DemandLoops is embedded in six B2B SaaS companies right now. Different industries, different stages, different tech stacks. And we keep running into the same problem at every single one of them.
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.
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 ✌