Speaker 1: Earlier this year, when Facebook CEO Mark Zuckerberg testified in Washington,
Speaker 1: d C. To defend his company, it became clear that
Speaker 1: senators didn't really understand what his business model was. In
Speaker 1: order to do that, we believe that we need to
Speaker 1: offer a service that everyone can afford, and we're committed
Speaker 1: to doing that. Well, if so, how do you sustain
Speaker 1: a business model in which users don't pay for your service? Senator?
Speaker 1: We run ads? Nice see in Silicon Valley. The episode
Speaker 1: was seen as embarrassing for senators. How could they be
Speaker 1: so clueless? A lot of people know Facebook's business model
Speaker 1: is advertising, but they may not know exactly how it works.
Speaker 1: In for example, when Facebook disclosed that Russia purchased a
Speaker 1: hundred thousand dollars and ads on Facebook to manipulate US politics,
Speaker 1: I got this question from a lot of smart people.
Speaker 1: Who was the Facebook salesperson that sold Russia those ads?
Speaker 1: Can we find them? That sounds like a pretty reasonable question,
Speaker 1: But when Facebook employees here that story, they immediately laugh
Speaker 1: because Facebook doesn't have sales people directly selling ads. In
Speaker 1: most cases, it all happens through a website. Anyone with
Speaker 1: a credit card can do it, and you don't even
Speaker 1: have to be in contact with Facebook at all, just
Speaker 1: the Facebook website. You don't even have to be who
Speaker 1: you say you are. So much has happened since that revelation,
Speaker 1: so many changes to Facebook's advertising system, so many apologies,
Speaker 1: so many more questions from Congress, And even that didn't
Speaker 1: stop Facebook from having its share of crises in the
Speaker 1: last election cycle as I tried to reform the system.
Speaker 1: But if there's anything this era of Facebook News has proven,
Speaker 1: it's that sometimes it's worth it to try to understand
Speaker 1: the basis by I'm Brad Stone and I'm Sarah Fryar,
Speaker 1: and this week on Decrypted, we're going to try to
Speaker 1: explain how Facebook's advertising system works so we can understand
Speaker 1: why some people love it so much and others think
Speaker 1: it could be so dangerous. Facebook's ad machine is easy
Speaker 1: and quick to use, and that's made it incredibly profitable.
Speaker 1: Wall Street is expecting it to bring in fifty five
Speaker 1: billion in revenue this year. But the same algorithms that
Speaker 1: make it so powerful can also create problems. For example,
Speaker 1: if enough people say on their profile that they hate Jews,
Speaker 1: Facebook software would make a category all by itself to
Speaker 1: let advertisers send their contents specifically to do hitters, and
Speaker 1: unfortunately that's not even a hypothetical. That's one of the
Speaker 1: problems Facebook had to solve, along with a lot of
Speaker 1: other things. But fixing oversight for the ad system isn't
Speaker 1: as easy as you might think, and some of those
Speaker 1: updates have had unintended consequences of their own stay with us.
Speaker 1: So Sarah back to that congressional hearing in April, the
Speaker 1: senators asked Mark Zuckerberg about whether Facebook sales user data
Speaker 1: to advertisers. Um, have you ever drawn the line on
Speaker 1: selling data to an advertiser? Yes, Senator, we don't sell
Speaker 1: data at all. So the way the add system works
Speaker 1: is advertisers can come to us. The company hates it
Speaker 1: when people think that because it doesn't sell data to advertisers.
Speaker 1: What it sells is access to certain kinds of people
Speaker 1: based on the information they share on face and then
Speaker 1: we help them get that message in front of people.
Speaker 1: But this is one of the it's widely mischaracterized about
Speaker 1: our system that we sell data, and it's actually everything
Speaker 1: you share on Facebook, all the articles you decide to
Speaker 1: click on or decide not to click on, everything you
Speaker 1: write in that textbox. All the things you're friends like
Speaker 1: help paint a picture of who you are. Facebook knows
Speaker 1: what stories you go to, and it knows as you're
Speaker 1: rolling what you stop to stare at, even if you
Speaker 1: never click on it. All of these things end up
Speaker 1: as signals that Facebook can you use to determine what
Speaker 1: kind of ads a person might be interested in seeing.
Speaker 1: Are they rich or poor, Asian or Latino, a mother
Speaker 1: or a student. The advertisers can't really see who is
Speaker 1: getting their ads, and Facebook can't at an individual level
Speaker 1: at least either. All they know is it works. The
Speaker 1: fact that Facebook doesn't really sell it's data, it just
Speaker 1: sells access to it is actually one of the reasons
Speaker 1: it's been so successful. It has all this information on
Speaker 1: now more than two billion people basically locked up, and
Speaker 1: it's only available to people or groups advertising directly on Facebook.
Speaker 1: And it's actually one of the most important parts of
Speaker 1: how Facebook works is we do not sell data. Advertisers
Speaker 1: do not get access to people's individual data. Those advertisers
Speaker 1: can use Facebook system to get really specific about what
Speaker 1: kind of people they want to reach. If enough people
Speaker 1: are into something, Facebook will auto create category for it,
Speaker 1: like breeding insects. It might be kind of hard to
Speaker 1: reach bug breeders if you're advertising in the New York Times,
Speaker 1: But on Facebook you can let exactly the right people
Speaker 1: know about your upcoming beekeeping conference. But what if you
Speaker 1: want only white people to apply for your apartment complex,
Speaker 1: or only young people to apply for your open job. Facebook,
Speaker 1: for a time actually allowed people to do that too.
Speaker 1: Of course, that's not how Facebook intended for the ad
Speaker 1: machine to work. And while there are some awkward, unintended
Speaker 1: consequences on the whole, the ad platform works very well
Speaker 1: for a lot of its users. Like a couple of
Speaker 1: months ago, I went with our producer Piaga Kari to
Speaker 1: visit a small business in New York. It's called Two
Speaker 1: Blind Brothers and they make T shirts. Should we give
Speaker 1: you guys quick toward? Yeah, let's do it. Um, well,
Speaker 1: you're pretty much looking at all of it already, but
Speaker 1: we have a few fun things, and here we have this. Uh.
Speaker 1: The company is run by two brothers, Brad and Brian.
Speaker 1: They're both visually impaired, and profits from their T shirt
Speaker 1: sales are donated to organizations that fight blindness, working on
Speaker 1: some stuff for kids. Uh, we have t shirts Brad
Speaker 1: and Brian say their company would never have even gotten
Speaker 1: off the ground without Facebook's ad system, which allowed them
Speaker 1: to start boosting posts on their business page without spending
Speaker 1: a lot of money. It works so well that Facebook
Speaker 1: actually published a case study about one of their Black
Speaker 1: Friday campaigns. The actual study is there on a wall
Speaker 1: in a frame, and it's been signed by Chief Operating
Speaker 1: Officer Cheryl Sandberg herself. So why did you start advertising
Speaker 1: on Facebook? You know, Facebook does a great job of
Speaker 1: baby stepping you into experimenting with the adds. So I
Speaker 1: remember we had put out this video and we went
Speaker 1: to bed that night. We woke up the next day
Speaker 1: and we're like, what the like? Normally when I put
Speaker 1: out a post on Facebook, it's like Grandma and like
Speaker 1: my sister that like it? And then this post just
Speaker 1: and So they had this dynamite video and they started
Speaker 1: targeting certain audiences with it. They could select for specific
Speaker 1: age groups or find people with an interest in blindness.
Speaker 1: But the brothers said it actually worked better when they
Speaker 1: could just click a little button that would get Facebook
Speaker 1: to do the job for them, choosing on its own
Speaker 1: who could see their ads right. They started using a
Speaker 1: tool called look Alike audiences. Yeah, so you know I can.
Speaker 1: I can get on the app platform and I can
Speaker 1: literally put into the to our audiences. I'm looking for
Speaker 1: somebody who uh likes tom shoes and like selling Degenerous
Speaker 1: because you know, we're featured on her show, and who
Speaker 1: is an online shopper and all these sort of broad
Speaker 1: categorical um characteristics. Or I can let Facebook analyze who's
Speaker 1: interacting with our brand and then they can decide on
Speaker 1: hundreds of factors using all their technology to figure out
Speaker 1: who that best audience is. But that is an audience
Speaker 1: that Facebook has determined for us looks and acts like
Speaker 1: our best and most exciting customers, and then we can
Speaker 1: take our ad creative and send it to that audience.
Speaker 1: So the lookalike feature just finds people who are similar
Speaker 1: to the users you've reached already. It's all automatic, and
Speaker 1: as the Brothers realize pretty quickly, it's incredibly effective. It's
Speaker 1: not just effective for businesses, though, it's effective for all
Speaker 1: kinds of advertising. This type of ready made targeting was
Speaker 1: one of the main tools Donald Trump used in its
Speaker 1: presidential election campaign. It allows someone who might not be
Speaker 1: in the mainstream conversation to find just enough people who
Speaker 1: share an interest, and then people who look like those
Speaker 1: people and expanded from there. So we know that Facebook's
Speaker 1: ad targeting is super efficient, and we know that businesses
Speaker 1: and political groups are using it. But a lot of
Speaker 1: these tools still feel somewhat mysterious to a lot of people.
Speaker 1: So we went to Facebook's headquarters in Menlo Park and
Speaker 1: asked them, Yeah, Thursday traffics bad. Bad. It was like
Speaker 1: telling me that I was going to get here. Hi,
Speaker 1: hi Hi. This is Nikila Shwinavasan, the product manager for
Speaker 1: Facebook Small Business Ads Growth. She knows inside and out
Speaker 1: the portal that most people come into when they want
Speaker 1: to buy ads, and she also knows that not everyone
Speaker 1: understands how it works behind the scenes. To your point earlier,
Speaker 1: I do empathize with the fact that it can feel
Speaker 1: like a black box because after I had boost posts,
Speaker 1: now what actually happens, right, And that's where the magic
Speaker 1: of the auction comes in. The auction is the heart
Speaker 1: of Facebook's advertising system. That's how it figures out how
Speaker 1: much advertisers are willing to pay to reach someone like
Speaker 1: those hard to reach beekeepers we mentioned earlier. But where
Speaker 1: Facebook really excels is figuring out who those people are
Speaker 1: in the first place. So when we think about that,
Speaker 1: there's a few dimensions, and obviously it's a it's a
Speaker 1: fairly complex system. But what we try to do is understand, Okay,
Speaker 1: what do we know about this person based on previous
Speaker 1: activity that they've had, previous ads, that they've interacted with um,
Speaker 1: And we consider also what is their likelihood of taking
Speaker 1: this particular action that the advertiser wants them to take,
Speaker 1: which is in this case actually Facebook uses stuff like age, gender,
Speaker 1: and location, of course, but that's not all. I asked
Speaker 1: Ninkila what characteristics exactly Facebook was using to find these
Speaker 1: target audiences. How many signals are all in the I
Speaker 1: really couldn't tell you, because there's a whole bunch like liking,
Speaker 1: commenting on an ad, liking a page, liking a post,
Speaker 1: um or activities that you have done that advertisers are
Speaker 1: able to then come back and retarget um. And then
Speaker 1: there's also signals that we can extrapolate based on what
Speaker 1: we know people like you tend to like. So if
Speaker 1: we know that you visited a particular retailer before, and
Speaker 1: there's another retailer that's selling a similar good, it's the
Speaker 1: likelihood of view interacting with that is also higher. So
Speaker 1: the answer to how many signals is I don't have
Speaker 1: a number. The software is so complicated that even people
Speaker 1: who work at Facebook can't see what's happening to the
Speaker 1: system as a whole. Knowing gets to know exactly who
Speaker 1: is seeing a particular ad. So what about Facebook? This Facebook? Now,
Speaker 1: like if you go look at an advertiser, do you
Speaker 1: know exactly whose ads that person reached? Know? We we
Speaker 1: did not look at individual level data, nor do we
Speaker 1: surface it to your point, We do it at an
Speaker 1: aggregate level. We surface that to the advertiser at an
Speaker 1: aggregate level where we're telling them, but they're now at
Speaker 1: an we don't go look at the individual level. Now. Yeah,
Speaker 1: So I guess, I mean, I guess it's one thing
Speaker 1: that's interesting about this whole system is it's it is
Speaker 1: so it's like, um, very automated and optimized. There are
Speaker 1: all these signals that go into it, and um, you know,
Speaker 1: out the other end comes something that's really useful and helpful. Um,
Speaker 1: but how do you how do you look for problems?
Speaker 1: Like how are you proactive about like the next iteration
Speaker 1: of it. Yeah, that's a great question. We hold ourselves
Speaker 1: even internally, to very high standards when we think about
Speaker 1: how we developed, and it turns out with so much
Speaker 1: content and only so many people, rooting out problems is
Speaker 1: very very hard. Facebook doesn't really resp on two problems
Speaker 1: until they are reported by users, and sometimes it's people
Speaker 1: saying they're annoyed by seeing a certain ad, and other
Speaker 1: times it's much more serious when it comes to political advertising.
Speaker 1: Obviously the stakes are high. There's still plenty of things
Speaker 1: that can and have gone wrong. That's despite the huge
Speaker 1: resources that Facebook is devoted to rooting out problems. There's
Speaker 1: just so much that goes unseen when you've tasked a
Speaker 1: smart set of computer programs with running your business. We've
Speaker 1: talked about the categories that get automatically created, like users
Speaker 1: who are saying into treason in Russia. Facebook had to
Speaker 1: go through and manually remove about five thousand of them.
Speaker 1: That could be problematic, right, but they did so only
Speaker 1: after a series of articles from the news organization Pro Publica.
Speaker 1: It seems as though they're not proactively looking at the
Speaker 1: potential problems themselves, like taking action when they think it
Speaker 1: might be the right thing to do, like the day
Speaker 1: before the midterms, Facebook stopped letting Trump turn and anti
Speaker 1: immigrant video into an ad on its network. It made
Speaker 1: the decision after major TV networks had already made it,
Speaker 1: and also the ad flip through its network in the
Speaker 1: first place. Also, once they decided to stop letting it
Speaker 1: be an ad, it was still an incredibly popular organic
Speaker 1: post on Facebook. Well, why change if you've got such
Speaker 1: a successful thing going. What we saw with the Two
Speaker 1: Blind Brothers is that Facebook makes a lot of money
Speaker 1: off small, really small businesses that are targeting incredibly niche audiences.
Speaker 1: There's no other service, I mean, maybe Google's ad platform
Speaker 1: that can cater to such specific needs. The main area
Speaker 1: they've tried to tame is politics. A lesson spending just
Speaker 1: isn't a very big part of Facebook's business. The company
Speaker 1: earlier this year even considered eliminating and entirely given the
Speaker 1: many headaches that generates for the company. But Sarah, the
Speaker 1: fixes generated headaches of their own, too, Right, You're totally right.
Speaker 1: I wrote this story a few weeks ago about how
Speaker 1: Facebook's new rules around political advertising now require anyone advertising
Speaker 1: about a sort of electoral issue to be verified, but
Speaker 1: the system was catching and taking down ads that hadn't
Speaker 1: gone through the verification process, and those ads sometimes had
Speaker 1: nothing to do with politics. Well, so there was this
Speaker 1: vacation Bible school that got blocked because it was located
Speaker 1: in Clinton, Indiana. In an insurance company in Clinton, Iowa
Speaker 1: that had an ad about its family baseball night taken
Speaker 1: down because they had the word Clinton in their ads,
Speaker 1: as in former President Bill Clinton or Secretary of State
Speaker 1: Hillary Clinton. But it also happens to be a common
Speaker 1: name of towns in the US. And I think we've
Speaker 1: heard so much about AI, but it's it sounds like
Speaker 1: Facebook came up with a logical solution to a need
Speaker 1: for more transparency and then signed an AI algorithm to
Speaker 1: do the work. But AI, despite the hype, it's not
Speaker 1: smart enough to figure out what actually might be political second,
Speaker 1: making advertising work parents that people. Facebook is devoting huge
Speaker 1: resources towards making sure it's more proactive about political behavior
Speaker 1: on its site in I recently went back to Facebook
Speaker 1: headquarters and took a tour of what it's calling the
Speaker 1: War Room. It's basically this room of a couple dozen
Speaker 1: people monitoring dat boards of articles that are trending and
Speaker 1: political ad money flowing so they can tackle problems related
Speaker 1: to election first one aunts so the biggest and most
Speaker 1: important thing we've done here in the last two years
Speaker 1: is huge advances and machine learning. That includes deleting fake accounts,
Speaker 1: sending fake news to fact checkers, and a lot of
Speaker 1: the issues that got it in trouble during the election.
Speaker 1: Facebook has been showing this war room off to reporters
Speaker 1: as a sign that it's taking political content on the
Speaker 1: platform extra seriously. And they also have that at archive
Speaker 1: right where everyone can see who's bidding on specific ads.
Speaker 1: That's a big step, but there are still issues. For example,
Speaker 1: the feature that allows users to see who's paying for
Speaker 1: each ad isn't perfect. A couple of Vice reporters realized
Speaker 1: that they could personally buy ads and claim that they
Speaker 1: were paid for by the Senate Republican Majority leader Mr. Racconnell.
Speaker 1: And in Virginia, a bunch of really savage attack ads
Speaker 1: ran against a House candidate, but the buyer's managed to
Speaker 1: completely obscure who they were or where the money came from.
Speaker 1: In two days before election day, Facebook was alerted by
Speaker 1: the federal government about foreign accounts that could be trying
Speaker 1: to influence selection that it had to take down in
Speaker 1: the day after the elections, the company announced it was
Speaker 1: strengthening its verification process. But meanwhile it's still dealing with
Speaker 1: a lot of influenced campaigns by foreign governments. So, Sarah,
Speaker 1: we've devoted this whole season of Decrypted to this idea
Speaker 1: of unintended consequences, and this is almost the biggest unintended
Speaker 1: consequence of all. Silicon Valley designed these systems to scale infinitely,
Speaker 1: very low human touch, um completely automated systems, and what
Speaker 1: they failed to recognize and which in this case had
Speaker 1: severe implications for the world, is that the problems scaled
Speaker 1: right along with the systems. Right when you're prioritizing convenience
Speaker 1: and speed and low touch, right, these are systems where
Speaker 1: you really don't have to have a person selling and
Speaker 1: placing and add um, it makes it so easy to
Speaker 1: miss the little things. And what Facebook says over and over,
Speaker 1: you know, they point to examples like two blind Brothers.
Speaker 1: They point to the many small businesses that Nkuila is
Speaker 1: helping out, and they say that the good that Facebook
Speaker 1: is done in helping these small businesses tell their stories
Speaker 1: and helping people have a voice in a way they
Speaker 1: wouldn't be able to afford to otherwise that outweighs the bad. Well,
Speaker 1: but to me, they just seem like different things. Like
Speaker 1: it was a system that was great for helping small
Speaker 1: businesses reach big or even very tailored audiences. But when
Speaker 1: it comes to a tug of war over ideas and
Speaker 1: you know, and the balance between informing people and maybe
Speaker 1: deceiving them and to get them to do things they
Speaker 1: wouldn't otherwise do, that's where the system kind of breaks down.
Speaker 1: And I think you can't just go and say just
Speaker 1: because we've done a lot of good things means that
Speaker 1: this service is good. I think, you know, so if
Speaker 1: you were to look at a government, for example, and say, well,
Speaker 1: the roads are really messed up, but the schools are great, Like,
Speaker 1: that's just not the kind of argument you would make
Speaker 1: about your government. You want all of it to work,
Speaker 1: and you want all of it to work in a
Speaker 1: way that's not dangerous for society. So let's take this
Speaker 1: to the to the logical extreme. And we touched on
Speaker 1: this earlier, like should Facebook just not allow a certain
Speaker 1: kinds of advertising? Should it say we haven't figured this
Speaker 1: how to make this work for political advertising and not
Speaker 1: accept it? Well, then you get into the question of
Speaker 1: what political advertising actually is, right, is it something that
Speaker 1: is is just a candidate putting out an ad to
Speaker 1: get you to vote for them, or is it about
Speaker 1: these issues? And that the issues are really what Russia
Speaker 1: tried to stoke tension around in the election. They weren't
Speaker 1: saying vote for Hillary, vote for Trump. They were saying
Speaker 1: you should get angry about immigration and refugees and race
Speaker 1: in America. And so I don't think that getting rid
Speaker 1: of a category of advertising really solves it. I think
Speaker 1: they have to think about how to proactively manage these
Speaker 1: systems with a little bit more human involvement, even if
Speaker 1: that doesn't scale. And verification obviously being so important, but
Speaker 1: it's been difficult for them to get that right. Why
Speaker 1: why is it so? Why is it so hard? Well,
Speaker 1: when you're trying to verify, you have to go by
Speaker 1: what the people say that and they want it to
Speaker 1: be fast, and in politics, they've already gotten so many
Speaker 1: complaints about how slow that system has been, where people
Speaker 1: send in their verification and it takes a few days
Speaker 1: to pross us. Meanwhile, you know the rival candidate in
Speaker 1: a campaign is spending tons of money on their ads,
Speaker 1: and that seems unfair. And so no matter what Facebook does,
Speaker 1: there's going to be this question of are they are
Speaker 1: they censoring or are they removing enough? And they have
Speaker 1: to figure out what the right balances, and maybe they'll
Speaker 1: never get there right and they can't be seen as
Speaker 1: favoring one side over another. And we should say this
Speaker 1: isn't just a Facebook problem, right, it's internet wide. It's
Speaker 1: an Internet problem, and it's a problem with this kind
Speaker 1: of this way that the assistance have been built over
Speaker 1: the years. These scale so well, they work so efficiently,
Speaker 1: they're so high margin. Wall Street loves it. Uh, but
Speaker 1: you know, when it comes to the real world consequences,
Speaker 1: it doesn't work to just wait for users to report
Speaker 1: when things go wrong. So perhaps eventually there will be
Speaker 1: a system wide fixed. For now, though, I'm tempted to
Speaker 1: say that it's incumbent on users to just be more vigilant,
Speaker 1: to be smarter. Um. There, you know our our advertisers
Speaker 1: out there that are trying to convince you to buy
Speaker 1: something new, maybe something you don't need. That's fairly innocuous,
Speaker 1: but in other cases they might be trying to get
Speaker 1: you to act in a way that you otherwise might
Speaker 1: not and that's it for this week's Decrypted. Thanks for listening.
Speaker 1: Do you have a story about advertising on Facebook? We
Speaker 1: want to hear from you. You can email us at
Speaker 1: Decrypted at bloomberg dot net or I'm on Twitter at
Speaker 1: Sarah Fryer and I'm at brad Stone. If you're a
Speaker 1: fan of the show, please take a moment to rate
Speaker 1: and review us. It really helps new listeners find the show.
Speaker 1: This episode was produced by pa good Kari and Mage
Speaker 1: mus Hendrickson. Our story editor was Anne vander Nay. Thanks
Speaker 1: to Akito, Emily Buso, and Liz Smith. Friend Chessca Levie
Speaker 1: is head of Bloomberg Podcast. We'll see you next week.
Speaker 1: M