tight before-and-after stories: what the attribution looked like on day one, what shipped, what the pipeline did. Expect more teardown-style posts as the first sprints wrap. And now that there are three operators in the practice instead of one, you’ll start seeing patterns from a much wider slice of B2B SaaS than I could ever cover alone.

The Best Part

The model is the news, but this team…this team is the story. 🥺

Three days in a room with Mary and Olivia, no client deliverables due, just the three of us being unreasonably picky about what this company should become. Building alone is fast. Building with people who challenge your assumptions and then out-execute your revised plan is something else entirely. I’d pick these two again every single time.

More soon. ✨

We’re just getting started. Thanks for following along, and if you’ve got questions about the new model, hit reply. I read every one.

See ya next week,
Kaylee ✌

I’ve been caught in two different client feedback loops recently where they’re trying to identify critical oversight in some of their processes. It will surprise no one that both of these led us right back to AI as the culprit.

Most teams are automating everything right now: outbound sequences, account scoring, content, nurture flows. And most of them have no idea if any of it works, because they never proved it manually first.

I’ve had principles I’ll live and die by for probably 7 years now but candidly haven’t revisited or updated them in a while. This week though I felt compelled to add a new one.

Here are the original principles. 👇

My newest principle: Earn the right to automate.

Pre-AI, your intelligence lived in your head, in your coworker’s head. It lived in notebooks, on whiteboards, in random Notes on your phone. All filled with insight about what the market wants, how your team really ships products/campaigns/you name it, what’s working / what’s not working, what bets you’re taking next.

In this “everybody must use AI” era, we skipped right over context and jumped straight to agentic everything.

Earning the right to automate means going back to that phase on purpose. Not indefinitely, but long enough to actually understand what you’re automating.

If the underlying process is broken, automation just accelerates the failure. Bad messaging doesn’t improve at scale, a scoring model built on flawed assumptions doesn’t get smarter at volume — it prioritizes the wrong accounts faster than any person can catch (until you’re digging deep when you’ve missed pipeline goal by 3x).

I’ve watched this play out twice now. Three months in to the “AI everything” era, pipeline is down. The team can’t diagnose it because the feedback loops are buried inside a system someone vibe coded so no one really understands it. The automation ran so fast it outpaced their ability to learn from it. And now oops, the team missed their goals.

The manual phase is the research phase. Remember when we used to do all research manually? 🙃 Just my two sense but if you don’t have a contextual layer embedded in your AI processes, you are just operationalizing a guess. Scale this up to a Series B SaaS company and now you’ve also got 250 teammates operationalizing their guesses. Sheesh.

Kieran Flanagan wrote about this this week from an architectural angle — he calls it the Context layer, the intelligence foundation that every agent in your system has to read from. His framing is org-wide which is more than true. I’m only credible enough to speak on demand gen. And truly this “manual phase” is how you build the demand gen piece of this contextual layer. You build this contextual layer by doing the work, understanding what’s working, building a POV for yourself. Then you can encode that understanding into your systems.

For outbound, that means writing and sending messages yourself before you build the sequence. For scoring, it means reviewing accounts by hand until the real intent patterns become obvious. For nurture, it means talking to prospects at each stage before you write the copy. Ads same thing. And so on.

Automation rewards the teams who understand what they were automating.

See ya next week,
Kaylee ✌

But most teams have never verified those assumptions.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday.

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The standard approach: buy an intent tool, define an ICP, pick a few firmographic filters, and build your motion around whoever surfaces. Over time you layer in more signals — job postings, technographic data, review site activity, LinkedIn engagement. The stack gets more sophisticated. The assumptions underneath it never get tested.

If the motion produces pipeline, the signals get credit. If it doesn’t, the copy gets blamed, or the sequence, or the channel. The signals keep running though.

The GTM Alpha Approach

A few months ago I built a custom AI skill called GTM Alpha — named for the concept Clay popularized — to identify non-obvious data points that predict buying behavior for a specific market. The stuff on the right side of the uniqueness spectrum: hard to find at scale, specific to your buyer’s world, more predictive than what your competitors are looking at.

You feed it your ICP, your product, your competitive landscape, and your best-customer profile. It generates ranked signal hypotheses — job posting patterns that indicate a company is mid-migration, review site signals that correlate with buying urgency in your category, scrapable web-based insights, that kind of thing.

Generating hypotheses is half the play though. The other half is the backtest. And almost no one does it (similarly to your ICP backtest).

What a Backtest Shows You

Last month I ran this with a client — an enterprise customer evidence platform (software that helps revenue teams collect, organize, and activate customer stories and proof points). They had decent intent coverage and a full demand gen motion running: outbound, content, paid, ABM. The motion was producing. But they couldn’t tell which signals were doing the work.

We took the GTM Alpha output and backtested it against their 30 most recent Closed Won accounts. The question: did accounts that eventually closed actually show these signals before they entered pipeline?

Three findings:

Job posting patterns held up. Accounts that posted for a Customer Marketing Manager or References Manager role in the 90 days before entering pipeline converted at a much higher rate than accounts without that signal. Nobody was tracking this before we ran the backtest.

G2 review spikes did not hold up. The team had treated G2 activity as a strong intent signal across the motion — it was influencing outbound prioritization and retargeting audiences. Fewer than 30% of their Closed Won accounts had shown elevated G2 activity in the 6 months prior to pipeline entry. The signal looked valid in the vendor’s aggregate data. It wasn’t predictive in their actual cohort.

LinkedIn content patterns held up at mid-market. Not at enterprise. Completely different stakeholder dynamics at each tier. Applying the signal universally was adding noise to segments where it didn’t belong.

One backtest changed how the entire demand gen motion was structured. The signal assumptions were wrong in specific, fixable ways. They just hadn’t been checked.

Where to Start

Most teams only pull their Closed Won accounts when they do this exercise. That’s too narrow. The dataset you actually want is four cohorts working together:

Closed Won — what signals did these accounts show before they entered pipeline? These are your positive examples. Look back 60-90 days pre-opportunity and document everything: job postings, LinkedIn activity, tools in their stack, review site behavior, leadership changes.

Closed Lost — same exercise, different outcome. Where did the signals diverge from Closed Won? Were they showing the same intent signals but missing something structural? Or did they show different signals altogether? Closed Lost accounts tell you which signals are necessary but not sufficient.

DQ’d MQLs that sales rejected — these accounts cleared your marketing qualification threshold but sales looked at them and said no. That’s a false positive. What signals were present that made marketing think they were ready? Understanding what triggered qualification without triggering a real opportunity is how you find the noise in your signal stack.

Leads that never reached MQL threshold — these came in but stalled before qualification. What was missing? Sometimes the most predictive signal is the one that separates accounts that make it to MQL from ones that don’t and why.

Think of these four cohorts as a Venn diagram. The signals that show up consistently in Closed Won and are absent or weak in the other three — that’s likely where your GTM Alpha lives. Not signals that correlate with intent broadly. Signals that are uniquely predictive of accounts that you close.

That’s the backtest. And once you know which signals hold up across all four cohorts, you build your motion around them and keep testing as new data, insights, signals come in. The idea is that your GTM Alpha should be fluid. This is how you outrun your competitors.

The GTM Alpha Skill accelerates the hypothesis generation. Give a try for yourself. I’d love to know what you find.

See ya next week,
Kaylee ✌

That’s real. But there’s a version of this problem taking shape inside GTM teams right now, and nobody’s talking about it yet.

I’ve spent years getting called in to clean up demand gen messes. New client, same story: six tools that don’t talk to each other, UTMs that break halfway through the funnel, ICP data baked into a scoring model that nobody’s validated since 2021, content that was “the old team’s stuff.” I joke that half my job is janitorial work before the actual strategy can start.

Pre-AI, this was manageable. Messy, yes. But the mess had a speed limit. Adding a new tool required a budget conversation. Building something custom required engineering resources. The chaos had friction built in.

AI took nearly all the friction away.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday.

Subscribe now

Everyone’s a product builder now

AI has made something genuinely remarkable possible: a non-technical employee can now build a functioning tool.

Your demand gen manager can spin up an account scoring workflow in an afternoon. Your SDR can build an enrichment pipeline that pulls from multiple data sources and outputs a prioritized list every morning. Your content person can automate a reporting dashboard. Your ops person can create a routing logic tool that actually works.

Five years ago, any one of those projects would have required an engineering ticket, a sprint, a product manager, and a multi-week wait.

It’s amazing on one hand because teams are moving so fast. And I love that. The builder in me is so pumped. People can now self-serve and solve their own problem set.

But this also means your employees are building tools inside your GTM stack that you have no visibility into, no inventory of, and no governance over. 🙃

The Jim and Tina problem

A month ago I was doing an audit for a client. Mid-market company, solid team, well-funded. In the course of mapping their marketing infrastructure, I found something interesting.

Two people on the same team had independently built almost identical lead prioritization tools. One lived in Notion with some AI automation layered on top. The other was a Zapier workflow feeding into a Google Sheet. Both were pulling from HubSpot. Neither was pulling from the same fields though. …And neither person knew the other’s tool existed.

Their outputs conflicted. SDRs were getting inconsistent signals depending on who they talked to. Nobody could figure out why their pipeline data looked different in different reporting contexts.

I call this the Jim and Tina problem - their real names have been redacted, obviously. Jim and Tina are both solving the same problem, independently, with AI tools that nobody asked them to build and nobody knows about.

This isn’t a one-off. I’m seeing versions of it at every client I work with now. The tools are different, the functions are different, but the pattern is the same: employees are building, nobody’s coordinating, and the mess is silently compounding.

Why the old playbook doesn’t catch this

Marketing has had a sprawl problem for years. The 2025 marketing technology landscape counted 15,384 martech tools. We’ve been talking about stack bloat for a decade or more.

But the old version of this problem was at least visible. A new tool purchase showed up in a contract. In a budget. In a vendor renewal. Even shadow IT left some footprint.

AI-built internal tools have minimal footprint so far. They live in someone’s personal Notion. They run on free tiers of Zapier. They’re a Claude prompt that gets copy-pasted every Monday morning. They’re invisible to procurement, invisible to IT, and invisible to the people responsible for GTM infrastructure.

Nobody owns this. That’s the problem.

If you asked your CMO who’s responsible for tracking what AI tools their team is building, they’d probably say IT or RevOps. If you asked RevOps, they’d say IT or Marketing Ops. If you asked IT, they’d say they don’t have visibility, it’s on the manager.

Nobody owns it. And the reason nobody owns it is that the problem is new enough that it doesn’t have a designated home yet.

The closest thing most orgs have is RevOps, and RevOps is already stretched. Managing the CRM, the attribution stack, the reporting layer, the sales enablement tooling, commission planning, the list goes on. Asking them to also audit every AI tool a 50-person GTM team is quietly building is just wild.

Over time, I think companies are going to need a dedicated internal AI governance function of sorts. Someone whose job is to know what’s being built, by whom, what data it’s touching, and whether it duplicates something that already exists.

We don’t have good models for what that role looks like yet. But the need is already here.

What this means for demand gen specifically

If you’re running demand gen at a B2B SaaS company right now, the AI tools your team is quietly building are touching the things you care most about: lead data, scoring signals, campaign attribution, funnel metrics.

When those tools are ungoverned, the downstream effects show up in the places you’ll notice last. A critical workflow that’s now broken by upstream changes. A pipeline report that’s falsely inflated. A segment pulling the wrong contacts because someone’s automation changed a field value.

These aren’t catastrophic failures. They’re the slow, hard-to-diagnose kind. The kind where you spend days ruling out obvious causes before someone finally surfaces the rogue workflow.

What you can do right now

You probably can’t stop your team from building. Nor should you. The productivity gains are real, and telling people to stop using AI tools is a battle you’ll lose.

But you can get visibility.

Start with a simple inventory. Ask your team, in a meeting or a quick async channel, to share any AI tools or automations they’ve built or are currently using. You’ll be surprised what surfaces. Make it a safe ask, not an audit. Frame it as “I want to make sure we’re not duplicating effort.”

Designate someone to own the map. It doesn’t need to be a full-time job yet. But someone on your team should be responsible for knowing what’s running. Even a shared doc with tool name, owner, what data it touches, and what it does is a massive improvement over nothing.

Establish a lightweight review before anything writes to your CRM. This is the highest-risk category. Reading data is relatively safe. Writing data back is where things go sideways. Create a simple norm: if a tool is writing to HubSpot or Salesforce, it needs a second set of eyes before it goes live.

Check for duplication before you build. Before you or your team starts a new AI project, spend five minutes asking whether someone’s already solved this. The Jim and Tina problem is mostly a communication problem. A quick “has anyone built X?” in Slack goes a long way.

None of these are complicated. They’re just not happening at most companies right now.

The companies that build governance habits early, before the mess gets unmanageable, are the ones that won’t need a janitor later.

See ya next week,
Kaylee ✌

Kyle Poyar and Brendan Short just published an excellent piece on AI-native GTM plays. Closed-lost re-engagement. Micro-campaigns. Champion tracking. All powerful, outbound-first plays. Every one ends with an AI agent drafting an email to a prospect.

I kept reading and thinking: nobody is writing about the demand gen side (or if they are, I’m not seeing it so please drop me a note so I can follow them if so).

These are 3 plays I’m running or building across clients right now. They’re demand gen specific and AI-native; meaning they either weren’t possible before or would have taken 10x longer without AI in the workflow.

Let’s get into it.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday. If you’re not subscribed yet, fix that below.

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Play 1: Infrastructure Debt Remediation

I joined a client engagement earlier last year. Opened HubSpot. 67 lead source values. Multiple duplicate UTM fields. Thousands of duplicate Contacts and Companies. Dozens of sync errors affecting the HubSpot <> Salesforce integration so badly that it was disconnected in order to clean up both instances.

The old playbook for this is brutal. You open a spreadsheet and start documenting. Weeks later, you’ve mapped half of it. You fix what you can, work around what you can’t, and pray that nothing breaks when you finally reconnect the sync.

What I did instead: I used AI to audit the full property list across both systems. It mapped fill rates for every field, flagged duplicates and near-duplicates across naming conventions, and drafted a consolidation plan (old value to new value, with reasoning for each). For lifecycle stage workflows, it QA’d each one against the Salesforce-side flow to flag sync gaps. For the integration rebuild, it generated 100+ property mapping rules by pulling field types, checking for conflicts, and catching where previous configurations created redundant paths.

I still made every decision around which lead sources to consolidate, what the lifecycle stages should be, whether a field gets deprecated or remapped, but AI did the pattern recognition across hundreds of fields and the cross-system comparison. Honestly, the stuff that used to take days of uninterrupted eyeball-scanning admin panels.

Play 2: Signal Identification and Backtest

Most teams treat signal data the same way: buy an intent tool, get a list of “surging” accounts, hand it to the BDR team, hope something sticks. The signals are generic and therefore so is the response if you’re lucky. And nobody can tell you whether those signals actually predicted anything after the fact.

I’ve been approaching this differently.

I built a custom AI skill called GTM Alpha (built off exactly what Clay coined the term for) that helps identify non-obvious data points for a specific company’s market. Not the standard firmographics and intent data everyone else already has. It’s trying to dig deep to find the stuff that sits in the right half of the uniqueness spectrum: hard to find at scale, specific to your buyer’s world, and more predictive than what your competitors are looking at.

The skill takes in your ICP, your product, your competitive landscape, and your best-customer profile, then generates ranked lists of data points you should be tracking. Things like specific job posting patterns that indicate a company is mid-migration, or public signals on review sites that correlate with buying urgency for your category.

But generating hypotheses is only half the play. The other half is backtesting.

This week I ran this with a client, a customer evidence platform. We took the GTM Alpha output (the hypothesized signals that should predict buying behavior for their specific product) and backtested it against their most recent cohort of Closed Won accounts. The question was simple: do accounts that eventually closed won actually show these signals before they entered the pipeline?

Where to start: Pick your last 20-30 Closed Won accounts. Before you generate new signal hypotheses, look at what those accounts had in common before they entered your pipeline. What was on their careers page? What were they posting about on LinkedIn? What tools were they hiring for? What review site activity did they have? You’ll start to see patterns that are specific to your market. Those patterns become your signal hypotheses. Then you test whether they hold up across a larger set. That’s your GTM Alpha.

🔗 Here’s the GTM Alpha Skill to try for yourself.

Play 3: Multi-Variant Ad Creative Production

Creative production velocity has been the single biggest bottleneck in paid media for most B2B teams I work with. Aside from budget, which is finite for most everyone, the ability to produce and test enough creative variants fast enough is always hard to prioritize design resources for.

You know the cycle. Typically you write the brief and send it off to design. Then design has a 3 to 5 day turnaround, and you get back 2-3 variants. You pick one and launch. Three weeks later, creative fatigue sets in and you need new variants. Back to the design queue. If you’re running persona-specific or industry-specific campaigns, multiply that cycle by the number of segments. Painful.

For most demand gen teams, this means you run 2-3 creative variants total because that’s all you can get through the production pipeline.

I’ve been using Claude Design + Canva to produce ad creative variants at a different pace:

Copy variations by persona.

Visual variants initiated in Claude Design, and tweaked to brand guidelines in Canva.

Format variants. Single image, carousel, and video thumbnail versions of the same campaign concept.

Stage variants. Top-of-funnel awareness creative (thought leadership, category education) vs. mid-funnel (proof points, case studies) vs. bottom-funnel (demo CTAs, pricing).

Instead of waiting days for 2-3 variants, you can have 10-15 ready in a single working session. You launch more variants, learn faster what resonates, and rotate creative before fatigue kills your CTR.

My next step is to create more of a closed-loop process so I can automate the performance learnings to create more informed future iterations.

Where to start: Copy variants on a single visual template before trying to generate entirely new visual concepts. Get the copy testing engine running first (5-7 headline/hook variants per campaign). Layer in visual variation once you have data on which messages are landing.

If you’re running AI-native demand gen plays I didn’t cover, reply to this email. I want to hear what’s working. Especially the unglamorous stuff.

See ya next week,
Kaylee ✌

I’ve been sitting with that question for a few weeks. Not in a panicky way. More like that low-grade background hum you get when something about your professional life has shifted and you haven’t fully processed it yet.

Last week I wrote about how your AI-powered marketing team is going to sound like everyone else’s. That piece was intentionally blunt, and a lot of people told me it hit a nerve. The sameness problem is a strategy problem though. You can fix it with better systems, better taste, better editorial standards. The thing underneath it, the thing I keep hearing in conversations with other demand gen operators, is harder to fix because it’s personal.

Your job changed. And it’s changing at a rate I’ve personally never felt before. Maybe this is what is was like to be a knowledge worker in the early 2000s, but honestly it feels like this is something unique to AI.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday. If you’re not subscribed yet, fix that below.

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The Job You Trained For Doesn’t Exist Anymore

I spent a decade getting good at demand gen. I learned how to build ABM programs, how to instrument a funnel, how to architect pipeline reporting and know exactly where the gap was. I could look at a paid media account and tell you within five minutes what was working and what was a waste of budget. That took yearsss to build.

And right now, today, I spend more time configuring Claude agents, writing skill files in markdown, and debugging data pipelines than I do on any of that. The skills I spent a decade developing haven’t disappeared. I still use them. But the ratio has shifted so dramatically that some weeks I wonder if I’d even recognize my own job description from two years ago.

I’m not complaining. I actually love the building. The agent architecture stuff caught me off guard, honestly, because I didn’t expect to enjoy it as much as I do. But there’s a strange grief that comes with watching your professional identity reshape in real time. The thing you were known for, the thing you were good at, is now table stakes at best and automated at worst.

And I don’t think enough people are talking about that part.

The Confidence Gap Is Real, and It’s Worse Than the Data Shows

Jasper’s 2026 State of AI in Marketing report found that 61% of CMOs feel confident about AI’s ROI. For individual contributors? 12%.

Read that again. Twelve percent.

That gap is telling me that leadership sees AI through the lens of output metrics: more content produced, faster campaigns launched, fewer people needed. The math works from a spreadsheet, maybe. But ICs are the ones actually using the tools eight hours a day. They see the rework cycles. They see the outputs that technically pass QA but miss the mark. They see the gap between what AI produces and what actually moves a buyer.

It’s not just a confidence gap about the tools, either. It’s a confidence gap about themselves. When the thing you used to do well is now done by software, and leadership is excited about the software, that math is so uncomfortable to swallow.

I’ve watched this land differently on different people. Some operators are energized. They’re the ones who were always tinkerers, always had a side project, always wanted to build systems. AI gave them permission to do the thing they were already wired for.

Others are… frozen, at least I think that’s the right word. Because the job they’re good at is morphing into something they didn’t really sign up for, and nobody gave them a roadmap for the transition. Right? These last 90 days or so have just literally rocked our whole world. And typically this “AI-pilled” future is just something they don’t believe in.

Both responses are totally rational. And most people are somewhere in the middle, toggling between excitement and dread on any given Tuesday. ← This is typically me. Even though I’m bought into the future of how powerful AI will be for us marketers, this growth spurt of learning, building, testing, consuming, failing, trying again. It’s a lot on top of trying to keep all the lights on with day-to-day tasks, too.

What I’m Watching on My Team and With Clients

I run a fractional demand gen shop. I’m embedded across multiple B2B SaaS companies at any given time. So I see this from a few angles: my own experience, my team’s, and my clients’.

On my team, the shift has been mostly positive but not without friction. My team members who leaned technical have taken to it fast. The ones whose strengths are more strategic and relational have had a harder adjustment. Not because they can’t learn the tools, but because the tools changed what “good” looks like in their daily work. One of my team members told me recently that she feels like she’s learning a new job inside the old one. That framing stuck with me. She’s right. That’s exactly what it is.

At my clients, the range is wider. I’m at one company right now where the VP of Marketing is fully bought in on AI and has restructured workflows, built agent systems, and cut production timelines in half. The team is keeping up, but the vibe is... compliance more than enthusiasm. They’re doing what’s asked because the boss is excited, not because they’ve independently decided this is how they want to work.

At another client, the marketing team hasn’t adopted AI in any meaningful way. Leadership keeps talking about it in all-hands meetings. Nobody has built anything noteworthy though. The team has an unspoken agreement that AI is a topic for Slack threads and quarterly planning decks, not for actual workflow changes. And leadership can’t figure out why adoption is stalling.

It’s stalling because the team doesn’t feel safe experimenting. They don’t know what happens if they build something that breaks. They don’t know if “learning AI” counts as productive work or if they’ll get dinged for not shipping while they’re ramping. Very grey territory.

The Identity Question Nobody’s Answering

MarTech published a piece recently about how AI agents will reshape every part of marketing. LinkedIn’s 2026 Skills on the Rise report moved “performance analysis” to the number one spot for marketing, bumping AI literacy down to second. The market data is telling us all that the job is changing.

But what’s the psychological cost of that change?

When I was coming up in demand gen, the path was clear. You started as a coordinator, moved to manager, became a director, maybe VP if you were good and lucky. Every step, you knew what skills to build. Campaign management, budget ownership, team leadership, executive communication. You could look ahead and see the shape of the career.

Now? I talk to demand gen managers who are being told they need to learn Python. I talk to campaign managers who are expected to build and maintain AI agents. I talk to directors who feel like they’re being evaluated on skills they haven’t had time to develop yet. The career ladder is dissolving all around us.

LinkedIn’s data shows “client prospecting” entered the top five marketing skills for the first time, and they connect it directly to the rise of independent and self-employed career paths. I translate that to: people are leaving their in-house jobs because this market was made for fractional/consulting. There is now all the potential to deliver the output of an FTE, in a fractional model with AI.

What This Feels Like (If We’re Being Honest)

I want to name a few things that I think a lot of demand gen operators are feeling but not saying out loud. Partly because I feel some of them too.

Imposter syndrome, but backwards. Classic imposter syndrome is feeling like you don’t belong despite evidence that you do. What I’m seeing is the opposite: people who were experts in their domain now feeling like beginners because the domain shifted.

Mourning work that used to feel satisfying. I used to find real satisfaction in building a tight campaign brief. Getting the ICP right, the messaging sharp, the channel mix dialed. That craft still matters, but it doesn’t take me three hours anymore. It takes maybe 30 minutes with Claude. And the 30-minute version is probably better as much as I hate to admit it. This time savings in the beginning felt like a win, but sometimes it feels like cheating.

Questioning your own market value. If AI can do 60% of what I used to do, is my 10 years of experience worth less now? More? I go back and forth on this. The optimistic answer is that experience becomes more valuable because you need to know what “good” looks like before you can direct AI toward it. The pessimistic answer is that companies are going to pay for the AI and expect a cheaper human to manage it. I think both are true in different companies, and that ambiguity is exhausting to try to plan for.

Performing enthusiasm you don’t always feel. This is the one I think about the most and hear the least about. There’s social pressure in the marketing world right now to be publicly excited about AI. Every LinkedIn post is about how AI changed someone’s workflow. Every conference has an AI track. If you’re not on the train, you’re implicitly behind. So people perform excitement even when what they actually feel is anxiety, confusion, or just fatigue. That performance is its own kind of work. And it’s isolating, because you can’t tell who’s actually thriving and who’s just performing thriving.

Where I’ve Landed (For Now)

I want to share where I am with all this, not as a prescription but because I think being honest about the process helps more than pretending I have it figured out.

I’ve accepted that the job I had two years ago is gone. The skills from that job are more valuable than ever, but the job as a daily experience is different now, and it’s going to keep changing. Fighting that has been less productive than just staying curious about what the job is becoming.

I’ve stopped measuring my value by how long something takes me. This was a hard one. When a campaign brief takes 30 minutes instead of three hours, my instinct is to feel like the work was easy and therefore less valuable. That’s wrong. The brief is better because I have a decade of context telling Claude what good looks like. The speed is a feature of my experience, not a devaluation of it.

I write Python now. Badly. I debug API connections by reading error messages and asking Claude what they mean. Six months ago I would have been embarrassed by that. Now I think of it as just another skill I’m building in real time.

And I’ve given myself permission to feel weird about it. Some days the new work lights me up. Some days I miss the old work. Two. things can be true.

So, to you, dear marketer…

Your experience is not deprecated. Your judgment, your pattern recognition, your understanding of what makes a buyer take action: those skills are your new superpower. The companies that figure this out will invest in experienced operators who direct AI. The ones that don’t will produce a lot of forgettable content very quickly.

Give yourself a real window to learn. I enrolled in the GTME School last January (2025) and started building then, it honestly took me until October to even feel like I had a grip on GTME skills. And then AI absolutely popped off between Jan - March of 2026 and I’ve been learning and relearning ever since. The people who look like they’re way ahead of you right now went through that same phase. They just didn’t post about it. 🙄

And if you’re a manager pushing AI adoption: ask your team how they’re feeling about it, not just whether they’re using it. Help them reach their own aha moment.

We’re in the middle of the biggest shift in how marketing work gets done since the internet. Maybe since mass media. And the coverage of it is almost entirely tactical (how to use the tools) or structural (how to reorganize the team). Almost nobody is writing about what it feels like.

I don’t have a framework for this one. Just the honest observation that every demand gen operator I know is going through some version of this, and the ones who are doing best are the ones who are honest about it rather than performing confidence they don’t feel.

We’ll figure it out. We always do.

See ya next week,
Kaylee ✌

A friend of mine got laid off two weeks ago. Her boss told her they were replacing her with Claude. Not like a “we’re restructuring”, or “your role is being eliminated”, like legit we’re replacing you with an AI tool.

She’s a senior demand gen manager with eight years of experience. She ran their ABM program, built their program from scratch, managed a $200K/quarter paid media budget. And her company decided an untrained robot could do her job, today.

I’m embedded in multiple B2B SaaS companies right now, and I can feel this wave building. More CEOs are looking at AI output demos and asking the same question: “If AI can do all that, why do we need such a big marketing team? Let’s trim it.”

I use AI more than almost anyone I know (doesn’t mean I always use it well, but I’m spending the majority of my days building, testing, iterating). And I think most companies are about to get this very, very wrong.

This is my list of warnings, or at least considerations.

👋 Hi, I’m Kaylee Edmondson. Looped In lands in your inbox every Sunday with one goal: to give you a sharper way to think about demand gen and growth in B2B SaaS. 2k+ marketers are already reading it. If you’re not subscribed yet, fix that below.

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The “Full-Stack Marketer” Mirage

I keep hearing this phrase in conversations: “We need full-stack marketers.” AI handles the execution, you just need a handful of strategic generalists who can prompt their way through any channel or function.

The full-stack marketer everyone imagines rarely exists, and doesn’t exist at the salary they want to pay at all. Someone who can genuinely operate across paid media, ABM, marketing ops, content strategy, product marketing, brand, field, analytics, and campaign execution is a senior director with 10+ years of experience at best, and they’re not taking your $120K IC role. The generalist who can “do it all with AI” is a bet that AI closes the depth and context gap. And right now, it just doesn’t.

The Sea of Sameness

Here’s what I think happens when every company compresses their marketing team and leans on AI for output: everything starts sounding the same. Because it is the same. The same models, trained on the same data, prompted by people following the same playbooks, producing content that reads like it all came from one room.

I already see it in my feed. The LinkedIn posts all read the same. The blog structures are interchangeable. The ad copy uses the same hooks. The AI-powered marketing team that leadership is so excited about is going to produce the exact same output as every other AI-powered marketing team. And when everyone’s content sounds identical, none of it will work. The whole point of marketing is to stand out. AI, by default, converges to the mean. And at some point I’m actually starting to worry if it forgets how to learn. Or worse, if we forget how to learn.

This is the part that should worry CEOs the most, because it’s the hardest problem to see from the top. The output will look professional. It’s grammatically clean. It hits the right keywords. But it has no edge. No POV. Nothing that makes a buyer stop scrolling and think, “this company gets it.” We’ve never been able to A/B test our way to that. And I really believe we won’t be able to prompt our way to it either.

Taste Is the New Moat

There’s a word that keeps coming up in every AI conversation I’m in right now: taste.

When production is basically free, the ability to produce stops being valuable. What becomes valuable is knowing what’s good and what to cut. Knowing when something technically works but feels off. Knowing that your competitor’s new positioning is weak even though it checks every messaging framework box.

That’s taste. And taste lives in experiences, but most definitely not in models (at least not yet).

The companies that compress their teams down to a handful of AI-prompters are going to produce more content than they ever have. They’re also going to produce the most forgettable content they’ve ever published. Because nobody on the team has the experience or the authority to say something is mid, kill it or this is close but the angle is wrong, or the market is tired of this framing, try something nobody else is doing.

Taste is the editorial layer that separates a brand with a point of view from one that’s just adding to all the noise we’re facing. I don’t think you can hire for it at the salary ranges I’m seeing (stacked with all the other requirements), and I’m pretty confident we’re not going to automate it anytime soon.

Creation Without Distribution

There’s another gap that compression makes worse. Very few marketing teams have figured out both creation and distribution. Most are decent at one and terrible at the other.

AI helps a lot on the creation side. I’ve seen it. I use it every day. We can produce more content, more ad variations, more email sequences, more landing pages in a fraction of the time. But distribution still requires human judgment, relationships, and a deep understanding of your buyer’s behavior. Getting the right content in front of the right people at the right time through the right channels has always been the harder half, and I don’t see AI solving that part yet.

Speed of output is different from speed of judgment.

When the team gets compressed, the people left are trying to do both. And I think they’re going to default to the side AI makes easier: creation. Which means you end up with a team producing a mountain of content that nobody sees because distribution strategy got deprioritized the moment headcount shrank. More content, and less pipeline. That’s the outcome I’d predict for most compressed teams within six months.

The Tribal Knowledge Problem

I see versions of this at every company I’m embedded in. The people who built the systems are the people who understand the systems. When the team gets compressed, you don’t just lose headcount. You lose the institutional knowledge of why things are set up the way they are. And that knowledge lives in people’s heads, and not in documentation, because rarely do people document this stuff.

I’ve never walked into a client engagement where the MOPs infrastructure was well-documented. Not once. In 10+ years. If this compression wave hits the way I think it will, companies are going to fire the people who built their systems and then spend the next six months paying contractors like me a premium to figure out what those people already knew.

What I Think Happens Next

I haven’t watched this play out yet. Not fully. But the signals are everywhere, and here’s my prediction for how it goes at most companies that compress too fast:

The reorg gets announced. “We’re building a lean, AI-powered marketing team.” The people who stay feel chosen. Cautious optimism.

Within a few weeks, the cracks show. Nobody can figure out why the lead routing broke. The scheduled reports stopped running and nobody knows which connector was triggering them. The team starts drowning in maintenance, and they haven’t even gotten to the part where they’re supposed to be building new things.

Pipeline starts slipping. Not because the team isn’t working hard, but because campaigns that were running on autopilot actually needed someone monitoring them. The content being produced is higher volume but substantially lower quality, and it’s blending into the same AI-generated sea as everyone else’s.

Leadership brings in a contractor to “help stabilize things.” The contractor spends the first three weeks doing discovery on what’s broken. This is effectively paying a premium for someone to rebuild context that walked out the door.

To Everyone CEO Considering This…

Document everything before touching the org chart. Do it while the people who built the systems are still around to verify the documentation is right. Audit what the team spends their time on, automate the repetitive work, measure real time savings.

Compress through attrition, not through RIFs. When someone leaves, let the team try to absorb the work with AI. If it works, that’s true efficiency. If it doesn’t, you’ve learned something about what that role drives for your business that AI can’t (yet).

And invest in taste. If you’re going to run a smaller team, those people need to be true unicorns. Likely not a junior hire, or a $120k generalist, but someone with enough experience to know what good looks like and enough authority to kill what doesn’t meet the bar.

The companies that figure this out will end up with smaller, sharper teams that use AI as a multiplier I have no doubt about that. The ones that just cut headcount and hope AI fills the gap are going to spend the next year wondering why their pipeline is flat (or declining) and their content sounds so mundane.

See ya next week,
Kaylee ✌

P.S. Next week I promise to be back on the AI adoption train, but this week’s entry needed to be a diary of the thoughts shuffling around in my brain 😅

The skill I built to solve this runs every Monday morning, reads the last seven days of my activity, and hands me a ranked list of the skills I should build next.

…yep. It’s a skill for skills. Whatever works, right?

A skill only pays off if you run it 5+ times a month. The build step is the easy part. Noticing what to build a skill around is harder (at least for me it was), and that’s where most operators stall. Memory is terrible at it. Your tools remember better than you do.

👋 Hi, I’m Kaylee Edmondson. Looped In lands in your inbox every Sunday with one goal: to give you a sharper way to think about demand gen and growth in B2B SaaS. 2k+ marketers are already reading it. If you’re not subscribed yet, fix that below.

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A One-Time Setup

Before the weekly audit ever runs, the skill onboards you with a 2-minute interview. Five questions, asked one at a time:

1. What’s your role, and which clients are you working on?

2. Which tools do you use most? (Slack, Gmail, Monday.com, Granola, Google Calendar, HubSpot, Google Drive, LinkedIn Ads, Google Ads, Salesforce)

3. What feels like it takes the most time in your week?

4. Do you already have any skills, templates, or automations set up?

5. What day of the week do you want the audit to run?

My answers got saved to a markdown file in my workspace called skill-audit-profile.md. Every future weekly run reads that file first. Self-reported time sinks get weighted heavier in the ranking. Existing automations get excluded from recommendations.

Then the skill schedules itself using a cron expression to fire at 9:00 AM on whatever day I picked. I picked Monday because I want the report waiting for me when I open my laptop at the top of a fresh week.

The Weekly Skill Audit: 3 Phases

Every Monday at 9:00 AM, the skill runs automatically. Three phases. I don’t have to prompt it. I don’t have to remember. By the time I’m pouring coffee, the report is sitting in my workspace.

Phase 1 is the scan. Phase 2 is the scoring. Phase 3 is the report.

Phase 1: Scan

The audit reads my profile first so it knows my role, my clients, my primary tools, and my self-reported time sinks. Then it scans the last 7 days of activity across whichever tools I marked as primary. Each tool has its own scan behavior:

• Granola: queries recent meetings, scans titles and transcripts for patterns

• Gmail: searches sent emails (in:sent newer_than:7d) for templated language and recurring email types

• Slack: searches recent messages for repeated phrases, status updates, recurring explanations

• Monday.com: checks recent board activity and item creation patterns

• Google Calendar: reviews the week’s events for recurring meeting types

• HubSpot: checks recent CRM activity for repeated workflows

• Google Drive: checks recently created or modified files for naming patterns

If a tool isn’t connected, the audit skips it and notes the gap in the final report.

Phase 2: Score

The audit looks for six specific pattern types. These are the categories baked into the skill:

1. Repeated creation (same type of deliverable made regularly)

2. Multi-step workflows (predictable sequences across tools)

3. Recurring communication (templated emails or messages)

4. Data gathering rituals (pulling info from multiple places on schedule)

5. Context switching overhead (bouncing between tools predictably)

6. Knowledge transfer moments (explaining the same thing to different people)

Every pattern that surfaces gets scored on five dimensions:

• Frequency: how many times it showed up this week

• Time per occurrence: estimated minutes per instance

• Complexity: how complicated the workflow is

• Automatable portion: what fraction a skill could realistically handle

• Estimated weekly time saved: the ranking number

The patterns get ranked by estimated weekly time saved. The audit also cross-references every candidate against my profile. Self-reported pain points get weighted heavier. Anything I already have a skill for gets dropped from the list before it even hits the report.

The scoring works as a filter. It removes the candidates that would burn a weekend for low payoff. The patterns that survive the rank order are the ones worth a Monday of building work. Most weeks only one or two patterns make the cut. That’s correct.

Phase 3: Report

The audit writes a markdown file to my workspace called weekly-skill-audit-[YYYY-MM-DD].md. The structure is fixed:

# Weekly Skill Audit — [date]
**Tools scanned:** [list]
**Activity window:** Last 7 days

## New Skill Opportunities This Week
(Ranked by estimated weekly time saved. Only NEW patterns not flagged in
previous reports.)

### 1. [Skill name] — [est. weekly time saved]
**Pattern:** [what was observed]
**Evidence:** [paraphrased examples]
**What the skill would do:** [2-3 sentences]
**Complexity to build:** [Low / Medium / High]

## Recurring Patterns (Still Present)
(Flagged before, still showing up. Brief status update.)

## Gaps
(Tools not connected, insufficient data, light weeks.)

After saving the report, the skill sends me a short summary message in conversation. Three or four sentences, no more. Top one or two new findings only. If I want the details I open the file.

What the report does not include: a drafted SKILL.md file, trigger phrases, worked examples, or a ready-to-ship skill. Building the skills it surfaces is a completely separate job I do later in the week, with a different tool, only if I agree with the audit’s ranking.

I Ran It on Myself

A normal Monday. The week behind it: three ABM kickoff prep sessions, a stack of ad reviews, twelve Granola meetings, and one Slack thread where I’d spent half an hour walking a new team member through tier logic.

The audit covered Granola, Gmail, Slack, Monday.com, Google Calendar, and Google Drive.

The top opportunity from the report:

## New Skill Opportunities This Week

### 1. abm-strategy — est. 180 min/week saved
**Pattern:** Knowledge transfer moments + multi-step workflow + recurring communication
**Evidence:** 3 ABM kickoff prep sessions this week each working through
the same ~8 questions (tier structure, budget allocation, BDR trigger
thresholds, ad format mix by stage, intent signal handoff). One Slack
thread explaining the same budget math to a new team member. Two Granola
calls where the first 20 minutes were spent re-covering tier logic.
**What the skill would do:** Intake the 8 standard ABM questions, return
a draft tier structure, budget allocation per tier, BDR trigger rules,
ad format recommendations by stage, and an ads-to-outbound signal
framework. One shot, based on my existing ABM playbook.
**Complexity to build:** Medium

The summary message the skill sent me after the report saved:

“1 new opportunity worth ~3 hours/week: ABM strategy intake (Medium complexity, est. 180 min/week). Full report in workspace.”

The audit’s job stops at description. It captures the opportunity in 2-3 sentences, ranks for complexity, and shows me the evidence.

I built abm-strategy the following week. Took me about an hour, in part because the audit had already done the work of articulating what the skill needed to do.

The audit has since flagged four more adjacent patterns (quarterly ABM performance review, account list QA, tier migration logic, ABM-to-outbound feedback loop) that I’d never have connected to each other without seeing them stacked in one report. Each one earned its own line item in subsequent weekly audits.

What does this mean?

(but hummed to the tune of 😏)

1. Discovery and building are different jobs.

The audit describes opportunities. It does not build skills. That separation is deliberate. When you mash discovery and building together, you prioritize whatever task you feel like automating in the moment, which is almost never the highest-leverage one. Forcing a beat between “this is worth building” and “I’m building it” gives you the chance to ask whether the audit’s #1 candidate is actually the one you should ship next. Sometimes I disagree with the rank. It can’t see into the future after all.

2. Your tools remember better than you do.

Your sent folder, your calendar, your call transcripts, and your Slack history are an honest record of your week. My memory is almost always a vibe check. The audit reads the actual artifacts, which is why it surfaces opportunities your brain would have missed.

3. The profile is the cheat code.

The one-time interview creates skill-audit-profile.md. Every future weekly run reads that file first. Self-reported time sinks get weighted heavier in the ranking. Existing automations get excluded from recommendations. Without the profile the audit still works, but with it, the audit picks skills you’ll build because they touch work you already admitted is painful. Two minutes of interview, six months of better recommendations. Worth it.

The objection I hear most often: “You need to be a developer to do this.” You don’t. SKILL.md is a markdown file with frontmatter. The audit’s scheduled task is set up for you by the skill itself. If you can answer five questions in an onboarding interview and pick a day of the week, you can run this. Writing SKILL.md is straightforward.

If you want this in your own setup, grab the /weekly-skill-audit SKILL.md template. Run the interview, pick your day, and let it scan your weeks.

And reply to this email with the skill you’re building next. I’ll feature the best replies in a future issue.

See ya next week,

Kaylee ✌

Marketing has signals. Lots of them really. Intent data flowing, website visitors getting deanonymized, engagement being tracked across channels. The signal supply chain is…supplied.

And sales isn’t doing as much with it as they probably could.

TL;DR: Marketing teams have gotten really good at sourcing signals in the last year or so, but not great yet at prioritizing and orchestrating them.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday.

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The Orchestration Layer

Over the last few years, B2B marketing teams got really good at signal sourcing. Intent data vendors matured. Deanonymization tools at the contact level became available. Engagement tracking got more granular. Product usage signals started flowing into CRMs. Praise be. Most marketing teams with any budget have some version of this infrastructure in place now.

But sourcing signals was only the first job.

The second job was building the orchestration layer: deciding which signals matter, how they combine, when they should trigger action, and routing them to the right motion with a defined owner and a timeline.

Most are still working on building that part. Marketing teams shipped the signal infrastructure, showed the dashboard to sales leadership, and mentally checked the box. “We gave them the data.” Job done.

Except raw signals without orchestration are 100% of the time getting ignored by our sales friends.

Separate Your Signals First

Before you build any orchestration, you need to sort your signals into three categories. You’ve probably seen some version of this framework, but stick with me, because how you separate these determines whether your tiering works downstream.

Fit signals are static-ish. Industry, employee count, ARR range, tech stack. They tell you whether an account even belongs in your universe, and they don’t change week to week. But worth revisiting quarterly or so. *Unless tech stack investment is a major signal for you, then prioritize it accordingly.

Relevance signals are where it gets interesting, because these are time-decaying. A new VP of Demand Gen got hired six weeks ago. The company just posted a role for an ABM Manager. They raised a Series B last quarter. Each of these tells you something about why now, but they all have a shelf life. A hiring signal from three months ago is very different from one that posted last Tuesday.

Engagement signals are the clearest in-market indicators: pricing page visits, repeat sessions on your site, ad clicks, community activity. These are the signals that say “this account is aware of us and actively doing something about it.”

A fit signal alone tells you an account could be a customer. A relevance signal alone tells you something changed at the company. An engagement signal alone tells you someone clicked on something. None of those individually are worth your time.

But a fit signal + a relevance signal + an engagement signal, all firing within a defined window? That’s a compound signal. That account is likely in an active buying window. The combination is the insight.

Tiering, Not Scoring

Once your signals are separated, the next step is tiering. And I specifically mean tiering, not scoring.

Tiering is simpler. Every account in your universe gets placed into a tier based on signal density, and each tier maps to a specific marketing motion.

Tier A: Fit confirmed + two or more relevance signals + at least one engagement signal, all within your defined window (I typically use 60 days, but this depends on your sales cycle). These accounts get your highest-touch motion. Immediate action.

Tier B: Fit confirmed + one relevance signal or one engagement signal. These accounts get an ABM motion. Progressive, multi-touch, account-specific. You’re building toward Tier A.

Tier C: Fit confirmed, but no active signals yet. Always-on demand gen. You’re keeping your brand in front of them so that when signals do fire, they already know who you are.

Tier D: Doesn’t meet fit criteria. Stop spending money and time here.

The tier determines the motion. The signals determine the tier, the tier determines what happens next, how fast, and who owns it.

On GTM alpha. Clay coined the term “go-to-market alpha” to describe the unique tactical advantages in your GTM strategy that your competitors haven’t found yet, borrowed from the finance concept of alpha as outperformance over a benchmark. Your GTM alpha lives in your specific signal combinations, the fit + relevance + engagement patterns that predict pipeline for your business. You can’t copy someone else’s signal architecture and expect it to work. Your ICP is different. Your sales cycle is different. Your data is different.

The Play Menu

Ok, so an account hits Tier A. Signals are converging. The system routes it to the BDR team. Now what?

At most companies, “now what” is a Slack notification that says something like “high intent account: Acme Corp.” Maybe there’s a link to the intent dashboard. Maybe there’s a lead score attached.

And then the BDR has to figure out what to do with it. What do they send? What angle do they take? How urgent is it? They’re making these decisions from scratch, every time, for every account.

This is where marketing needs to finish the job. The handoff to sales likely shouldn’t be a Slack notis. It should be a brief with three components:

1. Here’s what they’ve done.

The specific signals: “Their new VP of Demand Gen started eight weeks ago. They visited your pricing page and integrations page twice in the last ten days. And they just posted an open role for an ABM Manager.”

Some context a BDR can use. They can reference the hiring context in their outreach. They can speak to the integrations the prospect was researching. The signals become the talk track.

2. Here’s why it matters.

This is the context layer that marketing is positioned to provide because marketing generated most of these signals in the first place. What does it typically mean when a new demand gen leader is hired, the company is evaluating your integrations, and they’re building out an ABM function simultaneously?

It means they’re standing up a demand gen engine from scratch. They’re in build mode. They need help, and they need it now while the new leader still has a mandate to make changes.

That context gives the BDR confidence. They understand the “why” behind the outreach, and that shows up in how they write and how they talk.

3. Here’s the play.

Instead of leaving sales to decide what to do, marketing should be building a play menu: a pre-built set of outreach motions, each one mapped to a specific signal combination and persona.

Think of it like a matrix. Signal combination on one axis. Persona on the other. The play in each cell.

Building This with AI

The reason this orchestration layer hasn’t existed at most companies is that it was genuinely hard to build manually. Monitoring five to ten signal sources, cross-referencing them against your ICP, figuring out which combinations are firing on the same account within the same window, then generating a contextualized brief for Sales? Nobody had time for that.

That’s changed. I’m using Claude (both Cowork and Code) to build this across my client portfolio right now. Will share more on the skills and resources in the coming weeks.

Finding your GTM alpha in your pipeline data. Take your last 12 months of closed-won deals and feed them into Claude with your signal data. Ask it to surface the patterns. Which combinations of fit + relevance + engagement were present in the accounts that actually closed? That output becomes your tier definitions. That becomes your GTM alpha. And it takes hours instead of a quarter-long analysis project.

Building the signal-to-brief pipeline. I have agents running that pull from multiple signal sources, cross-reference what’s firing against my tier definitions for each client, and auto-generate briefs with all three components: what the account has done, why it matters, and which play from the menu to run. A rep opens their morning with the work already prioritized. The brief meets them where they are.

Generating the play menu itself. I feed Claude the signal taxonomy, the ICP definitions, and the tier structure, and it drafts play options mapped to each signal combination and persona. I edit these heavily. The plays need to sound human and reflect real sales conversations, not templates. But the scaffolding, the structure, the coverage across every tier and persona combination, that’s what AI handles.

Quarterly recalibration. Your tier definitions and play menus can’t be static. Every quarter, I run the same pipeline pattern-matching exercise on recent data to check whether the signal combinations that were predictive six months ago are still working. Signals shift. A relevance signal that used to be strong (like Bombora surges in a specific topic cluster) can get noisier as more companies game it. The system needs to stay alive.

Let’s get into it

Sourcing signals was step one. Most of us got stuck there. The actual work, the part that moves pipeline, is everything that comes after: separating your signals, tiering your accounts, building a play menu that arms your reps with context instead of alerts, and using AI to make the whole thing run without bumping into resource constraints.

If your marketing team has built the signal infrastructure but pipeline isn’t moving, look at the gap between detection and action. That’s your orchestration layer. And in my opinion, it’s marketing’s job to build it.

See ya next week,

Kaylee ✌

Spring break with my daughters. Set a global ‘away’ status on Slack, didn’t check LinkedIn, only 1 small-ish client fire drill. Just sisterly disputes over trivial things, and the kind of touching grass that reminds you demand gen will, in fact, survive without you for eight days.

I didn’t write anything new this week on purpose. So instead of forcing something, I pulled three of the most-read posts from the archive. If you’ve been around a while, maybe you missed one. If you’re newer, these are a solid entry point to what this newsletter is actually about.

👋 Hi, I’m Kaylee Edmondson. Looped In lands in your inbox every Sunday with one goal: to give you a sharper way to think about demand gen and growth in B2B SaaS. 2k+ marketers are already reading it. If you’re not subscribed yet, fix that below.

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The Top 3 from the archive:

1. What 12 Months of ABM Data Reveals About What Actually Works

This one hit harder than I expected. Most ABM programs fail before they ever launch because teams are optimizing for the wrong things. I broke down what the data shows after a full year of running these programs across multiple clients. If you run any kind of account-based motion, start here.

2. The Great ABM Unbundling Is Here

Your all-in-one ABM platform is expensive, underused, and designed for 2016. This piece is about why the “one big platform” era is ending and what a modern, unbundled ABM stack looks like — with real budget allocations and tool recommendations.

3. ABM Isn’t Dead–It Just Got Smarter: The 2025 Modern ABM Playbook

I wrote this one after a client asked me point-blank: “How can ABM still work after all the 6sense hype?” Here’s exactly what I told him. A practical breakdown of what modern ABM looks like right now (or at least in 2025 when I wrote this…which does feel like 20 years ago now in AI-world).

I’ll be back next week with fresh content. I’ve got a couple of ideas I’m working through that I can’t wait to get out of my brain and into your inbox.

See ya next week,
Kaylee ✌

By the end of the session, we had talked through additional potential use cases, and it felt like it was finally starting to click for a room full of people who had been using Claude to “help me rewrite this email.”

I’ve been playing and building in Claude Code and Claude Cowork for a few months now. I keep finding new use cases that save me time, help me think differently, iterate on concepts faster. And like I shared on LinkedIn earlier this week, I feel like I’m spending every waking moment possible in this new stack, yet still feel more behind in my craft than I ever have. So I’ll say this, if you’re building, exploring, testing in a new tool this week, you’re right where you’re supposed to be. There’s so much hype, especially on LinkedIn these days, and these tools are shipping new models, functionality, and features faster than ever. And that combined makes all of us feel like we’re falling behind. But as long as we keep building, and sharing what we’re learning, I think we’ll all turn out just fine.

So, let me open up a bit about what I’ve been testing and learning.

👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. Subscribe to join 2k+ readers who get Looped In delivered to their inbox every Sunday.

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Why Most Marketers Are Getting 10% of What Claude Can Do

Most demand gen teams I talk to are using AI for two things: writing first drafts of copy, and summarizing meetings. That’s fine. But you’re leaving a lot on the table.

The gap between “I use Claude” and “Claude runs half my workflows” really comes down to context.

I built what I call a “marketing brain.” It’s a set of markdown files that load automatically at the start of every Claude session: who I am, who my clients are, how I write, how I run campaigns, what tools I use, my ICP definitions, my messaging house. Claude reads all of it before I type a single word. That means when I ask it to do something, it already knows my business. It’s not starting from zero every time.

(I wrote about the marketing brain concept last week, so I won’t belabor it. If you missed it, go read that one first.)

Here are the 11 use cases I’m running (or building/finessing) right now. Some of these save me 30 minutes a week. A couple of them eliminated entire workstreams.

1. Discovery Call Research Briefs

Before every discovery call with a potential client, Claude pulls together a research brief. Company background, the prospect’s LinkedIn activity, any public talks or posts they’ve done, likely pain points based on role and company stage.

Last week I had a call with a Head of Demand Gen at a customer experience platform. Claude surfaced that she’d spoken publicly about whether marketing attribution is broken, that she was actively hiring a Demand Gen Manager and ABM Manager (suggesting the engine is early-stage), and flagged that she likely was the budget holder. So I should position DemandLoops as complementary to her hiring plan, not competitive with it.

90 seconds. That used to be 20-30 minutes of LinkedIn stalking and Googling.

2. Weekly Data Pulls from HubSpot + Salesforce

I’m embedded in a client right now where the HubSpot-to-Salesforce integration is... let’s just say it’s a project.

Every week, Claude pulls data from HubSpot, runs VLOOKUPs against Salesforce records, cleans up naming conventions, deduplicates contacts, and flags anything that looks off.

Not glamorous. But this used to eat 3-4 hours a week, and if you skip it, we had no idea what to go optimize for pipeline.

3. CRM Property Mapping Across 5 Systems

Same client. They run HubSpot, Salesforce, Vitally, NetSuite, and PandaDoc. Five systems. Trying to figure out which property maps to what across all of them in a spreadsheet made me want to quit consulting. (I’m being dramatic. But only slightly.)

I built a Claude assistant that takes the property lists from each system and creates a unified mapping doc. Markdown file for quick reference, structured spreadsheet for the full picture. Now when someone asks “where does this data live?” I can answer in seconds instead of opening five admin panels.

This is one of those use cases where Claude is serving as a bandaid solution. Eventually these systems will all be cleaned up, or replaced entirely, synced to the data warehouse, and integrated appropriately, but for now while we’re in the messy middle, post M&A (we’ve all been there), this Claude task is doing some heavy lifting.

4. Competitive Intel, Weekly

I have a competitive intel workflow that runs every week. Claude pulls from competitor websites, checks their ad libraries on Meta and LinkedIn, and flags what changed: messaging shifts, new product positioning, campaign themes, creative formats they’re testing.

The output is a structured report. What changed, what it probably means, whether we need to respond. Typically, quarterly competitive reviews are already stale by the time they ship. This approach keeps you within a week-ish of what competitors are doing.

5. Personalized ABM Ad Copy and Landing Pages

For one client’s ABM program, we’re building hundreds of individualized ads and landing pages. And I mean individualized. Not “Hi {Company Name}” personalization. Actually different messaging by industry, company size, and persona.

Claude generates the copy variations using our ICP definitions and messaging house as the foundation. We pipe account data through Clay for enrichment. The output is account-specific ad copy and landing page content.

6. Lead Scoring and Account Tiering

I’m building what I’m calling an enterprise appetite scoring matrix for a client. Six components for now: company size signals, tech stack indicators, buying intent, engagement depth, organizational complexity, and budget authority signals. I’ll also add in their GTM Alpha. Claude will weigh each one and assign a tier.

The part I find most useful is what I’ll call “synthetic attributes” for now. Data points that don’t actually exist in your CRM but can be inferred from combinations of other fields. For example: you might not have a “budget authority” field, but you can infer it from title seniority + company size + the presence of a procurement process. Claude is surprisingly good at this kind of inference when you give it a clear framework to work within.

7. Salesforce Flow Documentation

If you’ve ever inherited a Salesforce instance with 40+ automation flows and zero documentation, you know this pain.

Claude analyzes the flows, documents what each one does, flags redundancies, and identifies which ones are actually firing vs. sitting dormant. What would have been a two-week documentation project took about three hours. My output for the first run was far from perfect, but probably 60-70% there.

8. Campaign Consistency Checks

Messaging drift is real. Especially when you have three or four people writing copy across email, ads, landing pages, and social.

I built a workflow where Claude checks any new piece of copy against a client’s campaign strategy doc and messaging house before I launch it. Flags anything off-brand, off-message, or inconsistent with what they’ve already published. Takes about 10 seconds. Replaces what used to be a “can you review this” Slack thread that took a day to resolve.

9. Newsletter Topic Development

I use Claude to help me develop newsletter topics, but probably not in the way you’d expect. I don’t ask it to “give me 10 newsletter ideas.”

Instead, I have it pull from my meeting notes (via Granola), my Slack conversations, and current industry trends, then find the intersections. Where does my lived experience this week overlap with what the market is talking about?

This newsletter is a good example. Claude surfaced that the intersection of “I just trained a client team on AI workflows” and “the industry is obsessed with AI in marketing but nobody’s sharing specific use cases” was a strong topic. The pattern-matching was collaborative. The writing is mine.

10. Automated Content Maintenance

For clients with large content libraries, I’m building a series of Claude agents that pull existing site content, find what needs updating (outdated stats, broken cross-links, FAQ gaps), make the changes, and push them back for approval.

Nobody wants to do this work. I’m finding it sits undone for months. But it compounds. Outdated stats kill credibility. Broken links hurt SEO. FAQ pages that don’t reflect the current product confuse prospects. Claude is great for this grunt work.

11. Meeting Prep and Follow-Up

Every morning, Claude pulls my calendar, cross-references it with my meeting notes from prior conversations with the same people, and gives me a prep brief. After meetings, it processes the transcript and drafts follow-up emails, action items, and internal notes for my team.

The follow-up emails are where the time savings really add up. Claude knows my voice, knows the client context, and knows what was discussed. The drafts need light editing, not full rewrites. I was spending 15-20 minutes per follow-up before. Now it’s 2-3 minutes of editing.

How I’d Prioritize If You’re Starting From Scratch

If you’re looking at this list and wondering where to start, here’s the honest answer: start with the boring stuff.

Data pulls. Research briefs. Competitive monitoring. Copy QA. The work you do every single week that requires knowing your business well but follows a predictable pattern.

After that, move to the structured creative work. ABM copy, account scoring, content ideation. These still need your judgment. But Claude handles the 80% that’s pattern-matching, and you focus on the 20% that requires taste. Claude has very little natural taste.

Last priority: the one-off projects like documentation.

The through-line across everything I listed: the marketing brain is the multiplier. Every single use case works dramatically better because Claude already knows my clients, my ICPs, my voice, and my tech stack before I ask it to do anything. Without that context layer, you’re prompting from scratch every time.

What’s Next

I’m sure everything will change on us again tomorrow. And I’m also certain someone is going to reply to this and ask why I’m not using X, Y, Z hot new tool to do one of these workflows instead of Claude. For now, I’m just trying to familiarize myself with as much as possible while keeping my day job and still finding time to touch grass. So I’m sure there are other tools out there that could do these jobs better, please tell me if you have opinions here as I would love to try them out.

And if you’re a demand gen operator building workflows in Claude or any AI tool, I want to hear about it. Reply to this email and tell me what you’ve built. I’ll feature the best ones in a future issue.

I’m taking next week off to spend spring break with my kiddos. Hope you all are finding time to recharge, too! And then I’ll catch you back here the week after next!

Kaylee ✌

My feed looks and feels like this lately:

But what I’m observing inside marketing orgs right now – across clients, conversations, and peer communities – is a different story. Almost everyone is feeling behind. Almost no one can point to a concrete workflow where AI is producing better outcomes than what they had before. The mandate is real. The results mostly aren’t yet.

We’ve been here before. Just not quite like this.

B2B marketing has a long history of hype cycles, and they all follow the same arc: a new category emerges with genuinely compelling use cases, early adopters get results, the trade press picks it up, LinkedIn turns the volume to eleven, the board starts asking questions, budgets get allocated, tools get purchased, rollouts happen — and then, about 12-18 months later, most teams are sitting on a piece of expensive tech debt and wondering what went wrong.

I’ve watched it happen enough times to recognize the pattern by feel.

HubSpot and the inbound marketing wave in the early 2010s. The promise was that you could replace interruption-based marketing with content that buyers would come to you for. Real for some companies, absolutely. For many others: a blog nobody read, an ebook that generated zero pipeline, and a CMS contract that outlasted three CMOs.

Conversational marketing and the Drift era. The promise was that AI-powered chat would transform how B2B companies captured and qualified pipeline. Real for some companies. For many others: a chatbot icon on the website, a sales team that ignored the alerts, and a multi-year contract nobody wanted to renew.

Dark social. Zero-click content. ABM platforms at $150K+ annually. Each one has had a hype cycle. We are marketers after all. 😏

Which brings us to AI.

I want to be careful here, because I genuinely believe this one is different. Not different in kind, the hype cycle is running the same playbook, but different in scope and intensity. The investment levels are bigger. The board pressure is more universal. The category is broader, touching every function, every role, every workflow simultaneously. And the FOMO is more acute than anything I’ve ever seen, because unlike ABM or dark social, AI feels existential in a way that a marketing channel never did.

So what’s the diagnosis, doc?

Most companies aren’t buying AI tools because they’ve identified a specific problem and need to scale the solution. They’re just not. They’re doing it because they’re terrified of being the one who didn’t.

When you buy to reduce FOMO, you buy the wrong thing, for the wrong reasons, before you’re ready to use it. This was true for non-AI tech stacks and I believe it carries through to this era, too.

Devin Reed put it cleanly in a recent newsletter: “You can’t scale a process you haven’t built. You can’t automate thinking you haven’t documented.”

Generic prompt in. Generic content out.

Turns out this thing needs a brain

As I’ve spent my last several weeks obsessing over Claude Cowork, through much trial and error I’ve found the output is substantially better if you give Claude a “brain”. Claude is only as useful as what you tell it. Out of the box, it’s a generalist. But if you spend time upfront loading your context into Claude Cowork’s CLAUDE.md file, it becomes something closer to a second brain that knows your stack, your audience, your voice, and your opinions.

This is the prompt structure I’ve used. The good news is you can and should make this entirely your own. You can also go back and build new phases over time once you get into a new project and realize Claude has no idea what you’re talking about. To start, there are XX phases in this initial prompt. It interviews you one phase at a time, builds a structured file after each phase, waits for your approval, then moves on. When it’s done, you have a complete marketing brain that makes Claude useful from the first message of every future session.

And if you go implement this and have learnings, feedback, questions, etc. hit reply! I really read every one.

Phase Map

The Prompt

Copy everything and paste it into Claude Cowork as your first message.

👋 Hi, I’m Kaylee Edmondson. Looped In lands in your inbox every Sunday with one goal: to give you a sharper way to think about demand gen and growth in B2B SaaS. 2k+ marketers are already reading it. If you’re not subscribed yet, fix that below.

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“I hear you that ICPs are defined, but are they also operationalized? Meaning, are those definitions actually wired into your systems, your scoring, your routing? They’re breathing, not static.”

I’ve been thinking about it ever since. ICPs are almost always either nonexistent, far too broad, or living (and dying) in a spreadsheet.

👋 Hi, I’m Kaylee Edmondson. Looped In lands in your inbox every Sunday with one goal: to give you a sharper way to think about demand gen and growth in B2B SaaS. 2k+ marketers are already reading it. If you're not subscribed yet, fix that below.

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A few months back I wrote about rethinking how we segment ICPs – specifically why company size is often the wrong organizing principle. Today I want to go one level deeper: not just how you define your ICP, but whether your ICP definition actually does anything. And how to use AI to make the whole thing work the way it was supposed to.

I’ve found most companies have an ICP. Almost none have operationalized it. And nearly all of them will keep running on that static definition – until something forces them not to.

That “something” varies. Sometimes it’s a huge miss on pipeline one quarter. Sometimes it’s a churn problem that suddenly becomes impossible to ignore. Sometimes it’s a campaign that just...doesn’t work, and nobody can explain why. Sometimes it’s all three at once.

ICP is often static, until it’s not.

Why ICPs drift (and why neither reason is obvious)

There are two distinct failure modes here, and they’re both common.

The first: the ICP was wrong from the start, and nobody knew it. You built the definition off intuition, early wins, or a competitive benchmark, but not off rigorous analysis of your actual customers. Everything looked fine until churn started accumulating in accounts that fit the definition perfectly on paper. That’s not a sales problem. That’s a definition problem.

The second: the ICP was right once, and drifted. The business shifted its go-to-market. The market changed. The product expanded into new use cases. The pricing moved upmarket. Any of those transitions will change who your real ICP is, but if nobody updates the definition, your systems keep targeting the old version of the customer while the business is trying to sell to a different one.

Both versions of this problem have the same symptom: demand gen that feels like it should be working, but isn’t producing the results you’d expect.

And the fix for both starts in the same place.

Step zero: validate the definition before you build anything

Before you wire your ICP into any system, you need to know whether the definition you’re working with is actually correct. I see teams skip this all the time. They operationalize a definition that was never right to begin with, and then wonder why the machine isn’t producing.

Here’s the process I use when I come into a new engagement and suspect the ICP is off.

Pull these lists from your CRM:

The “good” cohorts:

• Closed-won customers

• Longest-standing customers

• Highest-paying customers

• Highest cross-sell / upsell customers

The “bad” cohorts:

• Churned customers

• Shortest contract length customers

• Lowest-paying customers

• Customers with the highest support ticket volume and/or lowest NPS

Or anything else that’s specifically relevant to your business that you’d want included as part of the ICP analysis.

The thought here is to build a before-and-after picture of your customer base. What do your best customers actually have in common? What do your worst ones have in common? And critically…what do those two groups not share?

Enrich everything with whatever your enrichment tool of choice is before you analyze anything.

The data quality problem is consistent across every brand I’ve worked with. Your CRM exports will have company names, maybe industry and size, and not much else. That’s not enough to do meaningful pattern analysis. Before you do anything analytical, run all eight cohort lists through Clay/ZoomInfo/Apollo/etc. to add firmographics, technographics, funding history, hiring signals, and whatever else is relevant for your business.

Then I’d load everything into Claude (or you can analyze them manually either way).

This is where it’s been getting interesting for me, and to be transparent this is a workflow I’m actively building and refining, not something I’ve run a hundred times. But the approach is sound, and I think it’s where a lot of demand gen teams are going to land over the next year.

Once you have your enriched cohort exports, drop them into a Claude Cowork session. You’re not asking Claude to make up attributes or hallucinate patterns just give it your first-party data and ask it to find what you’d miss doing this manually or what would take you two days in Excel.

Before you run any prompts, set up the project with three context files.

The prompts below will work without them. But they’ll work significantly better with them — especially when you’re running this analysis across multiple clients and need consistent, immediately usable output every time.

• icp-analysis-framework.md — a reusable file that tells Claude how to think about ICP analysis: what dimensions to look at, how to weight signals, when to flag something as uncertain versus confident, and what to watch out for (correlation vs. causation, survivorship bias, missing data). Build this once, drop it into every ICP project.

• icp-output-template.md — the exact output structure you want back. Narrative description, positive and negative fit signal tables across firmographics/technographics/demographics, fit scoring framework with point values and tier thresholds, and a plain-language summary card you can hand to a sales rep. Claude matches this format exactly instead of inventing something new each time.

• client-context.md — fill this out per engagement. Product description, current ICP hypothesis, average ACV and deal cycle, known hard disqualifiers, tech stack dependencies, any attributes the team already suspects matter, and what the output will be used for. This stops Claude from asking questions you already know the answers to and focuses the analysis on what’s actually uncertain.

Here are the prompts I’ve been using:

Prompt 1: Surface the patterns

“I’ve uploaded X CSV files representing different customer cohorts. The good cohorts are: closed-won customers, longest-standing customers, highest-paying customers, and highest expansion/upsell customers. The bad cohorts are: churned customers, shortest contract customers, lowest-paying customers, and customers with the highest support volume and/or lowest NPS scores.

Please analyze all cohorts and tell me: 1. The firmographic attributes that appear most consistently in the good cohorts but not the bad ones 2. The firmographic attributes that appear most consistently in the bad cohorts but not the good ones 3. Any technographic patterns (tech stack, tools) that differentiate good from bad 4. Any signals that appear to have been present at the time of sale for good vs. bad accounts

Weight each attribute by how strongly it differentiates good from bad cohorts. Flag any patterns where the sample size is too small to draw reliable conclusions.”

Prompt 2: Write the ICP definition

“Based on your analysis, please write a revised ICP definition that includes:
- A narrative description of our ideal customer profile
- Positive fit signals (demographic, firmographic, technographic), ranked by predictive strength
- Negative fit signals, ranked by predictive strength
- Positive behavioral and intent signals that suggest readiness to buy
- Negative signals that suggest poor fit or poor timing
- A suggested fit scoring framework with point values for each signal”

The output you’re looking for is a narrative with a structured breakout of patterns.

The reason I’d expect Claude to do this better than a human doing manual analysis isn’t speed (though it is faster). But Claude will surface cross-attribute correlations that are nearly impossible to spot manually. It’s not just “a lot of churned accounts are in healthcare.” It’s “healthcare companies that were Series B or earlier and didn’t have a dedicated RevOps function at time of sale.”

This narrative should give you a solid output, backed by data, to use as a conversation starter internally against your committee of stakeholders that care about your ICP. Typically people like Product, Sales, RevOps, Marketing, and Customer Success.

The goal is to leverage this process, the data, the documentation to gain internal alignment (which typically isn’t an easy feat).

Now operationalize it

Once you have a definition you, and your committee, trust, here’s where it needs to get wired in.

1. The CRM field

Your CRM is almost always the single system of record for your business. Which means if your ICP definition doesn’t live there, as a queryable, filterable, reportable field or set of fields, it effectively doesn’t exist operationally.

What that looks like is different for every company. Maybe it’s a single ICP Tier field with a simple picklist: Tier 1, Tier 2, Not ICP. Maybe it’s a series of fields capturing individual fit dimensions (industry fit, size fit, tech stack fit) that roll up into an overall score. There’s no single right answer. What matters is that there’s a method, it’s consistently applied, and anyone on the team can pull a report against it without exporting and shuffling things around manually.

In most companies I’ve inherited: none of that exists. The ICP is a document somewhere, and the CRM has no idea it was written.

The fix is purely mechanical. Decide on your structure, build the fields, and assign a value to every account in your database. Every downstream workflow like scoring, routing, TAL management, reporting, now has something to reference.

2. Scoring that accounts for fit, signals, and engagement

Most scoring models I inherit are 100% behavioral. Visited pricing page: +20. Downloaded a guide: +10. Hit 50 points: MQL.

The problem is that behavior without fit is noise. A VP of Operations at a 50-person out-of-ICP company who visits your pricing page four times is not a better lead than a VP of Supply Chain at a $2B target account who visited once.

Your model needs three dimensions: fit score (firmographic, technographic, demographic - built from your new ICP definition), signal score (hired a new critical role, just raised a round of funding, is slacking on their security posture, etc.) and engagement score (behavioral - 1st party signals typically from deanonymized activity on your website). Gate MQL status on a minimum fit threshold plus behavioral activity. Your MQL volume will drop. Your pipeline quality will go up.

3. Routing that reflects account value

If a Tier 1 account submits a demo request and hits the same queue, SLA, and rep assignment as a non-ICP startup, your ICP is not working hard enough for you. You defined it and then built a system that ignores it.

Tier 1 inbounds should route differently. Senior reps, faster SLA, Slack alert, different sequence. I set up a routing build at one client where Tier 1 inbounds had a 15-minute contact SLA during business hours. Strictly because their historical data showed a medium first-response time of 11 minutes for deals that closed.

4. A maintained TAL

Most target account lists get built once, saved to a shared drive, and not touched again. But a TAL is just a snapshot of your ICP in time. Companies raise funding. Headcount hits a threshold. A startup on your “watch” list announces a $40M Series B and is now squarely in your sweet spot. Which is all great…but if that TAL isn’t shifting, you’re likely not working an ICP. More likely working a snapshot.

I’d say you want at least a quarterly review cadence. Define three signals that move an account from “watch” to “active” (funding round, headcount milestone, a specific hire). Build a Clay or Apollo view that surfaces accounts hitting those signals monthly.

5. Reporting that filters by ICP fit

Most demand gen reporting: total leads, total MQLs, total pipeline, total revenue. Broken out by channel or campaign. Almost never by ICP fit.

I’ve seen campaigns that look like wins on blended pipeline numbers, where filtering for Tier 1 shows barely any ICP engagement at all. Without the filter you run it again. With the filter you kill it or completely rethink the targeting.

Add ICP Tier as a dimension in every report.

The part most people skip: keeping it dynamic

What I haven’t seen written about much, and what I think is the unlock: an ICP isn’t something you define once and operationalize. It’s an ongoing puzzle.

The market shifts. Your clients’ needs shift. Your business shifts. Any of those will change who your real ICP is and if you’re not actively maintaining the definition, you’ll drift back into the same problem you just fixed. Make this a recurring project that you prioritize.

I’m working on building a project in Claude Code that helps solve for some of the manual parts of this process. Will report back on if it stands up to the test.

Are you using AI for ICP analysis yet, or is that still on the “someday” list? Reply and tell me where you are with it. I’m genuinely curious how people are approaching this, and I’m building my own workflow in real time.

See ya next week,
Kaylee ✌

I’ve spent years inside demand gen orgs and consulting with them, and the pattern holds pretty consistently. Teams build out a content calendar, align on messaging for the quarter, get the creative approved, schedule the emails, and launch. Rinse and repeat the next quarter. Maybe they layer in a little segmentation. Maybe they have a nurture track or two.

And when pipeline is slow, they do more of it. More campaigns, more emails, more LinkedIn spend. Push the message out harder. R.A.M. (random acts of marketing) all around.

The problem isn’t that campaigns are bad. Campaigns are necessary. But it’s how most teams are running campaigns when they should also be running plays, and they’re treating those two things as if they’re the same motion.

They’re not. At least not in my mind.

👋 Hi, I’m Kaylee Edmondson. Looped In lands in your inbox every Sunday with one goal: to give you a sharper way to think about demand gen and growth in B2B SaaS. 2k+ marketers are already reading it. If you're not subscribed yet, fix that below.

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A campaign is how your brand talks to a market.

It’s your point of view, broadcast to a segment. A campaign tells a story about what you believe, what problem you solve, and why your category matters. Done well, it builds awareness, shapes perception, and warms up your total addressable market over time.

Think: a product launch, a seasonal push, an industry report rollout, a brand awareness series. These are all campaigns. They go out to a defined audience regardless of what that audience is doing right now. The message is relatively fixed. The trigger is the calendar.

A play is how you respond to a moment.

Something specific happens with a specific account or person, and you act on it. That’s the whole premise. A play exists because a signal exists. Take away the signal, and you have nothing to send. The activation (e.g. an email, an ad, a nurture, a dinner invite, etc.) is triggered by an insight you’ve gained.

A play looks like: your target account’s Head of Marketing just posted on LinkedIn about struggling with pipeline attribution. You have a relevant take on that problem. You reach out directly, referencing what they shared. I’d call that a play, not a campaign.

Or: a free trial user at one of your top 10 target accounts hit 80% of the usage threshold that typically predicts conversion. The play is triggered. Sales gets a task. A personalized email goes out.

Or: your best customer champion just started a new job at another company in your ICP. The moment they update their LinkedIn, a play fires.

These things can’t be scheduled.

A play requires a specific response to a specific moment.

That doesn’t mean it has to be manually written every time. Just that the activation should be designed around the trigger. The message, the timing, the ask, and the content all need to connect back to the trigger. If you swap out the trigger and the outreach still makes sense, you don’t really have a play.

The building blocks of a play.

Every play needs four things ideally to function:

A trigger. The specific condition that activates the play. Job change, pricing page visit, webinar attendance, competitive tool in their stack, funding announcement. No trigger, no play. This is the element most teams underdefine. “Website visitor” is too broad. “Pricing page visitor from a Tier 1 account with two or more visits in seven days” is a viable trigger.

A target. The play applies to a specific account, contact, or segment. Combined with your tier framework, this is how you control who gets what level of effort. A Tier 1 account hitting the same trigger as a Tier 3 account should get a meaningfully different play, or at least a different level of personalization within the same play.

An action. The actual thing you’re activating/launching. Could be an email, a LinkedIn message, an SDR task, a direct mail drop, a personalized landing page. The action has to be proportional to the signal. A pricing page visit from a cold account doesn’t warrant an exec-to-exec letter. A multi-visit, multi-stakeholder pattern at a named account does.

A clear connection between trigger and message. The prospect should be able to read your outreach and feel like it arrived at the right moment, even if they can’t articulate why. The message doesn’t always need to explicitly reference what they did, I personally prefer to take the “serendipitous” route. It should feel relevant to where they are right now.

The plays most teams are missing

After mapping this out with a handful of clients and building a play library over the past few months, a few categories consistently come up as gaps.

Most teams have some version of inbound signal response. If you fill out a demo request, someone follows up. Someone signs up for a webinar, they get a transactional calendar invite. Those are plays, even if teams don’t call them that.

Other, less commonly adopted, plays:

Job change plays. A former champion switches companies and lands somewhere in your ICP. This is one of the warmest possible signals you’ll ever see. They already know your product. They likely have an opinion on it. And they just stepped into a new role where they have both the mandate to make changes and the political capital to push something new through.

Proactive outreach plays with a real reason to reach out. Not “just checking in.” Not a generic sequence dressed up with a first name variable. An actual reason. Your team member is traveling to their city. They posted something on LinkedIn about a challenge you solve. Their competitor just went through a product sunset. Reason-to-reach-out plays are wildly underused.

Customer and expansion plays. The entire post-sale motion is usually absent from any plays discussion. A health score drop, an NPS promoter who hasn’t been asked for a referral, a champion who just got promoted, a usage spike that signals upgrade readiness. All of these are plays. All of them generate real revenue. Almost no demand gen team is running them systematically.

Multi-threading plays. A new decision-maker joins an account you’ve been working. A second contact is identified at a stalled deal. These moments are often caught by sales, but they rarely exist as a defined play with clear activation logic and outreach assets ready to go.

Campaigns and plays aren’t competing priorities

One thing worth being direct about: you still need campaigns.

Campaigns build the brand awareness and category credibility that make your plays land better. If someone’s never heard of you and you fire a play at them because there’s a potential partnership advantage, you’re going to get a much colder response than if you’d been building presence in their feed for the past few months.

Leverage them both. The ratio matters depending on where you are. If you’re early stage and relatively unknown, more of your energy goes into campaigns. As you build a bigger installed base, more signal data, and more brand presence in your category, plays start carrying more of the pipeline weight.

But even at the earliest stage, you should have plays. At minimum: inbound follow-up, job change for champions, and at least one proactive outreach play for your top accounts.

A plays library

I’ve been building out a reference library of plays that any B2B demand gen team can pull from, organized by signal type, account tier fit, and whether they’re core (run these regardless of stack maturity) or advanced (require specific tooling or higher personalization effort).

There are 70+ plays across five categories: 1st party signals, 2nd party signals, 3rd party signals, proactive outreach, and customer and expansion. Each one includes the specific trigger that activates it and a starting point for tier fit so you can figure out which accounts get what level of effort.

I’m sharing the full library here as a download. Start with the core plays. Get those running. Then layer in the advanced ones. It goes without saying, every company is different, use this template as a starting point, and customize based on what you know about your company.

[Plays Library]

*The goal is not necessarily to run all 76.

See ya next week,
Kaylee ✌

But another thing I’m seeing is that most teams are running the same plays off the same signals as every other company chasing their ICP. And then they wonder why the conversion rates fall flat.

I watched this play out with two clients last year. Different companies, same target personas. Both had decent signal coverage. Both were triggering outreach sequences off the same intent data. Both were getting okay-but-not-great results, and neither could figure out why.

TL;DR is that the problem was they’d never stopped to ask, “which signals matter for us, specifically?”

There’s a difference between building a signal stack and building your signal stack. This article is about the second one.

👋 Hi, I’m Kaylee Edmondson. Looped In lands in your inbox every Sunday with one goal: to give you a sharper way to think about demand gen and growth in B2B SaaS. 2k+ marketers are already reading it. If you're not subscribed yet, fix that below.

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Why Most Signal Stacks End Up Looking Identical

Everyone starts from the same “menu”.

Intent data platforms hand you a list of signals to track. Your ABM tool has recommended triggers built in. You look at what your competitors are doing and reverse-engineer their plays. Before long, you’ve got a list of 15-20 signals and a rough sense that you should be “acting on” all of them.

But the signals aren’t the edge. The edge is which signals are predictive for your product, your ICP, and your motion specifically.

If you’re showing up in communities asking, “what signals are working for everyone rn?”…you’re asking the wrong question.

Clay wrote a piece last year on what they call GTM alpha - the idea that winning teams use data others don’t have, in plays others can’t run. That framing is right. But most demand gen teams interpret that as “we need more signals” when the actual answer is almost always “we need fewer, better ones.”

Start With Your Best Customers, Not a Signal Menu

The process has to go backwards.

Before you open a signal tool or build a play, you need to understand what was actually true about the accounts that closed, stayed, and expanded. This is the work most teams skip because it feels slow. Or companies are convinced they can’t learn anything from the past. It feels like research instead of execution. But skipping it is what lands you in generic plays that everyone else is also running.

Here’s the exercise I walk clients through:

Pick 5-10 accounts you’d clone if you could. The ones where the deal moved fast, the champion was engaged, the expansion came without you having to chase it.
Write them down.

Then answer these questions for each one:

What was happening at that company in the 60-90 days before they came into your pipeline? Not just “they visited the website.” What was the business context? Were they in a growth phase? Had leadership changed? Were they mid-stack consolidation? Had they just shipped something new?

What did your sales team already know about them before the first call? What research had your AE done that gave them an edge in discovery?

If you had 10 interns researching an account before outreach, what would you have them look for? What information, if you had it, would change your message or your timing?

When you do this across 5-10 accounts, patterns emerge. They almost always do. You’ll start to see 3-5 behavioral or contextual signals that show up consistently in your best accounts. Those become your hypotheses. Everything else is noise until proven otherwise.

Creating Signal Tiering

Once you have your hypotheses, you need a way to organize them. Not all signals carry equal weight for your motion, and treating them like they do is how you end up with a 20-signal stack firing off random acts of marketing.

I use a simple three-tier framework.

Tier 1: High-conviction signals

These are specific, time-sensitive, and directly connected to a problem your product solves. They’re rare, likely for a smaller audience set, but when they fire, they mean something.

An example for a company that sells onboarding tooling: a company just hired its third Customer Success Manager in 60 days. This probably isn’t ironic timing, but is your sign the team is scaling a function that has a real, immediate problem you solve. The signal is specific. The timing matters. There’s a clear message to build around it.

Tier 1 signals are your plays. They’re what you build creative around and automate with care.

Tier 2: Supporting signals

These add context and confirm fit, but they’re not strong enough to trigger a play on their own. A Series B raise plus active SDR hiring is interesting. Layer it on top of a Tier 1 signal and it sharpens your targeting. Use it alone and you’re competing with everyone else who has the same data.

Tier 2 signals belong in your enrichment layer. They help you score and prioritize, but they don’t drive plays.

Tier 3: Noise signals

Every team has a few of these. They felt promising when you added them. You’ve been tracking them for 12-18 months. They haven’t correlated to anything meaningful. But nobody has had the conversation about cutting them because it feels like giving up.

Cut them. The cognitive overhead of managing signals that don’t convert is real, and it crowds out the space you need to think clearly about the ones that do.

Start by mapping everything you’re currently tracking into these three tiers. Most teams will find they’re heavily over-indexed on Tier 3, lightly invested in Tier 1, and confused about where Tier 2 fits.

Building Plays Around Your Tier 1 Signals

When you know which signals are predictive, play design gets cleaner.

A well-built signal-driven play has four components. Before you build anything, you need answers to all four.

The trigger: What exact condition fires the play? “Job change” is not a trigger. “New VP of Revenue Operations hired from a company with $50M+ ARR, into a company currently using a fragmented data stack” is a trigger. The more specific you can get here, the more relevant your outreach will be. Specificity is not over-engineering. Specificity is respect for your prospect’s time.

The context layer: What do you need to know about this account before you reach out? What enrichment should happen automatically before the sequence fires? Think about what a great AE would research before a cold call. Some of that can be automated now. Build it in.

The message: What is the one thing you want to communicate based on this signal? One. If you’re trying to say three things in your first touch, you’re not clear on why the signal matters. Go back and sharpen it.

The timing window: When does this signal stop being relevant? Most signals have a 2-4 week window before the context shifts. A new hire settles in. A compliance event gets handled. If you’re not building timing into your plays, you’re leaving a lot of relevance on the table.

A quick example from a previous client. They sell to mid-market HR teams. Their signal stack was pulling job change data on HR leaders and triggering generic sequences. Response rates were mid at best.

We went back through 12 months of closed-won deals and found that the accounts that moved fastest had one thing in common: the HR leader had been promoted into the role internally, rather than hired externally. Internal promotions meant they were inheriting a tech stack they didn’t choose and were actively evaluating what to keep. Turns out this was a Tier 1 signal. It was specific, time-sensitive, and directly tied to a buying moment.

They rebuilt the play around that one signal. The message became more specific/resonate. The timing changed. Results improved.

Maintaining Signal Hygiene Over Time

Signals decay. Honestly probably faster than we’re even estimating.

A play that worked six months ago may be producing diminishing returns now because the market shifted, because prospects have gotten wise to the trigger, or because three of your competitors started running the same sequence off the same data. This is the reality of signal-based marketing. Nothing stays alpha forever.

Quarterly signal audits, at minimum, are how you can stay ahead of decay. This doesn’t have to be a big process. Here’s what to do:

An easy way to do this is to organize signals to campaigns. Then pull down your campaign data to see which signals are correlating to pipeline and closed won. If a signals has been active with plays running against it for 90+ days and you can’t draw a line to revenue, this has likely become a Tier 3 signal that needs to be demoted or cut entirely.

Review anything you’re tracking but not acting on. Either build something around it or stop tracking it.

Talk to your sales team every quarter about what patterns they’re seeing in discovery. New signals show up there first. An AE who’s done 30 discovery calls in the last 90 days knows things about your buyers that no intent tool can surface.

And treat your signal stack the way you’d treat your tech stack. Regular pruning. Add new things intentionally. Question anything that’s been there a long time without proving itself.

One thing’s for sure, the signal-base marketing noise is only going to get louder. Every team is getting access to more data, better tooling, and faster workflows. The advantage is not having more signals, but in knowing which 2-3 signals are yours to own, and building plays that nobody else can replicate because nobody else did the work to find them.

See ya next week,
Kaylee ✌