The Marketing Millennials Ep. 376

The Marketing Metrics Everyone Misunderstands with Emily Popson, VP of Marketing at CallRail

Host: Daniel Murray
Guest: Emily Popson
Date: December 19, 2025

Episode Overview

In this no-nonsense deep dive, Daniel Murray chats with Emily Popson (VP of Marketing, CallRail) about the marketing metrics that are most misunderstood—and most unfairly maligned—on LinkedIn and in the wider B2B marketing world. Emily takes on the “war on MQLs,” challenges the idea that attribution is “garbage,” and advocates for smarter, AI-driven use of conversational and first-party data. This episode is packed with myth-busting, practical frameworks, and honest talk about the real challenges and nuances of modern marketing measurement.

Key Discussion Points and Insights

1. Emily’s Background and CallRail’s Mission

• [01:24] Emily shares her accidental entry into marketing via a SaaS startup after declining law school and her passion for democratizing lead intelligence for all businesses.
• Emily: “We provide lead intelligence ... so that any marketer can market with confidence.”
• Fun anecdote: Daniel’s wife is also a law school dropout-turned-marketer.

2. Myth #1: The “Death” of MQLs (Marketing Qualified Leads)

• [02:45–09:48]
• Emily pushes back on the anti-MQL trend on LinkedIn:
  • “For the last five years, there has been this war on these three little letters, and it's been so curious to me to watch... [it] causes such unnecessary confusion.” (05:00)
  • MQLs themselves aren’t the problem—it’s how you define and use them.
  • Historical misuse: Low-quality lead scoring led to sales frustration and eroded confidence.
  • “MQLs are nothing but a delivery system for what should be a core quality revenue opportunity.” (05:45)
  • It’s about redefining to fit your true ICP and using MQLs as a directional metric rather than the only goal.
  • Her team no longer sends MQLs to sales; instead, MQLs inform in-marketing journey steps.
• Daniel adds: “It's easy for a marketer to game an MQL... I think the problem is usually there's no alignment internally of what an MQL is.” (08:41)

3. Myth #2: Attribution Is Dead

• [10:59–24:49]
• Emily tackles the claim that “attribution is garbage”:
  • “Attribution is the act of attributing your efforts to revenue, to units, to growth. There's never ever... going to come a time when marketers don't have to answer... is what you're doing... delivering revenue growth?” (11:13)
  • Problems with overreliance on first/last touch and in-platform metrics.
  • Her advice: Bring together software data (GA4/Marketo/Bizible/CallRail), self-reported attribution (“How did you hear about us?”), and conversation intelligence.
  • Use AI to uncover patterns and common buying journeys; aggregate, triangulate, and automate insights.
  • Emily: “Attribution is entering a bit of a renaissance. It's just time to approach it differently, starting with collecting more data.” (12:53)
  • Cautions against single-channel, one-to-one dollar mapping.
  • Daniel shares operational tips: Manual audits, flagging conflicting influx sources between branded search and what prospects say.
  • E-commerce leads on incrementality testing—B2B can learn from their event-based, cross-channel evaluation mindset.

4. Practical Frameworks for Attribution and Channel Analysis

• [21:24–26:35]
• Emily’s stack: Session data, W-shaped Bizible attribution, influence reporting (frequency at which channels appear across winning deals), self-reported data, and experimenting with triangulation.
• Demand generation team is accountable for mapping data to business outcomes and making recommendations before the C-suite.
• Mac (Marketing Acquisition Cost) by channel, blended CAC, conversion rates—the fundamentals stay but the analysis gets smarter.
• Quote: “You can't, the old way stopped working five years ago where you're just relying on the output of a single set of attribution data.” (23:39)

5. The Power of Reporting, Dashboards, and Internal Narrative

• [25:28–27:02]
• Internal (marketing) dashboards vs. external-facing reports: Only surface what helps decision-making, not every data point.
• Emphasis on the importance of marketing being seen as a revenue driver, not a cost center.
• Emily: “You want to be seen as an investment center that delivers revenue growth. And you need to be able to do these things to maintain that standing...” (26:35)

6. Conversational Intelligence and First-Party Data

• [27:02–36:38]
• Myth: Conversation intelligence tools are only for sales.
• Emily reframes: Marketers should obsess over call transcripts, AI-powered insights, conversion signals, sentiment, and personalization.
  • Examples:
    • Conversion signals: Set AI to listen for readiness-to-buy in calls and feed signals to ad platforms.
    • Segmentation/personalization: Track sentiment, personalize follow-ups (scrub angry prospects from nurture lists, send recovery gifts).
    • Keyword/offer research: Extract trending pain points, product keywords, and seasonal opportunities from call word clouds.
    • Offer Testing: Directly surface what customers want (“do you have a Small Business Saturday offer?”).
    • Missed call analysis: Calls are high-intent—marketers should care deeply about missed leads and use AI “SDRs” to optimize response.
• Emily: “When people really need you or really want to do business with you, they do pick up the phone and dial. So if you are not answering every single one of those calls, you're ... missing opportunities to influence results.” (32:06)
• With AI, ingest transcripts in minutes and prompt for positioning, attribution sources, buyer intent, etc.
• Daniel: “Now with AI, it should be super easy to fix those problems.” (35:53)

7. Mindset: Trust (and Question) Your Own Data and Context

• [36:38–38:14]
• Emily’s closing advice: Marketers need confident judgment in tuning out LinkedIn hype and making their own calls for their own businesses.
  • “Don't live and die by what LinkedIn influencers are telling you.” (36:52)
• Daniel echoes: “When we say something like ‘you should do this’, always I would say test it. But don't, like, take our word at 100%... use it as a source point of nutrition.” (37:31)

Notable Quotes & Memorable Moments

• Emily: “The MQL is not worthless. Your definition of it is.” (05:56)
• Daniel: “It's easy for a marketer to game an MQL ... that's where the problem started happening.” (08:27)
• Emily: “Attribution is entering a bit of a renaissance… Bring in more data sets to really understand what our buying journeys look like.” (12:41)
• Daniel: “We used to have a Friday audit on attribution ... we would go through like: what did you hear? How did you hear about us compared to ... and if there was any conflicts we would make the changes.” (15:55)
• Emily: “You want to be seen as an investment center that delivers revenue growth.” (26:39)
• Emily: “Marketers need to care just as much, if not more, about what's happening with these [conversational intelligence] tools … you could be using these tools to extract unique details that help make your emails more personalized and more engaging...” (28:17)
• Daniel: “Now with AI, it should be super easy to fix those problems.” (35:53)

Important Timestamps

• 01:24 – Emily’s background and journey into marketing
• 02:45 – Debunking the MQL myth
• 10:59 – “Attribution is garbage”: why this is misleading
• 21:24 – Emily’s data and attribution stack in practice
• 27:23 – Why conversation intelligence isn’t just for sales
• 32:06 – High-intent leads and missed calls: Why marketers must care
• 36:38 – Final advice: “Trust your gut”
• 38:20 – Marketing hill to die on: “Attribution’s not garbage”

The Marketing Hill Emily Will Die On

Emily: “Attribution's not garbage and I want marketers to remain in the boardroom. And you're never gonna stop for the rest of your career having to prove your value and prove your impact. So evolve your attribution, don't ditch it.” (38:26)

Where to Find Emily Popson and CallRail

• LinkedIn: Emily Popson
• Website: CallRail.com
• Social: @callrail on all platforms

TL;DR

Emily and Daniel bust common B2B marketing myths:

• MQLs aren’t dead, but your definition might be.
• Attribution isn’t garbage—it’s evolving and demands triangulation.
• Stop ignoring conversation intelligence and missed calls; marketers can lead with AI-powered data.
• Ignore LinkedIn wars—do what works in your own nuanced reality, but root it in evidence and experiment.