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Apple Analytics: One Year Later

Seventeen months ago, at its annual developer conference WWDC, Apple announced that it would finally be launching something that many in the podcast industry have desired for a long time: better podcast analytics. Or, more accurately, better audience analytics from the historically impartial steward of the podcast ecosystem that’s still believed to facilitate the majority of all podcast listening. (For now, anyway.)

“It may look obscure,” tweeted Gimlet’s Matt Lieber at the time, “But this is the biggest thing to happen to the podcast business since Serial first went nuclear.”

Apple’s new in-episode analytics rolled out that December, six months after the initial announcement. At multiple points during those opening months, I tried developing ongoing reporting projects to gauge how the new data impacted the podcast business. But those early inquiries were premature and produced nothing particularly useful. A really smart person would later advise me that these things, which are somewhat tantamount to culture shifts, take time, and I’d be better off waiting a year.

So. It’s been almost twelve full months. Did Apple’s new analytics fundamentally change anything for publishers and the podcast business? After checking in with over a dozen sources throughout various corners of the podcast ecosystem, there seems to be a general consensus around the answer: no, not really, but it has brought some positives.

Let’s pause briefly and recall why we’re here for a second. Here’s a reminder of the Big Picture, or a primer if you’re new to this world: the narrative of the podcast business has long been defined by its crude analytics relative to other digital media channels. Podcast advertising campaigns are still bought and sold on the basis of the download, a rudimentary metric that more effectively conveys whether an episode has been shipped off to a consumer’s listening app rather than whether that consumer actually heard the episode — and, therefore, the ad. Compared to a broader digital media environment in which audience behavior is measurable to the nanosecond and where close user targeting are table stakes for advertisers, the relatively crude podcast analytics universe is perceived to be virtually prehistoric. (Never mind, of course, the prevalence of ad fraud, the Google-Facebook-Amazon digital advertising oligopoly, and the undermining of user privacy that afflicts the broader modern digital media environment. Modernity remains desired, with all its attendant tumors.)

That perception of prehistoric-ism is a precarious problem, because the podcast industry, broadly speaking, covets brand advertising dollars, which promises greater growth (bigger amounts), stability (longer campaigns), and, in theory, power (growth + stability = more capacity to impose will, probably?).

Nowadays, brand advertisers are thought to be accustomed to the taste of granular analytics, whether to better prove campaign effectiveness or to give them stronger impressions of it, and conventional wisdom argues that they probably won’t fully commit advertising dollars to podcasting unless publishers are able to provide similar levels of measurement granularity — or, at the very least, something markedly better than the rudimentary analytics universe they have now.

The premise and promise of Apple Podcast’s upgraded analytics, therefore, is a straightforward one: it could take the podcast ecosystem a step closer towards an analytics universe that’s able to engender the same kind of confidence in advertisers as any other digital media channel, thus increasing the possibility of brand advertisers meaningfully committing more podcasting dollars.

Of course, there were accompanying concerns. Some worried the new analytics would reveal podcast consumption to be less engaged than it was previously thought to be, or that it would trigger an apocalyptic CPM-cratering scenario. Others thought the revelations from the new data would cause considerable shake-ups or resizing in the podcast industry, as some publishers learn they are simply not as big and healthy as they thought they were. Others still, like Edison’s Tom Webster, posited that Apple’s new podcast analytics could create a feedback loop in which publishers are more motivated to play towards Apple’s platform, thus further narrowing the community’s focus on the finite world of Apple Podcast users — what he called “the optimization trap.” Meanwhile, direct response advertisers, whose dollars have historically helped grown the podcast ecosystem without granular analytics, began expressing concerns about having to compete with brand advertising dollars in the future. (A totally understandable position.)

When the new analytics layer finally rolled out last December, the feature was described to be in its beta phase. And what it offered seemed incremental but nonetheless helpful: publishers could now see aggregate in-episode listening analytics, which meant that they could now know whether anybody made it to that third midroll or the late-game twist in the narrative. Put another way: the podcast episode, as distributed through Apple Podcasts, was no longer a “black box.” (Notably, user data was kept anonymized, true to Apple’s practices.) During those early months, the general response seemed largely hopeful.

As the months rolled on, the initial concerns didn’t come to pass. Podcast consumption turned out to be as engaged as everyone thought they would be. CPM rates didn’t crater, suggesting that this particular version of apocalypse isn’t nigh (for now, at least). There were eye-catching shake-ups in various corners of the community, but the impacts felt localized, and while the new analytics may have played some direct role in those shifts, they were more likely the results of broader trends. It remains unclear if Webster’s Optimization Trap ensnared any significant chunk of publishers, but whatever the case, the Apple Podcast platform continues to be gamed in other ways. Meanwhile, direct response advertisers are still expressing concerns about having to compete with brand advertising dollars, most recently at the last IAB podcast upfronts, according to this Digiday write-up.

But twelve months after the fact, the legacy and impact of Apple’s new analytics is still very much a work in progress: trending positive, but complicated. The data has certainly prove useful, helping some publishers to better understand things like unlistened downloads, ad skipping, and episode retention rates. But based on the exchanges I’ve had, the general feeling seems to be that the data hasn’t fundamentally changed podcasting’s prehistoric perception among advertisers. Many argued that as long as the podcast business remains pegged to the download, trouble is afoot.

This isn’t to say that publishers weren’t able to secure more brand advertisers over the past year. (As many were quick to assure me.) Rather, some sources argues that until measurement actually shifts away from the download, the podcast ecosystem will never structurally unlock brand advertising dollars. One posited the nature of this problem has only worsened over the past year, given the increase in participation from competing platforms — like Google, Pandora, iHeart, Spotify, and so on — that, with their respective user bases and expertise in data and targeting, could potentially end up assuming control as the gatekeeper between brand advertisers and podcast publishers should any of them gain traction proper against Apple.

Some argued that things can only really change if the industry is able to successfully shift its analytics paradigm towards a “true” listening metric — that is, a universe in which publishers can sell advertising based on actual consumption, not episode delivery. And while there is some optimism around NPR’s Remote Audio Data (RAD) initiative (which, I’m told, might finally be widely deployed in the coming months), the prevailing suspicion is the publisher-led shift won’t come quickly enough. “We’re still pretty far from where we need to be,” a podcast executive told me.

We remain in the universe of podcast downloads, though, and while we’re here: most people I spoke with believe that the Interactive Advertising Bureau’s podcast measurement standards was a lot more influential over the past year than the new Apple analytics. “IAB V2 created a more even playing field,” National Public Media’s Bryan Moffett said. “There’s a common definition of a download, and we can all speak the same language.” There continues to be some debate over the nuances of the standards, but the podcast industry appears to have broadly aligned with the IAB on download measurements, so at least that hurdle seems to have been cleared. (Previously, the concern was around a lack of proper apples-to-apples comparisons among podcast downloads.)

Still, as mentioned, there were some concrete ways in which Apple’s in-episode analytics have helped publishers. For one thing, the new data allowed teams to better capture, understand, and convey listener engagement, and that contribution shouldn’t be downplayed. “I think the greatest benefit is knowing that the vast majority of people aren’t skipping the ads on our shows — especially when the hosts do a really engaging job with their reads,” said Alyssa Martino, Macmillan’s associate director of podcasts. “It’s hard to connect that specifically to spends since our shows sell well, but it’s great to have the data now to back up what we’ve known and said anecdotally for years.”

The new data also helped some publishers to build and improve new advertising products. Dave Shaw, the executive producer of podcasts at POLITICO, told me that they’ve successfully sold post-roll ad slots on the POLITICO Playbook Audio Briefing after being able to show that listeners stick around to the end. Anna Phelan, the editorial program manager at TED, tells me that the new analytics have helped them evaluate longer ad experiences that they’ve been integrating into WorkLife with Adam Grant. “We didn’t know how listeners would respond to the length or content, but we felt confident enough in the appeal of the content to take the risk,” Phelan said. “The high consumption rates that we saw, with almost no drop-off during the ad break, reassured us that the approach resonates with our audience and gives us permission to continue to develop other formats in this style.”

There is another way in which Apple’s in-episode analytics unambiguously proved useful: as editorial data. Almost every publisher I contacted talked about how they’ve been able to learn about episodes and experiments that worked (and what didn’t), and how the data has helped them feel more confident when shifting around resources or making structural adjustment to shows (cutting or expanding publishing schedules, shortening or lengthening episodes).

Those editorial benefits are important, but ultimately, they’re secondary to our advertising concerns here. And on that front, a good deal (though not all) of the sources I spoke with generally want more from Apple. Some expressed frustration over what feels like slow product iteration on the part of Apple’s new analytics dashboard. “I know it’s still supposed to be a beta, but let’s go already!” one executive told me. Several advocated for Apple to make more data available through an API, so that publishers can more effectively integrate Apple Podcast listening data — which, despite Apple’s majority facilitation, only represents one chunk of a show’s overall audience at the end of the day — into their central measurement dashboards, thus helping them paint better pictures of their audiences for advertisers to peruse.

There is still, it seems, a long way to go. One year after Apple’s new in-episode analytics rolled out to the public, its impacts seems to be somewhat muted — or, at least, nowhere near as revolutionary as many were hoping. As such, there is a certain sameness to the way this year is ending compared to the way it began. Maybe these things take longer than a year, or maybe those changes need to take different shapes. In any case, if there is be some revolution, it isn’t quite here yet.

In the meantime, the podcast industry will continue to grow in the way that it’s always been growing.