GTM Live – Breaking Open the Pipeline Black Box in GTM

Air Date: September 1, 2025
Hosts: Carolyn Dilks (A), Trevor Gibson (C), Amber Williams (B) of Passetto

Episode Overview

This episode explores the pervasive "pipeline black box" in B2B SaaS go-to-market (GTM) organizations: Why most teams operate without true visibility into what drives pipeline and revenue, why standard metrics like MQLs and new AI tools fail to solve the core issues, and how to rebuild systems and culture for granular, actionable revenue insights. The hosts candidly cut through common misconceptions, highlight operational and strategic pitfalls, and outline what leaders need to do to turn revenue into a "science" rather than an art based on gut feel or vanity metrics.

Key Discussion Points & Insights

1. Introducing Amber Williams & Setting the Stage [01:39]

• Amber Williams joins as Head of Revenue Operations; her expertise: uncovering visibility problems in RevOps and GTM.
• “More tech in 2025 does not make things easier for go-to-market teams.” – Amber [01:54]
• The team sets out the aim to break the status quo on flawed metrics and get practical about real revenue clarity.

2. The Pipeline Black Box & The 80/20 Visibility Gap [05:23]

• Most companies see only about 20% of what’s happening in their pipeline; 80% remains invisible and contains the true levers for growth.
• Teams overly trust tools and gut instinct, not data. Strategic pivots often happen without measuring actual causes.
• “Most companies...think that they have revenue visibility, but they're actually missing 80% of what is driving conversions.” – Amber [05:49]

Key Concepts Discussed:

• Revenue as a Science: Building a repeatable, measurable system for pipeline creation and conversion.
• Instrumentation vs. Old Data Architecture: New approach needed, focusing on event-level tracking and stitching touchpoints at person and account levels.

3. The Status Quo is Broken: Why Old Metrics Don’t Cut It [07:26]

• Standard GTM metrics (MQL/SQL, website traffic) still dominate, but are insufficient and often self-serving.
• Tools (like attribution software or sales automation) typically only “tell their own story”—not the holistic journey.
• “It can't be only ‘we got this number of leads.’ It has to be ‘we had a meaningful impact on revenue.’” – Trevor [18:45]

Notable Critiques:

• MQL volume is an especially poor proxy for pipeline, barely correlates to intent or revenue.
• Disconnected data creates finger-pointing between departments, with execs lacking real insight.

4. Measurement & Reporting: Volume ≠ Clarity [12:21, 14:39]

• Simplistic volume numbers (meetings, MQLs) give false comfort, but mask what actually converts.
• “The operational load of maintaining the status quo is crushing teams. Reporting is table stakes, but not strategic.” – Amber [12:21]
• True measures of efficiency and conversion are buried by operational noise and duct-taped systems.

5. The Silo Problem: Why Most Attribution Fails [21:17]

• Execs often believe tools like HubSpot or Gong “should” provide complete answers. In reality, data is siloed with no person- or account-level stitching.
• “There's no thread that ties everything together. CEOs want to know, ‘What happened with that $1,000 lead?’... but just get a volume report.” – Amber [24:09]
• The root visibility must follow individuals—then roll up to companies—across all engagement points.

6. Tech Stack Sprawl & Homegrown Fixes—Why Layering Fails [32:13]

• Layering new tech on legacy process just adds complexity and bias; rarely breaks the bad habits or surfaces what really matters.
• Organizational inertia and sunk-cost bias prevent real evolution until crisis forces a total rethink.
• “Companies are pretty complacent... until it’s a crisis. That’s when you have the luxury to finally make the changes.” – Trevor [37:27]

7. The AI Myth: Technology Isn’t a Silver Bullet [39:24]

• AI promises to “surface what matters,” but if the underlying data is poor, so are the insights.
• “AI isn’t going to solve garbage in, garbage out. You’ll just get a sophisticated answer that’s still wrong.” – Trevor [39:53]
• The real fix is to build granular, reliable, person-centric data foundations—then layer AI–not vice versa.
• “AI is medication for a symptom. It doesn’t fix the underlying problem. You’ve got to go deeper.” – Carolyn [42:57]

8. Building Revenue Science: Strategic & Operational Action [45:46]

• True revenue science requires viewing GTM as a full system (“factory”), with every part measured for impact and efficiency.
• Strategic actions require clarity on where your organization sits on a revenue visibility maturity scale—quantified, not anecdotal.
• “Build momentum by surfacing your gaps in quantifiable fashion, not with a big vague ask to ‘fix everything.’” – Carolyn [47:21]

9. Practical Application: What To Do If You Take Over a Broken Pipeline? [49:00]

Q: “If you went in-house today as head of GTM, marketing, or RevOps, what’s first?”

Amber [49:00]:

• “Unpack the true cost of the status quo... Say no to legacy reporting, let things break, and focus on what helps grow revenue.”
• Urges operators to prioritize important over urgent, and quantify the operational drag of maintenance.

Trevor [50:40]:

• "Don't just put out the loudest fires. Step back, map the moving parts, and plan for visibility gaps—then decide what's urgent and impactful."
• Fill your knowledge gaps first—identify where you cannot track or explain performance.

Carolyn [52:41]:

• “Make all campaigns and activities consistently trackable (e.g., rigorous UTM usage). Simultaneously, build a close relationship with finance to understand actual ROI on pipeline, not just volume.”
• The data’s not just for marketing—it’s the foundation for CFO and CEO significance.

Trevor’s Add (on Finance) [53:56]:

• “Finance is often left out or only receives surface metrics—their integration is essential for full-funnel efficiency analysis.”

Notable Quotes & Memorable Moments

• “So much of the status quo measurements are very simplistic volume measurements... Just the sheer fact of a volume number doesn't tell you a whole lot.” – Trevor [14:39]
• “MQLs are not a good indicator of pipeline. We're measuring just names—people who may never be in market.” – Carolyn [15:35]
• “Organizational change is hard... Even with new tools, you end up dragging the new thing into the mud of the old way.” – Trevor [32:13]
• “AI is not a silver bullet—it's really, really risky to bolt on AI when your underlying data's broken. It's just a sophisticated band-aid.” – Amber [44:39]
• “Let things break. See what breaks. Only fix what drives outcomes, not just legacy reporting.” – Amber [49:00]
• “You shouldn’t launch a campaign until you know how you’ll measure it. If you didn’t think about it up front, you won’t be able to after the fact.” – Trevor [54:41]
• “You might throw less paint at the wall, but every experiment you run is pointed and you can speak to what happened” – Amber [55:43]
• “Be scientific to the best of your ability—the scientific method is a thing and you can apply it here.” – Trevor [55:57]

Timestamps for Key Segments

• [01:39] Amber Williams introduction + revenue visibility problems
• [05:23] Pipeline black box & 80/20 theory outlined
• [07:26] The broken status quo of GTM measurement
• [12:21] Operational load & disconnect between exec strategy and data
• [15:35] Why MQLs are an especially bad metric
• [21:17] The silo problem – Why HubSpot/Gong/Marketo aren’t enough
• [24:09] CEOs want story, not vanity metrics
• [32:13] Tech layering/homegrown fixes multiply headaches
• [37:27] Why true change only happens in a crisis
• [39:53] AI is not a silver bullet without good data
• [49:00] What to fix first: Panel shares priorities for in-house turnarounds
• [54:41] Building a culture of measurement before execution
• [55:57] Applying the “scientific method” to GTM

Summary Takeaways

• Most GTM teams operate in a data fog—tracking vanity metrics, lacking person-level attribution, and relying on self-serving tool reports.
• Volume metrics and superficial AI solutions are not the answer; instead, rebuild your data foundation for holistic, cause-and-effect clarity.
• The real fix is operational as much as technical—prioritize what drives revenue, not legacy reporting or “the loudest fire.”
• Practical first steps: map where your visibility gaps are, stand up rigorous campaign-level tracking, and tie marketing ops closely to finance and true ROI.

Resources Mentioned

• Passetto Revenue Visibility Diagnostic: Entry-level service to pinpoint exactly where your data gaps are and what needs fixing first. (“If that's something you're interested in, definitely reach out…” – Amber [56:17])

If you are a CEO, CFO, or revenue leader ready to move beyond GTM guesswork and vanity dashboards, this episode delivers a candid roadmap to rebuilding true revenue clarity—no silver bullets, just modern, actionable steps.