Krystina Rubino
More posts from Krystina Rubino
There are sizable, meaningful gaps in the knowledge collection and publication of podcast listening and engagement statistics. Coupled with still-developing advertising technology because of the distributed nature of the medium, this causes uncertainty in user consumption and ad exposure and impact. There is also a lot of misinformation and misconception about the challenges marketers face in these channels.
All of this compounds to delay ad revenue growth for creators, publishers and networks by inhibiting new and scaling advertising investment, resulting in lost opportunity among all parties invested in the channel. There’s a viable opportunity for a collective of industry professionals to collaborate on a solution for unified, free reporting, or a new business venture that collects and publishes more comprehensive data that ultimately promotes growth for podcast advertising.
Podcasts have always had challenges when it comes to the analytics behind distribution, consumption and conversion. For an industry projected to exceed $1 billion in ad spend in 2021, it’s impressive that it’s built on RSS: A stable, but decades-old technology that literally means really simple syndication. Native to the technology is a one-way data flow, which democratizes the medium from a publishing perspective and makes it easy for creators to share content, but difficult for advertisers trying to measure performance and figure out where to invest ad dollars. This is compounded by a fractured creator, server and distribution/endpoint environment unique to the medium.
For creators, podcasting has begun to normalize distribution analytics through a rising consolidation of hosts like Art19, Megaphone, Simplecast and influence from the IAB. For advertisers, though, consumption and conversion analytics still lag far behind. For the high-growth tech companies we work with, and as performance marketers ourselves, measuring the return on investment of our ad spend is paramount.
Because podcasts lag other media channels in business intelligence, it’s still an underinvested channel relative to its ability to reach consumers and impact purchasing behavior. This was evidenced when COVID-19 hit this year, as advertisers that were highly invested or highly interested in investing in podcast advertising asked a very basic question: “Is COVID-19, and its associated lifestyle shifts, affecting podcast listening? If so, how?”
The challenges of decentralized podcast ad data
We reached out to trusted partners to ask them for insights specific to their shows.
Nick Southwell-Keely, U.S. director of Sales & Brand Partnerships at Acast, said: “We’re seeing our highest listens ever even amid the pandemic. Across our portfolio, which includes more than 10,000 podcasts, our highest listening days in Acast history have occurred in [July].” Most partners provided similar anecdotes, but without centralized data, there was no one, singular firm to go to for an answer, nor one report to read that would cover 100% of the space. Almost more importantly, there is no third-party perspective to validate any of the anecdotal information shared with us.
Publishers, agencies and firms all scrambled to answer the question. Even still, months later, we don’t have a substantial and unifying update on exactly what, if anything, happened, or if it’s still happening, channel-wide. Rather, we’re still checking in across a wide swath of partners to identify and capitalize on microtrends. Contrast this to native digital channels like paid search and paid social, and connected, yet formerly “traditional” media (e.g., TV, CTV/OTT) that provide consolidated reports that marketers use to make decisions about their media investments.
The lasting murkiness surrounding podcast media behavior during COVID-19 is just one recent case study on the challenges of a decentralized (or nonexistent) universal research vendor/firm, and how it can affect advertisers’ bottom lines. A more common illustration of this would be an advertiser pulling out of ads, for fear of underdelivery on a flat rate unit, missing out on incremental growth because they were worried about not being able to get download reporting and getting what they paid for. It’s these kinds of basic shortcomings that the ad industry needs to account for before we can hit and exceed the ad revenue heights projected for podcasting.
If there’s a silver lining to the uncertainty in podcast advertising metrics and intelligence, it’s that supersavvy growth marketers have embraced the nascent medium and allowed it to do what it does best: personalized endorsements that drive conversions. While increased data will increase demand and corresponding ad premiums, for now, podcast advertising “veterans” are enjoying the relatively low profile of the space.
As Ariana Martin, senior manager, Offline Growth Marketing at Babbel notes, “On the other hand, podcast marketing, through host read ads, has something personal to it, which might change over time and across different podcasts. Because of this personal element, I am not sure if podcast marketing can ever be transformed into a pure data game. Once you get past the understanding that there is limited data in podcasting, it is actually very freeing as long as you’re seeing a certain baseline of good results, [such as] sales attributed to podcast [advertising] via [survey based methodology], for example.”
So how do we grow from the industry feeling like a secret game-changing channel for a select few brands, to widespread adoption across categories and industries?
Below, we’ve laid out the challenges of nonuniversal data within the podcast space, and how that hurts advertisers, publishers, third-party research/tracking organizations, and broadly speaking, the podcast ecosystem. We’ve also outlined the steps we’re taking to make incremental solutions, and our vision for the industry moving forward.
Lingering misconceptions about podcast measurement
1. Download standardization
In search of a rationale to how such a buzzworthy growth channel lags behind more established media types’ advertising revenue, many articles will point to “listener” or “download” numbers not being normalized. As far as we can tell at Right Side Up, where we power most of the scaled programs run by direct advertisers, making us a top three DR buying force in the industry, the majority of publishers have adopted the IAB Podcast Measurement Technical Guidelines Version 2.0.
This widespread adoption solved the “apples to apples” problem as it pertained to different networks/shows valuing a variable, nonstandard “download” as an underlying component to their CPM calculations. Previous to this widespread adoption, it simply wasn’t known whether a “download” from publisher X was equal to a “download” from publisher Y, making it difficult to aim for a particular CPM as a forecasting tool for performance marketing success.
However, the IAB 2.0 guidelines don’t completely solve the unique-user identification problem, as Dave Zohrob, CEO of Chartable points out. “Having some sort of anonymized user identifier to better calculate audience size would be fantastic — the IAB guidelines offer a good approximation given the data we have but [it] would be great to actually know how many listeners are behind each IP/user-agent combo.”
2. Proof of ad delivery
A second area of business intelligence gaps that many articles point to as a cause of inhibited growth is a lack of “proof of delivery.” Ad impressions are unverifiable, and the channel doesn’t have post logs, so for podcast advertisers the analogous evidence of spots running is access to “airchecks,” or audio clippings of the podcast ads themselves.
Legacy podcast advertisers remember when a full-time team of entry-level staffers would hassle networks via phone or email for airchecks, sometimes not receiving verification that the spot had run until a week or more after the fact. This delay in the ability to accurately report spend hampered fast-moving performance marketers and gave the illusion of podcasts being a slow, stiff, immovable media type.
Systematic aircheck collection has been a huge advent and allowed for an increase in confidence in the space — not only for spend verification, but also for creative compliance and optimization. Interestingly, this feature has come up almost as a byproduct of other development, as the companies who offer these services actually have different core business focuses: Magellan AI, our preferred partner, is primarily a competitive intelligence platform, but pivoted to also offer airchecking services after realizing what a pain point it was for advertisers; Veritone, an AI company that’s tied this service to its ad agency, Veritone One; and Podsights, a pixel-based attribution modeling solution.
3. Competitive intelligence
Last, competitive intelligence and media research continue to be a challenge. Magellan AI and Podsights offer a variety of fee and free tiers and methods of reporting to show a subset of the industry’s activity. You can search a show, advertiser or category, and get a less-than-whole, but still directionally useful, picture of relevant podcast advertising activity. While not perfect, there are sufficient resources to at least see the tip of the industry iceberg as a consideration point to your business decision to enter podcasts or not.
As Sean Creeley, founder of Podsights, aptly points out: “We give all Podsights research data, analysis, posts, etc. away for free because we want to help grow the space. If [a brand], as a DIY advertiser, desired to enter podcasting, it’s a downright daunting task. Research at least lets them understand what similar companies in their space are doing.”
There is also a nontech tool that publishers would find valuable. When we asked Shira Atkins, co-founder of Wonder Media Network, how she approaches research in the space, she had a not-at-all-surprising, but very refreshing response: “To be totally honest, the ‘research’ I do is texting and calling the 3-5 really smart sales people I know and love in the space. The folks who were doing radio sales when I was still in high school, and the podcast people who recognize the messiness of it all, but have been successful at scaling campaigns that work for both the publisher and the advertiser. I wish there was a true tracker of cross-industry inventory — how much is sold versus unsold. The way I track the space writ large is by listening to a sample set of shows from top publishers to get a sense for how they’re selling and what their ads are like.”
Even though podcast advertising is no longer limited by download standardization, spend verification and competitive research, there are still hurdles that the channel has not yet overcome.
The conclusion to this article, These 3 factors are holding back podcast monetization, is available exclusively to Extra Crunch subscribers.
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