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The Business Intelligence Gap in Podcast Advertising


November 9, 2020


This article was originally published on TechCrunch.

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 investment by advertisers, 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 b) a new business venture that collects and publishes more comprehensive data that ultimately promotes growth for podcast advertising.  Let's explore.

Podcasts have always had challenges when it comes to the analytics behind distribution, consumption, and conversion. For an industry projected to exceed $1B 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, etc., 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?” 

A Brief Look at 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, US Director of Sales & Brand Partnerships at Acast, shared: “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. And 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 micro trends. 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 is just one recent case study on the challenges of a decentralized (or non-existent) 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 under-delivery 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 super-savvy 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 non-universal 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 3 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, non-standard “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 Creely, 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 non-tech 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 v. 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.

What is Now Holding the Channel’s Monetization Back?

Podcast advertising growth is inhibited by three major factors: 

  • Lack of macro distribution, consumption, and audience data
  • Current methods of conversion tracking
  • Idea of a “playbook” for podcast performance marketing

Because of these limiting factors, it’s currently more of an art than a science to piece disparate data from multiple sources, firms, agencies, and advertisers, into a somewhat conclusive argument to brands as to why they should invest in podcast advertising. 

1. Lack of macro distribution, consumption, and audience data

There were several resources that released updates based on what they saw in terms of consumption when COVID-19 hit. Hosting platforms, publishers, and third party tracking platforms all put out their best guesses as to what was happening. Advertisers’ own podcast listening habits had been upended due to lockdowns; they wanted to know how broader changes in listening habits were affecting their campaigns. Were downloads going up, down, or staying the same? What was happening with sports podcasts, without sports? 

At Right Side Up, we receive and analyze all of the available research from major publishers (Stitcher, aCast), to major platforms (Megaphone), and third party research firms (PodTrac, IAB, Edison Research). However, no single entity encompasses the entire space or provides the kind of interactive, off-the-shelf-customizable SaaS product we’d prefer, and that digitally native marketers expect. Plus, there isn’t anything published in real time; most sources publish once or twice annually. 

So what did we do? We reached out to trusted publishers and partners to gather data around shifting consumption due to COVID-19 ourselves, and determined that, though there was a drop in downloads in the short term, it was neither as precipitous nor as enduring as some had feared. This was confirmed by some early reports available, but how were we to evidence our own piecewise sample with another? Moreover, how could you invest 6-7 figures of marketing dollars if you didn’t have the firsthand intelligence we gathered and our subject matter experts on deck to make constant adjustments to your approach?  

We were able to piece together trends we’re seeing that point to increased download activity in recent months that surpass February/March heights. We’ve determined that the industry is back on track for growth with a less steep, but still growing, listenership trajectory. But even though more recent reports have been published, a longitudinal, objective resource has not yet emerged to show a majority of the industry’s journey through one of the most disruptive media environments in recent history. 

There is a need for a new or existing entity to create cohesive data points; a third party that collects and reports listening across all major hosts and distribution points, or “podcatchers,” as they’re colloquially called. As a small example: wouldn’t it be nice to objectively track seasonal listening of News/Talk programming, and schedule media planning and flighting around that? Or to know what the demographics of that audience look like, compared to other verticals? 

What percentage increase in efficiency and/or volume would you gain from your marketing efforts in the channel? Would that delta be profitable against paying a nominal or ongoing licensing or research fee for most brands?  

These challenges aren’t just affecting advertisers. David Cohn, VP of sales at Megaphone, agrees that “full transparency from the listening platforms would make our jobs easier, along with everyone else's in the industry. We'd love to know how much of an episode is listened to, whether an ad is skipped, etc. Along the same lines, having a central source for [audience] measurement would be ideal - similar to what Nielsen has been for TV.” This would also enable us to understand cross-show ad frequency, another black box for advertisers and the industry at large. 

Plus, in addition to the challenges around consumption and audience data tied to content, there’s the lack of data available to advertisers tied to not only delivery, but consumption of the actual ads themselves. A combination of lack of syndicated research and ad exposure tracking makes it difficult for advertisers to accurately model out reach and frequency, which is especially problematic for advertisers not optimizing to an immediate conversion goal. 

John Goforth, head of business development at Magellan AI agrees: “It won’t come as a surprise to most, but knowing whether listeners even heard an ad continues to be a pain for the entire industry. [Advertisers] WANT to spend more, but on the brand awareness side, sometimes attribution isn’t enough. They just want to know, for certain, their impressions were delivered. This is similar to the viewability conversation in display advertising. The big difference in podcasts is there isn’t an obvious, system-wide solution. Individual platforms can address it (as we’ve seen from Spotify with their SAI product), but no platform accounts for all of the listening.”

2. Current methods of conversion tracking

One of the most common concerns advertisers have post-launch are fears about lack of performance early on, in the first one to three weeks of a campaign. Conversion activity lags post-media placement because of the channel’s delayed consumption, so it can be disconcerting for brands to see media investment with no immediate return, especially if they’re used to seeing immediate return on ad spend from channels like Facebook or Google. We’ve had this conversation many times, and recently published anonymized results that illustrate the way that time-shifted consumption of media leads to further time-shifted response.

Conversion tracking has been a hot topic this year, with innovation from pixel based solutions like Chartable, Podsights, and Barometric. In the “legacy” way of doing things, direct conversion tracking has been limited to show specific Vanity URLs/Promo Codes. The industry standard is then to use a post-conversion survey, if available; usually some form of “How did you hear about us?” asked post-purchase in the checkout flow, or after the campaign’s key performance indicator (KPI). From that first party data, you can extrapolate a ‘multiplier’ to capture indirect conversion activity e.g. for every 1 person who remembered/cared to come through the attributable Vanity URL/Promo Code path, there were 2 others that didn’t according to the survey, therefore a multiplier of 3x should be applied.  

The promise of pixel-based solutions is to bring podcast advertising towards the view-through model used commonly in digital attribution. That is to say, how can we, as an industry, rethink the path of the listener through the RSS feed and through to the advertisers’ conversion confirmation page? This solution, if verified, universalized, and trustworthy, would completely change the level when comparing podcasts as a growth marketing channel to social or display by offering a 1:1 comparison. These solutions are not only promising for DR/ecommerce advertisers, but also for the perpetually coveted brand advertisers, who may want to explore website visits or device graph matches alongside brick and mortar visits and/or rewards card data. 

While the blue sky is certainly there for a pixel-based solution, it’s still singular channel measurement, and not everyone views it as the gold standard for podcast advertising in the same way they do for paid social. For starters, "Facebook’s ubiquity across devices puts it in an entirely different league from the rest of paid social,” says Matt Bahr, founder of Enquire Labs. He pegs "podcast pixel measurement right around Snap or TikTok’s new pixel: these platforms need measurement around the halo effect of a campaign. Otherwise, they’ll get outmuscled by Facebook.” He also points out that many brands are still choosing to supplement pixel-based measurement platforms with their own post-purchase survey data because "advertisers often trust their customers more than they trust the black box attribution platforms, as the customer has nothing to gain in offering up their attribution source.”

There are also challenges in implementing the new technology. Perhaps the largest issue is publisher adoption. Although compatibility rates vary widely by platform because of individual relationships between the third-party and the host/publisher, we have not found an active advertiser’s campaign that has above an 80% compatibility rate—that is to say, 8 out of 10 active shows are being tracked by a third-party. In fact, when we ask partners for coverage on existing media plans, we’ve seen as little as 10-15% of the shows on a given plan accept the technology. For example, Libsyn, reportedly one of the largest hosts in the space,does not currently accept third party pixels. 

Speaking to publisher adoption, Dave Zohrob, CEO of Chartable notes: “There are three obstacles. The first two are about opt-in: hosting platform opt-in and publisher opt-in. Only a few hosting platforms won’t work with us, but they’ve been remarkably stubborn. And some publishers won’t enable attribution, whether out of privacy concerns (despite our commitment to respecting privacy and compliance with regulations) or for some other reason. Finally, as Spotify grows its market share, many publishers do not get pass-through of their Spotify downloads which means we can’t run a reach/frequency calculation.”

Compounding the lack of 100% of coverage in a given campaign, the method by which download users are matched with conversion data relies on a percentage of deterministic matches, with probabilistic device-graph matches making up the balance for what can’t be found on deterministic matching. While this is the status quo for many attribution tools, the issue of <100% publisher compatibility with <100% 1:1 matching makes this solution, in the eyes and hearts of advertisers, not a standalone solution, but rather one to be utilized in addition to first party measurement. Getting show-level, not just publisher or platform, adoption as close to 100% as possible would eliminate this significant hurdle to successful implementation. 

Another challenge is baked into the podcast space itself. We have often told advertisers to wait 14-21 days after an episode launches to see the full response curve mature from their podcast ads, as both download and consumption of content matures. In other words, if your spot dropped yesterday, because of the nature of on-demand listening you won’t see the full volume of acquisitions for 14-21 days thereafter, regardless of an individual brand’s purchase consideration cycle. Accordingly, in order to capture those conversions through a pixel based solution you would have to set your view-through window to 14+ days, not the 24 hours that most digital advertisers are accustomed to. This is one of the factors marketers point to when we ask why they haven’t adopted this measurement methodology.

3. Idea of a “playbook” for podcast performance marketing

Many of the brands we talk to feel like they need an ad agency to place the buy for them—they don’t know where to start, as opposed to channels like paid social and paid search, where most in-house marketers have firsthand experience with channel management. There are also the gaps in data and the nascency of ad technology in the medium that we’ve outlined. The earliest days of podcast monetization leaned heavily on radio and broadcast best practices and industry professionals, and the legacy of traditional media has permeated the first generation of podcast advertisers, networks, and agencies. That’s why an outsized amount of the buying in this channel was historically done by a few ad agencies in the medium, an advantage that has eroded as more brands have begun buying and scaling the channel in-house, often with the help of consultants like us. 

The advantage of working with a buying agency is that you have a partner with tens of clients, years of experience, and the accompanying data to give you a leg up or head start, but that advantage has diminished over time with publisher and network consolidation and channel maturation. 

The challenge is that the ‘playbook’ for what shows are currently working best changes pretty dramatically every few months, as opposed to earlier in the medium, when there weren’t many shows to buy. Agencies could invest with the same partners again and again, finding similar results for mostly D2C companies who adopted the channel at its earliest stages of monetization.. Having successful strategies that you can implement across varied different shows, placements, etc. is good and worth touting, but anyone saying they have the best data on consumption, or downloads, or even ad performance is mistaken—no one does. In present state these are all partial, biased data sets, and won’t likely be relevant outside of a year.

The reality is that successful strategies for general growth marketing apply to podcasts: run a diversified portfolio, keep a proportionate balance of proven performers and unproven tests to maintain and improve CPA over time, and develop strong, honest relationships with media partners, to name a few. 

We’ve built our own podcast practice in a different way, where we focus on helping clients build the function as an internal competency. That’s how you can enable the kind of customization and attention that really make a channel like podcasting, for all its flaws and opportunities, sing. The biggest determining factor we’ve found in our work with advertisers is transparent, cohesive communications and goal alignment among all parties to the campaign. We prefer executing in-house with subject matter experts, whether consultant or full-time. This allows brands to own their media relationships and avoid paying media commissions, which incentivizes partners to scale spend, not necessarily results. 

How Do We Bridge the Business Intelligence Gap in Podcast Advertising?

To be clear, we fully expect the channel to continue to grow and thrive even if we aren’t able to address these challenges in the next 6-12 months. Even amongst all of the challenges in the medium, new advertisers, content, and innovations come out every week. And we aren’t slowing down our pace of investing in the channel at all. 

Here’s how we think about it for ourselves: we bridge this gap by staying constantly engaged with publishers and research partners the way we’ve outlined throughout this article. What we also have, that many do not, is a broad mix of conversion data from a variety of brands and business categories, rates and downloads from over 1,000 of the top DR-friendly podcasts, and other qualitative data points built into our homegrown suite of campaign management tools. From this, we’re confident that we can put together a relatively cohesive and complete picture of the marketplace for advertisers that work with us. While still missing consumption data, both in volume of downloads over time, as well as platform level (e.g. Spotify, Apple Podcasts, et. al.) distributions, once we have that data we can deepen and refine our data set over time to continue giving our advertisers the competitive edge in campaign data, forecasting, and attribution. 

We realize we’re lucky, and that not everyone buying in the channel has the opportunity, resourcing, or focus to accomplish such a task, but we can at least come together to push for a unified repertoire of reporting, and expect more from our media and third-party partners when it comes to public, free reporting of overall listening trends—preferably available in .csv for export, if we’re being picky! The mantra of “high tides raise all boats” is easy to say, it takes a commitment to broad publisher partnerships and willingness to proactively publish data and collaborate in order for us all to benefit. Advertisers and buying firms with substantial power should be using their leverage for unification of data sources and pertinent data. Companies like Chartable, PodSights, PodTrac, Megaphone, Art19 have all the data. Can we push them to put it all together, in the same way we pushed for IAB 2.0 compliance as an industry? 

There is also an opportunity for a new player to enter the space and innovate here: by standardizing download data (already 90% of the way there with IAB 2.0), collecting unique downloads vs corresponding listens for 90%+ of the marketable podcasts, and strike strategic partnerships with firms and brands to share data back and forth and publish provocative, useful trends. This opportunity likely emerges in the vein of a SaaS interface that is intuitive, easy to use, and can provide meaningful visualization and output. The common rejection of this vision is that “there isn’t any data” or “it’s in too many disparate places.” An entity or two with the sole focus of collecting and displaying this information could lift the handicap dragging audio advertising’s growth down, and develop substantial revenue and value as a result. Otherwise, without consistent, reliable, and relatively real-time data on consumption, there will be advertisers that will minimize their investment (exposure to uncertainty) or withhold altogether. We have to solve this problem if we want podcasting to continue to scale beyond its current and projected advertising revenue heights, and mainstream not only as a channel for listeners, but for advertisers as well.

If you’re interested in exploring podcast advertising with Right Side Up, send us a note at hello@rightsideup.co. We'd love to discuss ways we can work together.

Krystina Rubino joined Right Side Up to start its offline marketing practice when she realized too many brands leave offline channels on the table, favoring digital channels past diminishing returns. She has been obsessed with all forms of media for as long as she can remember; she’s an agency and marketing leader with deep experience in building brands and meeting growth goals, for companies of all stages and sizes. She’s spent her career helping companies and brands like Advil, DoorDash, P&G, Lyft, and StitchFix, develop profitable digital and offline media campaigns, often as vanguards in their category and the medium. Her favorite question to ask is “What’s next?” when helping grow a business or scale a customer acquisition campaign.

Grant Durando is a growth marketing leader, currently consulting on podcast and offline advertising at Right Side Up. He has helped some of the largest podcast campaigns start, scale, and optimize their offline growth campaigns; with many going on to be household names because of their ubiquitous podcast presence. After working with 60+ brands in offline growth marketing, he knows what it takes to efficiently dominate emerging media landscapes, and actively works with brands, ad tech, and media companies to push nascent spaces to their limits.

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Let's talk growth

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