| name | channel-and-campaign-measurement |
| description | Guidance for measuring marketing channel performance and campaign ROI across paid, organic, social, email, events, content, and community — including attribution approaches, metric selection, and stakeholder reporting. Trigger when a user asks how to measure, track, attribute, or report on any marketing channel or campaign type. |
| version | 2026-04-21 |
| episode_count | 63 |
Channel and Campaign Measurement
Overview
This skill covers how B2B marketers should measure, attribute, and report on marketing channel performance and campaign ROI. It addresses metric selection, attribution methodology, stakeholder communication, and channel-specific benchmarks across paid media, organic social, email, events, content, community, influencer programs, SEO, and more. All practices are sourced exclusively from Exit Five podcast guests; no general marketing knowledge has been added. Where guests disagree, both positions are presented in full.
Email Measurement
Primary Metrics
- Do not rely on open rates or click-through rates as primary success metrics for outbound or campaign email. Open rates are increasingly skewed by security bots, email filters, and Apple Mail Privacy Protection. (Source: Alex Fine, Episode #256; Sara McNamara, Episode #256; Beth O'Malley, Episode #241; Jay Schewedelson, Episode #241; Alyssa, Episode #312) (Note: this is contested — see Where Experts Disagree)
- Use reply rate as the primary performance metric for outbound and sales email. A declining reply rate (e.g., from 7–8% down to 5%, 3%, 1%) is a reliable indicator that emails are no longer landing in the primary inbox. (Source: Alex Fine, Episode #256)
- Monitor unsubscribe rate and unsubscribe reasons as primary deliverability indicators. Implement an unsubscribe reason form to gather qualitative data on why recipients are leaving. (Source: Sara McNamara, Episode #256)
- Track email replies as a high-value engagement metric. Replies signal genuine engagement and positive brand sentiment to inbox providers like Gmail. Monitor reply rates and consider replying yourself to build stronger relationships. (Source: Jaina Mistry, Episode #241)
- Move beyond opens and clicks to measure email's actual business impact. Track whether email drove pipeline, influenced a deal, increased brand awareness, or increased search volume. (Source: Beth O'Malley, Episode #241)
Defining What Each Email Is Supposed to Do
- Define the specific job each email campaign is meant to do, then measure it against that objective. A nurture email might be measured by engagement; a closing email should be measured by conversions or demo bookings. Opens and clicks are supporting metrics, not leading KPIs, unless they directly contribute to the campaign's stated goal. (Source: Joe, Episode #312)
- Segment email performance metrics by initiative type, not by "email" as a whole. Differentiate between top-of-funnel content (checklist signup), mid-funnel content (case study download), and bottom-funnel content (demo request). Each has different benchmarks. Reporting on "email" as a single channel masks actual performance. (Source: Jay Schewedelson, Episode #241)
Detecting Measurement Distortion
- Send test emails at 2 AM and isolate the first 30 minutes of clicks to measure bot inflation. Most clicks in that window are bot activity, not real users. Discount your overall click metrics by this bot percentage. (Source: Jay Schewedelson, Episode #241)
- Use inflated clicks on the "view in browser" link as an indicator of bot activity. If you see a 60% click-through rate on that link while other links are much lower, bots are likely inflating your click metrics. (Source: Jaina Mistry, Episode #241)
- Identify IT firewall security scanning by monitoring for recipients who open and click every email consistently. This pattern indicates an organization's IT security team is auto-opening all emails before releasing them to the inbox, which can delay delivery by up to 4 hours and inflate metrics. (Source: Beth O'Malley, Episode #241)
Deliverability Monitoring
- Check your ESP's built-in health monitoring tool (e.g., HubSpot's email health pane) to identify deliverability problems early. These tools benchmark performance against industry standards and flag technical misconfigurations faster than manual analysis. (Source: Sara McNamara, Episode #256)
- Monitor open rate trends over time as an early warning signal for deliverability problems — not as a success metric. A dramatic decline in open rates for regularly-sent newsletters is a strong indicator that deliverability issues are occurring. Monitor by inbox provider and cadence. (Source: Jaina Mistry, Episode #241) (Note: this is contested — see Where Experts Disagree)
- Monitor multiple metrics holistically, not single metrics in isolation. A change that improves one metric (e.g., consolidating links improves deliverability) might harm another (reduces clicks). Evaluate the full subscriber experience before declaring a change successful. (Source: Sara McNamara, Episode #256)
Engagement Depth and Segmentation
- Track structured clicks by content topic to build micro-segments, not just maximize click rate. Design emails with distinct content sections and tag everyone who clicks each section. Use these structured clicks to build micro-segments for future targeted campaigns. (Source: Jay Schewedelson, Episode #241)
- Monitor inactive and lurker engagement cohorts to identify hidden value. Use a 90-day engagement window to identify healthy segments. When you stop sending to a segment, measure whether usage or conversions drop afterward. Test re-engagement campaigns before removing inactive users. (Source: Alyssa, Episode #312)
- Move beyond vanity metrics and focus on engagement narratives and contact frequency. Dig into: How many contacts did this person receive before they converted? What's the engagement narrative over time? What do unsubscribe patterns reveal about frequency and timing? (Source: Alyssa, Episode #312)
Resend Strategy
- Resend emails to non-openers with a modified subject line indicating the resend (e.g., "Oops, you missed it"). This can generate an additional 25% of unique opens on newsletter sends. Be transparent about the resend rather than disguising it. (Source: Jay Schewedelson, Episode #241)
LinkedIn Measurement
Brand Awareness and Engagement Metrics
- Use dwell time as the primary engagement metric for LinkedIn brand awareness campaigns. LinkedIn's average CTR is 0.4%, making clicks a poor engagement metric. For company pages, aim for 3+ seconds average dwell time. For thought leader ads, target 5–6 seconds. Dwell time works across all formats. (Source: Anthony Blatner, Episode #243)
- Set up multiple conversion types: form submissions, page views, and high-intent pages. Track (1) any page view, (2) high-intent pages like pricing or demo pages, and (3) form submissions. This creates a funnel view showing how many people from LinkedIn reach your site and how many convert. (Source: Anthony Blatner, Episode #243)
- Benchmark your LinkedIn engagement against competitors using LinkedIn's built-in competitor reports. Compare engagement metrics (comments, shares, reactions) against named competitors to quantify relative share of voice. (Source: Anthony Blatner, Episode #243)
- Request a Share of Feed report from your LinkedIn rep to measure competitive share of voice. This shows your share of impressions versus competitors to a specific audience. (Source: Anthony Blatner, Episode #243)
- Run LinkedIn brand lift studies for campaigns with $20k+ monthly spend. These Nielsen-backed studies measure brand lift and product consideration through surveys and provide third-party validation of campaign impact. (Source: Anthony Blatner, Episode #243)
Revenue and Pipeline Attribution
- Set up LinkedIn's Revenue Attribution Report by connecting Salesforce to Business Manager. This free built-in tool shows which campaigns influenced deals in your pipeline. (Source: Anthony Blatner, Episode #243)
- Use self-reported attribution to measure LinkedIn's impact on pipeline. Ask prospects directly in discovery calls, surveys, or CRM notes: "How did you hear about us?" LinkedIn often shows up in self-reported attribution even when absent from click-based attribution. (Source: Dave Gerhardt, Episode #317)
- Correlate LinkedIn organic impressions to demo bookings to measure organic social impact. Create a spreadsheet plotting weekly demo bookings against LinkedIn organic impressions over a 52-week period. Map both metrics on the same chart to identify correlation patterns. Present this to finance as evidence of channel effectiveness. (Source: Pranav Piyush, Episode #191)
- Measure LinkedIn founder brand success by tracking inbound lead volume and deal metrics, not just vanity metrics. Track the percentage of inbound leads that reference LinkedIn, and monitor deal metrics for LinkedIn-sourced leads: conversion rate, deal cycle length, and deal size. (Source: Kait Stephens, Episode #156)
Audience and Targeting Validation
- Upload target account lists to LinkedIn to measure impression coverage. Use LinkedIn's Companies Report feature to monitor how many impressions you're serving to specific target companies. This validates whether your targeting is reaching your intended audience before worrying about conversions. (Source: Anthony Blatner, Episode #243)
- Monitor frequency metric to validate budget sizing for your audience. If frequency is 1–2, you're not getting enough impressions per person — shrink your audience or increase budget. If frequency jumps to 20+ within days, you're overspending on a small audience — lower your budget. (Source: Anthony Blatner, Episode #243)
- Manually measure LinkedIn ad effectiveness using target account list matching and VLOOKUP. Upload your target account list into LinkedIn ads, download engagement data, and use VLOOKUP in Google Sheets to match engagement against your target accounts. (Source: Taylor Udell, Episode #190)
- Optimize for comment quality and ICP relevance rather than raw impressions. Track which posts attract comments from your actual ICP versus broader audiences. A post with lower impressions but comments from decision-makers is more valuable than a post with high impressions from tangential audiences. (Source: Adam Robinson, Episode #157)
LinkedIn Content Performance Framework
- Track LinkedIn content performance using this metrics framework: (1) Post publishes — benchmark 3/week; (2) Impressions — benchmark 10–15% lift; (3) Content downloads from gated content in the featured section; (4) Website traffic from organic; (5) MQLs from gated content; (6) Self-attributed pipeline — track mentions of the executive's name in self-reported attribution. Review metrics weekly for wins, every two weeks for performance trends, and monthly for deep dives. (Source: Devin Reed, Episode #196)
- Track both social metrics (leading indicators) and business metrics (lagging indicators) to measure LinkedIn success. Social metrics (follower growth, impressions, ICP follower quality) should trend upward week-over-week. Business metrics (inbound demos, sales calls, content mentions in sales conversations) may take 1–3 months to materialize. (Source: Tommy Clark, Episode #171) (Note: this is contested — see Where Experts Disagree)
Budget Calculation
- Calculate LinkedIn ad budget by working backward from audience size and frequency goals. Identify your target audience size, decide how many times you want to reach each person (typically 5–10 times per month), and multiply by expected CPM. Example: 10,000-person audience × 5–10 impressions × $100 CPM = required budget. (Source: Tim Davidson, Episode #127)
- Set different CPL benchmarks by channel; accept higher CPL on LinkedIn than Google. LinkedIn typically supports higher CPLs ($300–500+) because targeting is more precise and impacts full-funnel awareness. Google should generally stay in the $100–300 range. Don't apply the same CPL target across all channels. (Source: Kym Parker, Episode #201)
Social Media Measurement (General)
Organic Social
- Measure organic social performance by engagement quality and ICP audience reach, not vanity metrics. Use platform analytics to verify content is reaching your target ICP by job title, company size, and location. Test different formats and cadences and compare engagement and ICP reach month-over-month. Avoid relying on total follower counts. (Source: Pranav Piyush, Episode #239)
- Analyze comment quality and commenter profile, not just like counts. Read comments and assess who is commenting — is it your ICP? Are they customers? A post with 500 likes and 30 comments from target customers is more valuable than 500 likes and 30 generic comments. (Source: Dave Gerhardt, Episode #139)
- Prioritize audience quality and business relevance over social media growth metrics. High engagement from the wrong audience will not translate to product adoption or revenue. Favor depth and niche relevance over top-of-funnel reach. (Source: Dave Gerhardt, Episode #133)
- Use organic content performance data to inform paid media strategy. Track which organic content pieces get the most engagement and comments. Feed these validated messages into paid media campaigns rather than guessing what will work. (Source: Dave Gerhardt, Episode #134)
- Use organic content performance to identify and validate high-interest topics. When a post outperforms others, you've identified a topic with genuine audience interest. Use this signal to decide what to write about next, what to feature in newsletters, and what to build content series around. (Source: Dave Gerhardt, Episode #134)
Social Media Time Horizons
- Allocate 6+ months before expecting attribution or results from social strategy. Most companies abandon social strategies too early. Budget conservatively (e.g., $7–8K/month initially) so leadership can tolerate the ramp-up period. Communicate that the buying cycle is 200+ days, so social's role is building awareness and preference over time. (Source: Chris Cunningham, Episode #347) (Note: this is contested — see Where Experts Disagree)
- Commit to 6–12 months of consistent LinkedIn posting before evaluating ROI. Posting for 3 weeks and seeing no direct sales is not a valid test. The channel builds trust through osmosis — people see your content repeatedly and eventually convert. Measure success through indirect signals like inbound partnership requests and podcast invitations, not just direct sales. (Source: Dave Gerhardt, Episode #171) (Note: this is contested — see Where Experts Disagree)
- Measure social media impact by asking sales reps how prospects found you. Ask closed customers how they first discovered your company. Track how many mention social media and which specific videos or accounts they reference. This qualitative approach reveals social's impact on awareness without requiring complex attribution infrastructure. (Source: Chris Cunningham, Episode #347)
Instagram
- Set realistic, outcome-focused Instagram goals tied to business capacity. Define goals around actual business outcomes (brand awareness, relationship building, lead generation) rather than vanity metrics like follower counts. Align follower targets with your business capacity. For B2B, focus on building long-term loyalists through dark social (comments, DMs, story replies). (Source: Jenn Herman, Episode #168)
- Establish a 6–8 week testing period with consistent posting to measure Instagram performance. Track reach as the primary success metric. Identify positive outliers (posts with 2–3x baseline reach) and negative outliers (posts with 40% of baseline) to understand what resonates. (Source: Jenn Herman, Episode #168)
Content Measurement
Attribution Approaches
(Note: there is genuine disagreement among guests about the right approach to content attribution — see Where Experts Disagree)
- Measure content campaigns using time-based attribution instead of direct last-click. For example: "This content campaign ran for 8 weeks and generated X signups." This approach works better for brand and awareness content where attribution is unclear. (Source: Dave Gerhardt, Episodes #172 and #262)
- Measure content influence using quality held meetings (QHM) as a conversion metric. Track content engagement through event tracking and cookie tracking, then measure whether users book and actually attend a meeting. Use this to create attribution reports showing how much pipeline content influenced, regardless of conversion channel. (Source: Chelsea Castle, Episodes #262 and #172)
- Aggregate and measure qualitative content feedback as a primary metric. Track comments on LinkedIn/YouTube, DMs from readers, and mentions of how someone applied your content to solve a problem. Collect these signals in a centralized place. Five comments from your ideal customer saying "this helped me close a deal" is more meaningful than 1,000 views from random traffic. (Source: Chelsea Castle, Episode #172)
- Correlate podcast listens to demo bookings to measure podcast impact. Plot weekly podcast listens against weekly demo bookings on a scatter chart. If listens and demos move together consistently, you have evidence of correlation. (Source: Pranav Piyush, Episode #191)
Content Quality and SEO Metrics
- Calculate the monetary value of your organic traffic by determining what it would cost to acquire the same traffic through Google Ads. Multiply estimated cost-per-click for your organic keywords by traffic volume to derive total traffic value. Use this to present a concrete ROI figure to finance stakeholders. (Source: Andrei Țiț, Episode #269)
- Calculate organic share of voice to benchmark brand visibility against competitors. Compare your organic keyword share and traffic against 3–4 direct competitors. Track this metric over time to identify if competitors are gaining ground. A declining share of voice indicates algorithm changes or competitive pressure. (Source: Andrei Țiț, Episode #269)
- Analyze branded vs. non-branded traffic to identify SEO gaps. Filter out branded search volume from total organic traffic to understand the true value of non-branded keyword rankings. If branded traffic represents the majority of your organic value, you are not yet ranking for high-value commercial and transactional keywords. (Source: Ross Simmonds, Episode #224)
- Benchmark demo-led content conversion rates against traditional blog content. Track and compare signup conversion rates between demo-led pages and traditional blog articles to justify resource allocation toward higher-converting content formats. (Source: Madhav Bhandari, Episode #183)
Podcast ROI
- Measure podcast ROI by relationship depth and brand awareness, not direct attribution. Measure success by: (1) download growth and audience size, (2) qualitative feedback from listeners, (3) brand awareness lift (people recognizing you at events), and (4) depth of connection with audience. Accept that podcast ROI is long-term and relationship-based. (Source: Erin May, Episode #337)
Short vs. Long Sales Cycle Content Measurement
- In businesses with short sales cycles (days to weeks), measure content effectiveness rapidly by tracking traffic, email signups, sales conversations initiated, and product signups. Leverage your short cycle to validate content strategy faster. (Source: Erin May, Episode #337)
AI Search and AEO Measurement
- Before committing resources to AI search optimization, determine whether your site is already receiving meaningful traffic from AI search sources. Use analytics tools to track referral traffic from ChatGPT, Perplexity, Google AI Overviews, and other LLMs. If AI traffic is negligible, focus on foundational brand-building first. (Source: Andrei Țiț, Episode #269)
- Evaluate AI search traffic by conversion quality and lead intent, not click volume. AI search generates fewer clicks but higher-quality leads. Track the conversion rate and signup rate of AI search traffic separately. Even if AI traffic represents 0.5% of total referrals, it may drive a disproportionately high percentage of conversions (e.g., 12% of signups). (Source: Andrei Țiț, Episode #269)
- Recognize that AEO traffic volume is currently small but high-intent; optimize for conversion rate rather than traffic volume. For EagleView, AEO traffic was 0.1% of total traffic but converted at 5% vs. lower rates on other channels. (Source: Marcy Comer, Episode #324)
- Select one AEO tracking tool and standardize across the team to avoid conflicting metrics. Different tools track different metrics and can produce conflicting results. Pick one tool, run a free trial to validate it fits your needs, and standardize reporting around it. (Source: Clare Schmitt, Episode #324)
- Use LLM visibility tools (such as Ahrefs Brand Radar) to track how often your brand is mentioned in AI-generated summaries across ChatGPT, Perplexity, Google AI Overviews, and other LLMs. Monitor mentions, impressions, competitive share, and competitive reach. Identify visibility gaps by finding keywords that trigger AI summaries containing competitor mentions but not your brand. (Source: Andrei Țiț, Episode #269)
Paid Media Measurement
Google Ads
- Optimize paid ads (especially Google Ads) by continuously filtering out low-quality leads and training the platform on high-quality signals. Feed the platform clear signals about what constitutes a good lead (MQL, SQL, or customer-like profiles) and assign higher bid values to those signals. This process takes 2–3 years to optimize but becomes ROI-positive. (Source: Michael Cole, Episode #212)
Out-of-Home and TV
- Measure out-of-home campaign impact by tracking lift in performance metrics during and after campaign periods. Track website visits, demo requests, opportunities created, and paid campaign performance during the 4–8 weeks when out-of-home campaigns are running and compare to baseline periods. Early campaigns showed 30–50% lift across performance metrics. For TV specifically, measure analytics lift within a 15-minute window after a spot airs. (Source: Amrita Gurney, Episode #287)
Return Visitor Lift
- Track return visitor percentage as a signal of campaign effectiveness. A baseline might be 20% return visitors; after launching foundational campaigns, this could increase to 25%. This incremental lift indicates campaigns are driving awareness and repeat touchpoints, which is critical in B2B where buyers need multiple exposures before becoming an MQL. (Source: Tas Bober, Episode #154)
Influencer and Creator Measurement
Attribution Approach
(Note: there is broad agreement to avoid UTM links in influencer posts — see Where Experts Disagree for the tactical nuance between time-series and baseline uplift methods)
- Do not put UTM parameters on influencer posts. Platforms like LinkedIn deprioritize posts containing external links, killing organic reach. Influencers also won't use them reliably. (Source: Dave Gerhardt, Episode #163; Kevin White, Episodes #286 and #179; Domi de Saint-Exupéry, Episode #332)
- Use time-series attribution to measure influencer campaign impact. Compare signup and demo request volume during the campaign period against baseline periods before and after. Supplement with self-reported attribution on your site (ask users how they heard about you). (Source: Kevin White, Episodes #286 and #179)
- Measure influencer impact using signup baseline uplift. Compare signup baseline before and after a post goes live. Apply your standard conversion rates (free trial to paid) and LTV to estimate revenue impact. This avoids false precision while allowing you to compare influencers and identify underperformers. (Source: Domi de Saint-Exupéry, Episode #332)
- Measure influencer campaign impact through directional signals rather than perfect attribution. Track signup spikes on launch day, monitor engagement metrics (likes, comments) on the creator's post, use self-reported attribution in your signup form, and correlate timing of posts with traffic patterns. (Source: Natalie Taylor, Episode #162)
- Measure influencer marketing by awareness and consideration, not click-through rates. B2B buyers see content, remember it, and later search for or reach out to the company. Measure by brand awareness lift, consideration, and how many people mention the influencer's content when they do convert. (Source: Bruno Estrella, Episode #180)
Defining Success Before You Start
- Define success metrics and KPIs before engaging any creators. Align internally on primary and secondary KPIs. Typical B2B metrics include: Executive/C-suite (pipeline generation, category association); Sales (pipeline generation); Product (trials, freemium signups); CS (account retention, expansion). Pick one primary and one secondary metric. (Source: Brianna Doe, Episode #305)
- Treat influencers as audience builders, not salespeople. Measure influencer success on their contribution (impressions, engagement, audience reach) and then measure conversion separately through your own funnel. (Source: Brianna Doe, Episode #305)
Creator Selection
- Prioritize creator engagement rates over follower count when selecting influencers. Analyze engagement metrics (comments, likes, shares) rather than follower count. A creator with 2,000 highly engaged followers may generate as much engagement as someone with 10,000 disengaged followers. (Source: Natalie Taylor, Episode #163)
ABM Measurement
- Measure ABM differently than traditional demand gen; expect different ROI profiles. ABM campaigns will look inefficient on traditional marketing metrics (cost per impression, CTR) because you're limiting audience size and buying expensive impressions for small, targeted groups. Prepare finance and stakeholders upfront. Focus on account-level metrics (progression through engagement stages, conversion rates, deal value). (Source: Drew Pinta, Episode #331)
- Measure ABM success across the full funnel, not just meetings booked. Track qualified opportunities, conversion rates, and deal value. Use multi-touch attribution and deal story analysis (e.g., LLM-based review of all account activity in Salesforce, Gong, Slack) to understand what marketing activities influenced deal progression. (Source: Brian Kotlyar, Episode #331)
- Track account coverage percentage as a key metric to ensure adequate reach in your ABM strategy. Calculate what percentage of your target account list you reach in a given month through paid media. If coverage is below 30–35%, you don't have an effective ABM strategy. Use this metric to justify diversifying away from single-channel spending. (Source: Richard Meyer, Episode #341)
- Target 8+ touches per ABM contact every few months as an engagement benchmark. For ABM accounts, aim to reach each key contact approximately 8 times every few months through various channels. This frequency does not show diminishing returns in enterprise ABM. (Source: Drew Pinta, Episode #331)
- Prove ABM ROI by combining easy-to-attribute tactics with harder-to-measure brand plays. Use easy-to-attribute tactics (CPL-based lead gen, webinar signups, event attendance) alongside brand-building efforts. Make friends with your RevOps person to connect marketing activities in your CRM to closed revenue. Set expectations that ABM is a long-term play (6–18 months) and measure progress through intermediate signals like response rates and meeting bookings. (Source: Chris Rack, Episode #150)
- Use LinkedIn's Company Intelligence tool to measure account-level impact beyond individual conversions. When connected to CRM sync and Conversions API, it shows exponentially more influence and conversions than last-click attribution alone, because it captures the full buying committee's exposure to your brand across organic posts, ads, and website interactions. (Source: Davang Shah, Episode #338)
Event Measurement
Aligning Metrics to Event Type
(Note: there is genuine disagreement about the primary metric for event ROI — see Where Experts Disagree)
- Distinguish between brand-building and demand-generation events; measure each by different success criteria. Brand events should be measured by awareness, pitch testing, and prospect engagement rather than immediate lead generation. Demand events should focus on meeting bookings and pipeline. (Source: Holly Xiao, Episode #270)
- Align event metrics to the funnel stage the event targets. Top-of-funnel events (trade shows): measure pipeline generation and new leads. Middle-of-funnel events (roadshows): measure bookings and renewal rates. Bottom-of-funnel events (customer events): measure product adoption, NPS, and expansion. (Source: Kristina DeBrito, Episode #227)
- Use source attribution for top-of-funnel events and influenced attribution for middle/bottom-funnel events. For trade shows, measure source attribution — the event is the first touch. For roadshows and customer events, measure influenced attribution — the event influenced the deal but wasn't necessarily the first touch. (Source: Kristina DeBrito, Episode #227)
Revenue and Pipeline Metrics
- Measure event ROI by calculating the total prospect revenue and retention revenue represented by attendees in the room. Mix prospect and existing customer attendance intentionally — happy customers sell prospects on value. Track this metric across events to demonstrate cumulative impact. (Source: Sydney Sloan, Episode #289)
- Measure event ROI across three pillars: revenue, target account penetration, and retention/expansion. Track: (1) new revenue and pipeline generated, (2) penetration of target accounts (getting the right people in the room), and (3) retention and expansion metrics (NRR, renewal rates, customer health). (Source: Stephanie Christensen, Episode #227)
- Define primary growth metrics and secondary brand metrics for each event. Primary metric: usually revenue-related (pipeline, bookings, NRR). Secondary metrics: brand awareness, NPS, social mentions, press coverage. Stay focused on the primary metric while tracking secondary metrics. (Source: Stephanie Christensen, Episode #227)
- Track and communicate the correlation between event attendance and annual sales quota achievement. Measure and share data showing how many accounts need to attend your event for a sales rep to hit their annual quota. This ties event attendance directly to rep compensation. (Source: Stephanie Christensen, Episode #227)
- Measure event ROI over medium to long-term influence, not immediate pipeline. Recognize that events deliver pipeline over 2–3 quarters, not immediately. Establish a portfolio approach and ensure strong SDR follow-up processes post-event to capture leads. Document wins that trace back to past events to validate long-term ROI. (Source: Jason Lyman, Episode #263)
Brand and Customer Event Metrics
- Measure brand-focused events through site traffic, social mentions, press coverage, and NPS. For events where the primary goal is brand awareness, measure secondary metrics like website traffic post-event, social media mentions, press coverage, app usage, and NPS score changes. (Source: Stephanie Christensen, Episode #227)
- Measure customer event impact through product adoption, health scores, NPS, and renewal rates. For customer-focused events, measure success through customer-centric metrics: product adoption rates, health score improvements, NPS changes, renewal rates, and expansion revenue. (Source: Kristina DeBrito, Episode #227)
- Measure event quality using NPS, not just ticket sales. Send an NPS survey immediately after the event to quantify attendee satisfaction. Treat the event like any other product — measure it on NPS alongside other metrics. (Source: Dave Gerhardt, Episode #294)
Real-Time Event Tracking
- Integrate ticket sales data into Slack in real-time to monitor demand and attendee fit. Use tools like Zuttle and Zapier to automatically send ticket purchase notifications to a Slack channel, including attendee details (company, role, revenue tier, stated reasons for attending). This gives real-time visibility into ICP fit and enables you to tailor event content based on actual attendee profiles. (Source: Dave Gerhardt, Episode #147)
Community Measurement
(Note: there is genuine disagreement about whether to measure community by engagement activity or by membership retention and access — see Where Experts Disagree)
Engagement-Based Metrics
- Measure community as a full business unit with acquisition, conversion, and retention metrics. Track: new trial signups per month, trial-to-paid conversion rate, churn rate, monthly active users (MAU) percentage, post volume, comment volume, and post-to-comment ratio. (Source: Matthew Carnevale, Episode #213)
- Target 40% monthly active users as a top-tier community engagement benchmark. Aim for at least 40% of community members to have meaningful engagement (posting, commenting, attending events) at least once per month. (Source: Matthew Carnevale, Episode #213)
- Track post-to-comment ratio as a leading indicator of community engagement health. Monitor the average number of comments per post each month. Track this ratio month-over-month; an increasing ratio signals improving engagement quality. (Source: Matthew Carnevale, Episode #213)
- Track the direct relationship between engagement and churn to justify engagement investment. Monitor your community's churn rate over time and correlate it with engagement metrics. As engagement increases, churn should decrease. (Source: Matt Carnevale, Episode #233)
- Use NPS as a primary community health metric. Conduct NPS surveys twice per year as a formal checkpoint, and maintain continuous feedback loops through daily community interactions, member messages, and churn exit surveys. (Source: Dave Gerhardt, Episode #307)
Access-Based Metrics
- Shift community KPI from engagement to access; optimize for yearly retention, not daily activity. Measure membership success by whether members retain their membership year-over-year and use the resource when they need it. A member who logs in once every 60 days but finds critical resources is more valuable than a daily commenter who derives no lasting value. (Source: Greg Isenberg, Episode #146) (Note: this is contested — see Where Experts Disagree)
Community Analytics for Content Strategy
- Use community platform analytics to identify popular topics and inform content strategy. Regularly review your community platform's analytics (e.g., Circle's built-in analytics) to see which posts and topics generate the most engagement. Use these insights to inform external content strategy and community programming. (Source: Matt Carnevale, Episode #233)
- Use subscriber count as the primary leading indicator for audience-building success. Track email opt-ins as the core metric for audience growth. Supplement with engagement metrics: exclusive content consumption, referral activity, and social amplification. (Source: Anthony Kennada, Episode #145)
Landing Page and Website Measurement
- Track bounce rate, scroll depth, time on page, and navigation patterns alongside conversion metrics. Monitor bounce rate as an indicator of message-channel mismatch, scroll depth and time spent to understand user behavior flow, and navigation patterns to identify what content is actually resonating. (Source: Lee Reshef, Episode #220)
- Understand that homepage traffic is typically generic and low-intent; optimize for clarity over specificity. Homepage traffic usually comes from organic search, direct traffic, or PR/partnerships. Visitors have unclear or low intent. Save specific, persona-targeted messaging for dedicated landing pages. (Source: Lee Reshef, Episode #220)
- Measure landing page success through engagement signals, not just conversions. Track scroll depth, time on page, interaction with specific content blocks, tab clicks on value proposition sections, and heatmap/session recording data. High engagement without immediate conversion is a positive signal in B2B. (Source: Tas Bober, Episode #154)
- When landing pages don't generate on-page conversions, track alternative engagement signals. Track: (1) scroll depth, (2) FAQ clicks, (3) return to main website, and (4) overall campaign lift via incrementality testing. Use heat mapping tools (Hotjar, Crazy Egg, Microsoft Clarity) to identify which sections get the most engagement. Do not kill campaigns based on low on-page conversion rates alone. (Source: Tas Bober, Episode #185)
- Implement two essential tools to optimize landing pages: a heat mapping tool and a web analytics tool. Heat mapping is free up to a certain threshold (Hotjar offers 2,000 free sessions). Focus on heat map data to identify which sections get the most engagement and which objections users are trying to resolve. (Source: Tas Bober, Episode #185)
- Build an ungated content hub and use your own analytics product to track engagement and attribute it to pipeline. Do not gate content behind email forms. Use analytics/attribution product to track engagement (time spent, videos watched) and match that engagement to known prospects via email or CRM data. Measure lift: compare conversion rates for users who spend 5+ minutes on the hub vs. those who don't. (Source: Emir Atli, Episode #165)
Interactive Demo Measurement
- Measure interactive demo impact by comparing sales cycle length, not just demo usage. Track whether opportunities that used an interactive demo have a shorter sales cycle compared to those that didn't. (Source: Natalie Marcotullio, Episodes #176 and #122)
- Track ungated demo engagement at account level using IP and cookie tracking. Even when demos are ungated, track which accounts viewed them using IP address and cookie-based tracking. This provides intent signals for retargeting and ABM. (Source: Natalie Marcotullio, Episode #122)
- Ungated demos drive 40–60% visitor engagement vs. 10–15% for videos. Data shows 40–60% of website visitors will engage with ungated interactive demos, and 30–40% will complete the entire demo, compared to 10–15% for product videos and 2% for free trials/demo signups. (Source: Natalie Marcotullio, Episode #122)
Webinar Measurement
- Track multiple audience signals to understand webinar attendee journey and inform personalized follow-up. Use: (1) person-level enrichment tools (Apollo, RocketReach) to understand who is attending; (2) ad exposure tracking (HockeyStack, Demandbase) to see if someone has been receiving your ads; (3) CRM data (HubSpot) to track historical engagement. Combine these signals to understand each attendee's position in their journey. (Source: Eoin Clancy, Episode #326)
- Set realistic expectations for live webinar attendance. Expect approximately 20–25% of webinar registrants to attend live. Accept this as normal consumption behavior and optimize for making recorded content easily accessible to the remaining 75–80% of registrants. Focus on measuring total content consumption rather than live attendance. (Source: Dave Gerhardt, Episode #137)
PR Measurement
- Create a homegrown PR metrics framework with feature pieces, meaty mentions, and quarterly targets. Track: (1) feature pieces (articles primarily about your company); (2) meaty mentions (paragraph or two with a quote); (3) general mentions. Set quarterly targets based on business activities and planned announcements. Distinguish between earned coverage you generated versus inbound coverage driven by external events. (Source: Priscilla Barolo, Episodes #302 and #193)
- Develop custom PR metrics that reflect your actual business priorities rather than accepting generic media metrics. Be skeptical of inflated media counts during major news cycles (e.g., pandemic coverage) that don't reflect your team's work. (Source: Priscilla Barolo, Episode #193)
Partnership Measurement
- Measure partnership success across multiple dimensions, not just direct pipeline. Measure: (1) partner attach rate (revenue from customers who use both products), (2) brand lift and awareness, (3) content engagement and reach, (4) event attendance and lead quality, and (5) long-term customer retention. This prevents partnerships from being treated as a direct-response channel. (Source: Jared Fuller, Episode #174)
Referral Program Measurement
- Keep referral program measurement simple: use a single CRM field or spreadsheet rather than complex tracking platforms. Maintain a simple dedicated field in HubSpot or a basic spreadsheet to track referral source and deal velocity. This reduces friction for sales teams while still capturing the data needed to identify top referral sources. Start simple before investing in specialized referral tracking tools. (Source: Sandra Rand, Episode #265)
Where Experts Disagree
1. Is open rate a useful primary metric for email performance?
Support summary: 5 vs 1
Position A — Open rates are unreliable; use alternatives as primary metrics:
Five guests argue that open rates are increasingly unreliable due to bot inflation, security scanning, and Apple Mail Privacy Protection, and should be deprioritized as primary metrics:
- Alex Fine (Episode #256) recommends reply rate as the only meaningful metric for outbound email. A declining reply rate (from 7–8% down to 5%, 3%, 1%) reliably indicates emails are no longer landing in the primary inbox.
- Sara McNamara (Episode #256) recommends monitoring unsubscribe rate and unsubscribe reasons as primary deliverability indicators, and using ESP health pane tools rather than manually analyzing open rates.
- Beth O'Malley (Episode #241) advocates moving beyond opens and clicks to measure email's actual business impact on pipeline, deal influence, and brand awareness.
- Jay Schewedelson (Episode #241) demonstrated that bots inflate click metrics significantly (the 2 AM send test shows most early clicks are bots) and recommends segmenting by initiative type rather than relying on aggregate open/click rates.
- Alyssa (Episode #312) recommends moving beyond open rates and click-through rates to focus on engagement narratives, contact frequency, and unsubscribe patterns.
Position B — Open rate is useful as a trend signal for deliverability:
One guest argues that while open rates alone are not a reliable success metric, they remain valuable as an early warning system:
- Jaina Mistry (Episode #241) argues that a dramatic decline in open rates over time — especially for regularly-sent newsletters — is a strong indicator that deliverability issues are occurring. She recommends monitoring open rates by inbox provider and cadence as an early warning system, not as a success metric.
Context dependency: Partially context-dependent. The "avoid open rates" position applies most strongly to outbound sales email and campaign performance measurement. The "open rate as trend signal" position applies specifically to newsletter/broadcast email deliverability monitoring. For newsletters, both positions can coexist — you can monitor open rate trends for deliverability signals while not using open rate as a success KPI.
Recommendation for users: Do not use open rate as a primary success metric for any email type. For outbound email, use reply rate. For broadcast/newsletter email, use unsubscribe rate and business impact metrics as primary KPIs, while monitoring open rate trends as a secondary deliverability signal only.
2. Should you use UTM links in influencer/creator social media posts?
Support summary: 4 vs 1 (though all agree to avoid UTMs; the split is on measurement method)
All guests agree: do not use UTM links in influencer social posts. The reasons are consistent — platforms deprioritize posts with external links (killing organic reach), and influencers won't use them reliably.
The tactical disagreement is between two alternative measurement approaches:
Position A — Time-series attribution:
- Dave Gerhardt (Episode #163) recommends comparing signup volume during the campaign period against baseline periods, using custom vanity URLs or landing pages if trackable links are needed.
- Kevin White (Episodes #286 and #179) recommends time-series analysis (compare signup/demo request volume during campaign vs. baseline) supplemented by self-reported attribution on your site.
- Natalie Taylor (Episode #162) recommends measuring through directional signals: signup spikes on launch day, engagement metrics on the creator's post, self-reported attribution, and timing correlation with traffic patterns.
Position B — Baseline uplift model:
- Domi de Saint-Exupéry (Episode #332) recommends comparing signup baseline before and after a post goes live, then applying standard conversion rates and LTV to estimate revenue impact. This allows you to compare influencers and identify underperformers.
Context dependency: These approaches are not mutually exclusive and can be used together. The baseline uplift model is a specific implementation of time-series attribution. The genuine disagreement is narrow — it's about whether to apply LTV/conversion rate modeling to the uplift (Domi's approach) or to use raw volume correlation (Kevin White's approach).
3. Should community success be measured by daily engagement activity or by membership retention and access?
Support summary: 3 vs 1
Position A — Measure engagement activity:
Three guests argue community health should be measured through active engagement metrics:
- Matthew Carnevale (Episode #213) tracks community as a full business unit with MAU percentage, post volume, comment volume, and post-to-comment ratio. Targets 40% MAU as a top-tier benchmark.
- Matt Carnevale (Episode #233) tracks the direct relationship between engagement metrics and churn rate. Exit Five reduced churn by at least 0.5% per month as engagement increased.
- Dave Gerhardt (Episode #307) uses NPS as primary community health metric, with continuous feedback loops through daily community interactions.
Position B — Measure access and retention:
One guest argues against optimizing for daily engagement:
- Greg Isenberg (Episode #146) argues that chasing daily engagement metrics becomes unsustainable as communities grow and leads to optimizing for viral posts rather than member value. He recommends measuring yearly retention and access as the primary KPI — whether members retain their membership year-over-year and use the resource when they need it. A member who logs in once every 60 days but finds critical resources is more valuable than a daily commenter who derives no lasting value.
Context dependency: Greg Isenberg's position may apply more to large, mature communities where daily engagement optimization becomes unsustainable, while the engagement-focused approach may be more appropriate for growing communities where building activity is the primary challenge. However, both are presented as general principles, not stage-specific guidance.
Trend note: None identified.
4. What is the primary metric for measuring event ROI?
Support summary: 5 vs 1
Position A — Revenue and pipeline are the primary metrics:
Five guests argue event ROI should be measured primarily through revenue-related metrics:
- Sydney Sloan (Episode #289) measures event ROI by calculating total prospect revenue and retention revenue represented by attendees in the room, tracking this across events to demonstrate cumulative impact.
- Stephanie Christensen (Episode #227) recommends measuring events across three pillars: new revenue/pipeline, target account penetration, and retention/expansion. Defines primary metric as revenue-related and secondary metrics as brand/NPS. Also tracks correlation between event attendance and annual sales quota achievement.
- Kristina DeBrito (Episode #227) aligns event metrics to funnel stage: TOFU events measured on pipeline/new leads, MOFU on bookings/renewal rates, BOFU on product adoption/NPS/expansion.
- Jason Lyman (Episode #263) recommends measuring event ROI over 2–3 quarters with a portfolio approach, emphasizing strong SDR follow-up to capture pipeline.
- Stephanie Christensen (Episode #227) defines primary metric as revenue-related (pipeline, bookings, NRR) with secondary metrics as brand awareness, NPS, social mentions.
Position B — Community and relationships are the primary ROI:
One guest argues the real ROI of in-person events is harder to measure than pipeline:
- Dave Gerhardt (Episode #294) argues the real ROI of in-person events is the community and relationships built — connections that compound over time and drive word-of-mouth growth. Recommends NPS surveys immediately after events to quantify attendee satisfaction, treating the event like a product measured on NPS.
Context dependency: Partially context-dependent. Dave Gerhardt's position applies specifically to community-building events (like Exit Five DRIVE), while the revenue-focused positions apply to demand generation and customer events. Holly Xiao's practice (Episode #270) also supports distinguishing event types by purpose. The disagreement partially dissolves when you account for event type, but there is still a genuine philosophical difference about what events are fundamentally for.
Recommendation for users: Clarify the event's primary purpose before selecting metrics. For demand generation and customer events, use revenue-related primary metrics. For community-building events, NPS and relationship depth may be more appropriate primary metrics, with revenue as a secondary, longer-term signal.
5. Should content ROI be measured through direct attribution or time-based/qualitative methods?
Support summary: 3 vs 2 vs 1
Position A — Time-based attribution (3 supporters):
- Dave Gerhardt (Episodes #172 and #262) advocates measuring content initiatives over weeks and months rather than individual pieces with direct attribution. Compares it to billboard measurement — you can't measure one billboard's exact impact but can measure a campaign over time.
- Pranav Piyush (Episode #191) uses correlation analysis (plotting weekly content metrics against weekly demo bookings on scatter charts) as a five-minute alternative to complex attribution.
Position B — Pipeline influence attribution (2 supporters):
- Chelsea Castle (Episodes #262 and #172) uses event tracking and cookie tracking to identify content consumption, then measures whether users book and attend meetings (quality held meetings / QHM). Creates attribution reports showing pipeline content influenced, regardless of conversion channel.
Position C — Qualitative feedback as primary metric (1 supporter):
- Chelsea Castle (Episode #172) also recommends collecting qualitative signals (LinkedIn/YouTube comments, DMs, requests for follow-up content) in a centralized place. Argues five comments from ideal customers saying "this helped me close a deal" is more meaningful than 1,000 views from random traffic.
Context dependency: Partially context-dependent. Time-based attribution works best for brand/awareness content with long sales cycles. Pipeline influence attribution works better for mid-funnel content where tracking infrastructure exists. Qualitative feedback is most relevant for thought leadership content. However, the core disagreement about whether to invest in technical attribution infrastructure vs. simpler time-based methods is genuine regardless of context.
Trend note: None identified.
6. How long should you wait before evaluating ROI from a social media strategy?
Support summary: 2 vs 1
Position A — Commit 6+ months before evaluating ROI:
- Chris Cunningham (Episode #347) recommends allocating 6+ months before expecting attribution or results. ClickUp took 3 months before seeing significant views and 4–5 months before clear pipeline attribution emerged. Budget conservatively so leadership can tolerate the ramp-up.
- Dave Gerhardt (Episode #171) recommends committing to 6–12 months of consistent LinkedIn posting before evaluating whether the channel works. Posting for 3 weeks and seeing no direct sales is not a valid test.
Position B — Track leading indicators from week one:
- Tommy Clark (Episode #171) recommends tracking social metrics (follower growth, impressions, ICP follower quality) as leading indicators from week one to validate the strategy is working before waiting for lagging business outcomes. Business metrics (demos, sales) may take 1–3 months to materialize, but social metrics should trend upward week-over-week if the strategy is directionally correct.
Context dependency: Partially context-dependent. Tommy Clark's position is compatible with a long-term commitment but argues you don't have to wait months to know if the strategy is directionally correct. The disagreement is real but narrow — it's about intermediate checkpoints, not the overall time horizon.
Recommendation for users: Commit to 6–12 months before making a final go/no-go decision on social. However, track leading indicators (follower growth, impressions, ICP follower quality) from week one to validate the strategy is directionally correct and to give leadership intermediate signals of progress.
What NOT To Do
- Do not use open rates or click-through rates as primary success metrics for email campaigns. They are increasingly unreliable due to bot inflation, security scanning, and Apple Mail Privacy Protection. (Source: Alex Fine, Sara McNamara, Beth O'Malley, Jay Schewedelson, Alyssa — Episodes #256, #241, #312)
- Do not measure all email initiatives with the same metrics. Reporting on "email" as a single channel masks the actual performance of different initiative types and leads to incorrect conclusions. (Source: Jay Schewedelson, Episode #241)
- Do not require creators to use UTM links in their social media posts. Social platforms deprioritize posts containing links, killing organic reach from the start. (Source: Dave Gerhardt, Episode #163; Kevin White, Episodes #286 and #179; Domi de Saint-Exupéry, Episode #332)
- Do not abandon social strategies after 3 weeks because you see no direct sales. That is not a valid test. The channel builds trust through osmosis over time. (Source: Dave Gerhardt, Episode #171)
- Do not measure ABM campaigns using traditional demand gen metrics (cost per impression, CTR). ABM will look inefficient on these metrics by design. (Source: Drew Pinta, Episode #331)
- Do not measure events solely on immediate pipeline. Events deliver pipeline over 2–3 quarters, not immediately. (Source: Jason Lyman, Episode #263)
- Do not measure influencer marketing by CTR or immediate conversions. B2B buyers don't typically click on ads or sponsored content when they see it. (Source: Bruno Estrella, Episode #180)
- Do not measure all events by the same success criteria. Brand events and demand events serve different purposes and require different metrics. (Source: Holly Xiao, Episode #270)
- Do not rely on follower count alone when selecting influencers. A creator with 2,000 highly engaged followers may generate as much engagement as someone with 10,000 disengaged followers. (Source: Natalie Taylor, Episode #163)
- Do not optimize for a single metric in isolation when making email changes. A change that improves one metric might harm another. Always evaluate the full subscriber experience. (Source: Sara McNamara, Episode #256)
- Do not obsess over AI search traffic volume. AEO traffic is currently small but high-intent. Optimize for conversion rate rather than traffic volume. (Source: Marcy Comer, Episode #324)
- Do not run multiple AEO tracking tools in parallel. Different tools produce conflicting results, creating confusion when reporting to leadership. Pick one and standardize. (Source: Clare Schmitt, Episode #324)
- Do not measure community success solely by daily engagement activity without considering whether members are retaining their memberships and deriving lasting value. (Source: Greg Isenberg, Episode #146)
- Do not measure influencer success on their contribution to the entire funnel. Their role is to build trust and awareness with a new audience. Conversion responsibility shifts to your nurture flows and follow-up strategy. (Source: Brianna Doe, Episode #305)
- Do not kill campaigns based on low on-page conversion rates alone. Track alternative engagement signals (scroll depth, FAQ clicks, return to main website, campaign lift) before making that decision. (Source: Tas Bober, Episode #185)
- Do not measure LinkedIn success by likes, comments, and impressions alone. Track the percentage of inbound leads that reference LinkedIn and monitor deal metrics for LinkedIn-sourced leads. (Source: Kait Stephens, Episode #156)
- Do not accept agency-provided PR metrics without scrutiny. Develop custom metrics that reflect your actual business priorities and be skeptical of inflated media counts during major news cycles. (Source: Priscilla Barolo, Episodes #302 and #193)
- Do not measure partnerships solely by direct revenue or pipeline generated. This prevents partnerships from being treated as a direct-response channel and misses the full value of trust-building and network effects. (Source: Jared Fuller, Episode #174)
- Do not over-engineer referral tracking with expensive platforms. Start with a simple CRM field or spreadsheet before investing in specialized referral tracking tools. (Source: Sandra Rand, Episode #265)
- Do not apply the same CPL target across all channels. LinkedIn typically supports higher CPLs than Google because targeting is more precise and impacts full-funnel awareness. (Source: Kym Parker, Episode #201)
Sources
| Episode | Guest | Date |
|---|
| Episode #347 | Chris Cunningham | 2026-04-16 |
| Episode #341 | Richard Meyer | 2026-03-28 |
| Episode #338 | Davang Shah | 2026-03-17 |
| Episode #337 | Erin May | 2026-03-12 |
| Episode #332 | Domi de Saint-Exupéry | 2026-02-23 |
| Episode #331 | Drew Pinta | 2026-02-19 |
| Episode #331 | Brian Kotlyar | 2026-02-19 |
| Episode #326 | Eoin Clancy | 2026-02-04 |
| Episode #324 | Clare Schmitt | 2026-01-27 |
| Episode #324 | Marcy Comer | 2026-01-27 |
| Episode #317 | Dave Gerhardt | 2026-01-01 |
| Episode #312 | Alyssa | 2025-12-15 |
| Episode #312 | Joe | 2025-12-15 |
| Episode #307 | Dave Gerhardt | 2025-11-27 |
| Episode #305 | Brianna Doe | 2025-11-20 |
| Episode #302 | Priscilla Barolo | 2025-11-10 |
| Episode #294 | Dave Gerhardt | 2025-10-16 |
| Episode #289 | Sydney Sloan | 2025-10-09 |
| Episode #287 | Amrita Gurney | 2025-10-02 |
| Episode #286 | Kevin White | 2025-09-29 |
| Episode #270 | Holly Xiao | 2025-08-04 |
| Episode #269 | Andrei Țiț | 2025-07-31 |
| Episode #265 | Sandra Rand | 2025-07-17 |
| Episode #263 | Jason Lyman | 2025-07-10 |
| Episode #262 | Chelsea Castle | 2025-07-07 |
| Episode #262 | Dave Gerhardt | 2025-07-07 |
| Episode #256 | Sara McNamara | 2025-06-19 |
| Episode #256 | Alex Fine | 2025-06-19 |
| Episode #243 | Anthony Blatner | 2025-05-05 |
| Episode #243 | Tagg Bozied | 2025-05-05 |
| Episode #241 | Jay Schewedelson | 2025-04-28 |
| Episode #241 | Jaina Mistry | 2025-04-28 |
| Episode #241 | Beth O'Malley | 2025-04-28 |
| Episode #239 | Pranav Piyush | 2025-04-21 |
| Episode #233 | Matt Carnevale | 2025-03-31 |
| Episode #227 | Stephanie Christensen | 2025-03-13 |
| Episode #227 | Kristina DeBrito | 2025-03-13 |
| Episode #224 | Ross Simmonds | 2025-03-03 |
| Episode #220 | Lee Reshef | 2025-02-17 |
| Episode #213 | Matthew Carnevale | 2025-01-23 |
| Episode #212 | Michael Cole | 2025-01-21 |
| Episode #201 | Kym Parker | 2024-12-12 |
| Episode #196 | Devin Reed | 2024-11-25 |
| Episode #193 | Priscilla Barolo | 2024-11-14 |
| Episode #191 | Pranav Piyush | 2024-11-07 |
| Episode #190 | Taylor Udell | 2024-11-04 |
| Episode #185 | Tas Bober | 2024-10-17 |
| Episode #183 | Madhav Bhandari | 2024-10-10 |
| Episode #180 | Bruno Estrella | 2024-09-30 |
| Episode #179 | Kevin White | 2024-09-26 |
| Episode #176 | Natalie Marcotullio | 2024-09-16 |
| Episode #174 | Jared Fuller | 2024-09-09 |
| Episode #172 | Dave Gerhardt | 2024-09-02 |
| Episode #172 | Chelsea Castle | 2024-09-02 |
| Episode #171 | Tommy Clark | 2024-08-29 |
| Episode #171 | Dave Gerhardt | 2024-08-29 |
| Episode #168 | Jenn Herman | 2024-08-19 |
| Episode #165 | Emir Atli | 2024-08-12 |
| Episode #166 | Arielle Gordis | 2024-08-12 |
| Episode #163 | Dave Gerhardt | 2024-08-01 |
| Episode #163 | Natalie Taylor | 2024-08-01 |
| Episode #162 | Natalie Taylor | 2024-07-29 |
| Episode #157 | Adam Robinson | 2024-07-11 |
| Episode #156 | Kait Stephens | 2024-07-08 |
| Episode #154 | Tas Bober | 2024-07-01 |
| Episode #150 | Chris Rack | 2024-06-17 |
| Episode #147 | Dave Gerhardt | 2024-06-06 |
| Episode #146 | Greg Isenberg | 2024-06-03 |
| Episode #145 | Anthony Kennada | 2024-05-30 |
| Episode #139 | Dave Gerhardt | 2024-05-09 |
| Episode #137 | Dave Gerhardt | 2024-05-02 |
| Episode #134 | Dave Gerhardt | 2024-04-22 |
| Episode #133 | Dave Gerhardt | 2024-04-18 |
| Episode #127 | Tim Davidson | 2024-03-25 |
| Episode #122 | Natalie Marcotullio | 2024-03-04 |
