Revenue Vitals

→ WHAT IT COVERS Jon Miller, Marketo co-founder and former Demandbase CMO, explains why the MQL-based demand generation playbook he helped build in the early 2000s no longer reflects how B2B buying actually works, and outlines a replacement measurement framework centered on brand health, account journeys, and post-sale engagement. → KEY INSIGHTS - **Brand as measurement baseline:** Before tracking pipeline, define the single perception you want your ICP to hold about your company, then measure...

→ WHAT IT COVERS Posetto's Amber and Carolyn debrief their four-day Above the Fold conference in Fort Lauderdale, covering AI-powered product marketing tools, SEO strategy shifts driven by AI-generated content risks, attribution model limitations, brand investment ROI, and how internal politics shape a CMO's measurement priorities. → KEY INSIGHTS - **AI-Powered Messaging Compliance:** Mojo PMM (by Eric Holland) uses AI to enforce brand messaging standards across sales, marketing, and CS teams.

→ WHAT IT COVERS Matthew Sciannella, VP of Innovation at Refine Labs, joins Revenue Vitals to break down why B2B attribution models fail, how brand spend should be measured against contribution margin rather than sourced revenue, and why strong product marketing — not channel execution — is the common thread across every high-performing demand generation program.

→ WHAT IT COVERS Matt Green, CRO of Sales Assembly, explains why attribution wars between sales and marketing stem from competing scorecards rather than people problems. He advocates for shared revenue accountability, discusses buyer-centric forecasting methods, and reveals why small in-person events outperform scaled digital outreach in building pipeline during 2026.

→ WHAT IT COVERS Carolyn and Amber address listener questions about defending modern measurement models against legacy metrics like MQLs, tracking pipeline influences beyond last touch attribution, gaining executive buy-in for new frameworks, and navigating organizational resistance when leadership demands familiar volume metrics despite their proven ineffectiveness in current B2B environments.

→ WHAT IT COVERS Stage four of revenue transformation focuses on architecting a new GTM measurement system that replaces department silos with journey-focused analytics, multidimensional tracking, and unified metrics across engagement, prospecting, and closing stages. → KEY INSIGHTS - **Remove Department Silos:** Replace marketing versus sales pipeline tracking with journey analytics that measure how both teams work together across the full buyer lifecycle, tracking what marketing signals and...

→ WHAT IT COVERS A $25M enterprise SaaS company discovers 80% of their pipeline lacks visibility into origin and influence, contributing to declining win rates of 3-5% and revealing $3.5M in recoverable revenue through data analysis. → KEY INSIGHTS - **Pipeline Visibility Gap:** 80% of opportunities in a two-year period had no tracked prospecting trigger, making it impossible to identify what channels or activities initiated deals, forcing decisions based on gut feel rather than data-driven...

→ WHAT IT COVERS Stage three of revenue transformation occurs when marketing leaders realize their data model is fundamentally broken. No amount of hard work fixes a system that cannot connect activities to outcomes or prove marketing's revenue impact. → KEY INSIGHTS - **The GTM Triangle:** Three forces keep teams stuck: activity-based rewards instead of outcome metrics, perceived risk of overhauling systems, and urgency culture that prioritizes quick fixes over systematic change.

→ WHAT IT COVERS Stage two of revenue transformation addresses the QBR fire drill problem where marketing leaders struggle to connect activities to pipeline impact, face credibility risks, and spend days manually assembling unreliable data from disconnected systems. → KEY INSIGHTS - **Contact-to-Deal Architecture:** Contacts must be tied to opportunities in CRM to measure marketing impact.

→ WHAT IT COVERS Paceto analyzes a $500M cybersecurity SaaS company's full funnel data in 14 days, revealing that product trials generate 55% of pipeline but convert at only 5% win rate versus hand raisers at 10%. → KEY INSIGHTS - **Product trial economics:** Product trials drove 55% of prospecting volume but converted at 5% win rate with $2-3K ACV, while hand raisers converted at 10% win rate with $10K ACV, delivering twice the revenue yield per opportunity created.