.png){kind=small}
Most revenue teams are sitting on a mountain of data and still making decisions on gut feel.
Your CRM tracks activities. Your conversation intelligence tool records meetings. Your enrichment provider pulls firmographic data. Your intent data vendor flags "surging" accounts. And your reps? They ignore most of it because none of it tells them what to actually do.
This is the gap that RevOps was supposed to close. But most RevOps teams spend their time maintaining systems, not building intelligence from them. The data exists. The connections between that data do not.
STACK is a framework we built to solve this. It takes five layers of revenue data, runs them through your existing sales methodology, and produces a single actionable score for every deal in your pipeline. We call that score the Signal Quotient, or SQ.
This article explains what STACK is, how it works, and why we believe signal stacking is the most valuable thing RevOps can do for a revenue team.
STACK stands for five data layers that, when combined, give you a complete picture of every deal in your pipeline:
S - Signals
External buying signals that indicate intent, timing, or fit. Job postings, funding rounds, technology changes, leadership moves, competitor evaluations, LinkedIn engagement patterns. These are the signals that exist outside your CRM but directly affect whether a deal will close.
T - Transcripts
Meeting recordings and call transcripts from tools like Gong, Fathom, or Chorus. Not just the fact that a meeting happened, but what was actually said. Who attended. What objections were raised. What commitments were made. What was promised and what was dodged.
A - Activities
CRM activity data. Emails sent and received, meetings booked, tasks completed, deal stage changes, property updates. The behavioural fingerprint of how a deal is actually progressing versus how a rep says it is progressing.
C - CRM
The structured data sitting in your CRM. Deal amounts, close dates, associated contacts, company properties, lifecycle stages, custom fields. This is your source of truth for pipeline state, but on its own it only tells you what has been entered, not what is actually happening.
K - Knowledge
Institutional and contextual knowledge. Your ICP definition, historical win/loss patterns, average sales cycle length by segment, seasonal trends, competitor positioning. The knowledge that experienced reps carry in their heads but never makes it into a system.
Each layer on its own is limited. CRM data is only as good as what reps enter. Transcripts are rich but unstructured. Signals are noisy without context. Activities show motion but not direction. Knowledge is powerful but usually locked in someone's head.
Stack them together and you get something fundamentally different: a layered, evidence-based view of every deal in your pipeline.
The Signal Quotient is the output of the STACK process. It is a single score, applied at deal level, that quantifies how likely a deal is to close based on evidence from all five data layers.
Think of it like a credit score for your pipeline. A credit score does not tell you one thing about a person. It synthesises hundreds of data points into a single number that predicts behaviour. The SQ does the same for deals.
Here is how it works at a high level.
Each of the five STACK layers generates data points that map to your chosen sales methodology. If you run MEDDIC, those data points map to Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, and Champion. If you run SPICED, they map to Situation, Pain, Impact, Critical Event, and Decision.
The framework scores each methodology criterion based on evidence, not rep self-assessment. Did the transcript show the economic buyer was on the call? That is evidence. Did the rep tick a checkbox saying they identified the economic buyer? That is opinion. STACK prioritises evidence over opinion, every time.
The SQ is then calculated as a weighted composite across all criteria. Deals with strong evidence across multiple layers score high. Deals where the CRM says everything is fine but the transcript tells a different story score low. Deals where external signals suggest urgency but internal activity has gone quiet get flagged.
The result is a number that tells you three things: how healthy this deal actually is, where the gaps are, and what the rep should do next.
The revenue intelligence market is not short on tools. There are platforms for conversation intelligence, intent data, CRM analytics, and pipeline forecasting. The problem is not a lack of data. It is a lack of synthesis.
Most teams run these tools in parallel. The conversation intelligence platform has its own dashboard. The intent data goes into a separate report. CRM analytics live in yet another tool. Nobody is combining them into a single, deal-level view.
This is like trying to diagnose a patient by looking at their blood pressure, heart rate, and X-rays in three different hospitals. Each data point is useful. None of them is sufficient. The diagnosis comes from layering them together.
Signal stacking is the discipline of combining data from multiple sources, at the deal level, to create intelligence that no single source can produce on its own. It is not a technology play. It is an operational discipline. And it is, in our view, the single highest-leverage activity a RevOps team can perform.
Here is why.
It replaces rep opinion with evidence. Every sales leader has experienced the deal that was "90% likely to close" right up until it wasn't. The SQ does not care what the rep thinks. It cares what the data shows. When there is a gap between rep confidence and signal evidence, you catch it early.
It makes your sales methodology actually work. Most companies adopt MEDDIC or SPICED and then rely on reps to self-assess. That is like giving someone a health checklist and asking them to grade themselves. STACK automates the assessment by pulling evidence from transcripts, activities, and signals. The methodology stops being a training exercise and starts being an operating system.
It compounds over time. Every deal you run through STACK adds to your Knowledge layer. You learn which signals predict closed-won deals in your market. You learn which transcript patterns indicate a deal is stalling. You learn which activity patterns separate high-performing reps from the rest. The framework gets smarter the longer you use it.
It gives RevOps a strategic seat. When RevOps can tell a CRO "your pipeline SQ dropped 12 points this quarter, here is exactly why, and here are the three deals that need immediate attention," that is a different conversation than "here is your pipeline report." STACK turns RevOps from a reporting function into an intelligence function.
Running STACK is not a one-time audit. It is a continuous process that layers into your existing revenue operations. Here is what it looks like.
Phase 1: Connect the layers. You start by connecting the five data sources. CRM data and activity data typically come from HubSpot, Salesforce, or whatever you are running. Transcripts come from your conversation intelligence tool. Signals come from enrichment and intent data providers, or from custom monitoring using tools like Clay. Knowledge is codified from your team's historical patterns and ICP definition.
Phase 2: Map to your methodology. Each data point from each layer gets mapped to a criterion in your sales methodology. A transcript mention of budget maps to MEDDIC's Metrics. A signal showing a competitor evaluation maps to Decision Criteria. A CRM activity gap maps to Champion engagement. This mapping is where the framework becomes specific to your business.
Phase 3: Score and surface. The SQ is calculated for every active deal and surfaced where your reps already work. That might be in Slack, in the CRM, or in a dedicated dashboard. The score is not a vanity metric. Each SQ comes with a breakdown showing exactly which criteria are strong, which are weak, and what specific action would improve the score.
Phase 4: Act and learn. Reps take action based on the SQ recommendations. Did the score say no economic buyer has been identified? The next step is clear. Did the score flag that external signals show the prospect evaluating a competitor? The rep knows to address it. And every outcome, whether the deal closes or not, feeds back into the Knowledge layer to improve future scoring.
STACK is not for every company. It works best in environments where the sales cycle is complex enough that multiple data sources matter, and where there is enough pipeline volume that manual deal review cannot keep up.
If you are a B2B company with an average deal cycle longer than 30 days, a sales team of five or more reps, and at least a basic RevOps function, you are in the right territory. If you are already running a sales methodology like MEDDIC, SPICED, or BANT but feel like it is not being applied consistently, STACK is specifically designed to solve that problem.
If you are an early-stage company with three deals in the pipeline and a founder doing all the selling, you do not need a framework. You need conversations.
This article is the starting point. We are building out a full series that goes deeper on each element of STACK and the Signal Quotient:
GTM Layer builds scalable revenue systems for B2B companies. We specialise in revenue architecture, CRM operations, and signal-driven intelligence that turns data into action. The STACK Framework and Signal Quotient (SQ) are proprietary methodologies developed by GTM Layer.
If you want to understand what your pipeline's SQ looks like, get in touch.
%20(1).jpeg)