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Podcast advertising has a business intelligence gap

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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.

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.

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.

Advertisers may pull out of campaigns for fear of under-delivery, missing out on incremental growth because they were worried about not getting what they paid for.

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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What exactly is an algorithm and how does it work?

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The world of computing is full of buzzwords: AI, supercomputers, machine learning, the cloud, quantum computing and more. One word in particular is used throughout computing – algorithm.

In the most general sense, an algorithm is a series of instructions telling a computer how to transform a set of facts about the world into useful information. The facts are data, and the useful information is knowledge for people, instructions for machines or input for yet another algorithm. There are many common examples of algorithms, from sorting sets of numbers to finding routes through maps to displaying information on a screen.

[Read: What audience intelligence data tells us about the 2020 US presidential election]

To get a feel for the concept of algorithms, think about getting dressed in the morning. Few people give it a second thought. But how would you write down your process or tell a 5-year-old your approach? Answering these questions in a detailed way yields an algorithm.

Input

When you get dressed in the morning, what information do you need? First and foremost, you need to know what clothes are available to you in your closet. Then you might consider what the temperature is, what the weather forecast is for the day, what season it is and maybe some personal preferences.To a computer, input is the information needed to make decisions.

All of this can be represented in data, which is essentially simple collections of numbers or words. For example, temperature is a number, and a weather forecast might be “rainy” or “sunshine.”

Transformation

Next comes the heart of an algorithm – computation. Computations involve arithmetic, decision-making and repetition.

So, how does this apply to getting dressed? You make decisions by doing some math on those input quantities. Whether you put on a jacket might depend on the temperature, and which jacket you choose might depend on the forecast. To a computer, part of our getting-dressed algorithm would look like “if it is below 50 degrees and it is raining, then pick the rain jacket and a long-sleeved shirt to wear underneath it.”

After picking your clothes, you then need to put them on. This is a key part of our algorithm. To a computer a repetition can be expressed like “for each piece of clothing, put it on.”

Output

Finally, the last step of an algorithm is output – expressing the answer. To a computer, output is usually more data, just like input. It allows computers to string algorithms together in complex fashions to produce more algorithms. However, output can also involve presenting information, for example putting words on a screen, producing auditory cues or some other form of communication.

So after getting dressed you step out into the world, ready for the elements and the gazes of the people around you. Maybe you even take a selfie and put it on Instagram to strut your stuff.

Machine learning

Sometimes it’s too complicated to spell out a decision-making process. A special category of algorithms, machine learning algorithms, try to “learn” based on a set of past decision-making examples. Machine learning is commonplace for things like recommendations, predictions and looking up information.

For our getting-dressed example, a machine learning algorithm would be the equivalent of your remembering past decisions about what to wear, knowing how comfortable you feel wearing each item, and maybe which selfies got the most likes, and using that information to make better choices.

So, an algorithm is the process a computer uses to transform input data into output data. A simple concept, and yet every piece of technology that you touch involves many algorithms. Maybe the next time you grab your phone, see a Hollywood movie or check your email, you can ponder what sort of complex set of algorithms is behind the scenes.


This article is republished from The Conversation by Jory Denny, Assistant Professor of Computer Science, University of Richmond under a Creative Commons license. Read the original article.

Published October 22, 2020 — 10:00 UTC

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Microsoft wants to cut down pollution from its business travel  

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Microsoft announced a new effort today to reduce pollution coming from some of its employees’ flights. It plans to buy credits for sustainable aviation fuel to cover travel on the commercial flight routes most frequented by its employees during business trips.

It will buy the credits from Dutch company SkyNRG, which will then supply cleaner-burning fuel to Alaska Airlines. The less-polluting flights will be operated by Alaska Airlines for travel between Seattle-Tacoma International Airport (near Microsoft’s corporate headquarters) and San Francisco, San Jose, and Los Angeles international airports.

The fuel that SkyNRG provides would be made in the US with used cooking oil or other plant oils. When it’s burned, the fuel could emit 75 percent fewer CO2 emissions compared to traditional, kerosene-based jet fuel, SkyNRG claims.

This is Microsoft’s latest move to address their greenhouse gas emissions. In January, it pledged to remove more planet-heating carbon dioxide than it emits by 2030. It also said that by 2050, it would draw down all the emissions it’s ever released since its founding. Despite the splashy announcement, the technology needed to capture significant amounts of carbon dioxide doesn’t yet exist. Right now, the clearest way to avert more catastrophic climate change is to put out less pollution in the first place. When it comes to business travel, that means taking fewer flights and switching to cleaner fuels.

“We hope this sustainable aviation fuel model will be used by other companies as a way to reduce the environmental impact of their business travel,” Judson Althoff, executive vice president of worldwide commercial business at Microsoft, said in a statement.

Business travel accounted for about three percent of Microsoft’s carbon footprint during its 2019 fiscal year, according to a company factsheet. That climate pollution, equivalent to 392,557 metric tons of carbon dioxide, is roughly the same amount that 84,809 passenger vehicles might produce in a year. Although it’s a small fraction of Microsoft’s overall emissions, pollution from the company’s business travel has grown steadily since 2017.

Until the COVID-19 pandemic grounded flights en masse this year, aviation was one of the world’s fastest growing sources of global greenhouse gas emissions. If the industry was a country, it would be one of the top ten carbon polluters in the world. Driven by concerns for the climate, activists sparked a worldwide trend shunning air travel in 2017.

The pandemic devastated domestic and international travel this year, resulting in a nearly 47 percent drop in emissions from the sector during the first seven months of 2020. Microsoft says it’s currently allowing some employees to travel for “critical services and sales,” and expects more travel to resume when COVID-19 case numbers decline.

When more planes do start flying again, airlines will have to keep net emissions for international flights at 2019 levels, thanks to a decision by the United Nations’ aviation body, the International Civil Aviation Organization (ICAO), earlier this year. Reducing flights is still the best way to reduce emissions, but the airline industry, and frequent fliers like Microsoft are looking at alternatives. Batteries are still too heavy to power large, electrified commercial planes, which leaves cleaner-burning fuels as the best option to reduce pollution from flights.

Back in 2016, ICAO estimated that if sustainable aviation fuel powers every international flight by 2050, it would slash emissions by 63 percent. Microsoft’s announcement is one small step in that direction.

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Hide your Kindle SHAME with this case that looks like a REAL book

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I love ereaders, but regular books are better.

I’m not saying Kindles and the like aren’t worth your time, far from it. They’re amazing. Take, for example, my Kindle Paperwhite. It hold thousands of books, so I’m never at a loss for something to read. The backlight means I can devour novels in low-light. It’s easy to hold, so I can get comfortable everywhere. What’s not to like?

Well, I’ll tell you: it doesn’t make me look smart.

Books, and let’s be honest here, are a PR tool. Like… have you ever actually read a book? Me neither. Instead, I spend around 12 hours every day on public transport holding a copy of Gravity’s Rainbow so people really know just how smarter and more interesting and cleverer I am than them.

You can’t do that with a Kindle.

Whenever anyone looks at you using an ereader their first though will be “NERD.” That’s not what I want. Not at all. I need to be seen as a swashbuckling Lord Byron, not a basement-dwelling incel like Elon Musk.

Here we reach the crux of the issue: there are times I need to use a Kindle. Like if I’m going on holiday. Or want to read some ridiculous fantasy novel. Plus, Infinite Jest is playing havoc with my Vitamin D deficient wrist bones.

Thankfully there’s a solution, and it’s called the BookBook Kindle Paperwhite case from Twelvesouth. Confuddled? Take a look:

bookbook kindle case front on
GAZE UPON THY DOOM.

You see that? I’ll ask one more time: DO YOU SEE THAT? Not only does it look like an actual book, it looks like an old book. And if there’s one universal truth about people who read old books it’s that they’re supremely sophisticated and are brain geniuses because old books are basically written in a different language and are very, very boring.

The BookBook Kindle case: a good case

That sub-head says it all really.

The BookBook Kindle case is made for the latest version of the Paperwhite and fits it perfectly. Be a bit shit if it didn’t, right?

It offers decent protection, I’ve been able to throw it into my bag and leave it there with little to no damage. Obviously, it makes the Kindle a little heavier and harder to use with one hand, but not by much at all. It’s still easier to hold than a regular book.

Another cool thing: the case automatically wakes and puts your device to sleep when you open and close it.

Also, it only takes a few moments to slip the Kindle Paperwhite out if you do want to read it like that. Quick note though: if you’ll want to be taking the Kindle in and out constantly, maybe look at getting a sleeve rather than a case.

There’s a magnet on the back of the Kindle insert section to keep the device there when you’re reading laying down, but there’s something even cooler hidden in the back: a stand.

Have a look:

bookbook kindle case stand
GAZE UPON THE STAND (AND THE IRRITATING BLURRING AT THE TOP OF THE KINDLE I DIDN’T REALIZE WAS THERE UNTIL IT WAS TOO LATE BUT NOW YOU’RE SIMPLY GOING TO HAVE TO ACCEPT)

This is amazing for reading while you’re eating, or in any other situations where you don’t want to use your hands like a goddamn animal. I’m a big fan.

There really aren’t very many negatives. If I’m being picky, the zip curves into the spine a bit meaning it doesn’t always open in a totally smooth way, but I got used to this pretty quickly. Also, I’m not sure I’d trust it holding up if you got it wet (say, in the bath), but you can just take your waterproof Kindle out the case if you plan on doing that.

And the biggest negative? Well, you’re not gonna think much of this Kindle case if you believe hiding an ereader in a fake book is pathetic. But hey, you can’t please everyone!

bookbook kindle case - side angle and zips
GAZE UPON THE MERELY OKAY ZIPPING SYSTEM.

The conclusion: yes, the BookBook Kindle case is a Kindle case that looks like a book

If you think that’s cool (me), that’s great. If you don’t (losers), that’s also fine.

Basically, the BookBook Kindle case is a simple idea well executed. Plus, it’ll help everyone know how astute, educated, and downright brainy you are. And that’s what you want, right? For the world to know? At last, for the world to know precisely how fucking great you are? So it can quiver in fear? At your smarts? Make the world grovel at your smart little feet? Grovel, world, mewl and roll in your own dirt for my amusement, for I am Big Brain, look upon my works, ye mighty, and tremble.

Or you may just think it’s neat. The BookBook Kindle case costs $50 and you buy it here.

For more gear, gadget, and hardware news and reviews, follow Plugged on Twitter and Flipboard.

Published October 22, 2020 — 09:23 UTC

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