Update, May 20: Google's I/O announcements this week: 1B users on AI Mode, information agents launching this summer; make this more urgent, not less.
More soon.
Podcast attribution has always been a little broken. We measure what we can. Promo codes, post-purchase surveys, the occasional self-reported lift study. We treat the gap between those numbers and reality as the cost of doing business.
Sponsors know it. Hosts know it. Networks know it. We mostly nod and move on.
But the gap doesn’t work in everyone’s favor equally. It works in the brand’s favor in the short term, but ultimately hurts everyone.
Here’s how it actually plays out. A campaign runs. The brand gets reach, brand lift, halo, audience trust, and a slow trickle of orders that bypass the discount code entirely. The show gets paid for the impressions it can prove and is told that next campaign’s budget depends on the numbers in the wrap-up report. So the show invoices for what attribution tools could see, while the brand quietly takes everything attribution tools couldn’t. If the show doesn’t have a serious measurement framework attached (a Superfiliate or Popfly, and something with real post-purchase capture), the campaign often just ends with more value extracted than was ever counted. And that hurts everyone. The show has a sponsorship that ends, and the brand loses quality business intelligence on what’s working (or not).
The brands aren’t necessarily doing this on purpose. The system rewards it. CMOs ask “what’s the ROI” and the only honest answer is “what we measured.” So the measured number becomes the number that determines whether the show gets renewed.
The result is that a lot of shows that are actually doing the work of moving brand affinity get cut, and the budget moves to channels that can prove something even if that something is smaller. Performance marketing wins by default. Brand work loses by default. Even when brand work was the thing that actually moved the needle.
That equilibrium is about to break, and not for the reason you’d think.
The question for sponsorship in the future won’t just be “did people use the code.” It will be “did our sponsorship change what AI says about this brand to the next thousand people who ask?”
Those are two different metrics with two different stories. The first one captures a small slice of the audience that decided to buy right now and bothered to find the discount. It’s real, but it’s a fraction of the impact. The second one captures whether the brand is showing up in the consideration set at all. Whether it’s described in flattering or unflattering terms. Whether it’s positioned as the authority in its category or as an afterthought.
That second metric is the harder thing to measure. It’s also the thing that compounds. And until very recently, it was the thing shows weren’t allowed to invoice against.
The new top of funnel
Bain published a study last year that put real numbers on it. 80% of consumers now rely on AI-written results for at least 40% of their searches. 60% of searches on traditional engines now end without the user clicking through to anything at all.
Read that second one twice. The majority of search behavior now never produces a website visit.
The implication for sponsorship: a meaningful share of the audience that used to land on a brand’s website after hearing a podcast ad never gets there anymore. They ask the chat tool, get an answer, act on it. Whether that answer mentions the brand or not is determined upstream of the click.
That’s the new top of funnel for a huge chunk of high-intent buyers. It’s growing fast. And it’s almost entirely invisible in the wrap-up reports we send to sponsors today.
Which is why I started looking for a way to measure it.
What it actually looks like
A few weeks ago I met Michael Rueckert, founder of Centium AI. His tool runs prompts across the major LLMs (ChatGPT, Claude, Gemini, Perplexity, Grok), tracks how often a given brand shows up in the answers, what other brands it competes with, what attributes get attached to it, and which sources the models are pulling from to make those calls.
I’d been trying to figure out how to add a qualitative layer to my sponsor wrap-up reports for months. Impressions and code redemptions are necessary but not sufficient. They tell you what happened. They don’t tell you whether the brand actually moved.
Michael offered to build me a dashboard for any brand in my network. I picked Good Ranchers. They’re an active sponsor across Long Run Labs right now, running a campaign with seven podcasts, and Brad Mader on their team has been experimenting with new ways to measure podcast performance, and new audiences in general. The fit was natural.
The baseline came back this week. A few things stood out.
What we found
Good Ranchers ranks #6 in “curated meat bundles” against ButcherBox, Crowd Cow, Porter Road. That’s a real defensible position in a competitive category. Most brands would happily take it.
But across all 120 prompts Centium AI ran, Good Ranchers had a 27% visibility rate on Gemini and a 2% visibility rate on ChatGPT. Same brand. Same category. Two of the most-used LLMs in the world. A 13x difference.
The reason for the gap is structural. Gemini leans heavily on Reddit, YouTube, and recent web content where Good Ranchers shows up organically. ChatGPT and Claude lean on editorial authority. Good Housekeeping. Garage Gym Reviews. Wirecutter-style listicles. And in those listicles, Good Ranchers is rarely cited.
That’s a fixable problem, and it’s exactly the kind of problem a podcast sponsorship is built to fix. More on the mechanics in a minute.
The other thing the data showed: Good Ranchers does not currently get associated with athletes by AI. The perception data tracks which traits get most-frequently attached to a brand, and “Targets Athletes / Fitness Community” came back as a low-frequency association. ButcherBox owns that lane. Trifecta and Factor own the prepared-meals version of it. Good Ranchers is essentially absent. It also hasn’t been a focus for them.
This is the part I find most interesting from a sponsorship strategy lens. Good Ranchers competes with ButcherBox in nearly every other category prompt and wins on American sourcing in head-to-head comparisons. But in the athlete category specifically, the brand isn’t in the conversation at all. It hasn’t been a focus for them, but there’s a logical overlap between the audience and what the brand offers.
That’s exactly what an endurance podcast network campaign is built to move. And now we have a baseline number to measure it against.
How sponsorships actually move the AI layer
The fair question to ask at this point is how, mechanically, a podcast campaign ends up changing what ChatGPT says about a brand. It’s worth walking through, because the answer is different than most people assume, and the mechanisms have different confidence levels.
There are three paths, and a well developed campaign moves all three.
The first is direct transcript ingestion. Podcast episodes are not just audio anymore. They’re YouTube uploads with auto-captions, Spotify and Apple transcripts, Podscribe indexes, and Substack writeups that quote the conversation directly. All of that is crawlable text. When a host says “I’ve been enjoying Good Ranchers ribeyes the night before (or after) my long run for six months,” that sentence becomes searchable, attributable text attached to the brand. The YouTube and Substack layer is the highest-confidence piece here. The audio-native transcript layer is growing fast but harder to verify from the outside. Either way, the campaign is producing text, not just impressions.
The second is community amplification, and it’s the strongest mechanism we have evidence for. Listeners who hear a brand discussed authentically post about it. r/running threads asking “did anyone else hear that Good Ranchers ad on FTLR?” (or more likely, “does anyone else keep hearing about Good Ranchers on running podcasts all of a sudden?”) YouTube creators referencing the episode. Substack comments. Those posts become training data and retrieval source material the next time someone asks an LLM about the category. This is most of what’s driving Gemini’s 27% visibility number for Good Ranchers. It’s organic, hard to manufacture, and disproportionately responsive to authentic host integrations versus scripted reads.
The third is editorial seeding, and this is where networks earn their fee (and renewal) versus just placing ad reads. A host who’s a credentialed athlete, actively using the product during a campaign window, with real performance data, is a different kind of source than a brand’s PR firm pitching cold. Writers at Outside, Runner’s World, Trail Runner, and review sites will hear that pitch. The output is the editorial coverage ChatGPT and Claude actually cite. This mechanism doesn’t happen automatically. It requires the host to be willing, the network to facilitate, and the brand to coordinate. But when it works, it’s the highest-leverage piece of the campaign, because editorial coverage is the layer the most-used LLMs weight most heavily.
The integrated version of this is what moves the dial. Ad reads alone move mechanism one and a little of mechanism two. A real campaign moves all three.
The clock problem
The other thing brands need to understand before they buy into this kind of measurement is that LLM associations don’t update on the same cadence as a Shopify dashboard. There are three different clocks running, and they matter for when you can expect to see lift.
The retrieval layer updates in days to weeks. This is what’s happening any time an LLM does a live web search to answer a question, which is most of what Perplexity does and an increasing share of what ChatGPT, Gemini, and Claude do. New Reddit threads, new Substack posts, new YouTube content start showing up in citations almost immediately. This is where a campaign’s first signal appears.
The fine-tuning and instruction layers update in weeks to months. This is where editorial coverage lands. A piece in Outside that mentions a brand in May might not show up in base ChatGPT responses until summer.
The base model layer updates in months to years. Knowledge cutoffs are real and the foundational associations are slow to shift. No single campaign moves this layer in isolation. Consistent presence over a year of campaigns does.
What this means for measurement: a 30-day post-campaign remeasure captures the retrieval-layer lift. A 90 to 120-day remeasure starts to capture the editorial layer. Annual measurement is what tells you whether the base associations are trending the right way. The right cadence for a serious sponsor is baseline at campaign start, lift measurement at 30 days, second measurement at 90 days, and an annual brand health check that aggregates across campaigns.
This reframes the question in a useful way. Performance marketing answers “did this week’s spend produce this week’s orders.” AI affinity measurement answers “is our brand on a positive trajectory in the consideration set over a 6 to 12 month window.” Both are real jobs. Most brands need both. The mistake is judging a brand investment on a performance timeline, which is what’s been happening to podcast sponsorship for the last decade.
Why this changes the deal
Here’s why I think this matters beyond Good Ranchers.
Brands have started to narrow the attribution gap with better measurement frameworks. Superfiliate, Popfly, Podscribe, the post-purchase survey infrastructure that companies like Good Ranchers actually run well. These tools surface orders that would have been invisible and let shows invoice for more of the value they create. But none of them touch the AI layer, and the AI layer is the one growing fastest.
The concept marketers are now organizing around is what Bain calls the day one list. It’s the short list of brands a buyer already has in mind on the day they decide to start shopping. Before they search. Before they click anything. Before any performance marketing has a chance to influence them. Bain’s research finds that 85% of B2B buyers purchase from their day one list, and the directional pattern in consumer categories is the same. The decision is mostly made before the search ever happens.
This changes the job description for marketing. Performance channels are good at capturing demand that already exists. They are not good at putting your brand on the day one list in the first place. That work belongs to earned media. Recommendations from trusted sources. Conversations with friends. The things people read and listen to in the months before they’re ready to buy.
Podcasts are extraordinarily good at this work. A host an audience trusts, talking about a brand they actually use, threaded through hours of content the listener chose to spend time with. That is day-one-list manufacturing. It’s classic business development: educate your customer on your product before they need it, so when they’re ready to decide, you’re on the short list.
The other piece worth naming: PR is back. For a while now, editorial coverage has been hard to justify because the impact wasn’t measurable. Brand budgets shifted to performance channels, agencies contracted, and the work got rebranded as “content marketing” or “thought leadership.” The output looked similar but the discipline of placing your brand in trusted publications got quietly hollowed out.
Now those editorial mentions are the highest-value training data in the world. ChatGPT cites Good Housekeeping. Claude cites Garage Gym Reviews. Gemini sees Reddit and YouTube. Both matter, but it’s the editorial side that’s been most undervalued for the last decade and is suddenly the most leveraged. Podcasts are an unusually clean entry point into that work, for the reasons in mechanism three above.
What changes when you add AI brand affinity measurement is that the wrap-up gets a new column. One that the brand can’t easily ignore.
Where this is going
A few specific predictions, since I think they’re worth putting in writing.
The sharper end of the brand-side market will start asking agencies for AI brand affinity data alongside impressions. Not all of them. The smart ones first. The ones already losing sleep about how much LLM mediation is changing their funnel.
This becomes table stakes for any sponsorship deal above a certain size. Networks that can deliver baseline-and-remeasure reports will price differently than networks that can’t. The conversation shifts from “what’s your CPM” to “what’s your AI lift.”
The savvier brands will start designing campaigns specifically to move LLM associations. Different creative, different host briefs, different pacing. We’ll get good at it. The current “we read the script” model will look as dated as banner ads.
The flip side is the risk for brands that don’t move. AI consideration sets aren’t quickly recoverable. If your brand isn’t in the answer when ChatGPT generates “best meat delivery for athletes” today, you have to displace whoever is, and displacement takes editorial coverage, organic community presence, or both. Six months of doing nothing means six months of competitors getting cited while you don’t. The status quo is the most expensive option, and most brands haven’t realized it yet.
The brands working with networks that are building this measurement now will be in better shape eighteen months from now than the ones still arguing about whether AI mediation is real.
What I’m doing about it
Every brand that signs a sponsorship deal above $30k across Long Run Labs going forward gets the option to bake a Centium AI dashboard into their package. Baseline at the start, re-measure at the end. The wrap-up report includes the AI lift alongside the standard numbers.
Michael and I are also working on a category-wide study across endurance sports, looking at how AI talks about every meaningful brand in running, GPS, hydration, recovery, and a few adjacent verticals. Methodology and findings to come.
And I’m going to keep writing about what I learn here.
If you’re a brand thinking about how to measure your podcast spend differently, hit reply. If you’re a brand or operator thinking about this stuff too, I’d love to compare notes. The space is wide open right now.
A quick ask
Most of what I publish here comes from real campaigns, real conversations with brands, and real data we’re pulling across the network. The Good Ranchers and Centium AI angles in this post are exactly the kind of insight paid subscribers have told me is worth far more than the $80/year (or $8/month) they’re paying.
One subscriber, Dr. Neal Sivula, an integrative veterinarian in Ohio (about as far from endurance sports as it gets), emailed me after the first two AI posts with five concrete workflows he was already implementing in his practice: client retention analysis, inventory gap analysis, turning continuing education transcripts into study guides, identifying podcast guests, and streamlining a webinar speaker series. He upgraded to paid the same week.
Real attribution data, real sponsor takeaways, real frameworks you can apply right away. If that’s the kind of thing adding value to how you think about your business, consider upgrading to a paid membership. It helps make it possible!
Thanks to Brad Mader at Good Ranchers for being game to share his data publicly, and to Michael Rueckert at Centium AI for building the tool that made any of this possible.
Hear more from Michael on his Second Nature episode.
This is all so fascinating. What a time. Thank you for sharing your perspective!
Good post Jon!