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Spotify acquires content recommendation startup MightyTV

Spotify this morning announced its latest move to expand its marketing and advertising horizons: it has acquired and shut down content recommendation service MightyTV, a startup that (as its name implies) focused on video recommendations, with an app that used a Tinder-style swipe interface to help guide you to TV and film choices compatible with your own tastes. As part of the deal, MightyTV’s founder and CEO Brian Adams will become Spotify’s VP of technology, focused its marketing and advertising platforms.
The company declined to disclosed the terms of the deal. The company launched less than a year ago and had raised just over $4 million, according to CrunchBase. The deal includes MightyTV’s team of eight, who will be based across Spotify’s New York City, Toronto and Stockholm offices.
Adams, notably, was the co-founder and CEO of AdMeld, an advertising optimization platform for publishers that was acquired by Google in 2011, reportedly for $400 million. He then joined Google to run the Doubleclick Publisher Platform, before leaving to start his own company again in 2015.
MightyTV’s Tinder-style mobile app for iOS and Android let you quickly indicate whether you liked or disliked a given title, which helped customize MightyTV’s suggestions to your own personal tastes. As with Tinder, the idea is that the app’s recommendations would then improve over time, the more you used the product.

From a consumer perspective, what made MightyTV interesting was not necessarily its Tinder-like interface — though that was fun — but that it combined different approaches to making its suggestions, combining both those that come from the aggregated user ratings as well as those that better understood one’s individual tastes.
Spotify earlier this month acquired another technology startup, Sonalytic, which also had an angle on improving recommendations. Like an improved Shazam, its tech could identify song snippets and even songs playing at live events. But it also had a machine-learning music recommendation technology that could help you find the music you liked for a given context, like road trips or gym workouts, for example.
Improving recommendations is an area that’s a battlefield for all of today’s streaming services as they seek to expand their user bases beyond early adopters to more casual listeners who may not always know exactly what they want to listen to.
But in addition to this, Spotify’s interest in MightyTV and its leadership is related to how the company hopes to develop its programmatic audio advertising and native brand ads, the company’s announcement indicated.

Spotify launched programmatic audio globally last summer, allowing advertisers to target audiences based on age, gender, genres, and playlists in real-time.
Spotify has not yet spelled out how it plans to use MightyTV’s tech in its programmatic play, but one option could be that Spotify could create tools for users to help them discover new music, and then use those tools both to collect more interesting data on their users, as well as to serve ads to them using those parameters.
Those can both be audio ads along the lines of the kinds of ads that Spotify already presents to users, but it could also be used to help promote music (and related products like concerts or merchandise) on behalf of labels and artists to listeners who may be most receptive.
Building out a bigger marketplace for both labels and artists to sell goods and services beyond basic music would make sense for Spotify. Margins on streaming music are thin-to-negative (something Spotify is trying to change, as we’ve reported).
But services like programmatic ads that promote other products present Spotify as an enabler and platform for a more sustainable streaming music business for everyone, Spotify itself as well as the artists and rightsholders that work with it. (Building out that wider platform and community behind it was also behind several other acquisitions that Spotify has made in the last year, including Preact, Cord Project and Soundwave, and CrowdAlbum.)
“The content recommendation system MightyTV has built is incredibly aligned with how we think about advertising technology and marketing personalization,” said Jason Richman, VP of Product at Spotify, in a statement. “Brian and his team will help us continue to innovate on free monetization and extend our leadership position in programmatic audio.”
“Spotify has built the leading marketplace for fans and creators,” said Adams. “It’s an enormous opportunity for me and the team to help create native brand experiences that stay true to a product that millions love.”

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Nuzzel launches a ‘Newswire’ for sponsored content

For the first time, news aggregator Nuzzel will include advertising.
That doesn’t mean you’re going to see banner ads popping up all over the startup’s app and website. Instead, the Nuzzel Newswire consists of sponsored links in Nuzzel’s email newsletter, pointing to a blog post, press release or news article of the advertiser’s choosing.
So why call it a “newswire” when it’s really just sponsored content? Nuzzel founder and CEO Jonathan Abrams told me he’s pitching this as an alternative to paying for press release distribution through a service like PRNewswire, where he said “probably few people will really read” it.
“Of course you could do both,” Abrams said. “But the idea is that this is a way to get your content in front of influencers that a traditional press release service may not deliver, since they take anything and release so many press releases every day.”
In contrast, he said the Newswire will only run one sponsored story per day.

Nuzzel, for those of you who haven’t tried it, offers a number of ways to keep track of the news, whether that’s following feeds/newsletters curated by Nuzzel or other users, or by connecting your Twitter account to see the stories that are getting the most shares from the people you follow.

Abrams declined to reveal anything about the size of Nuzzel’s audience, except that “it’s fair to say that at this point, the real value of the Nuzzel Newswire is the quality of the audience vs. huge size” — a point underlined on the Newswire website, which highlights a few individual Nuzzel users.
As for why the ads are only running in the newsletters, Abrams said he might add them to other products in the future, but he’s starting with email “because we thought native ads in email newsletters would get really good engagement and not as many people were doing it.”

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Foursquare is launching an analytics platform to help retailers understand foot traffic

While Foursquare started as a social check-in app, the company has always said there is a bigger picture — mainly related to unique ways of leveraging its database of check-ins at nearly 100 million public places.
There’s no better example than when Foursquare predicted that Chipotle same-store sales would fall 29 percent after the Mexican chain was hit with E. coli outbreaks. The actual decline announced by Chipotle ended up being a spot-on 30 percent.
As you can imagine, these analytics can be very valuable to retailers, allowing them to better understand customers’ habits as well as predict store traffic.
So today the company is announcing Foursquare Analytics, a foot-traffic dashboard for brands and retailers. The platform is available for retailers with any number of stores, no matter how small. Previously the only way for companies to access this data was through one-off deals with Foursquare.
Retailers will be able to use the dashboard to see foot-traffic data across metrics like gender, age and new versus returning customers — on a national or citywide scale. They also can compare their foot traffic against a set of competitors and their category as a whole.

The data is collected via Foursquare’s existing database of locations (which powers more than 100,000 apps, including Snapchat), as well as anonymized in-store-visit data collected from users of Swarm and Foursquare who have opted in to always-on location sharing. Foursquare then normalizes this data to make sure it accurately represents the U.S. population as a whole.
As a demonstration, Foursquare ran a case study analyzing T.J. Maxx’s recent retail success. As part of the study, they were able to determine things like what percent of foot traffic comes from high-frequency regular customers versus new ones.
Early partners include TGI Friday’s, Taco Bell, H&M and Equinox — but the platform is available now for retailers of any size.

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Propulse employs deep learning to power product recommendations for retailers

Propulse Analytics is launching today with $1.4 million in seed financing to equip retailers with deep learning for product recommendations. Backed by Wobemail Online Services LLP and Stradigi Ventures, the Canadian startup is leveraging the features of product images to better grasp customers’ tastes and ultimately increase sales for retailers.
The inspiration for Propulse came from CEO Eric Brassard’s experience working for Saks Fifth Ave. He explained to me that top salespeople on the floor can earn almost $200,000 per year. The art of selling clothing is recognizing the tastes and personalities of customers quickly and recommending appropriate items.
More traditional applications of machine learning to product recommendations are powered by collaborative filtering. Retailers monitor the products all customers view before making a purchase, and recommend similar products to similar customers.
This model has proven very effective for companies like Amazon, Spotify and Netflix. However, the model tends to benefit larger companies over smaller ones. Models improve with data, and it can be a challenge to overcome the cold start problem if you’re just starting out and don’t have much more to go on than an IP address for location and weather data.

Propulse is focused on marketing its service to small and medium retailers. These businesses are small with respect to Nike or Zara, but are often still household names. Frank + Oak was an early customer of Propulse. The fashion retailer has switched strategies quite often as of late, but has always staked its brand on personalization.
Ethan Song, CEO of Frank + Oak, told TechCrunch that his company is seeing 2.5X conversions with a new recommendations page powered by Propulse, though it’s tough to say how much of that is a result of marketing and the existence of the page itself versus the technology powering it.
“The marketing is key to drive the customer to go to that page but product drives conversion,” asserted Song. “If you recommend the wrong products you could convert less not more.”
Retailers get charged either by volume, the more compute used to power the recommendations or performance. In the latter case, Propulse gets compensated on improvement. This requires companies to track changes in conversions, but it’s an easier pill to swallow for retailers that are pitched AI solutions to improve revenue on a daily basis.

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Zenreach raises $30M to help businesses boost their marketing with free Wi-Fi

Zenreach is announcing that it has raised $30 million in Series C funding.
The round comes from venture capital firms like Maverick Ventures, Founders Fund (Founders Fund’s Peter Thiel joined the board last year), 8 VC, Bain Capital Ventures, First Round Capital and SV Angel — plus two celebrity investors, NBA star Kevin Durant and actor/VC Ashton Kutcher.
Founder and CEO Jack Abraham (pictured above with Durant and Kutcher) previously sold his local shopping startup Milo to eBay for $75 million. He said that Zenreach, like Milo, is “bringing the online and offline world together.”
Specifically, businesses that use Zenreach can offer their customers free Wi-Fi. All the customer needs to do is register using their email address, phone number or Facebook account.
Zenreach then uses that information to create a customer database for the business, which in turn allows them to deliver targeted promotions — businesses could create different messages and offers for new customers, loyal customers, customers who need to be enticed back and so on. And because Zenreach can recognize devices once they’ve been registered, it can help businesses measure when these efforts actually bring people back into the store.
While there are other companies offering CRM tools for brick-and-mortar merchants (Square, for example, is trying to do this on the payments side), Abraham said his company’s approach is particularly compelling because it doesn’t require any change in consumer behavior or any additional training for store staff: You just install the Zenreach router and it automatically starts collecting email addresses.

In fact, Abraham said the company collects an average of 10 email address per day for each business.

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Abraham also said Zenreach works across industries. He argued that consumers expect free Wi-Fi wherever they go nowadays, including “doctor’s offices and dentists and spas and auto repair” — not just at coffee shops.

“Every merchant wants to offer it, and should offer it,” Abraham said. “And it’s becoming expected.”
Zenreach has now raised a total of $80 million. It’s also made some notable hires in the past few months, including former Groupon vice president Zach Finley, who’s now executive vice president of growth and operations, and Derek Seibert, who held a number of roles at Uber and is now Zenreach’s director of growth.

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Facebook introduces a new ad format — product-focused ‘collection’

Facebook is introducing a new ad format that should help retailers showcase their products.
The last big addition to Facebook’s ad lineup came last year, with the launch of Canvas, a fast-loading, rich media ad. Like Canvas, a collection is created specifically for mobile, and seems designed to win users over in the Facebook app — before directing them to the advertiser’s mobile website.
Facebook monetization director Maz Sharafi told me the goal with the collection format is “to really build a great new shopping experience for people and to help marketers really drive discovery and sales in mobile.” Collections are also designed to be easy for merchants to set up, with Facebook doing the heavy lifting of choosing the right products to showcase from the merchants’ broader catalog.
Sharafi added that collections take advantage of a number of broader trends: The growth of mobile commerce and video, plus the increasing importance of fast performance on smartphones.

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So the ad shows up in users’ News Feeds — it leads with a video or image, followed by four product images below. If someone taps on the ad, it opens up a broader catalog of up to 50 different products. Then if they tap on a specific product, they’ll go to the advertiser’s website or app to make the purchase.

Sharafi said that while the advertiser can choose the initial four products that show up in a collection, the rest are automatically selected by Facebook, based on the merchant’s preferences and on user targeting, aiming to create an experience that’s “as relevant as possible for the consumer.”
Facebook is also announcing a change to the way advertisers can measure their results. For units like collections and Canvas, it won’t just tell advertisers when someone clicks an ad — it will also measure outbound clicks, namely when someone clicks out of the ad to the business’ website. This will also be available to advertisers on Instagram.

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TripleLift to Build Super-Smart Programmatic Real-Time Bidding Platform with VoltDB

TripleLift, an ad tech firm that delivers both programmatic and native advertising solutions, announced its partnership with VoltDB, which is an enterprise database for tech innovation companies and e-commerce firms. By partnering with VoltDB, TripleLift aims at delivering precision-based native programmatic ads at a magnified scale and pace. VoltDB will enable TripleLift in delivering a true real-time bidding auction platform for both demand-side and server-side vendors.
While the companies go programmatic, there will be a much larger emphasis on creating sponsored content. Regarded as the fastest-growing native format for the second part of the decade, adoption of sponsored content and programmatic native-display campaigns will remain CMO’s prerogative. Reason—high cost of adoption and limited inventory. That’s the area where TripleLift and VoltDB are expected to work on.
Michael Harroun, Head of Backend Engineering at TripleLift, says –
“We were analyzing our streaming data on a per-batch basis with Spark, but this introduced a latency that fell above the real-time requirements for us and our customers. Couple that with the inefficiencies associated with managing multiple Spark clusters in four separate data centers globally, and we knew we needed a database refresh.”
Harroun adds, “The VoltDB Fast Data platform has not only reduced our footprint but provided us with the ability to meet the real-time demands of mobile ad placement while ensuring the accuracy of a growing number of transactions to keep our clients’ ad budgets and actual spend aligned.”
According to Business Insider, native display ad revenue in the US will account for nearly 74% of the total display ad revenue by 2021. The revenue growth from native display advertising campaigns, which include native in-feed ads, will be largely attributed to the dominance of social media platforms like Facebook, Twitter, LinkedIn, `and YouTube.
Sponsored content, which is categorized separately from native-display due to the direct relationship between publishers and brands in creating the format, will be the fastest-growing native format over the next five years. However, the high cost to produce these ads and the limitation in inventory will limit the format.
“Two of the biggest challenges in digital advertising are delivering the right content to the right audience at the right moment and then accurately billing the publisher and advertiser for that content. The key to overcoming these challenges is consistently ingesting, analyzing and acting on live data streams,” said David Flower, President and CEO of VoltDB.
“TripleLift recognized the need to go beyond batch analysis to satisfy the real-time demands of its business. The VoltDB Fast Data Platform enables digital advertising platforms like TripleLift to more efficiently and effectively harness the power of real-time insights to deliver differentiated offerings that meet their customers evolving needs.”
The partnership between TripleLift and VoltDB comes amidst the growing challenge that advertisers face in dealing with ad blockers and malvertising biddings. With many consumers opting for ad-blockers while browsing online, native advertisements — which have a similar look and feel to the editorial content — are driving the next wave of innovation in the digital advertising market.

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LinkedIn Launches 'Trending Storylines'

) by rolling out “Trending Storylines,” which the professional social network will use to serve up highlights to the biggest trending news stories of the day.
But unlike Facebook, which got into trouble with its “Trending Topics” feature that had been known to serve up fake or controversial news, LinkedIn is sticking mainly to business news and will have a team of editors write up a news blurb when a big story breaks and then provide links to the most relevant news story or stories related to the news. The idea is that from that point LinkedIn users will engage and post their own links regarding the news story. (See more: Facebook's Trending News Section Makes Multiple Gaffes.)
Wanting to Be an Everyday Social Network
In an interview with Business Insider, Tomer Cohen, vice president of LinkedIn, said the goal is to expose its users to different viewpoints outside of their network. Take Snap Inc. (SNAP
Snap Inc

) for one example. A LinkedIn user may have a network of people that hate the company and, as a result, is not aware that the stock surged on the first day of its IPO. A “Trending Topic” on that news would have given the LinkedIn user a broader view of Snap. Cohen said “Trending Topics” is also a play on LinkedIn’s mission to boast productivity since users will have more information about a hot topic or industry before heading into a meeting or job interview. As for concerns that LinkedIn could get in trouble if it serves up political news that alienates one group from the other or, worse, fake news, Cohen said the fact that humans will guide the editorial content and that the majority will be business-related news and not political stories, it should be OK.
The ultimate goal for LinkedIn is to act more like Facebook

Read more: LinkedIn Launches 'Trending Storylines' | Investopedia
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How Post Intelligence Uses AI And Deep Learning To Help You Not Suck On Social Media

You suck at Twitter TWTR -0.23%. Like many users, you've tried to come up with relevant or witty tweets but failed. You've either been unable to grasp the concept and use cases of the service, or you've gotten bored after succumbing to an overwhelming feeling of knowing nothing you could possibly say could add anything to the conversation. Not everyone can be tweet machines. Before you write off Twitter as useless, perhaps give Post Intelligence a try. Launching today, Post Intelligence seeks to bolster your tweets by providing relevant content to help boost your Twitter confidence.
Post Intelligence isn't just some janky app that dumps a bunch of trending topics in your lap for you to tweet about. Instead, Post Intelligence analyzes your tweets (if you've got any to analyze) through deep learning artificial intelligence. Over time it recognizes your tone, quirks and suggests topics and themes to tweet about.
Aside from content suggestions, Post Intelligence offers personalized trending topics, post engagement prediction, historical reports, sentiment graphing and engagement analysis. All things that new Twitter users don't care about on the surface, but desperately need. What they do care about is finding a reason to use Twitter, or any social network for that reason. Every day brings new users and every day in the world of social is more overwhelming than the last.

"Social media and a lot associated tasks are difficult to do for most humans. Things like building a following, making engaging posts can be difficult," Post Intelligence co-founder Bindu Reddy tells me. "Some people do well but most people fail on Twitter. Twitter is too smart for most people, too difficult for them to master. Post Intelligence says 'how do we make your social media tasks easier to do, create more engaging posts, more followers, more engagement and so on?' It then creates a deep learning model for each user."
Both Reddy and co-founder Arvind Sundararajan are former Google GOOGL -1.18% executives who believe in a future that is full of artificial intelligence and deep learning tools, embedded in our daily lives. They applied that forward thinking when building out Post Intelligence.

The app not only analyzes real-time data from Twitter but it looks at content associated with URLs in public social media posts, Wikipedia and real-time Wikipedia traffic data using word embedding. It then slaps on a proprietary algorithm to solve the jumble and then constructs a current and comprehensive entity model. What does all this mean? It does a bunch of things in the background and tells you what to tweet about based on what's hopping.
Continues Reddy, "The thing that really fascinates me about AI is doing things humans cannot do or are difficult. I can't go to the moon, but AI can help me go to the moon. What is it that AI can do that human brain cannot?"
In the case of Post Intelligence, it means creating a recommendation system and public source categorizer that analyzes various sources of data using word embedding and extracting entities. These entities are categorizes across various dimensions. Then a cosine similarity (surely you remember that from math class) is used to compute the relevancy of a data source. This helps provide content and topic recommendations personalized to each user.
Post Intelligence does kind of get a bit wonky when you log in with over 170,000 tweets in your history. It's hard to analyze a trend when you tweet like a madman in a shed full of caffeine and knives. My Twitter history is hard to nail down to certain trending topics, though the content suggestions seemed to be on point with political suggestions, as I have been a bit of an armchair activist lately.
Keeping yourself in the global conversation can be time consuming and frustrating. Regular social media users, especially on Twitter, end up quitting because of the feeling of screaming into an empty and slowly expanding void. Post Intelligence, which carries the ability to not only suggest content, but share to multiple pages, platforms and schedule. All this helps new users enter into and become a part of that global conversation.
For regular social media users all this artificial intelligence and deep learning mumbo jumbo amounts to training wheels for social media. For social media influencers and brands it means that a lot of time is going to be saved finding relevant, real-time content to Tweet about. Post Intelligence also offers sponsored content (through strategic partnerships with over 60 brands such as Inverse, Distractify, Bored Panda, Simplemost and Life & Style Magazine) to earn ad revenue and pump up their following and engagement.
Speaking about engagement, because I know how much influencers and brands love engagement, Post Intelligence takes engagement to another level. I've seen influencers starve to death while trying to consume their engagement statistics. Ok, maybe nothing that drastic, but if you've been around social media influencers as long as I have, you know how excited they get when talking about engagement. Like a shark at a Vampire pool party.
The next level here is that Post Intelligence uses a post-engagement predictor. That is, it constructs a deep neural network based on the user's social media feed -- including past post-engagements and contents of each post -- to predict how much engagement a new post will get -- before it's freaking posted.
Not only that, but the recommendation engine will deliver relevant sponsored content to influencers based on the whims of their audience. It's hard enough to assume you know what your audience is thinking, but even harder to think you can accurately predict that with your squishy human brain. With this type of artificial intelligence, sponsored content becomes a bit more engaging and entertaining rather than an intrusion of annoying social media trash with an #ad hashtag.
Over time the model will learn tweeting habits and the hope for the development team is that Post Intelligence will be able to handle posting to social media without much guidance from the user, aside from adding that personal touch that makes social media social.
Post Intelligence is not just for Twitter. It also works for Facebook FB +0.40% and oddly enough -- Pinterest. With future builds set to encompass Instagram and Snapchat, Post Intelligence is looking to apply its deep learning model to all the social networks.
With all this deep learning, artificial intelligence just serves to do what we humans should have been doing all along -- talking to each other. Says Reddy, "The hope here is that people will have more interesting, focused conversations." Conversation sure beats staring into the abyss and hoping that something, someone, somewhere stares back.
Post Intelligence is launching its beta today for the web and for Google Android and Apple iOS in the near future.

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After YouTube boycott, Google pulls ads from more types of offensive content

Google is pulling display ads from being placed alongside a wider range of content on YouTube and other sites, in the wake of a spike of criticism that its automatic, programmatic advertising seemingly cannot stop mainstream brands from appearing alongside extremist and offensive material.
Last week a number of brands and publishers in Europe said they would pull advertising from Google’s network after their adverts were revealed to be being displayed alongside content such as videos promoting terrorism and anti-Semitism — a long-standing issue with online ad networks that is arguably coming to a head now given rising concern about extremist movements using online channels to spread divisive messaging and build influence among voters in democratic societies.
In response to criticism last week from advertisers, including the U.K. government, the Guardian newspaper and French ad giant Havas, Google said it would be expanding controls to give them more say over where their ads appear on YouTube and the Google Display Network.
More brands have since joined the boycott.

Google is now providing more detail on its response — and says it’s already started making changes, evidently hoping to stem the flow of brands away from its ad network. Chief business officer Philipp Schindler writes today that Google has “already begun ramping up changes” in three areas: its ad policies; enforcement of the policies; and new controls for advertisers.
“Recently, we had a number of cases where brands’ ads appeared on content that was not aligned with their values. For this, we deeply apologize,” he writes. “We know that this is unacceptable to the advertisers and agencies who put their trust in us. That’s why we’ve been conducting an extensive review of our advertising policies and tools, and why we made a public commitment last week to put in place changes that would give brands more control over where their ads appear.”
Among the changes Schindler covers in the blog is what he describes as “a tougher stance on hateful, offensive and derogatory content.”
And not just for ad display purposes; the suggestion is Google will be removing more types of offensive content from YouTube entirely — a tacit admission that hosting such content is becoming increasingly problematic for a company that has historically sat firmly in the U.S. “free speech” camp, yet which finds itself in the political firing line more and more, accused of helping spread hate online by providing a platform plus financial incentives for content intended to expand societal divisions.

In Germany the government is even considering new legislation to set standards for social media companies to promptly remove hate speech content from their platforms — with the country last week accusing internet companies of failing to act swiftly enough on user complaints. (Although, in that instance, Google was commended for improved responses to user complaints about illegal content on YouTube, versus Facebook and Twitter being criticized for getting worse at swiftly handling complaints.)
“We know advertisers don’t want their ads next to content that doesn’t align with their values. So starting today, we’re taking a tougher stance on hateful, offensive and derogatory content,” writes Google’s Schindler today. “This includes removing ads more effectively from content that is attacking or harassing people based on their race, religion, gender or similar categories. This change will enable us to take action, where appropriate, on a larger set of ads and sites.
“Finally, we won’t stop at taking down ads. The YouTube team is taking a hard look at our existing community guidelines to determine what content is allowed on the platform — not just what content can be monetized.”
He says Google will also be tightening safeguards for ad display pertaining to its YouTube Partner Program.
Among the new tools for advertisers that Google says it will be introducing in the “coming days and months” are:
stricter default settings for ads so they are less likely to appear beside “potentially objectionable content,” as Google puts it — with brands having to actively opt in to advertise on “broader types of content if they choose”
new account-level controls to make it easier for advertisers to exclude specific sites and channels from all of their AdWords for Video and Google Display Network campaigns, and enabling them to manage brand safety settings across all their campaigns “with a push of a button”
additional controls aimed at making it easier for brands to exclude “higher risk content and fine-tune where they want their ads to appear”
Google also says it will be beefing up resources, accelerating reviews and giving advertisers and agencies “more transparency and visibility” — with expanded availability of video-level reporting to all advertisers “in the coming months.”
The company says it will be hiring “significantly” more staff to handle the issue, as well as developing additional tools — saying it will seek to apply AI and machine learning to “increase our capacity to review questionable content for advertising.”
Advertisers with complaints about where their ads are appearing will also have access to a “new escalation path to make it easier for them to raise issues” in the future — with Google also claiming it will soon be able to resolve these cases “in less than a few hours.”
“We believe the combination of these new policies and controls will significantly strengthen our ability to help advertisers reach audiences at scale, while respecting their values,” adds Schindler. “We will continue to act swiftly to put these new policies and processes in place across our ad network and YouTube. But we also intend to act carefully, preserving the value we currently provide to advertisers, publishers and creators of all sizes. In the end, there’s nothing more important to Google than the trust we’ve built amongst our users, advertisers, creators and publishers. Brand safety is an ongoing commitment for us, and we’ll continue to listen to your feedback.”
We’ve reached out to Google with questions. At the time of writing the company had not responded but a spokeswomen told Bloomberg the timing and implementation of the new policies is still being set. In terms of granularity, she added that eventually Google plans to disable ads based on the tighter criteria on individual web pages rather than entire publications.

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