So What? Marketing Analytics and Insights Live
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In this episode, hosts Christopher Penn and John Wall break down complete show performance beyond standard download counts.
You will master strategies where nontraditional podcast analytics reveal hidden audience growth across newsletters, social channels, and video platforms. This shift will allow you to harness advanced nontraditional podcast analytics to pinpoint top content and unlock broader reach. By learning this framework, you will identify your true audience drivers and eliminate wasted marketing effort.
Watch the video here:
So What? Nontraditional Podcast Analytics
Can’t see anything? Watch it on YouTube here.
In this episode you’ll learn:
- Why traditional podcast analytics aren’t prescriptive
- What specific, prescriptive podcast analytics can show you
- How to get started
Transcript:
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher Penn – 00:00
Happy Thursday, folks. This is So What?, the Marketing Analytics and Insights live show. I’m Chris, here with John. Katie is off kayaking up a mountain or something; I don’t know.
John Wall – 00:42
She is in the great outdoors.
Christopher Penn – 00:44
The great outdoors, yes.
Christopher Penn – 00:46
Jay Baer has a sign in his office that I love and want to steal. It says, “Avoid outdoor sports and enjoy all of the indoor hobbies.”
John Wall – 00:51
Avid endorsement—enjoy all of the indoor hobbies.
Christopher Penn – 00:56
Exactly. Enjoy all the indoor hobbies. This week, we’re talking about nontraditional podcast analytics. I was looking the other day, and we haven’t done an episode specifically on podcast analytics since 2019, so it has been a long time.
John Wall – 01:16
Yeah, that’s it. There has been a lot of movement over the last three or four years on this front. I’m interested in what you’re using. It has come to the point for me that podcasting is like many other marketing tactics where we focus primarily on the end result—how many leads came in and whether any of them closed—because intermediate tracking can be such a mess. I’m excited to see what you’ve been testing.
Christopher Penn – 01:42
This is interesting, and I wanted to get your take to start. You’ve mentioned on our other podcast, Marketing Over Coffee, that particularly in sponsorships—and with audiences in general—people don’t follow a show format; they follow a person. Can you elaborate on that?
John Wall – 02:05
One of the biggest shifts over the last 10 or 15 years is that podcasting used to be strictly topic-driven. If you were interested in tech, you listened to This Week in Tech; if you liked music, you listened to Brian Ibbott’s Coverville or followed Adam Curry.
Over time, celebrities began translating their existing fame into the medium. They show up with built-in audiences. If you follow the hosts on SmartLess because you find them entertaining, you tune in on audio platforms or YouTube.
John Wall – 02:47
There has been a shift toward needing to show up with an established brand and audience that grows from there. While plenty of niche podcasts still serve targeted audiences around specific topics, the major sponsorship dollars targeting millions of listeners lean heavily toward celebrity-driven shows. You don’t see the exact same organic influencer growth in podcasting that you see on other media channels.
John Wall – 03:21
On TikTok and Instagram, creators can appear out of nowhere and amass a couple million followers quickly. In podcasting, reaching tens of millions organically without an existing brand behind you is far rarer. That is the general landscape as I see it today.
Christopher Penn – 03:50
To address that landscape, we evaluated how we manage our own show, In-Ear Insights, which Katie and I host for Trust Insights. We record on Mondays and release on Wednesdays. You can subscribe at TrustInsights.ai/tipodcast or find us on most podcast platforms.
If you want to check out John and me, we also host Marketing Over Coffee. The primary challenge with our show is distribution reach. When we record in StreamYard, the media file goes to Libsyn.
Christopher Penn – 04:33
Libsyn distributes the audio feed to Apple Podcasts, Spotify, and other directories. We then publish the video version on YouTube, post the full transcript to the Trust Insights website as a blog post, and embed both the YouTube video and MP3 player there.
Every Wednesday, we feature it in our newsletter, Inbox Insights, which you can subscribe to at TrustInsights.ai/newsletter. On Fridays, our account manager, Kelsey, sends an “in case you missed it” email referencing it.
Christopher Penn – 05:18
Kelsey processes the recording through Opus Clip to extract short video clips. I also edit clips for social channels via our Agorapulse management system. Content is distributed as shorts, regular posts, blog posts, YouTube videos, and newsletter features.
The limitation of traditional podcast analytics is that most producers open Libsyn or Transistor, look at download totals, and treat that number as their entire performance metric. When evaluating the full distribution matrix, downloads represent a small slice of total reach.
John Wall – 06:04
That has changed the landscape significantly. It used to be that publishing to Libsyn and getting indexed by Apple Podcasts covered 90% of your audience. Today, the reality is the exact opposite.
Christopher Penn – 06:26
To audit this effectively, we addressed two questions: A, where are all the places we currently publish or should publish? B, what does the aggregated data reveal?
I initiated this process using deep research tools to benchmark against industry standards, focusing on branded podcasts designed to educate, entertain, and attract prospective clients rather than monetize through ad placements.
Christopher Penn – 07:21
We do monetize slightly through Libsyn programmatic ads—our August earnings were 96 cents.
John Wall – 07:27
Excellent. Up and to the right.
Christopher Penn – 07:31
I compiled research from Edison Research, Sounds Profitable—led by Bryan Barletta and Tom Webster—and the Pew Research Center to establish how industry peers measure multi-channel performance and attract new listeners.
I instructed the model to evaluate measurement frameworks and audience growth tactics, setting a benchmark goal of reaching 1,000 monthly downloads for In-Ear Insights across all combined channels.
John Wall – 11:31
Combining channel metrics changes the approach completely. You can gain significant traction on LinkedIn or YouTube, which offer more active distribution mechanics than a static MP3 feed.
Christopher Penn – 11:48
The initial step was establishing a comprehensive episode ledger. To analyze historical performance properly, I expanded the scope from a 30-day window to a full year of data.
I utilized Claude Code to engineer the data extraction scripts and handle multi-source aggregation.
Christopher Penn – 12:30
I extracted audio statistics from Libsyn and video metrics from YouTube. Claude Code flagged that YouTube API exports limit outputs to 500 rows per call, requiring month-by-month extraction to evaluate content performance trends accurately.
It also identified that the So What? live stream aired across multiple channels, prompting me to pull performance data from my personal YouTube channel alongside the company channel.
Christopher Penn – 13:13
Next, I exported a year’s worth of social media performance data from Agorapulse covering Facebook, Instagram, LinkedIn, Threads, and YouTube across both personal and corporate profiles.
I pulled Google Analytics data and extracted click-stream records for every link included in our newsletters over the past year.
Christopher Penn – 14:09
It took about an hour and a half to write and execute the export scripts, producing a master spreadsheet tracking dates, URLs, and click counts.
To determine the optimal analytical approach, we leveraged our internal library of 1,400 analytical techniques spanning 23 disciplines—including agronomy, economics, and meteorology—to match our dataset structure with appropriate statistical methods.
Christopher Penn – 15:05
The model selected techniques tailored to our data structure, including holdout testing to assess timing variables and compositional destination share analysis to evaluate platform performance.
Christopher Penn – 15:52
It executed cumulative sum management and change-point detection on downloads to determine whether specific episodes underperformed after standard baseline periods, such as the 13-day mark.
It also applied panel longitudinal family summaries—a method derived from epidemiology—to evaluate performance patterns across episode cohorts. Finally, the data was compiled into our SAINT (Summary, Analysis, Insights, Next Steps, Timeline) framework.
Christopher Penn – 16:47
This created a structured summary for decision-making while insulating the primary deliverable from verbose processing logs.
Christopher Penn – 17:27
The execution phase required 17 hours of automated processing and code generation.
The audit revealed technical configuration issues within Libsyn. Specifically, 42% of downloads lacked proper user-agent attribution, while Apple Podcasts and Spotify accounted for the remainder.
Christopher Penn – 18:20
The analysis highlighted a primary distribution bottleneck: brand account reach lags significantly behind personal profile reach.
While we should continue posting on corporate channels, cross-posting to my personal LinkedIn profile drives the vast majority of engagement. My personal profile has 48,000 followers compared to 2,000 on the brand page, averaging 1,500 impressions per post versus 42 impressions on the company page.
Christopher Penn – 19:20
Personal channels remain our primary organic driver for podcast awareness.
John Wall – 19:28
Is that primarily because corporate page reach across major social networks has declined over the past five or six years?
Christopher Penn – 19:44
Corporate accounts across most social platforms yield low organic distribution and serve primarily defensive positioning or IP verification functions.
Audiences follow individual personalities and creators rather than corporate entities.
Christopher Penn – 20:28
Algorithm updates from Meta and other networks explicitly prioritize peer-to-peer interactions over corporate page content, causing personal profile distribution to outpace company pages consistently.
Christopher Penn – 20:50
The newsletter analysis showed subscribers generated 28,000 clicks to podcast and live-stream links out of 529,000 total email clicks over the year.
Interestingly, two of the top 10 most-clicked podcast links in the newsletter were among the top 10 overall downloaded episodes, indicating the newsletter audience differs from directory subscribers on Apple Podcasts or Spotify.
John Wall – 21:35
That aligns with distinct audience segments: dedicated subscribers who consume updates via email vs. general podcast app commuters.
Christopher Penn – 21:48
Comparing content formats revealed that live streams yield higher total watch time due to longer runtimes, whereas audio podcast episodes achieve higher relative completion rates. Short-form video clips generate higher total view counts due to their 60-second length, but audio episodes sustain higher average engagement depth.
John Wall – 22:30
Live streams benefit from algorithmic feed placements where users drop in passively, whereas podcast listeners make intentional choices to consume an episode.
Christopher Penn – 22:43
I would prefer StreamYard to offer integrated, cross-platform audience analytics directly within its dashboard to streamline tracking across endpoints.
John Wall – 22:59
StreamYard currently offers limited native reporting, requiring users to pull performance data manually from each destination network.
Christopher Penn – 23:19
Given limited internal bandwidth for non-billable production, the analysis evaluated whether to deprecate any active channel. The output recommended keeping the full mix, as removing the live stream would negatively affect overall reach across the Trust Insights media ecosystem.
Christopher Penn – 23:59
The download trend analysis identified an unexplained spike in downloads during April and May before metrics returned to baseline levels.
John Wall – 24:26
Unexplained traffic anomalies can stem from search indexing, LLM training pulls, or delayed engagement trailing live events attended earlier in the year. Seasonal drops during July and August are typical for B2B audiences.
Christopher Penn – 25:16
Because our primary model is B2B consulting, performance naturally aligns with commercial business cycles, where summer slowdowns are standard.
Christopher Penn – 25:47
The longitudinal pacing analysis identified three episodes that performed significantly below historical download benchmarks: “AI-Forward Content Remixing,” “Does AI Belong on the Org Chart?”, and “What Is Agentic SEO?” The remaining 10 episodes met or exceeded historical pacing.
Monitoring early download velocity provides a signal to adjust promotion strategy—either boosting promotion for high-performing episodes or adjusting strategy for underperforming topics.
John Wall – 26:53
Tracking the long-tail performance curve is valuable. Episodes often receive initial traffic during the first three weeks, but specific industry events can reactivate older episodes months later.
Christopher Penn – 27:26
If a previous guest publishes a book, gives a high-profile talk, or makes news, search volume for that archived episode spikes immediately.
Christopher Penn – 27:51
Immediate technical action items include resolving the directory feed limits and optimizing promotion on high-performing platforms. Because email click preferences do not map directly to top audio downloads, we should test formatting adjustments in the newsletter.
Christopher Penn – 28:16
I plan to test adding dedicated sub-sections in our newsletter explicitly listing the five most recent podcast episodes alongside the five most recent YouTube videos to improve content discovery.
John Wall – 28:42
Subscribers scan quickly for topics of interest, so presenting those options clearly increases click-through rates across formats.
Christopher Penn – 28:57
Another recommendation is auditing raw download counts against IAB-certified standards in Libsyn to account for measurement variations.
John Wall – 29:10
Raw totals count every server request, whereas IAB standards filter out automated web crawlers, bot traffic, and re-connection attempts from unstable mobile players.
IAB numbers provide a conservative metric suitable for external advertisers, whereas raw data offers full visibility for internal analysis before filtering.
John Wall – 31:39
Unless you fully understand the filtering criteria applied by external tools, retaining raw log data ensures you do not inadvertently filter out valid engagement.
Christopher Penn – 31:52
Nontraditional podcast analytics requires measuring the surrounding promotional ecosystem—newsletters, social distribution, web referrals, and clip performance—rather than relying strictly on RSS download totals.
Christopher Penn – 32:49
I am updating our feed settings directly. Libsyn was set to limit the podcast RSS feed output to the last 100 episodes. Increasing this parameter to 1,000 episodes ensures complete catalog availability.
John Wall – 33:45
Expanding that feed limit allows podcast directories and web crawlers to index the remaining 900 historical episodes immediately.
John Wall – 34:59
Automating the repurposing and syndication of short-form video clips across platforms like LinkedIn provides high leverage without adding heavy production overhead.
Christopher Penn – 35:29
For the next iteration of this analysis, we will pull search query data from Google Search Console and Bing Webmaster Tools.
Google Search Console now allows properties beyond standard websites, providing query visibility across linked YouTube and social profiles.
John Wall – 37:04
Incorporating backlink data from Ahrefs will also highlight external domains referencing specific episodes and establishing topic authority.
Christopher Penn – 37:54
Analyzing competitor backlink profiles in Ahrefs identifies industry publications and blogs to target for link placement and guest pitches.
Christopher Penn – 38:20
Checking Ahrefs confirms 311 external pages currently link to the In-Ear Insights podcast landing pages.
Christopher Penn – 39:04
External links include referrals from CMO.com, The Tilt, and Social Media Examiner, representing clear targets for ongoing media outreach.
Christopher Penn – 39:38
When introducing new data streams—such as backlink reports or competitor benchmarks—you must re-run the dataset against your analytics catalog to identify newly applicable analytical techniques.
John Wall – 40:34
Prioritizing tactics like automated LinkedIn short-form distribution and backlink acquisition yields the highest return for available operational time.
Christopher Penn – 41:46
If you have questions regarding multi-channel transcript and podcast analytics, join our free Slack community at TrustInsights.ai/analyticsformarketers, where over 4,700 practitioners discuss analytics daily.
Subscribe to the show, check out the Trust Insights podcast at TrustInsights.ai/tipodcast, and sign up for our weekly newsletter at TrustInsights.ai/newsletter.
Christopher Penn – 42:34
Join our free Analytics for Marketers Slack group at TrustInsights.ai/analyticsformarketers. See you next time.
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This really reframes how I think about “podcast performance.” The part about personal profiles crushing brand pages (48k vs 2k followers, 1,500 vs 42 impressions) hit hard — we keep pouring effort into the company page out of habit. And the point that newsletter clickers and Apple/Spotify listeners are basically two different audiences explains a lot of the weird data I’ve been staring at. Treating downloads as just one slice of the whole ecosystem instead of the scoreboard is the mindset shift I needed. Thanks for sharing the actual numbers, not just the theory.
The piece that keeps breaking every nontraditional dashboard is clip-level attribution. Full-episode distribution is trackable (RSS, YouTube watch time, embed plays), but a captioned 9:16 clip posted natively to Reels, Shorts, and TikTok carries no link, strips the source from the caption, and often gets reshared from the guest’s account, not the show’s. A workable pattern: treat clips as a reach metric and the profile link as the attribution layer – one UTM’d link in bio per campaign window, a weekly count of bio-clicks by platform, and a rule that any clip still producing profile clicks after a month graduates into the back-catalog rotation. It will never be exact, but it turns “the clips are doing something” into a number you can defend in a budget meeting.
One addition from building in this space (we make Video To Reel, a phone app that turns an episode upload into captioned vertical clips – disclosure stated): the clip pipeline itself deserves a metric. Time-to-post per episode is the number that predicts whether a show sustains clip promotion past month three, and it is free to measure.