The federal government’s lawsuit isn’t likely to derail the company’s market dominance.
The suit is the first antitrust action against the company to result from investigations by American regulators.
Googling something was all we once did with Google. Now we spend hours a day using its maps, videos, security cameras, email, smartphones and more.
In the suit, the Justice Department is expected to argue that Google used anticompetitive practices to safeguard its monopoly position as the dominant force in search and search-advertising, which sit at the foundation of the company’s extensive advertising, data mining, video distribution, and information services conglomerate.
It would be the first significant legal challenge that Google has faced from U.S. regulators despite years of investigations into the company’s practices.
A 2012 attempt to bring the company to the courts to answer for anti-competitive practices was ultimately scuttled because regulators at the time weren’t sure they could make the case stick. Since that time Alphabet’s value has skyrocketed to reach over $1 trillion (as of today’s share price).
Alphabet, Google’s parent company, holds a commanding lead in both search and video. The company dominates the search market — with roughly 90% of the world’s internet searches conducted on its platform — and roughly three quarters of American adults turn to YouTube for video, as the Journal reported.
In the lawsuit, the Department of Justice will say that Alphabet’s Google subsidiary uses a web of exclusionary business agreements to shut out competitors. The billions of dollars that the search giant collects wind up paying mobile phone companies, carriers and browsers to make the Google search engine a preset default. That blocks competitors from being able to access the kinds of queries and traffic they’d need to refine their own search engine.
It will be those relationships — alongside Google’s insistence that its search engine come pre-loaded (and un-deletable) on phones using the Android operating system and that other search engines specifically not be pre-loaded — that form part of the government’s case, according to Justice Department officials cited by the Journal.
The antitrust suit comes on the heels of a number of other regulatory actions involving Google, which is not only the dominant online search provider, but also a leader in online advertising and in mobile technology by way of Android, as well as a strong player in a web of other interconnected services like mapping, online productivity software, cloud computing and more.
A report last Friday in Politico noted that Democrat Attorneys General would not be signing the suit. That report said those AGs have instead been working on a bipartisan, state-led approach covering a wider number of issues beyond search — the idea being also that more suits gives government potentially a stronger bargaining position against the tech giant.
A third suit is being put together by the state of Texas, although that has faced its own issues.
While a number of tech leviathans are facing increasing scrutiny from Washington, with the US now just two weeks from Election Day, it’s unlikely that we are going to see many developments around this and other cases before then. And in the case of this specific Google suit, in the event that Trump doesn’t get re-elected, there will also be a larger personnel shift at the DoJ that could also change the profile and timescale of the case.
In any event, fighting these regulatory cases is always a long, drawn-out process. In Europe, Google has faced a series of fines over antitrust violations stretching back several years, including a $2.7 billion fine over Google shopping; a $5 billion fine over Android dominance; and a $1.7 billion fine over search ad brokering. While Goolge slowly works through appeals, there are also more cases ongoing against the company in Europe and elsewhere.
Google is not the only one catching the attention of Washington. Earlier in October, the House Judiciary Committee released a report of more than 400 pages in which it outlined how tech giants Apple, Amazon, Alphabet (Google’s parent company) and Facebook were abusing their power, covering everything from the areas in which they dominate, through to suggestions for how to fix the situation (including curtailing their acquisitions strategy).
That seemed mainly to be an exercise in laying out the state of things, which could in turn be used to inform further actions, although in itself, unlike the DoJ suit, the House report lacks teeth in terms of enforcement or remedies.
A victory for the government could remake one of America’s most recognizable companies and the internet economy that it has helped define.
A nationwide operation of 1,300 local sites publishes coverage that is ordered up by Republican groups and corporate P.R. firms.
Dear Mr. Zuckerberg, Mr. Dorsey, Mr. Pichai and Mr. Spiegel: We need universal digital ad transparency now!
The negative social impacts of discriminatory ad targeting and delivery are well-known, as are the social costs of disinformation and exploitative ad content. The prevalence of these harms has been demonstrated repeatedly by our research. At the same time, the vast majority of digital advertisers are responsible actors who are only seeking to connect with their customers and grow their businesses.
Many advertising platforms acknowledge the seriousness of the problems with digital ads, but they have taken different approaches to confronting those problems. While we believe that platforms need to continue to strengthen their vetting procedures for advertisers and ads, it is clear that this is not a problem advertising platforms can solve by themselves, as they themselves acknowledge. The vetting being done by the platforms alone is not working; public transparency of all ads, including ad spend and targeting information, is needed so that advertisers can be held accountable when they mislead or manipulate users.
Our research has shown:
- Advertising platform system design allows advertisers to discriminate against users based on their gender, race and other sensitive attributes.
- Platform ad delivery optimization can be discriminatory, regardless of whether advertisers attempt to set inclusive ad audience preferences.
- Ad delivery algorithms may be causing polarization and make it difficult for political campaigns to reach voters with diverse political views.
- Sponsors spent more than $1.3 billion dollars on digital political ads, yet disclosure is vastly inadequate. Current voluntary archives do not prevent intentional or accidental deception of users.
While it doesn’t take the place of strong policies and rigorous enforcement, we believe transparency of ad content, targeting and delivery can effectively mitigate many of the potential harms of digital ads. Many of the largest advertising platforms agree; Facebook, Google, Twitter and Snapchat all have some form of an ad archive. The problem is that many of these archives are incomplete, poorly implemented, hard to access by researchers and have very different formats and modes of access. We propose a new standard for universal ad disclosure that should be met by every platform that publishes digital ads. If all platforms commit to the universal ad transparency standard we propose, it will mean a level playing field for platforms and advertisers, data for researchers and a safer internet for everyone.
The public deserves full transparency of all digital advertising. We want to acknowledge that what we propose will be a major undertaking for platforms and advertisers. However, we believe that the social harms currently being borne by users everywhere vastly outweigh the burden universal ad transparency would place on ad platforms and advertisers. Users deserve real transparency about all ads they are bombarded with every day. We have created a detailed description of what data should be made transparent that you can find here.
We researchers stand ready to do our part. The time for universal ad transparency is now.
Jason Chuang, Mozilla
Kate Dommett, University of Sheffield
Laura Edelson, New York University
Erika Franklin Fowler, Wesleyan University
Michael Franz, Bowdoin College
Archon Fung, Harvard University
Sheila Krumholz, Center for Responsive Politics
Ben Lyons, University of Utah
Gregory Martin, Stanford University
Brendan Nyhan, Dartmouth College
Nate Persily, Stanford University
Travis Ridout, Washington State University
Kathleen Searles, Louisiana State University
Rebekah Tromble, George Washington University
Abby Wood, University of Southern California
Four inflection points transformed Mr. Biden from a pauper during the primaries to a powerhouse against President Trump.
The social network, which prohibits misinformation related to the coronavirus, has also banned other types of content in recent days.
Year after year, a lack of transparency in how ad traffic is sourced, sold and measured is cited by advertisers as a source of frustration and a barrier to entry in working with various providers. But despite progress on the protection and privacy of data through laws like GDPR and COPPA, the overall picture regarding ad-marketing transparency has changed very little.
In part, this is due to the staggering complexity of how programmatic and other advertising technologies work. With automated processes managing billions of impressions every day, there is no universal solution to making things more simple and clear. So the struggle for the industry is not necessarily a lack of intent around transparency, but rather how to deliver it.
Frustratingly, evidence shows that the way data is collected and used by some industry players has played a large part in reducing people’s trust in online advertising. This is not a problem that was created overnight. There is a long history and growing sense of consumer frustration with the way their data is being used, analyzed and monetized and a similar frustration by advertisers with the transparency and legitimacy of ad clicks for which they are asked to pay.
There are continuing efforts by organizations like the IAB and TAG to create policies for better transparency such as ads.txt. But without hard and fast laws, the responsibility lies with individual companies.
One relatively simple yet largely spurned practice that would engender transparency and trust for the benefit of all parties (brands, consumers and ad/marketing providers) would be for the industry to come together and have all parties open their SDKs.
Why open-sourcing benefits advertisers, publishers and the ad industry
Open-source software is code that anyone is free to use, analyze, alter and improve.
Auditing the code and adjusting the SDKs functionality based on individual needs is a common practice — and so too are audits by security companies or interested parties who are rightly on the lookout for app fraud. By showing exactly how the code within the SDK has been written, it is the best way to reassure developers and partners that there are no hidden functions or unwanted features.
Everyone using open-source SDKs can learn exactly how it works, and because it is under an open-source license, anyone can suggest modifications and improvements in the code.
Open source brings some risks, but much bigger rewards
The main risk from opening up an SDK code is that third parties will look for ways to exploit it and insert their own malicious code, or else look at potential vulnerabilities to access back-end services and data. However, providers should be on the lookout and be able to fix the potential vulnerabilities as they arise.
As for the rewards, open-sourcing engenders trust and transparency, which should certainly translate into customer loyalty and consumer confidence. After all, we are all operating in a market where advertisers and developers can choose who they want to work with — and on what terms.
Selfishly but practically speaking, opening SDKs can also help companies in our industry protect themselves from others’ baseless claims that are simply intended to promote their products. With open standards, there are no unsubstantiated, false accusations intended for publicity. The proof is out there for everyone to see.
How ad tech is embracing open source
In the ad tech space, companies such as MoPub, Appodeal and AppsFlyer are just a few that have already made some or all of their SDKs available through an open-source license.
All of these companies have decided to use an open-source approach because they recognize the importance of transparency and trust, especially when you are placing the safety and reputation of your brand in the hands of an algorithm. However, the majority of SDKs remain closed.
Relying on forward-thinking companies to set their own transparency levels will only take our industry so far. It’s time for stronger action around trust and data transparency. In the same way that GDPR and COPPA have required companies to address privacy and, ultimately, to have forced a change that was needed, open-sourcing our SDKs will take the ad-marketing space to new heights and drive new levels of trust and deployment with our clients, competitors, legislators and consumers.
The industry-wide challenge of transparency won’t be solved any time soon, but the positive news is that there is movement in the right direction, with steps that some companies are already taking and others can easily take. By implementing measures to ensure brand-safe placements and helping limit ad fraud; improving relationships between brands, agencies, and programmatic partners; and bringing clarity to consumer data use; confidence in the advertising industry will improve and opportunities will subsequently grow.
That’s why we are calling on all ad/marketing companies to take this step forward with us — for the benefit of our consumers, brands, providers and industry at large — to embrace open-source SDKs as the way to engender trust, transparency and industry transformation. In doing so, we will all be rewarded with consumers who are more trusting of brands and brand advertising, and subsequently, brands who trust us and seek opportunities to implement more sophisticated solutions and grow their business.
The platform is trying to address growing concern that falsehoods could lead to instability. Most of the changes will start on Oct. 20.
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.
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.
Political ads will be banned indefinitely after polls close on Nov. 3 and the company plans new steps to limit misinformation about the results.
Even when you’re excellent at making the sale, you still need people to know you exist in the first place.
Content is excellent at making the case for your product or service, but it also excels at providing value to potential customers in a more tangential way, introducing them to your brand and building awareness and authority.
Here’s how utilizing content marketing and digital PR can make huge strides in getting your brand name out there.
Ranking on-site content for awareness keywords
When on-site content you created ranks well in the search engine results pages (SERPs), that doesn’t just mean you get more traffic (although that’s certainly a major benefit).
You’re also getting your brand name in front of searchers because you’re appearing in the results. You’re building authority because Google appears to believe you have the best answer for their query. You’re giving the searcher and answer to their question and beginning to build trust.
So how do you know which keywords/topics to target and what kind of content to create? You perform keyword research, which basically means examining what keywords people are searching for, how many people search for them per month and how hard it’ll be to rank for them.
When your goal is to build awareness, it’s important that the keywords and topics you target have high volume. In other words, they’re searched a lot. Awareness objectives mean reaching as many people as possible so more people know that your brand exists and begin to understand what it’s about.
As the world reopens and revenue teams are unleashed to meet growth targets, many B2B sellers and marketers are wondering how they can best prioritize prospect accounts. Everyone ultimately wants to achieve predictable revenue growth, but in uncertain times — and with shrinking budgets — it can feel like a pipe dream.
Slimmer budgets likely mean you’ll need more accurate targeting and higher win rates. The good news is your revenue team is likely already gathering tons of prospect data to help you improve account targeting, so it’s time to put that data to work with artificial intelligence. Using big data and four essential AI-based models, you can understand what your prospects want and successfully predict revenue opportunities.
Big data and CDPs are first steps to capturing account insights
Capturing and processing big data is essential in order to know everything about prospects and best position your solution. Accurately targeting your campaigns and buyer journeys necessitates more data than ever before.
Marketers today rely on customer data platforms (CDPs) to handle this slew of information from disparate sources. CDPs let us mash together and clean up data to get a single source of normalized data. We can then use AI to extract meaningful insights and trends to drive revenue planning.
That single source of truth also lets marketers dive into the ocean of accounts and segment them by similar attributes. You can break them down into industry, location, buying stage, intent, engagement — any combination of factors. When it’s time to introduce prospects to your cadence, you’ll have segment-specific insights to guide your campaigns.
AI realizes data-based insights
You might find that your data ocean is much deeper than you expected. While transforming all that data into a single source to drive actionable insights, you’ll also need the right resources and solutions to convert raw data into highly targeted prospect outreach.
This is where AI shines. AI and machine learning enable revenue teams to analyze data for historical and behavioral patterns, pluck out the most relevant intent data, and predict what will move prospects through the buyer journey.
The decision to narrow the case to search could set off separate lawsuits from states over Google’s power in other business segments.
The $3.1 billion acquisition of DoubleClick in 2007 was a “game changer.” A growing number of antitrust experts say it’s the sort of deal that should no longer be possible.
There are still many ways that voter misinformation can spread on the social network, even as it moves to cut off new political ads on Oct. 27.
The attorney general is said to have set a deadline over the objections of career lawyers who say they need more time to build the case.
The Biden ad, part of a $45 million one-week television and digital purchase that is by far the campaign’s largest to date, comes as the Democratic nominee pushes back against President Trump’s attacks.
Meditation apps, tinctures, stress-busting gummies — spending on commercials for all of them is rising amid the pandemic’s climbing death toll and economic strain.
Earlier this week, an advertising agency emerged with a video bragging about an ad-campaign concept: We’ll invade gaming-filled Twitch chat rooms and post ads for your brand for cheap. The attached video was exactly the kind of cringe you might expect from “brand engages with video game culture,” with edgy yet inoffensive quotes, footage of fake games, and digitally altered voices.
But what looked like a fake ad concept has turned out to be very real—and after examining how Twitch works, the whole thing looks like a possible FTC violation.
More like “king of steaming-mad Twitch users”
The ad campaign, run by the Ogilvy agency on behalf of Burger King, relied on a common Twitch trope of donating to game-streaming hosts. “Affiliate” Twitch users are eligible to receive cash from viewers, either in the form of flat-rate subscriptions or variable one-time donations, and hosts often encourage this by adding voice-to-text automation to the process. So if you pay a certain amount, a voice will read your statement out loud—and hosts usually retroactively react to weird and offensive statements made by these systems instead of pre-screening them. (They’re busy playing a game, after all.)
The world’s biggest social network is working out what steps to take should President Trump use its platform to dispute the vote.
It’s been less than a year since Group Nine Media acquired PopSugar — but it’s been a uniquely challenging time in digital media.
Brian Sugar founded the eponymous women’s lifestyle site with his wife Lisa Sugar . Post-acquisition, he’s become president for the entirety of Group Nine (which also owns Thrillist, NowThis, The Dodo and Seeker) and also joined the company’s board.
That job probably looks very different from what he expected last fall. The company had to lay off 7% of its staff back in April, which Sugar described as “one of the worst days of my career.” At the same time, he remains confident about the online advertising business. In his view, it’s TV advertising that’s taken a “huge punch” in the face and will never recover.
“We like to think of ourselves as one of the fastest, most innovative publishers out there,” Sugar told me. “And now’s the time for us to kind of show that off.”
You can read an edited, updated and condensed transcript of our conversation below, in which I talked to Sugar about how his role has evolved, how he motivates the team during difficult times and what gets lost in the shift to remote work.
TechCrunch: Obviously, it’s been a crazy couple of months since we last talked. What does your job look like now?
Brian Sugar: Well, I feel like a data miner, searching for answers. I feel like a hackathon engineer. And I feel like a therapist. You know, we like to think of ourselves as one of the fastest, most innovative publishers out there. And now’s the time for us to kind of show that off.
[We’ve just been] looking at data on how people are consuming our content across platforms. And on our site, we’ve come up with some really interesting ideas that we’ve implemented. We’ve been having these really cool hackathon Fridays to build stuff quickly, because a lot of people feel like they have a little bit more time on their hands — because you don’t have to travel to meetings, you can get more work done. Some people feel they’re more efficient.
We’re extremely optimistic. All our brands are extremely optimistic, and so is [the whole] company.
You mentioned launching some new products to respond to how audience behavior is changing. Are there any examples?
The first one [is] the PopSugar Fitness thing. We were planning on launching a paid workout subscription service in May, but everybody was working from home [in March], and we decided to pull the launch all the way up to as fast as we can launch it. We launched it that following weekend. Since the launch in late March, over the past few months, we’ve had 200,000 people sign up, and we have 50,000 monthly active users on it.
The Trump campaign is trying to make sure that Mr. Trump’s message will be almost impossible to miss even during the Democrats’ biggest week.
We’ve aggregated many of the world’s best growth marketers into one community. Twice a month, we ask them to share their most effective growth tactics, and we compile them into this Growth Report.
This is how you stay up-to-date on growth marketing tactics — with advice that’s hard to find elsewhere.
Without further ado, on to our community’s advice.
Accessing social groups through referrals
Excerpt from Demand Curve’s Growth Training.
A surprising benefit of referrals is how they often lead to social partnership opportunities.
Consider this process:
- Find your happiest users.
- Figure out what social groups they belong to. This could be anything from a female founders group, to university alumni networks, to a restaurant management trade association.
- How do you find out? Just ask them what groups they belong to. Don’t be afraid of conversation.
- Ask the happy user to connect you with the heads of those groups. Solve a problem they collectively have — even if it’s only tangentially related to your business. What matters is that more of these ideal customers know and trust you. You can also refer speakers, offer deals, write content for them or offer free office hours.
- Down the road, these people inevitably send you referrals.
- Reach out cold to people in other, similar groups. Reference the endorsement of the original group and provide a case study (with their permission).
Going through groups can be a high-leverage way to land and expand into ideal audiences.
At last month’s Early Stage virtual event, channel growth experts joined TechCrunch reporters and editors for a series of conversations covering the best tools and strategies for building startups in 2020. For this post, I’ve recapped highlights of talks with:
- Ethan Smith, founder and CEO, Graphite
- Susan Su, startup growth advisor, executive-in-residence, Sound Ventures
- Asher King-Abramson, founder, Got Users
If you’d like to hear or watch these conversations in their entirety, we’ve embedded the videos below.
Ethan Smith: How to build a high-performance SEO engine
Relying on internet searches to learn about growth topics like search engine optimization leads to a rabbit hole of LinkedIn thinkfluencer musings and decade-old Quora posts. Insights are few and far between, because SEO has changed dramatically as Google has squashed spammy techniques “specialists” have pushed for years.
Ethan Smith, owner of growth agency Graphite, says Google didn’t kill SEO, but the channel has evolved. “SEO has built a negative reputation over time of being spammy,” Smith says. “The typical flow of an SEO historically has been: I need to find every single keyword I possibly can find and auto-generate a mediocre page for each of those keywords, the user experience doesn’t really matter, content can be automated and spun, the key is fooling the bot.”
Artificial intelligence has disrupted this flow as algorithms have abandoned hard-coded rules for more flexible designs that are less vulnerable to being gamed. What SEO looks like today, Smith says, is all about trying to “figure out what the algorithm is trying to accomplish and try to accomplish the same thing.” Google’s algorithms aren’t looking for buckets of keywords, they’re looking to distill a user’s intent.
The key to building a strategy around SEO as a company breaks down into six steps surrounding intent, says Smith:
- Target by intent