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?
Read part 1 of this article, Podcast advertising has a business intelligence gap, on TechCrunch.
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.
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 one person who remembered/cared to come through the attributable vanity URL/promo code path, there were two 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 toward 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/e-commerce 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, eight 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, but 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 versus 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 go mainstream not only as a channel for listeners, but for advertisers as well.