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Speaker 1: Welcome to tech Stuff, a production from I Heart Radio.

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Speaker 1: Hey there, and welcome to tech Stuff. I'm your host,

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Speaker 1: Jonathan Strickland. I'm an executive producer with I Heart Radio

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Speaker 1: and a love of all things tech, and I figured

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Speaker 1: i'd do UH an episode or two about tech and privacy.

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Speaker 1: I've titled this episode how Tech Spies on Us, But

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Speaker 1: right from the get go, I have to admit that

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Speaker 1: that is a little disingenuous because in many cases, not all,

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Speaker 1: but in many cases, we're essentially giving tech permission to

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Speaker 1: just harvest information about us, which is a little different

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Speaker 1: than spying. It's kind of like saying, no, I'm cool

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Speaker 1: with there being a live camera on me all the time,

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Speaker 1: just broadcasting to some other location. You can't really call

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Speaker 1: that spying if you were in on it at the top.

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Speaker 1: But the way tech tends to go about this UH

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Speaker 1: usually follows a pathway that isn't transparent, and it's not

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Speaker 1: always obvious, and sometimes it relies on people not really

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Speaker 1: being aware of what they've agreed to. So I really

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Speaker 1: wanted to go into that so that we can all

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Speaker 1: have a deeper understanding of how tech gathers information and

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Speaker 1: then how companies leverage that and exploit our information, and

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Speaker 1: furthermore to kind of, you know, think about whether or

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Speaker 1: not we're okay with this. Some of you might be,

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Speaker 1: and there's I'm not casting any shade, I'm not making

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Speaker 1: any judgments, but others might not be. And that's why

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Speaker 1: I wanted to kind of go through this and really

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Speaker 1: get down to why it seems like our tech has

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Speaker 1: almost a preternatural ability to know things about us. So

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Speaker 1: let me paint for you a hypothetical situation. You are

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Speaker 1: hanging out with a friend and you're both chatting a out,

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Speaker 1: you know, all sorts of stuff, and the two of

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Speaker 1: you decided to go to a nice restaurant with an

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Speaker 1: outdoor seating area because you know, we're still kind of

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Speaker 1: post pandemic type stuff, and you know you're being super

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Speaker 1: responsible with regard to health concerns and all of that. Anyway,

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Speaker 1: you have a conversation with your friend who is telling

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Speaker 1: you all about this movie they just watched. And after

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Speaker 1: your meal, you say goodbye to your friend and you

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Speaker 1: head on home. Later that same day or evening or

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Speaker 1: night whatever, you're on the internet and you see something

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Speaker 1: kind of weird. You actually start seeing ads for that

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Speaker 1: film your friend was talking about popping up on different

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Speaker 1: sites and services, and you don't remember seeing ads for

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Speaker 1: this particular movie before, So maybe it's cherry picking. Maybe

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Speaker 1: it's the whole van as always by the corner thing

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Speaker 1: where you only notice something after it's been brought to

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Speaker 1: your attention. But maybe it's not. So what the heck

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Speaker 1: is going on? Was your phone spying on you? Was

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Speaker 1: it listening in on your conversationation that you had with

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Speaker 1: your friend at dinner and then sent that information off

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Speaker 1: somewhere to be processed and analyzed, then sold to advertisers,

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Speaker 1: And then when you get back and now you have

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Speaker 1: targeted ads aimed at you because your phone was eavesdropping

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Speaker 1: on you. Now, I mentioned this sort of thing in

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Speaker 1: a recent tech News episode, and I'm not gonna make

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Speaker 1: you wait through this whole episode to answer that hypothetical question.

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Speaker 1: Your tech is most likely not listening in on your conversations,

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Speaker 1: not because that would be hard to do it. It

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Speaker 1: actually wouldn't be hard to do that, but because there's

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Speaker 1: really no need to do it. There's no need to

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Speaker 1: go that far. Also, if tech were actually actively listening

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Speaker 1: in on you all the time, governments around the world

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Speaker 1: would be extremely interested in that, either to use it

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Speaker 1: for their themselves as a kind of surveillance of their

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Speaker 1: own citizens and people who are within the country, or

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Speaker 1: they would want to go after tech companies for violating

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Speaker 1: various privacy and security laws. Now, the point is that

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Speaker 1: the way our modern tech works, and the way companies collect, share,

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Speaker 1: and barter our data, means there's no need to listen

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Speaker 1: in to what we're saying. Our tech knows who we are,

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Speaker 1: it knows where we go, it knows what we're doing,

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Speaker 1: and because the people around us also carry similar tech,

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Speaker 1: it knows who we're with, and by cross referencing data,

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Speaker 1: it knows what sort of connections exist between us. So

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Speaker 1: today I thought I talk a bit more about how

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Speaker 1: our tech collects data and the history of that, from

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Speaker 1: the stuff that we willingly surrender to the stuff that

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Speaker 1: we might not even realize was being shared. We'll talk

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Speaker 1: about how this has evolved and how geolocation has played

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Speaker 1: a really big part in this, and this will lead

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Speaker 1: us to some recent events in which we've seen big

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Speaker 1: companies like Facebook and Google try to skirt around attempts

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Speaker 1: to give users the option to opt out of some

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Speaker 1: forms of data collection now, as always in tech stuff,

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Speaker 1: anyone who's listened to a textuff episode knows I love

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Speaker 1: going through history because it's not like there was just

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Speaker 1: a switch that was flipped one day and then suddenly

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Speaker 1: all of our personal information got hoovered up by the Internet. Also,

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Speaker 1: we should keep in mind that there are a lot

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Speaker 1: of different privacy laws around the world that can restrict

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Speaker 1: this kind of you know, data collection and how data

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Speaker 1: companies can use that information. So, for example, the European

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Speaker 1: Union has laws that provide a decent amount of protection

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Speaker 1: for citizens. But as I am located in the United States,

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Speaker 1: where the Internet first got its start, and thus where

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Speaker 1: the attitudes and practices regarding private information on the Internet

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Speaker 1: got their start, at least with regard to you know,

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Speaker 1: the web and the Internet in general, I'm mostly going

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Speaker 1: to be focusing on the good old us of a

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Speaker 1: which isn't really that old, and frequently isn't that good either.

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Speaker 1: So here in the United States, a right to privacy

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Speaker 1: was not one of the guarantees laid out in the Constitution,

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Speaker 1: at least not explicitly. However, the various forms of government,

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Speaker 1: including the Supreme Court, over the course of decades and

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Speaker 1: several court cases has essentially found that four of the

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Speaker 1: first five amendments to the Constitution extend over towards a

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Speaker 1: right to privacy. So those would be the first Amendment.

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Speaker 1: That's the one that guarantees the right to assembly, uh,

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Speaker 1: the freedom of religion, the freedom of speech, and the

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Speaker 1: freedom of expression. Then we skip over the second Amendment

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Speaker 1: and we head over to Amendment number three, which prevents

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Speaker 1: the government from being able to station soldiers in private

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Speaker 1: homes of citizens. Then there's the Fourth Amendment, which protects

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Speaker 1: citizens from unreasonable search and seizure. That's a big part

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Speaker 1: of privacy obviously. And then we've got Amendment number five,

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Speaker 1: which has a little bit of Monica in my life. Wait, no,

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Speaker 1: I'm sorry, that's mambo number five. Amendment number five says

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Speaker 1: that US citizens enjoy certain legal protections. For example, you

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Speaker 1: can't be tried for the same crime twice, right, that's

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Speaker 1: double jeopardy. That obviously doesn't include stuff like the appeals process,

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Speaker 1: but that's separate. Also, citizens can take the Fifth Amendment

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Speaker 1: to avoid self incrimination during a trial, so they cannot

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Speaker 1: be compelled to confess to a crime. Uh. That again

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Speaker 1: relates to privacy. Now, none of those four amendments provide

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Speaker 1: explicit rights to privacy, but they all extend towards that

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Speaker 1: direction in different ways. So that was back in seventeen

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Speaker 1: eighty nine, which means it was like two centuries before

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Speaker 1: we had to worry about the Internet, but a century

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Speaker 1: after the Constitution was ratified. That being an eight ninety

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Speaker 1: an article written by Justice Louis Brandy and Samuel Warren

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Speaker 1: argued for a more explicit right to privacy. It was

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Speaker 1: the early days of photography in the eighteen nineties, and

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Speaker 1: already the two saw the potential for people to have

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Speaker 1: their privacy violated due to the fact that now it

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Speaker 1: was easier to capture a moment forever and then potentially

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Speaker 1: distribute it widely through the press. In nineteen fourteen, we

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Speaker 1: got the founding of the Federal Trade Commission, or FTC. Now,

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Speaker 1: the primary responsibility of the FTC is to ensure that

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Speaker 1: companies within the United States are playing fair. The FTC

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Speaker 1: can go after companies that use deceptive commercial practices, but

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Speaker 1: later on, really like starting around the nineteen seventies, the

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Speaker 1: FTC would also focus on companies that violated citizen privacy,

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Speaker 1: going global. For a moment in nineteen forty eight, the

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Speaker 1: United Nations drafted the Declaration of Human Rights, which included

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Speaker 1: within it article number twelve, of which states, quote no

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Speaker 1: one shall be subjected to arbitrary interference with his privacy, family, home,

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Speaker 1: or correspondence, nor two attacks upon his honor and reputation.

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Speaker 1: Everyone has the right to the protection of the law

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Speaker 1: against such interference or attacks. End quote. Now, apart from

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Speaker 1: the non inclusive pronoun usage there, this was a really

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Speaker 1: big step toward positioning privacy as a basic human right.

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Speaker 1: In nineteen sixty a legal scholar named William Prosser published

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Speaker 1: an article titled Fittingly Enough Privacy, and in that article

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Speaker 1: processor outlined four cases in which infringing on privacy should

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Speaker 1: allow for the victim to pursue a civil lawsuit against

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Speaker 1: the perpetrator. Those four cases were intrusion upon seclusion or solitude,

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Speaker 1: so in other words, someone's barging in on you when

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Speaker 1: you're just trying to be alone, or intrusion into private affairs. Similarly,

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Speaker 1: public disco cosure of embarrassing private facts, which when you

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Speaker 1: think about that, the Internet and the way it works

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Speaker 1: would just catch on fire if people were really holding

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Speaker 1: that seriously. Today publicity which places a person in a

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Speaker 1: false light in the public eye was another case. And

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Speaker 1: appropriation of one's name or likeness. So if someone were

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Speaker 1: to go about posing as Jonathan Strickland and it's not me,

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Speaker 1: it's not someone else who's actually named Jonathan Strickland like

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Speaker 1: they were trying to pose as me, I should be

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Speaker 1: allowed to sue that person based on that that criteria. Now,

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Speaker 1: obviously in these cases, uh, there's a lot of leeway

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Speaker 1: because there also are issues where it kind of starts

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Speaker 1: to track a little bit toward issues with the First Amendment. Right,

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Speaker 1: So in case of public disclosure of embarrassing private facts,

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Speaker 1: if you're talking about a public figure, there's a lot

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Speaker 1: more leeway there because it may be that that public

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Speaker 1: fig years private facts have a public bearing, like if

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Speaker 1: it's a politician or something. So it does get a

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Speaker 1: little dicey, but these matters always do. We'll skip ahead

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Speaker 1: to nineteen seventy four, though I should say that there

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Speaker 1: were some court cases and some scholarly articles that further

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Speaker 1: the framing of privacy. By nineteen seventy four, the US

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Speaker 1: passed the Privacy Act, which placed limits on how federal

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Speaker 1: agencies can collect and use personally identifiable information not granted.

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Speaker 1: These restrictions were all about government use, federal government use,

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Speaker 1: not state government, and not corporate use, so it's not

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Speaker 1: like this applied across the board. In nineteen six, the

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Speaker 1: US government passed the Telephone Consumer Protection Act and established

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Speaker 1: they Do Not Call Registry, and these were meant to

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Speaker 1: reduce the number of solicitation calls that citizens would get,

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Speaker 1: you know, the spam calls we would call them today,

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Speaker 1: and it would give people the opportunity to opt out

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Speaker 1: of being on that regis streets, so that in theory,

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Speaker 1: you wouldn't get those cold calls. Now, this is one

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Speaker 1: of those things that in recent years has become an

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Speaker 1: area of focus because stuff like robo calls and spoofing

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Speaker 1: have really sidestepped the protections that were in place all

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Speaker 1: the way back in six rendering them almost meaningless. And

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Speaker 1: you've heard a lot of calls even in you know,

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Speaker 1: high areas of Congress to have a new version of

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Speaker 1: this too address things like companies that use spoofing and

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Speaker 1: robo calls to do a widespread targeting of spam. Over

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Speaker 1: in Europe, the EU adopted the Data Protection Directive in

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Speaker 1: nine and then in the EU replaced that with the

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Speaker 1: General Data Protection Regulation or g d p R. This

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Speaker 1: is the set of rules and restrictions that really prevents

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Speaker 1: largely like big tech companies from following the same source

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Speaker 1: of strategies in the EU that they follow here over

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Speaker 1: in the United States. So you'll hear a lot about

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Speaker 1: companies having to readjust how they work in the EU,

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Speaker 1: because if they were to continue to operate as they

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Speaker 1: do in the United States, they could be held legally

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Speaker 1: liable for lots of violations. There are other rules that

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Speaker 1: are important. There's KAPPA. That's the Children's Online Privacy Protection

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Speaker 1: Act here in the United States. That act places much

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Speaker 1: tighter restrictions on companies when it comes to the collection

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Speaker 1: and use of data about people who are younger than thirteen.

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Speaker 1: That law and the enforcement of it have created some

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Speaker 1: fairly tricky situations for companies and for others, like notably

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Speaker 1: content creators on platforms like YouTube. But I'm pretty sure

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Speaker 1: I'm gonna have to do just a full episode on

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Speaker 1: KAPPA and its consequences in the future, because those consequences

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Speaker 1: include stuff that I think are valuable and stuff that

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Speaker 1: because of the interpretation of the law and the implementation

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Speaker 1: of policies on platforms like YouTube can also be harmful.

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Speaker 1: It's complicated, so that's why it would require its own episode.

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Speaker 1: In the US past, the Graham Leach Blightly Act, which

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Speaker 1: requires financial institutions to explain how they use and share

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Speaker 1: private consumer data. So this was for things like credit

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Speaker 1: organizations banks. That kind of thing also requires that those

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Speaker 1: companies provide a means for customers to opt out of

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Speaker 1: having their information shared, which is a pretty good point

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Speaker 1: to focus on for a second. In the United States,

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Speaker 1: the general approach the default is to require companies to

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Speaker 1: provide an opt out option, but a lot of companies

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Speaker 1: bury these sorts of settings or features so that they're

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Speaker 1: not easy to find or activate, and often any disclosure

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Speaker 1: of there being an opt out feature can be buried

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Speaker 1: inside a long terms of service end user agreement page

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Speaker 1: which most people don't take the time to read. And

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Speaker 1: I'm just as guilty of this as other people. I

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Speaker 1: don't I'm not you know, I'm not throwing shade here, folks,

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Speaker 1: I am. I've done this, and some people argue that

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Speaker 1: it would be better to have an opt in approach,

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Speaker 1: so this would be where you would sign up for

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Speaker 1: a service. You would be told, hey, do you mind

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Speaker 1: if we use your information to do X, Y and

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Speaker 1: Z in a very clear and transparent way, not buried

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Speaker 1: in like pages and pages of terms of service, and

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Speaker 1: you would then click a box to allow it to happen.

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Speaker 1: But since so much revenue depends intrinsically on the ability

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Speaker 1: of companies to collect and share data, there's an extreme

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Speaker 1: financial incentive to go the opt out route. More on

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Speaker 1: that later. On the state level, presently, two states in

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Speaker 1: the US have passed privacy laws meant to protect the

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Speaker 1: private information of citizens of those states, and they are California,

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Speaker 1: which passed the California Consumer Privacy Act in which went

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Speaker 1: into effect in twenty twenty, and then Virginia, which very

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Speaker 1: recently passed the Consumer Data Protection Act just this past

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Speaker 1: March in but that law won't go into effect until January.

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Speaker 1: The other forty eight states of the US pretty much

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Speaker 1: just followed the general federal rules to some extent. So

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Speaker 1: that's the legal background on privacy in the United States

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Speaker 1: and to a lesser extent, the EU without going into

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Speaker 1: like super detailed analysis. The idea being privacy is clearly

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Speaker 1: a thing, but it's not as heavily protected from a

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Speaker 1: legal standpoint in the United States as it could be. Meanwhile,

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Speaker 1: obviously technology advances at an incredible pace and frequently leaves

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Speaker 1: the legal system in the dust. But what about the

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Speaker 1: actual collection of personal information in the tech age. Well,

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Speaker 1: again we need to think back historically. Now, in the

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Speaker 1: old old days, you know, pre digital age, businesses essentially

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Speaker 1: kept an eye on customers, particularly repeat customers, and kept

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Speaker 1: an eye on what they were buying. This was necessary

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Speaker 1: in order to just keep stores stocked with the stuff

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Speaker 1: people actually needed. And I think this kind of tracking,

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Speaker 1: which is being really generous, uh, it was much more

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Speaker 1: general approach. It's pretty easy to understand and to forgive.

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Speaker 1: So imagine that you are a store owner and you

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Speaker 1: notice that every two months farmer Betty comes in and

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Speaker 1: she buys two bags of grain. You would learn pretty

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Speaker 1: quickly that you need to make sure you have grain

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Speaker 1: stocked every two months because typically that's when she would

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Speaker 1: come in and buy them. So you want to make

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Speaker 1: sure you had that on hand. And you would do

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Speaker 1: this with all your customers, whatever it was that they

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Speaker 1: were buying. You would make sure that you were stocked

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Speaker 1: up on and you might also try and experiment and

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Speaker 1: stock some stuff was related to the products that your

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Speaker 1: regulars were buying, and assuming that you speculate correctly, everyone

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Speaker 1: benefits from that. You make more sales and your customers

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Speaker 1: end up getting stuff that they need, even if they

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Speaker 1: didn't know they needed it at the time. Fast forward

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Speaker 1: a whole lot to the point where we had electronic

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Speaker 1: means of keeping inventory. The invention of stuff like computer

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Speaker 1: systems and the bar code in the nineteen seventies that

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Speaker 1: made it possible for us to keep an up to

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Speaker 1: date electronic record of inventory and sales, and these weren't

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Speaker 1: directly tied to customers just yet. That would still largely

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Speaker 1: depend heavily on observation, but now it was much easier

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Speaker 1: to spot buying trends and respond to them in close

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Speaker 1: to real time. That particular branch would lead to data

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Speaker 1: driven marketing, in which experts and marketing would look at

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Speaker 1: various goods and services and use data to determine which

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Speaker 1: regions they should focus on and which one's might prove

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Speaker 1: to be less profitable. So let's say you are uh,

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Speaker 1: the owner of a chain of grocery stores in the Southeast.

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Speaker 1: You might notice that grocery stores in Atlanta are selling

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Speaker 1: a lot of a particular type of product, and then meanwhile,

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Speaker 1: a store in you know, Charlotte isn't selling that so much,

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Speaker 1: but it is selling a different product at very high volume.

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Speaker 1: These are the sort of dad trends you would want

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Speaker 1: to know so that you could make sure you had

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Speaker 1: the right stock on hand, you could do advertising campaigns,

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Speaker 1: and you can maximize your sales and minimize your waste.

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Speaker 1: It was incredibly valuable data that was undeniable. Information was

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Speaker 1: the key to maximizing profit and reducing costs as much

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Speaker 1: as possible. In the nineteen eighties we saw the rise

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Speaker 1: of direct marketing, in which marketers would customize and personalized

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Speaker 1: efforts and aim them at specific shoppers. Now we're no

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Speaker 1: longer looking at regions, we're looking at individuals. And typically

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Speaker 1: they did this through direct mail sales. And this was

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Speaker 1: a pretty primitive approach and based solely on the customer's

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Speaker 1: past purchases. So I can give you an example of this.

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Speaker 1: I grew up in the eighties. I was one of

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Speaker 1: those kids who occasionally would buy comic books, and of

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Speaker 1: course stuff on the backs of comic books would sometimes

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Speaker 1: be really tempting, Like man I really would like that

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Speaker 1: hand buzzer. That seems like that will be a real hit,

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Speaker 1: And you go and you send your like dollar seventy

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Speaker 1: five or the mail and you buy one. And then

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Speaker 1: next thing you know, you start getting catalogs for all

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Speaker 1: these sort of novelty gifts. And that's how I ended

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Speaker 1: up on a billion mailing lists that were all aiming

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Speaker 1: at me in different ways for different types of weird

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Speaker 1: or novelty type stuff. That was kind of the approach.

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Speaker 1: It was pretty primitive. It was not, you know, super sophisticated,

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Speaker 1: but it did set a foundation that particular branch would

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Speaker 1: later extend further into st like loyalty programs in which

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Speaker 1: stores would issue cards or tokens, often using a bar

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Speaker 1: code right that they would scan, and that would link

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Speaker 1: purchases to specific customers. Now you actually know who it

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Speaker 1: is that's buying stuff and how frequently they're buying it,

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Speaker 1: and when linked to other information such as a person's

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Speaker 1: email address or their snail mail address, that can allow

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Speaker 1: stores to proactively reach out to a customer and alert

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Speaker 1: them of sales, maybe offer up coupons, all in an

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Speaker 1: effort to sell more stuff. And it gave the stores

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Speaker 1: way more information about the preferences of their customers on

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Speaker 1: that individual basis. That's sort of a microcosm of what

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Speaker 1: we're looking at with personal data on the Internet. I'll

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Speaker 1: explain more after this short break. Okay, we have gotten

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Speaker 1: up to the ninety nineties and the birth of the

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Speaker 1: World Wide Web. Now, the original purpose of the web

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Speaker 1: was to create a collection of documents that could be

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Speaker 1: linked to one another using hypertext links. So it was

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Speaker 1: just a means of sharing information and linking information together

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Speaker 1: which could allow you to create a type of contextualization.

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Speaker 1: So think of the average experience you might have on

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Speaker 1: a site like Wikipedia. Sure you might start off reading

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Speaker 1: about sloths, but then through a series of clicking on

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Speaker 1: various links in different articles, you ultimately end up reading

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Speaker 1: about the communist revolution in Cuba. It didn't take too

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Speaker 1: long after the initial launch of the earliest web pages

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Speaker 1: for commerce to follow onto the web. In the digital

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Speaker 1: magazine hot Wired introduced something new, the banner ad. Hot

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Speaker 1: Wired was an online branch of the print magazine Wired.

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Speaker 1: Now hot Wired doesn't exist anymore, but Wired now occupies

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Speaker 1: both print and digital formats. However, the banner ad became

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Speaker 1: a really important stepping stone in our story. The first

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Speaker 1: banner ad was for a T and T, which reportedly

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Speaker 1: paid hot Wire a fee of thirty dollars so that

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Speaker 1: the banner ad would be placed at the top of

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Speaker 1: hot Wired pages for the duration of three months during

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Speaker 1: that time, according to one source, at least I could

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Speaker 1: not verify this. It's widely reported, but I feel like

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Speaker 1: they're all pulling it from the same source, So take

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Speaker 1: this with a grain of salt. But apparently that banner

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Speaker 1: ad enjoyed a click through rate of forty so quick aside,

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Speaker 1: just in case you're not familiar with online ad terms,

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Speaker 1: click through is what it sounds like. It's how many

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Speaker 1: people actively clicked on an ad, which would then link

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Speaker 1: the person to some other page. It would send them

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Speaker 1: to a page that might have a little more information

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Speaker 1: about a specific service or product, and frequently it would

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Speaker 1: also include some means of signing up or purchasing that

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Speaker 1: service or product. So if you're an advertiser, you want

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Speaker 1: a pretty decent click through rate because one, it shows

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Speaker 1: your ad was effective, and too, it makes your client

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Speaker 1: happy because presumably they're going to get more sales, and

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Speaker 1: click through is indescribably effective. It is it's insane when

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Speaker 1: nearly half of all the people visiting a web page

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Speaker 1: are clicking on an AD that's on that page. That's incredible.

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Speaker 1: These days, the average click through on display ads is

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Speaker 1: somewhere in the neighborhood of point three five per cent,

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Speaker 1: so less than half a percentage point. Banner ads, by

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Speaker 1: the way, get even less, sometimes as low as point

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Speaker 1: zero five percent click through. So yeah, was unthinkably successful.

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Speaker 1: It probably said a lot of very unrealistic expectations, if

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Speaker 1: I'm being honest. But it was also a brand new thing.

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Speaker 1: People had yet to develop ad blindness to banner ads, right.

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Speaker 1: They They hadn't sort of trained themselves to just ignore

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Speaker 1: everything that's at the top or the right of the

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Speaker 1: page or sometimes the bottom. They were looking at everything.

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Speaker 1: And also ad blockers were not yet a thing, so

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Speaker 1: it wasn't like people were using means to prevent ads

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Speaker 1: from showing up in the first place. Now, in those

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Speaker 1: early days, the general thought was that a conversion, that is,

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Speaker 1: converting someone from viewing an AD to acting on it,

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Speaker 1: was pretty much limited to sales. So, in other words,

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Speaker 1: people thought of ads as only being successful if someone

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Speaker 1: was actively buying stuff through interacting with that ad. But

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Speaker 1: pretty early on that attitude began to change because there

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Speaker 1: were other aspects of conversion to consider. Clicking on the

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Speaker 1: ad showed interest, and interest alone could be a conversion.

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Speaker 1: It might mean that the company that was in charge

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Speaker 1: of of uh, whatever projects our service the ad was

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Speaker 1: linked to, might have to do some extra work, but

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Speaker 1: you have at least identified a a prospect. Downloading a

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Speaker 1: coupon was another type of conversion, and companies began to

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Speaker 1: have a way to track data trends through online interactions,

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Speaker 1: at least to how well their ads were kind of doing.

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Speaker 1: At the same time, a developer named Lou Montoli created

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Speaker 1: a new type of computer file that would transform the

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Speaker 1: experience of using the World Wide Web. These files, called cookies,

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Speaker 1: are integral to how we experience the web. The cookies

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Speaker 1: keep track of which websites we visit, how frequently we

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Speaker 1: visit them, and what we do on those sites, including

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Speaker 1: stuff like if we make a purchase or if we

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Speaker 1: click through an ad. They allow us to do stuff

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Speaker 1: like log into a service online, like you know, put

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Speaker 1: in your user name and password, and then you can

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Speaker 1: stay logged in even if you navigate away from the

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Speaker 1: site and you come back to it later, you're still

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Speaker 1: lugged in. Well, that's thanks to cookies. That way, when

434
00:27:03,480 --> 00:27:05,359
Speaker 1: we come back, we're already lugged in. We don't have

435
00:27:05,400 --> 00:27:08,399
Speaker 1: to go through that process again unless you're using something

436
00:27:08,400 --> 00:27:11,800
Speaker 1: that has really high security, in which case you would

437
00:27:11,800 --> 00:27:14,400
Speaker 1: have to do it because the security is the most

438
00:27:14,400 --> 00:27:18,919
Speaker 1: important part. Similarly, if you're using a site that allows

439
00:27:18,920 --> 00:27:22,119
Speaker 1: you to set certain preferences, the cookie file on your

440
00:27:22,160 --> 00:27:25,320
Speaker 1: computer tells that site what those preferences are, so that

441
00:27:25,359 --> 00:27:28,280
Speaker 1: when you return, those preferences are already in place for

442
00:27:28,320 --> 00:27:31,760
Speaker 1: you automatically. It creates a level of convenience that makes

443
00:27:31,760 --> 00:27:34,800
Speaker 1: the web more usable. Now, when you visit a site,

444
00:27:34,880 --> 00:27:38,359
Speaker 1: the site can request the information stored in that cookie file.

445
00:27:38,400 --> 00:27:41,640
Speaker 1: It has to because if the site needs to adjust

446
00:27:41,720 --> 00:27:44,440
Speaker 1: things for you, it has to know what to do right.

447
00:27:44,960 --> 00:27:46,399
Speaker 1: And this is where we start to get to a

448
00:27:46,400 --> 00:27:51,600
Speaker 1: point where personal information becomes a hot commodity online. The cookie,

449
00:27:51,800 --> 00:27:54,960
Speaker 1: which had clear benefits for users, would have even more

450
00:27:55,040 --> 00:27:59,760
Speaker 1: significant benefits for businesses online. So let's switch back to

451
00:27:59,800 --> 00:28:02,960
Speaker 1: the ads side of the story. At first, companies would

452
00:28:02,960 --> 00:28:05,720
Speaker 1: follow a T and T S lead and purchase banner

453
00:28:05,800 --> 00:28:09,399
Speaker 1: space or right rail space on a website for a

454
00:28:09,480 --> 00:28:11,560
Speaker 1: set amount of money for a set amount of time.

455
00:28:12,040 --> 00:28:14,520
Speaker 1: But it didn't take long for that to change. In

456
00:28:16,160 --> 00:28:20,560
Speaker 1: Netscape and info seek migrated to a new pricing model.

457
00:28:21,200 --> 00:28:25,400
Speaker 1: Instead of creating a one size fits a few approach

458
00:28:25,560 --> 00:28:28,840
Speaker 1: to selling web page landscape, which meant companies would have

459
00:28:28,880 --> 00:28:31,760
Speaker 1: to pay the same amount whether their ads were working

460
00:28:31,760 --> 00:28:34,560
Speaker 1: on that page or not, and for you know, whatever

461
00:28:34,640 --> 00:28:37,280
Speaker 1: duration they had picked. The new approach was the good

462
00:28:37,320 --> 00:28:43,280
Speaker 1: old CPM. CPM stands for cost per milay, with milla

463
00:28:43,400 --> 00:28:46,719
Speaker 1: being the Roman word for thousand. So it sounds at

464
00:28:46,760 --> 00:28:48,640
Speaker 1: first like you're saying cost per million, but you're really

465
00:28:48,640 --> 00:28:51,760
Speaker 1: saying cost per thousand. So the thousand in this case

466
00:28:52,080 --> 00:28:56,360
Speaker 1: tends to mean impressions. That is the number of times

467
00:28:56,400 --> 00:29:01,320
Speaker 1: the ad is presumably seen by visitors to that particular website.

468
00:29:01,840 --> 00:29:04,360
Speaker 1: So what this really breaks down to is that web

469
00:29:04,400 --> 00:29:07,680
Speaker 1: pages that are really popular and they get lots of

470
00:29:07,760 --> 00:29:12,600
Speaker 1: visitors can demand a higher CPM because more folks go there,

471
00:29:12,960 --> 00:29:15,080
Speaker 1: So ads that are placed there are going to have

472
00:29:15,160 --> 00:29:19,160
Speaker 1: higher visibility than an ad placed on you know, old

473
00:29:19,240 --> 00:29:23,360
Speaker 1: Joe Bob's Duct Tape Museum website. A site that gets

474
00:29:23,400 --> 00:29:26,400
Speaker 1: a lot of traffic can demand more money per thousand impressions,

475
00:29:26,680 --> 00:29:29,200
Speaker 1: kind of like how in the United States, if you

476
00:29:29,280 --> 00:29:31,440
Speaker 1: want to have a commercial played during the Super Bowl,

477
00:29:31,480 --> 00:29:35,080
Speaker 1: it's gonna cost you millions of dollars compared to, you know,

478
00:29:35,200 --> 00:29:38,680
Speaker 1: putting an ad on some niche television channel for late

479
00:29:38,760 --> 00:29:43,080
Speaker 1: night TV. On the flip side, for advertisers, they can

480
00:29:43,080 --> 00:29:47,120
Speaker 1: negotiate a rate with websites for a certain number of impressions,

481
00:29:47,160 --> 00:29:50,240
Speaker 1: which effectively replaced the duration limit for an ad. So

482
00:29:50,280 --> 00:29:53,240
Speaker 1: instead of saying I want an ad running on this

483
00:29:53,320 --> 00:29:57,200
Speaker 1: page for three months, you would say, all right, the

484
00:29:57,280 --> 00:30:01,040
Speaker 1: web page has a CPM rate of ten dollars, meaning

485
00:30:01,080 --> 00:30:03,200
Speaker 1: that you have to pay ten dollars for every one

486
00:30:03,320 --> 00:30:08,600
Speaker 1: thousand views, and you might negotiate for a million impressions,

487
00:30:09,000 --> 00:30:11,920
Speaker 1: which would mean that the advertiser would have to pay

488
00:30:11,920 --> 00:30:15,680
Speaker 1: the website ten thousand dollars to carry that ad in

489
00:30:15,760 --> 00:30:18,520
Speaker 1: order to generate a million impressions. And if the page

490
00:30:18,560 --> 00:30:20,720
Speaker 1: got a million impressions in a couple of days, boom,

491
00:30:20,800 --> 00:30:24,400
Speaker 1: that ad campaign was done. If it takes weeks, well,

492
00:30:24,480 --> 00:30:28,560
Speaker 1: then the campaign would last longer. And obviously that CPM

493
00:30:28,720 --> 00:30:32,120
Speaker 1: was different depending on the popularity of the website. Website

494
00:30:32,120 --> 00:30:34,120
Speaker 1: that's not that popular would have a low CPM, and

495
00:30:34,120 --> 00:30:37,719
Speaker 1: it would also take a longer time to reach whatever

496
00:30:37,800 --> 00:30:41,080
Speaker 1: the agreed upon number of impressions was. By the way,

497
00:30:41,120 --> 00:30:44,400
Speaker 1: this is also why there are a lot of websites

498
00:30:44,440 --> 00:30:47,400
Speaker 1: that have things like quizzes and galleries where you have

499
00:30:47,520 --> 00:30:52,240
Speaker 1: to scroll through each item. Like any website where it's

500
00:30:52,520 --> 00:30:57,720
Speaker 1: top ten movies that feature ghosts that have hair in

501
00:30:57,760 --> 00:31:00,720
Speaker 1: front of their face, and every single tree is its

502
00:31:00,760 --> 00:31:04,360
Speaker 1: own web page. That's because every one of those web

503
00:31:04,400 --> 00:31:08,360
Speaker 1: pages you go to, that's another impression. So it's a

504
00:31:08,400 --> 00:31:11,520
Speaker 1: way of expanding the number of impressions and AD would

505
00:31:11,600 --> 00:31:15,280
Speaker 1: get by making you have to reload the page over

506
00:31:15,320 --> 00:31:17,920
Speaker 1: and over and over again. If all ten of those

507
00:31:17,920 --> 00:31:21,040
Speaker 1: things were on one page, you would get one impression

508
00:31:21,160 --> 00:31:23,720
Speaker 1: for that article. These things, by the way, also have

509
00:31:23,880 --> 00:31:26,880
Speaker 1: massive drop off rates, like if they're not really compelling,

510
00:31:26,920 --> 00:31:30,479
Speaker 1: people will bail on them within like two or three entries,

511
00:31:31,480 --> 00:31:35,320
Speaker 1: so there's a diminishing returns thing going on with them.

512
00:31:35,400 --> 00:31:39,040
Speaker 1: That's just some insight into how web pages generate revenue.

513
00:31:39,320 --> 00:31:42,600
Speaker 1: I've been on that side, and it is not always

514
00:31:42,640 --> 00:31:47,160
Speaker 1: fun anyway. A big part of all this is that

515
00:31:47,400 --> 00:31:51,880
Speaker 1: the ad campaign wasn't tied to other performance metrics, right,

516
00:31:51,920 --> 00:31:54,240
Speaker 1: So it wasn't whether or not people click through the

517
00:31:54,280 --> 00:31:57,680
Speaker 1: ad or whether they actually made a purchase through the ad.

518
00:31:57,720 --> 00:32:01,000
Speaker 1: This was just about how many people actually were exposed

519
00:32:01,120 --> 00:32:03,800
Speaker 1: to the ad itself. The ad had to do the

520
00:32:03,840 --> 00:32:07,680
Speaker 1: rest of the work. Then you had the emergence of

521
00:32:08,000 --> 00:32:11,640
Speaker 1: double Click, that company which is now owned by Google.

522
00:32:11,800 --> 00:32:15,440
Speaker 1: It was actually the focus of a big antitrust lawsuit

523
00:32:15,560 --> 00:32:18,040
Speaker 1: that I'm not going to get into, but it was

524
00:32:18,080 --> 00:32:21,080
Speaker 1: a big deal anyway, and let companies know which of

525
00:32:21,120 --> 00:32:24,360
Speaker 1: their ads were most effective. So double click would give

526
00:32:24,440 --> 00:32:27,720
Speaker 1: feedback to companies about which ads got the most impressions

527
00:32:27,760 --> 00:32:30,320
Speaker 1: and click throughs, and those companies could then get a

528
00:32:30,360 --> 00:32:33,000
Speaker 1: better idea of where they needed to spend their digital

529
00:32:33,040 --> 00:32:37,720
Speaker 1: marketing dollars. Double clicks relationship with advertisers meant that there

530
00:32:37,760 --> 00:32:40,200
Speaker 1: was a lot of rapid innovation in the web marketing

531
00:32:40,200 --> 00:32:44,200
Speaker 1: space as companies began to hone in on what strategies

532
00:32:44,240 --> 00:32:49,000
Speaker 1: worked and which ones didn't work. From there, you add

533
00:32:49,040 --> 00:32:52,520
Speaker 1: all sorts of changes in the space. Some ads moved

534
00:32:52,560 --> 00:32:55,640
Speaker 1: to pay per click models, so that was kind of

535
00:32:55,640 --> 00:32:59,080
Speaker 1: going back to that conversion approach, where in this model

536
00:32:59,160 --> 00:33:02,520
Speaker 1: and advertiser would only pay a website for the number

537
00:33:02,560 --> 00:33:06,280
Speaker 1: of clicks that an ad received. That created an incentive

538
00:33:06,480 --> 00:33:09,360
Speaker 1: for the website operators to try and make sure they

539
00:33:09,360 --> 00:33:12,880
Speaker 1: were pairing ads with pages that would potentially drive the

540
00:33:12,960 --> 00:33:16,160
Speaker 1: most traffic. So if you had a web page about

541
00:33:16,560 --> 00:33:20,760
Speaker 1: I don't know, chainsaws, you probably wouldn't try and pair

542
00:33:20,920 --> 00:33:24,560
Speaker 1: that page with an ad for perfume. Now I'm not

543
00:33:24,640 --> 00:33:28,360
Speaker 1: saying there's no crossover between people who are interested in

544
00:33:28,440 --> 00:33:31,880
Speaker 1: chainsaws and those who are interested in perfume, but from

545
00:33:31,880 --> 00:33:35,120
Speaker 1: a marketing standpoint, you're probably not going to get the

546
00:33:35,280 --> 00:33:40,440
Speaker 1: view that is the best use of your resources. And

547
00:33:40,520 --> 00:33:43,680
Speaker 1: another big change around this time was to how people

548
00:33:43,720 --> 00:33:46,640
Speaker 1: were accessing information on the web in general. While it

549
00:33:46,720 --> 00:33:50,400
Speaker 1: might be theoretically possible to navigate the webs simply by

550
00:33:50,440 --> 00:33:53,680
Speaker 1: following various hypertext links to get from point A to

551
00:33:53,760 --> 00:33:58,160
Speaker 1: point B, to actually do it would be monumentally inefficient.

552
00:33:58,240 --> 00:34:00,680
Speaker 1: I mean, you might have to pass through hundreds or

553
00:34:00,840 --> 00:34:03,720
Speaker 1: thousands of sites to get from A to B. If

554
00:34:03,760 --> 00:34:07,400
Speaker 1: you're limiting yourself to just following links that are on

555
00:34:07,520 --> 00:34:09,719
Speaker 1: the page of A and trying to get to be

556
00:34:09,840 --> 00:34:12,120
Speaker 1: that way, this can be a fun game by the way,

557
00:34:12,120 --> 00:34:14,520
Speaker 1: a kind of seven degrees of separation kind of game,

558
00:34:15,040 --> 00:34:17,160
Speaker 1: but it's not an efficient way to navigate the web.

559
00:34:17,480 --> 00:34:20,839
Speaker 1: So Internet search engines were way more useful. You would

560
00:34:20,880 --> 00:34:23,760
Speaker 1: put in your query in the search bar and boom,

561
00:34:24,040 --> 00:34:26,880
Speaker 1: you got results that and, in theory at least best

562
00:34:26,920 --> 00:34:30,520
Speaker 1: match whatever it was you're looking for. But this meant

563
00:34:31,000 --> 00:34:36,000
Speaker 1: that search engines were incredible aggregators for user behavior. Not

564
00:34:36,200 --> 00:34:39,919
Speaker 1: only could search engines keep itally on which search terms

565
00:34:39,920 --> 00:34:42,799
Speaker 1: were the most popular at any given time, they could

566
00:34:42,840 --> 00:34:46,839
Speaker 1: also consult those cookie files on users computers and see

567
00:34:46,880 --> 00:34:50,000
Speaker 1: what other sites users have been visiting, and search engines

568
00:34:50,040 --> 00:34:53,440
Speaker 1: could sell ads to. In fact, search engines made a

569
00:34:53,480 --> 00:34:57,719
Speaker 1: subtle shift from being all about indexing the web and

570
00:34:57,800 --> 00:35:01,320
Speaker 1: became more about monetizing search and getting into the data

571
00:35:01,360 --> 00:35:05,799
Speaker 1: collection and advertising businesses. That's what happened to Google. I

572
00:35:05,800 --> 00:35:08,560
Speaker 1: mean years ago you might call a you know, Google,

573
00:35:08,560 --> 00:35:11,840
Speaker 1: a search company. Uh this was before the days of

574
00:35:11,920 --> 00:35:15,279
Speaker 1: tons of other Google products emerged, like Google Cloud and

575
00:35:15,280 --> 00:35:18,560
Speaker 1: Android and all that stuff. But at its heart, what

576
00:35:18,680 --> 00:35:21,640
Speaker 1: Google really was wasn't a search engine. It was an

577
00:35:21,680 --> 00:35:25,400
Speaker 1: advertising company. Companies could pay Google to have their sites

578
00:35:25,440 --> 00:35:28,799
Speaker 1: pop up in searches as you know, results in a

579
00:35:28,840 --> 00:35:31,840
Speaker 1: search result, they would be ads, they would be clearly

580
00:35:31,880 --> 00:35:35,319
Speaker 1: marked his ads, but they would be advertised spots. And

581
00:35:35,360 --> 00:35:38,680
Speaker 1: it gave those companies way more visibility than they might

582
00:35:38,719 --> 00:35:43,280
Speaker 1: otherwise have. And Google can target specific users with ads

583
00:35:43,320 --> 00:35:47,520
Speaker 1: that might appeal to them through a collection and analysis

584
00:35:47,560 --> 00:35:50,600
Speaker 1: of all that personal data, both from the users cookies

585
00:35:50,640 --> 00:35:53,880
Speaker 1: and their own searches history, and and if that person

586
00:35:53,920 --> 00:35:56,440
Speaker 1: happens to be using Chrome or an Android device, well

587
00:35:56,440 --> 00:36:00,600
Speaker 1: that's another kettle of fish entirely. So things took a

588
00:36:00,840 --> 00:36:06,000
Speaker 1: massive downturn shortly after search engines began to dominate the

589
00:36:06,120 --> 00:36:09,160
Speaker 1: data collection space, and that's because of the dot com

590
00:36:09,200 --> 00:36:12,600
Speaker 1: bubble bursting in two thousand, two thousand one, and the

591
00:36:12,640 --> 00:36:16,520
Speaker 1: world slid into an economic recession in general that particularly

592
00:36:16,600 --> 00:36:20,080
Speaker 1: hit the online world hard. That was further exacerbated by

593
00:36:20,080 --> 00:36:22,880
Speaker 1: the terrorist attacks on the United States on September eleven,

594
00:36:23,000 --> 00:36:27,359
Speaker 1: two thousand one. But the industry did continue. There were

595
00:36:27,400 --> 00:36:30,160
Speaker 1: just fewer players. Some of the bigger companies were able

596
00:36:30,200 --> 00:36:32,880
Speaker 1: to survive all that turmoil, and the fact that the

597
00:36:33,040 --> 00:36:37,680
Speaker 1: smaller competitors were largely wiped out meant that these companies.

598
00:36:37,840 --> 00:36:42,040
Speaker 1: So companies like Google and Amazon now had an even

599
00:36:42,040 --> 00:36:45,520
Speaker 1: more dominant position in the online world, so it would

600
00:36:45,560 --> 00:36:48,560
Speaker 1: be very hard to overtake those companies, and both Google

601
00:36:48,600 --> 00:36:52,279
Speaker 1: and Amazon are famous for jealously guarding their dominant positions.

602
00:36:53,280 --> 00:36:55,239
Speaker 1: Move forward a few more years and you get the

603
00:36:55,320 --> 00:36:58,520
Speaker 1: rise of social network platforms. My Space was one of

604
00:36:58,560 --> 00:37:02,000
Speaker 1: the first really big social networks. It launched in two

605
00:37:02,040 --> 00:37:05,359
Speaker 1: thousand three, but Facebook soon followed in two thousand four.

606
00:37:06,280 --> 00:37:08,680
Speaker 1: My Space would be the most popular social network until

607
00:37:08,680 --> 00:37:12,360
Speaker 1: about two thousand eight. That's when Facebook overtook it. But

608
00:37:12,440 --> 00:37:17,160
Speaker 1: Facebook also introduced Facebook Ads in two thousand seven. Facebook

609
00:37:17,200 --> 00:37:21,280
Speaker 1: offered even more data points about people than search engines could.

610
00:37:21,800 --> 00:37:25,760
Speaker 1: I mean, people use social networks to connect with friends

611
00:37:25,920 --> 00:37:29,120
Speaker 1: and to share stuff about themselves and the people they like.

612
00:37:29,480 --> 00:37:33,239
Speaker 1: So people were willingly giving up tons of information that

613
00:37:33,360 --> 00:37:37,840
Speaker 1: an advertiser might find extremely relevant. And so Facebook's business

614
00:37:37,880 --> 00:37:41,960
Speaker 1: model largely fell into the realm of collecting and analyzing

615
00:37:42,000 --> 00:37:45,760
Speaker 1: information about users, both from their activities on the platform

616
00:37:45,800 --> 00:37:50,879
Speaker 1: itself and through the consultation of cookies, and Facebook could

617
00:37:51,000 --> 00:37:54,799
Speaker 1: use that information to place highly relevant ads on specific

618
00:37:54,920 --> 00:38:02,880
Speaker 1: user pages. This became the absolute foundation of Facebook strategy

619
00:38:03,000 --> 00:38:06,560
Speaker 1: for the desktop experience, and how they generate revenue, and

620
00:38:06,600 --> 00:38:11,239
Speaker 1: the value proposition Facebook has for advertisers is incredible. They

621
00:38:11,239 --> 00:38:15,200
Speaker 1: can say, Hey, practically everyone in the world is on

622
00:38:15,239 --> 00:38:18,239
Speaker 1: our platform, and we know what they all like and

623
00:38:18,280 --> 00:38:23,360
Speaker 1: what they dislike because of cookies and their activities on Facebook. Plus,

624
00:38:23,719 --> 00:38:28,160
Speaker 1: we've developed algorithms that keep people on Facebook longer. So

625
00:38:28,239 --> 00:38:31,279
Speaker 1: if you advertise on us, we can make sure that

626
00:38:31,360 --> 00:38:33,759
Speaker 1: the people who are most likely to respond to your

627
00:38:33,880 --> 00:38:37,480
Speaker 1: ad are the people who see that ad, and we

628
00:38:37,520 --> 00:38:40,520
Speaker 1: can make sure that they see your ad a lot.

629
00:38:41,719 --> 00:38:44,640
Speaker 1: This also began to introduce the era of read targeting.

630
00:38:45,000 --> 00:38:47,480
Speaker 1: So if you've ever had the experience of going to

631
00:38:47,800 --> 00:38:52,480
Speaker 1: different websites but seeing the same ads play on those websites,

632
00:38:53,000 --> 00:38:57,080
Speaker 1: you've been experiencing red targeting. Maybe you were doing some

633
00:38:57,200 --> 00:39:00,479
Speaker 1: comparison shopping for I don't know how, with a brand

634
00:39:00,480 --> 00:39:04,280
Speaker 1: new toaster, but for whatever reason, you didn't pull the trigger.

635
00:39:04,520 --> 00:39:06,880
Speaker 1: Maybe you went as far as looking at a specific

636
00:39:06,920 --> 00:39:10,839
Speaker 1: toasters page on a shopping site like Amazon, but you

637
00:39:10,920 --> 00:39:15,120
Speaker 1: didn't move forward with the purchase. But now suddenly everywhere

638
00:39:15,160 --> 00:39:18,680
Speaker 1: you go you seem to be seeing ads for that toaster,

639
00:39:19,360 --> 00:39:23,880
Speaker 1: or maybe it's a different but similar toaster. This is retargeting.

640
00:39:24,280 --> 00:39:26,600
Speaker 1: The cookies on your computer have a record of you

641
00:39:26,800 --> 00:39:29,799
Speaker 1: visiting that toaster page, and they also include the fact

642
00:39:29,880 --> 00:39:32,440
Speaker 1: that you didn't actually purchase the toaster when you went

643
00:39:32,520 --> 00:39:35,960
Speaker 1: to that page. Those cookies allow various websites that have

644
00:39:36,120 --> 00:39:39,680
Speaker 1: deals with ad companies that are working with this toaster

645
00:39:39,760 --> 00:39:44,640
Speaker 1: manufacturer to dynamically insert ads for that toaster on all

646
00:39:44,680 --> 00:39:47,279
Speaker 1: the various sites that you go to with the goal

647
00:39:47,360 --> 00:39:49,800
Speaker 1: of convincing you to finally follow through on your purchase

648
00:39:49,840 --> 00:39:53,840
Speaker 1: and buy that dang toaster. All right, we are still

649
00:39:53,880 --> 00:39:58,080
Speaker 1: only just approaching the surface level of water from the

650
00:39:58,120 --> 00:40:02,080
Speaker 1: tip of the iceberg. The really valuable stuff is below

651
00:40:02,160 --> 00:40:05,160
Speaker 1: the surface, and we have Apple to thank for it.

652
00:40:05,760 --> 00:40:18,240
Speaker 1: I'll explain more when we get back. On June seven,

653
00:40:18,320 --> 00:40:22,320
Speaker 1: Apple launched the iPhone. While other smartphones predated the iPhone,

654
00:40:22,360 --> 00:40:24,240
Speaker 1: it's safe to say that the iPhone was the first

655
00:40:24,360 --> 00:40:29,760
Speaker 1: truly successful consumer smartphone that appealed to the mainstream market. Previously,

656
00:40:29,800 --> 00:40:34,680
Speaker 1: smartphones pretty much just targeted geeks and executives. Now everybody

657
00:40:34,719 --> 00:40:38,680
Speaker 1: wanted one. The smartphone would open up brand new opportunities

658
00:40:38,800 --> 00:40:42,200
Speaker 1: for data collection. In two thousand eight, Apple launched the

659
00:40:42,239 --> 00:40:45,880
Speaker 1: first iPhone to include a GPS chip. This allowed for

660
00:40:45,960 --> 00:40:49,160
Speaker 1: really useful features for the users, such as real time

661
00:40:49,200 --> 00:40:51,239
Speaker 1: maps that can give you an accurate view of your

662
00:40:51,280 --> 00:40:55,680
Speaker 1: current location. That was huge, right, enormous benefit, But it

663
00:40:55,760 --> 00:40:58,920
Speaker 1: also meant that along with all the data that companies

664
00:40:58,960 --> 00:41:02,080
Speaker 1: could access thanks to cookies and social media posts and

665
00:41:02,120 --> 00:41:06,680
Speaker 1: search engine activity, they can now also add location data

666
00:41:06,880 --> 00:41:09,680
Speaker 1: to the mix as well. Now companies can know what

667
00:41:09,800 --> 00:41:12,319
Speaker 1: you were doing online and where you were in the

668
00:41:12,360 --> 00:41:15,640
Speaker 1: actual world. Now, to be clear, companies could do that

669
00:41:15,680 --> 00:41:18,360
Speaker 1: for folks who were accessing the Internet on desktops or

670
00:41:18,440 --> 00:41:21,239
Speaker 1: laptops as well. It's just that we don't tend to

671
00:41:21,360 --> 00:41:24,279
Speaker 1: carry desktops around with us at all, and for those

672
00:41:24,280 --> 00:41:26,799
Speaker 1: of us who do use laptops, we don't have them

673
00:41:26,880 --> 00:41:31,240
Speaker 1: on an active all the time. But a smartphone that's different.

674
00:41:31,520 --> 00:41:33,919
Speaker 1: That's a device that can ping back to home base

675
00:41:34,239 --> 00:41:36,719
Speaker 1: several times a day, sometimes more than a hundred times

676
00:41:36,719 --> 00:41:39,520
Speaker 1: a day, and that pain can include stuff like how

677
00:41:39,600 --> 00:41:42,319
Speaker 1: much screen time you've spent on the device that day,

678
00:41:42,640 --> 00:41:46,240
Speaker 1: what apps you've been using, what sites and services you've accessed,

679
00:41:46,600 --> 00:41:50,440
Speaker 1: and where in the world you happen to be. And again,

680
00:41:50,840 --> 00:41:53,919
Speaker 1: this allows companies to target more specific ads your way,

681
00:41:54,239 --> 00:41:56,839
Speaker 1: and now those ads could be location based as well

682
00:41:56,880 --> 00:42:00,200
Speaker 1: as activity based. So maybe you're wandering around a new

683
00:42:00,239 --> 00:42:03,279
Speaker 1: city and you start seeing ads for specific locations like

684
00:42:03,440 --> 00:42:07,560
Speaker 1: restaurants or shops or amusement parks or whatever. On the

685
00:42:07,600 --> 00:42:09,879
Speaker 1: one hand, that could be really useful as you try

686
00:42:09,960 --> 00:42:12,440
Speaker 1: to find things that you might want to experience in

687
00:42:12,480 --> 00:42:15,239
Speaker 1: a new place. But on the other it indicated that

688
00:42:15,320 --> 00:42:18,880
Speaker 1: the smartphone was really gathering a ton of information about you.

689
00:42:19,719 --> 00:42:22,120
Speaker 1: And then there's that scenario I mentioned at the top

690
00:42:22,160 --> 00:42:24,640
Speaker 1: of this episode. If you hang out with other folks

691
00:42:24,640 --> 00:42:28,840
Speaker 1: and everyone happens to have a smartphone, everyone is generating data,

692
00:42:28,920 --> 00:42:31,759
Speaker 1: whether they're actively using their phones or not. And part

693
00:42:31,760 --> 00:42:34,919
Speaker 1: of that data isn't just what they're doing or where

694
00:42:34,960 --> 00:42:37,960
Speaker 1: they are, but also who there with. And now our

695
00:42:38,000 --> 00:42:41,560
Speaker 1: relationships with one another, the time we spend with each other,

696
00:42:41,600 --> 00:42:45,000
Speaker 1: and the places where we spend it, that all becomes

697
00:42:45,120 --> 00:42:48,720
Speaker 1: part of the data grab as well. It represents another

698
00:42:48,760 --> 00:42:52,560
Speaker 1: way that companies can leverage and exploit the information we generate.

699
00:42:53,280 --> 00:42:56,160
Speaker 1: One of the industries that grew out of all of

700
00:42:56,200 --> 00:43:00,680
Speaker 1: this was the data brokerage industry. These are companies that

701
00:43:00,800 --> 00:43:06,319
Speaker 1: collect and maintain massive data repositories about well about us.

702
00:43:06,920 --> 00:43:09,960
Speaker 1: These companies buy and sell personal information as if it

703
00:43:10,000 --> 00:43:13,320
Speaker 1: were any other commodity, because for a lot of entities

704
00:43:13,320 --> 00:43:17,080
Speaker 1: out there, that's exactly what our personal information is. So

705
00:43:17,239 --> 00:43:19,520
Speaker 1: even for companies that might not have the means to

706
00:43:19,560 --> 00:43:23,960
Speaker 1: collect your personal data directly through their own services, can

707
00:43:24,040 --> 00:43:27,760
Speaker 1: pay to get hold of those sweet ones and zeros

708
00:43:27,800 --> 00:43:31,280
Speaker 1: through using data brokers. The U. S State of Vermont

709
00:43:31,440 --> 00:43:34,040
Speaker 1: passed a law a couple of years ago that mandated

710
00:43:34,080 --> 00:43:37,440
Speaker 1: that any company that bought or sold third party personal

711
00:43:37,520 --> 00:43:41,439
Speaker 1: data had to register with the Secretary of State. As

712
00:43:41,440 --> 00:43:45,520
Speaker 1: a result, one hundred twenty one companies registered with the

713
00:43:45,640 --> 00:43:49,400
Speaker 1: Vermont state government. The companies didn't have to provide information

714
00:43:49,480 --> 00:43:52,560
Speaker 1: about who was in their databases. They didn't have to

715
00:43:52,600 --> 00:43:56,040
Speaker 1: say how many people were in them. The rules didn't

716
00:43:56,040 --> 00:44:00,680
Speaker 1: require that these companies make available any information about consumers

717
00:44:00,719 --> 00:44:03,960
Speaker 1: to those consumers, which means if you wanted to check

718
00:44:04,000 --> 00:44:08,200
Speaker 1: to see how much dirt any of those companies might

719
00:44:08,280 --> 00:44:11,840
Speaker 1: have on you, and you, you know, lived in Vermont,

720
00:44:12,080 --> 00:44:14,279
Speaker 1: well you would kind of still be out of luck

721
00:44:14,320 --> 00:44:16,839
Speaker 1: because the law didn't go that far. The law did

722
00:44:16,880 --> 00:44:19,720
Speaker 1: require that the companies had to inform the government about

723
00:44:19,760 --> 00:44:23,280
Speaker 1: any kind of opt out features that the companies provided,

724
00:44:23,640 --> 00:44:26,719
Speaker 1: but that only assumed that they actually were providing an

725
00:44:26,719 --> 00:44:30,959
Speaker 1: opt out feature for the average consumer. What this means

726
00:44:31,040 --> 00:44:33,920
Speaker 1: is that there are more than one companies trafficking and

727
00:44:34,000 --> 00:44:37,560
Speaker 1: personal data, and yours could be among them, and to

728
00:44:37,680 --> 00:44:41,319
Speaker 1: opt out of that system, you would have to contact

729
00:44:41,440 --> 00:44:44,200
Speaker 1: each and every one of these data brokers and go

730
00:44:44,280 --> 00:44:48,120
Speaker 1: through whatever process they might have in order to opt out,

731
00:44:48,880 --> 00:44:53,040
Speaker 1: because by default we are all opted into that system.

732
00:44:53,080 --> 00:44:55,759
Speaker 1: In some cases, we did have a choice, in the

733
00:44:55,800 --> 00:44:58,360
Speaker 1: sense that the choice was whether or not we wanted

734
00:44:58,360 --> 00:45:02,000
Speaker 1: to use a specific service or platform or visit a

735
00:45:02,040 --> 00:45:05,960
Speaker 1: specific website. Again, this is frequently where those long user

736
00:45:06,000 --> 00:45:08,720
Speaker 1: agreements come in, the ones most of us skip right over,

737
00:45:08,840 --> 00:45:11,040
Speaker 1: so that we just click on that I agree button

738
00:45:11,080 --> 00:45:13,560
Speaker 1: and get on with it. We might not be aware

739
00:45:13,760 --> 00:45:15,560
Speaker 1: that we just gave consent to have all of our

740
00:45:15,640 --> 00:45:18,640
Speaker 1: data collected, but that's arguably on us if we didn't

741
00:45:18,680 --> 00:45:22,200
Speaker 1: bother to read the terms and conditions. But in other cases,

742
00:45:22,560 --> 00:45:25,040
Speaker 1: we might not ever have really had a chance to

743
00:45:25,120 --> 00:45:28,560
Speaker 1: opt out of a specific data brokers collections at all

744
00:45:29,080 --> 00:45:31,919
Speaker 1: because we never had direct contact with some of those

745
00:45:32,000 --> 00:45:36,280
Speaker 1: data brokers. Because again information is bought, sold, and traded

746
00:45:36,360 --> 00:45:39,200
Speaker 1: like crazy. So some of these companies are just buying

747
00:45:39,280 --> 00:45:42,359
Speaker 1: up data that was collected by someone else and we

748
00:45:42,440 --> 00:45:46,520
Speaker 1: only ever had contact with that initial point. So as

749
00:45:46,560 --> 00:45:48,480
Speaker 1: an example, let's say that you sign up for a

750
00:45:48,560 --> 00:45:53,360
Speaker 1: social networking platform. We'll call it space Look. So you

751
00:45:53,440 --> 00:45:57,160
Speaker 1: sign up on space Look, and there's this long, boring

752
00:45:57,280 --> 00:46:00,160
Speaker 1: passage of information that you've got to scroll through so

753
00:46:00,200 --> 00:46:02,400
Speaker 1: that you can click the I Agree button, so you

754
00:46:02,480 --> 00:46:04,880
Speaker 1: zoom past all the dull stuff so that you can

755
00:46:04,920 --> 00:46:08,280
Speaker 1: finally get to uploading photos of your adorable kitty cat online.

756
00:46:08,800 --> 00:46:12,520
Speaker 1: But in that dull passage, there are terms that explain

757
00:46:12,600 --> 00:46:16,120
Speaker 1: that space Look will be collecting information about you and

758
00:46:16,160 --> 00:46:19,520
Speaker 1: then using the information to serve you ads. And in addition,

759
00:46:19,920 --> 00:46:23,640
Speaker 1: space Look might also sell or share your personal information

760
00:46:23,920 --> 00:46:28,399
Speaker 1: with other third party entities. So you've effectively signed over

761
00:46:28,440 --> 00:46:31,440
Speaker 1: your personal data to space Look for it to do

762
00:46:31,520 --> 00:46:34,080
Speaker 1: whatever it wants with it within the confines of the

763
00:46:34,120 --> 00:46:37,560
Speaker 1: agreement that you've clicked on, and your info might get

764
00:46:37,600 --> 00:46:40,920
Speaker 1: sent to various data brokers that otherwise you have never

765
00:46:40,960 --> 00:46:47,080
Speaker 1: heard of and never contacted. But wait, it gets worse. Recently,

766
00:46:47,520 --> 00:46:50,160
Speaker 1: Facebook and Google have been in the News for putting

767
00:46:50,200 --> 00:46:51,920
Speaker 1: up a bit of a fuss when it comes to

768
00:46:52,000 --> 00:46:55,080
Speaker 1: the types of data collection that are available to them.

769
00:46:55,440 --> 00:46:58,960
Speaker 1: Facebook got upset at Apple for a new change in

770
00:46:59,040 --> 00:47:03,040
Speaker 1: privacy settings on iOS devices, and it requires Facebook to

771
00:47:03,080 --> 00:47:06,880
Speaker 1: inform users about how it wants to collect data regarding

772
00:47:07,200 --> 00:47:10,719
Speaker 1: user activity on those iOS devices. So like iPhones and

773
00:47:11,320 --> 00:47:15,160
Speaker 1: you know, iPads, things like that, that activity includes the

774
00:47:15,280 --> 00:47:18,480
Speaker 1: use of other apps on the device. So, in other words,

775
00:47:19,320 --> 00:47:23,360
Speaker 1: Facebook collects information on users app activity even if that

776
00:47:23,440 --> 00:47:27,319
Speaker 1: activity isn't directly involving Facebook itself. So if you do

777
00:47:27,360 --> 00:47:30,640
Speaker 1: any banking on your phone, or shopping, or maybe you

778
00:47:30,680 --> 00:47:34,239
Speaker 1: play certain mobile games, well, Facebook wants to know about

779
00:47:34,280 --> 00:47:39,320
Speaker 1: all that because that data is valuable. Apples change requires

780
00:47:39,320 --> 00:47:43,360
Speaker 1: Facebook to alert users that it wants to collect that data,

781
00:47:43,520 --> 00:47:46,920
Speaker 1: kind of like this, this app wants to know your location.

782
00:47:47,040 --> 00:47:49,800
Speaker 1: Is that okay? It's similar to that, And it gives

783
00:47:49,920 --> 00:47:52,960
Speaker 1: the chance for users to opt out of that process

784
00:47:53,200 --> 00:47:57,040
Speaker 1: right then and there in that alert. And Facebook hates

785
00:47:57,320 --> 00:48:00,319
Speaker 1: that because if you give people the choice is to

786
00:48:00,440 --> 00:48:05,000
Speaker 1: not be tracked, often they take that choice. They just

787
00:48:05,280 --> 00:48:08,879
Speaker 1: you know, have to know about it first. So if

788
00:48:08,920 --> 00:48:12,759
Speaker 1: you aren't allowed to bury that notice in page two

789
00:48:12,800 --> 00:48:15,920
Speaker 1: of settings or in a deep user agreement, Well, then

790
00:48:15,920 --> 00:48:18,520
Speaker 1: a lot of folks might just take the option to

791
00:48:18,640 --> 00:48:22,640
Speaker 1: opt out, which means that's a hit on Facebook's revenue,

792
00:48:23,000 --> 00:48:26,120
Speaker 1: and Facebook and a fan of that, it has launched

793
00:48:26,160 --> 00:48:29,360
Speaker 1: a little bit of a campaign that essentially argues that

794
00:48:29,440 --> 00:48:32,560
Speaker 1: Apple's new rules are harmful to small businesses and that

795
00:48:32,600 --> 00:48:36,400
Speaker 1: people should feel badly about opting out, which is um,

796
00:48:36,560 --> 00:48:40,319
Speaker 1: super disingenuous. If you ask me, Facebook, I don't think

797
00:48:40,440 --> 00:48:44,080
Speaker 1: is at all concerned about small businesses except to the

798
00:48:44,160 --> 00:48:49,560
Speaker 1: extent at which those small businesses spend money on Facebook. Similarly,

799
00:48:50,080 --> 00:48:53,440
Speaker 1: Google was in the news when internal documents leaked showing

800
00:48:53,520 --> 00:48:57,759
Speaker 1: that the company had leaned hard on Android device manufacturers

801
00:48:58,440 --> 00:49:04,480
Speaker 1: to hide geo tracking opt out features deep in settings.

802
00:49:04,560 --> 00:49:07,160
Speaker 1: And so Google was essentially saying, hey, that's cool that

803
00:49:07,200 --> 00:49:09,239
Speaker 1: you want to make this Android smartphone, do us a

804
00:49:09,280 --> 00:49:12,480
Speaker 1: solid for the geo tracking stuff. Hide that like on

805
00:49:12,600 --> 00:49:15,120
Speaker 1: page two or three of your settings, so that nobody

806
00:49:15,160 --> 00:49:18,040
Speaker 1: ever bothers to go that far. And the goal was

807
00:49:18,080 --> 00:49:20,120
Speaker 1: just to make it harder for people to find where

808
00:49:20,160 --> 00:49:22,800
Speaker 1: they could turn off geo tracking, so that the company

809
00:49:22,840 --> 00:49:26,120
Speaker 1: could continue to collect that sweet data without too many

810
00:49:26,160 --> 00:49:31,719
Speaker 1: people opting out dirty pool Google. Just a few years ago,

811
00:49:31,880 --> 00:49:34,480
Speaker 1: all this data would have been alarming, but it also

812
00:49:34,800 --> 00:49:36,839
Speaker 1: would have been kind of burdened by the fact that

813
00:49:37,200 --> 00:49:41,000
Speaker 1: there's just so much information that's hard to do anything

814
00:49:41,040 --> 00:49:44,240
Speaker 1: with it. So it's one thing to collect enormous amounts

815
00:49:44,239 --> 00:49:47,440
Speaker 1: of personal information, but it's another to actually find a

816
00:49:47,520 --> 00:49:50,359
Speaker 1: meaningful use for all that data. You get a lot

817
00:49:50,400 --> 00:49:53,360
Speaker 1: of noise along with a little bit of signal. But

818
00:49:53,440 --> 00:49:57,360
Speaker 1: over the past few years, data analysis has advanced dramatically

819
00:49:57,520 --> 00:50:01,400
Speaker 1: and it's become much more sophisticated. Big data, which was

820
00:50:01,400 --> 00:50:04,240
Speaker 1: a buzz term that came largely from the marketing world,

821
00:50:05,040 --> 00:50:07,840
Speaker 1: is a real thing now, and when paired with systems

822
00:50:07,840 --> 00:50:11,719
Speaker 1: that use strategies like machine learning, advertising companies are able

823
00:50:11,760 --> 00:50:15,120
Speaker 1: to get incredibly detailed looks at who each of us

824
00:50:15,239 --> 00:50:19,719
Speaker 1: happens to be. Our data can be parsed and contextualized

825
00:50:20,080 --> 00:50:24,000
Speaker 1: in millions of ways that are incredibly valuable to countless

826
00:50:24,000 --> 00:50:28,480
Speaker 1: people and organizations. Some of those might be largely benign,

827
00:50:28,880 --> 00:50:32,560
Speaker 1: or at least no more malicious than your typical capitalistic endeavor,

828
00:50:32,960 --> 00:50:36,440
Speaker 1: but some might be way more malevolent. And of course

829
00:50:36,560 --> 00:50:40,160
Speaker 1: I haven't even touched on the burden of good stewardship

830
00:50:40,280 --> 00:50:44,040
Speaker 1: when it comes to protecting data. Many of those one

831
00:50:44,160 --> 00:50:47,680
Speaker 1: data broker companies have been targets of hackers, and more

832
00:50:47,760 --> 00:50:50,360
Speaker 1: than a few have had some pretty nasty data breaches

833
00:50:50,520 --> 00:50:54,319
Speaker 1: over the years. So I guess the whole point of

834
00:50:54,320 --> 00:50:57,279
Speaker 1: this episode is to really explain how technology in the

835
00:50:57,320 --> 00:51:02,680
Speaker 1: digital age largely centers around the collection and exploitation of information,

836
00:51:03,040 --> 00:51:05,800
Speaker 1: and that a lot of that information comes from people

837
00:51:05,880 --> 00:51:08,919
Speaker 1: like us, and that if we feel strongly about that,

838
00:51:09,280 --> 00:51:13,560
Speaker 1: we have to take steps to address this issue. Unfortunately,

839
00:51:13,680 --> 00:51:18,439
Speaker 1: right now, those steps are often laborious and convoluted. It's

840
00:51:18,600 --> 00:51:24,240
Speaker 1: easy to get discouraged. It's easy to prioritize convenience over privacy.

841
00:51:24,360 --> 00:51:27,399
Speaker 1: It's easy to give into the statement that Mark Zuckerberg

842
00:51:27,440 --> 00:51:31,560
Speaker 1: famously made in two when he said privacy is dead.

843
00:51:32,239 --> 00:51:37,040
Speaker 1: But as we see implementations of systems that exploit our data,

844
00:51:37,160 --> 00:51:42,759
Speaker 1: and as these become undeniably more invasive, it might benefit

845
00:51:42,880 --> 00:51:45,799
Speaker 1: us to look at them more closely and act in

846
00:51:45,840 --> 00:51:49,560
Speaker 1: our own self interest, because I assure you most of

847
00:51:49,560 --> 00:51:52,080
Speaker 1: these companies are not going to do that for us.

848
00:51:52,960 --> 00:51:56,880
Speaker 1: I'm talking to you, Stephanie, yes, you. I've personalized this

849
00:51:56,920 --> 00:52:01,160
Speaker 1: episode for every listener, and you, step phany need to

850
00:52:01,200 --> 00:52:05,400
Speaker 1: take action. I'm kidding. I didn't personalize this podcast, and

851
00:52:05,440 --> 00:52:08,840
Speaker 1: anyone not named Stephanie is probably just confused, and anyone

852
00:52:08,840 --> 00:52:11,759
Speaker 1: who is named Stephanie is probably flipping out. But no,

853
00:52:11,880 --> 00:52:13,799
Speaker 1: I just wrote that as a joke. But it is

854
00:52:13,840 --> 00:52:16,040
Speaker 1: the sort of thing that websites and apps and other

855
00:52:16,080 --> 00:52:21,000
Speaker 1: Internet related services can do for reals. In fact, technically,

856
00:52:21,360 --> 00:52:24,040
Speaker 1: with enough work, a podcast like this could probably do

857
00:52:24,120 --> 00:52:25,959
Speaker 1: it too. It would just require me to go through

858
00:52:26,760 --> 00:52:29,279
Speaker 1: a very long list of names and record them and

859
00:52:29,320 --> 00:52:33,480
Speaker 1: then have dynamic insertion of that statement in the podcast

860
00:52:33,840 --> 00:52:36,600
Speaker 1: and targeted to specific listeners. It would be a lot

861
00:52:36,600 --> 00:52:38,440
Speaker 1: of work, is what I'm saying, something that I am

862
00:52:38,480 --> 00:52:42,239
Speaker 1: not willing to do, but it could be done, and

863
00:52:42,320 --> 00:52:46,719
Speaker 1: maybe that's not always a good thing. All right. That

864
00:52:46,800 --> 00:52:50,400
Speaker 1: wraps up this very long soapbox edition of tech Stuff.

865
00:52:50,440 --> 00:52:53,920
Speaker 1: Hope you guys learned something in that and found some

866
00:52:54,040 --> 00:52:57,080
Speaker 1: value in that discussion. I will be doing some more

867
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Speaker 1: episodes about privacy related materials like Kappa. I do want

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Speaker 1: to talk about that and the intent of that legislation

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Speaker 1: as well as the actual impact of it, So look

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Speaker 1: forward to that in the future. But If you have

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Speaker 1: suggestions for things I should cover in future episodes of

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Speaker 1: tech Stuff, reach out to me. The best place to

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Speaker 1: do that is on Twitter. The handle for the show

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Speaker 1: is tech Stuff H s W and I'll talk to

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Speaker 1: you again really soon. Tech Stuff is an I Heart

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Speaker 1: Radio production. For more podcasts from My Heart Radio, visit

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Speaker 1: the I Heart Radio app, Apple Podcasts, or wherever you

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Speaker 1: listen to your favorite shows.