1 00:00:00,960 --> 00:00:04,320 Speaker 1: From UFOs two, ghosts and government cover ups. History is 2 00:00:04,400 --> 00:00:08,000 Speaker 1: riddled with unexplained events. You can turn back now or 3 00:00:08,080 --> 00:00:16,320 Speaker 1: learn the stuff they don't want you to now. Hello everyone, 4 00:00:16,440 --> 00:00:19,680 Speaker 1: my name is Matt, and this is stuff they don't 5 00:00:19,720 --> 00:00:23,040 Speaker 1: want you to know. Nett a couple of questions for you. 6 00:00:23,480 --> 00:00:27,840 Speaker 1: Start the show one and you don't have to answer 7 00:00:27,880 --> 00:00:30,040 Speaker 1: if you don't want to. Look, I'm gonna answer. Okay, 8 00:00:30,320 --> 00:00:35,479 Speaker 1: what was the last thing you bought? Beatles rock band 9 00:00:36,000 --> 00:00:39,479 Speaker 1: for the three sixty That was literally last thing you 10 00:00:39,560 --> 00:00:42,120 Speaker 1: bought today? Oh no, the last thing I bought today 11 00:00:42,200 --> 00:00:45,840 Speaker 1: was food? Okay, something, but yes, something that is not 12 00:00:46,120 --> 00:00:50,520 Speaker 1: was not marketed to me? Okay, okay, so you're sure 13 00:00:50,520 --> 00:00:53,680 Speaker 1: it wasn't marketed to you? Oh you know what, Perhaps 14 00:00:53,680 --> 00:00:56,880 Speaker 1: it was. There is a sign. There is a sign 15 00:00:56,920 --> 00:01:00,840 Speaker 1: downstairs in our lobby that yeah is prom moating their food. 16 00:01:00,880 --> 00:01:04,120 Speaker 1: I guess. Yeah. We have a place downstairs in our 17 00:01:04,160 --> 00:01:08,240 Speaker 1: building where all the podcasters have been at least once, 18 00:01:08,880 --> 00:01:12,760 Speaker 1: even the vegetarians and vegans in the crowd. Uh. Wherein 19 00:01:13,560 --> 00:01:19,119 Speaker 1: they sell you know, sandwiches and wraps and yogurt and granola, 20 00:01:19,160 --> 00:01:21,120 Speaker 1: and they have a salad bar and a hot bar 21 00:01:21,240 --> 00:01:25,360 Speaker 1: and you know if you you feel very uh, very 22 00:01:25,400 --> 00:01:29,240 Speaker 1: American and in poor health sometimes if you shop at 23 00:01:29,240 --> 00:01:34,040 Speaker 1: the hot food bar, because they charge by the pound, yeah, 24 00:01:34,120 --> 00:01:38,160 Speaker 1: which is a quick indicator of people who eat too 25 00:01:38,240 --> 00:01:40,480 Speaker 1: much and no offense to carry in. The people that 26 00:01:40,600 --> 00:01:43,800 Speaker 1: run it, hopefully, they yeah, they're wonderful people. That's why 27 00:01:43,840 --> 00:01:45,800 Speaker 1: I go there actually, And the food is pretty good. 28 00:01:46,040 --> 00:01:48,360 Speaker 1: The food is great. But man, yeah, they charge more 29 00:01:48,400 --> 00:01:52,360 Speaker 1: than uh, let's say, their competitors around the area, right, 30 00:01:52,400 --> 00:01:55,360 Speaker 1: But we're also paying for the convenience before we get 31 00:01:55,360 --> 00:01:58,279 Speaker 1: too derailed. The reason I ask you you already clocked 32 00:01:58,320 --> 00:02:04,160 Speaker 1: at matt is because it is strange and frightening how 33 00:02:05,560 --> 00:02:10,120 Speaker 1: oblivious most people are to the sheer up mount, this 34 00:02:10,200 --> 00:02:14,600 Speaker 1: shear magnitude of advertising that plagues us throughout a day. 35 00:02:14,600 --> 00:02:16,960 Speaker 1: And maybe plague isn't a fair word, how about inning 36 00:02:17,080 --> 00:02:21,040 Speaker 1: dates existence. I think we have to become somewhat blind 37 00:02:21,240 --> 00:02:24,560 Speaker 1: to some of it, because literally, wherever you look, there's 38 00:02:24,600 --> 00:02:29,240 Speaker 1: some logo, there's something selling you something. And oh yeah, well, 39 00:02:29,600 --> 00:02:31,560 Speaker 1: looking around this room, it's not so bad. It's mostly 40 00:02:31,760 --> 00:02:36,760 Speaker 1: soundproofing and tiling. Yeah, those rocket speakers though, yeah, look 41 00:02:36,800 --> 00:02:43,120 Speaker 1: at that apple? Oh man, uh, dixie cup? Oh no, 42 00:02:43,320 --> 00:02:45,919 Speaker 1: they got us. So just in the space of looking around, 43 00:02:45,960 --> 00:02:49,799 Speaker 1: we've already seen several logos, and if we count logos 44 00:02:49,840 --> 00:02:54,880 Speaker 1: as advertisement, then we quickly realize that this stuff is everywhere. 45 00:02:55,400 --> 00:03:00,560 Speaker 1: I guess the fancy word for it is ubiquitous. However, however, listeners, 46 00:03:01,240 --> 00:03:04,520 Speaker 1: you probably have already heard Matt and I talked about 47 00:03:04,600 --> 00:03:10,240 Speaker 1: Edward Burnet's and other other things in advertising, so you 48 00:03:10,320 --> 00:03:15,079 Speaker 1: are already relatively aware. I would say they're relatively aware, right, Matt, Yeah, sure. 49 00:03:15,120 --> 00:03:17,720 Speaker 1: And if you ever listened to George Carlin, or you 50 00:03:17,760 --> 00:03:21,280 Speaker 1: know a lot of these other comedians, or watched I 51 00:03:21,320 --> 00:03:26,200 Speaker 1: don't know anything on NPRS website or PBS with Bill Moyers, 52 00:03:26,240 --> 00:03:28,800 Speaker 1: maybe you know more about this than we do. Or 53 00:03:28,919 --> 00:03:33,800 Speaker 1: maybe you have taken a survey somewhere at a mall 54 00:03:34,080 --> 00:03:37,480 Speaker 1: or in a grocery store and it's hit you, Oh, 55 00:03:37,720 --> 00:03:40,440 Speaker 1: these people are taking the questions they're asking me and 56 00:03:40,520 --> 00:03:43,800 Speaker 1: trying to build something that I will be much more 57 00:03:43,840 --> 00:03:48,680 Speaker 1: likely to buy. But do you know how far this 58 00:03:48,840 --> 00:03:52,560 Speaker 1: science has evolved in a very short span of time. Uh, 59 00:03:52,800 --> 00:03:55,920 Speaker 1: we would like to today introduce you to the concept 60 00:03:56,000 --> 00:04:02,400 Speaker 1: of neuro marketing. Neuro marketing, so Matt lay it on. Mean, man, 61 00:04:02,440 --> 00:04:05,520 Speaker 1: I want some stolen cold definition here, all right, here 62 00:04:05,520 --> 00:04:08,040 Speaker 1: we go. So this is the definition of neuro marketing. 63 00:04:08,400 --> 00:04:12,080 Speaker 1: It's the process of researching the brain patterns of consumers 64 00:04:12,120 --> 00:04:15,400 Speaker 1: to reveal their responses to a particular advertisement or a 65 00:04:15,440 --> 00:04:19,960 Speaker 1: brand or product, and use that to develop new advertising 66 00:04:20,000 --> 00:04:25,560 Speaker 1: campaigns and branding techniques to better influence the your consumers, 67 00:04:25,640 --> 00:04:29,599 Speaker 1: the human beings. Yeah weird. Huh. So this means that 68 00:04:29,600 --> 00:04:32,640 Speaker 1: they're using things like m r s and CT scans, 69 00:04:32,960 --> 00:04:37,839 Speaker 1: even measuring the amount of moisture or sweat your body admits. Uh. 70 00:04:37,960 --> 00:04:40,840 Speaker 1: The people doing this, who call themselves neuro marketers, claim 71 00:04:40,920 --> 00:04:45,720 Speaker 1: that they can interpret this data and interpret it in 72 00:04:45,800 --> 00:04:49,440 Speaker 1: such a way that they are able to make predictions 73 00:04:49,640 --> 00:04:52,560 Speaker 1: about what consumers should buy and what kind of products 74 00:04:52,600 --> 00:04:54,600 Speaker 1: they want to see. Now, now here's a good thing 75 00:04:54,640 --> 00:04:57,640 Speaker 1: and the bad thing. But it's still debatable whether or 76 00:04:57,640 --> 00:05:00,200 Speaker 1: not this is effective, or whether it works, or how 77 00:05:00,240 --> 00:05:03,960 Speaker 1: effective it is spoiler alert. Yeah, um, but but it 78 00:05:04,080 --> 00:05:08,400 Speaker 1: is kind of an emerging um science, I guess y um. 79 00:05:09,720 --> 00:05:12,640 Speaker 1: We do know that there are there are there are 80 00:05:12,640 --> 00:05:15,400 Speaker 1: several pieces of the puzzle here that we do know. Yeah, 81 00:05:15,440 --> 00:05:19,240 Speaker 1: oh yeah, we know that for instance, Uh, the brain 82 00:05:19,600 --> 00:05:25,480 Speaker 1: unconsciously prepares your decision. So before you or I Matt 83 00:05:25,640 --> 00:05:30,760 Speaker 1: or you listener would consciously decide to go left or 84 00:05:30,880 --> 00:05:33,680 Speaker 1: right somewhere, you know, so you're walking around a trash 85 00:05:33,680 --> 00:05:37,280 Speaker 1: can or an obstacle or something. Before you consciously realize 86 00:05:37,279 --> 00:05:39,359 Speaker 1: that you're gonna go right or you're gonna go left, 87 00:05:39,560 --> 00:05:42,840 Speaker 1: your brain has already made the decision. There's a bit 88 00:05:42,920 --> 00:05:45,800 Speaker 1: of a lag time, like you're on a crappy computer 89 00:05:46,120 --> 00:05:49,960 Speaker 1: connection or something. So in two thousand eight, some scientists 90 00:05:50,000 --> 00:05:52,960 Speaker 1: in Germany published a study showing this, and here's what 91 00:05:53,000 --> 00:05:57,480 Speaker 1: they did. Uh, they measured somebody using their left or 92 00:05:57,560 --> 00:06:00,479 Speaker 1: right hand for a task, and they found that several 93 00:06:00,600 --> 00:06:04,440 Speaker 1: seconds before human being consciously decides what they're going to do, 94 00:06:04,880 --> 00:06:09,480 Speaker 1: the outcome can be predicted by looking at unconscious activity 95 00:06:09,560 --> 00:06:11,919 Speaker 1: in the brain. So they have them wired up to 96 00:06:11,960 --> 00:06:15,919 Speaker 1: an e g. A real one, an academic lab level, 97 00:06:16,120 --> 00:06:19,719 Speaker 1: not like commercial stuff as we'll find, and they saw 98 00:06:19,760 --> 00:06:22,120 Speaker 1: that some part of their brain a couple of seconds 99 00:06:22,120 --> 00:06:25,400 Speaker 1: before they said left, some part of their brain spiked 100 00:06:25,480 --> 00:06:28,640 Speaker 1: up and the decision was already made. Spooky stuff that 101 00:06:28,680 --> 00:06:32,480 Speaker 1: comes from an article in the Telegraph by Alex Hannaford. So, 102 00:06:32,480 --> 00:06:36,080 Speaker 1: so what happens in in our brains when we're watching 103 00:06:36,240 --> 00:06:39,599 Speaker 1: a television commercial about a sad one about puppies and 104 00:06:39,640 --> 00:06:43,360 Speaker 1: how they need to be adopted. I mean, well, there 105 00:06:43,440 --> 00:06:45,720 Speaker 1: there's something that happens you. You might tear up, you 106 00:06:45,800 --> 00:06:49,480 Speaker 1: might um, you might laugh hilarious, something you just find 107 00:06:49,560 --> 00:06:53,120 Speaker 1: so funny, maybe a beer commercial. So what's happening in 108 00:06:53,120 --> 00:06:55,480 Speaker 1: our brains when when we're watching these things? Well, one 109 00:06:55,520 --> 00:06:57,880 Speaker 1: thing is for certain, there there are brain waves that 110 00:06:57,920 --> 00:07:02,400 Speaker 1: are correlated to these heightened uh states of attention and 111 00:07:03,200 --> 00:07:07,000 Speaker 1: emotional reactions. So according to the researchers who are studying 112 00:07:07,040 --> 00:07:09,840 Speaker 1: these e g s, they're really saying that when you're 113 00:07:09,840 --> 00:07:14,520 Speaker 1: watching something that's compelling like this or causing an emotional response, uh, 114 00:07:14,560 --> 00:07:18,120 Speaker 1: the the electrical responses in your brain are hyper focused 115 00:07:18,240 --> 00:07:21,400 Speaker 1: on some of these areas and then the other um, 116 00:07:21,480 --> 00:07:25,520 Speaker 1: the other activity kind of subsides. So you're there's less 117 00:07:25,560 --> 00:07:28,560 Speaker 1: thinking going on, less activity going on in other places 118 00:07:28,600 --> 00:07:34,480 Speaker 1: besides that focused fear or um um, some finding something's 119 00:07:34,480 --> 00:07:38,200 Speaker 1: funny or romantic or whatever whatever emotion they're going for. 120 00:07:38,920 --> 00:07:41,640 Speaker 1: I see, So they're kind of hacking past the critical 121 00:07:41,680 --> 00:07:44,800 Speaker 1: thinking aspect of the brain to see what triggers that 122 00:07:44,840 --> 00:07:50,119 Speaker 1: immediate emotional response. So let's think of every advertisement, every 123 00:07:50,160 --> 00:07:54,440 Speaker 1: logo as essentially a pitch or proposition, right why you 124 00:07:54,480 --> 00:07:57,880 Speaker 1: should buy this. If these pitches are going to succeed, 125 00:07:58,120 --> 00:08:00,800 Speaker 1: then they have to reach that sub just level of 126 00:08:00,840 --> 00:08:03,920 Speaker 1: the brain where you develop initial interest in the product, 127 00:08:04,000 --> 00:08:07,320 Speaker 1: the inclination to buy it, where you have that brand loyalty. 128 00:08:07,440 --> 00:08:10,600 Speaker 1: At least that's according to a guy named A. K. 129 00:08:10,880 --> 00:08:13,880 Speaker 1: Pre Deep, the founder and chief executive of a company 130 00:08:13,920 --> 00:08:19,040 Speaker 1: called neuro Focus. Now, neuro Focus being a neuro marketing firm, 131 00:08:19,040 --> 00:08:21,480 Speaker 1: clearly as some bias, they're not gonna They're gonna be 132 00:08:21,520 --> 00:08:23,920 Speaker 1: the ones on the side of the argument that says 133 00:08:24,000 --> 00:08:27,800 Speaker 1: neuro marketing does work well, sure, because that's their product, right, 134 00:08:27,880 --> 00:08:31,040 Speaker 1: their brand. And to me that okay, I just want 135 00:08:31,040 --> 00:08:33,960 Speaker 1: to pause really fast. Yeah, that's a fascinating thing to me, 136 00:08:34,000 --> 00:08:35,720 Speaker 1: and I know we're going to talk about it later, 137 00:08:36,000 --> 00:08:40,680 Speaker 1: but that the idea of selling a brand is it's 138 00:08:40,720 --> 00:08:43,439 Speaker 1: in their interests to sell their brand just as much 139 00:08:43,440 --> 00:08:45,600 Speaker 1: as they are looking at how these other brands are 140 00:08:45,640 --> 00:08:48,720 Speaker 1: selling their brand, and that will come into play later. 141 00:08:48,920 --> 00:08:51,280 Speaker 1: Make sure wonder, Yeah, it's a meta pitch for sure. 142 00:08:51,679 --> 00:08:55,640 Speaker 1: So we know that the neuro Focus company has done 143 00:08:55,679 --> 00:08:58,760 Speaker 1: marketing tests where they take volunteers, they give them an 144 00:08:58,760 --> 00:09:01,840 Speaker 1: e G. Sensor app and an eye tracking device and 145 00:09:01,840 --> 00:09:05,079 Speaker 1: then just have them watch commercials or you know, use 146 00:09:05,360 --> 00:09:08,760 Speaker 1: the internet and visit a website or even watch the trailer, 147 00:09:09,280 --> 00:09:12,959 Speaker 1: and the researchers are able to watch the volunteers brain 148 00:09:13,000 --> 00:09:18,439 Speaker 1: patterns and the the eye movements, so they know exactly 149 00:09:18,559 --> 00:09:22,200 Speaker 1: what's firing when they look at this stuff. Um, and 150 00:09:22,240 --> 00:09:24,760 Speaker 1: you can. You can go online and find videos and 151 00:09:24,840 --> 00:09:29,040 Speaker 1: look at the brain patterns and eye movements for particular commercials. 152 00:09:29,080 --> 00:09:31,040 Speaker 1: There are several places if you just go to YouTube 153 00:09:31,040 --> 00:09:34,240 Speaker 1: and check out neuromarketing to do a search, you'll be 154 00:09:34,280 --> 00:09:36,800 Speaker 1: able to see exactly what the readout looks like for 155 00:09:36,880 --> 00:09:40,680 Speaker 1: these scientists. And so people who believe that this does 156 00:09:40,760 --> 00:09:43,640 Speaker 1: work say stuff very similar to what doctor Predep said, 157 00:09:43,720 --> 00:09:47,280 Speaker 1: which is by measuring brain waves, were able to measure attention, emotion, 158 00:09:47,679 --> 00:09:53,439 Speaker 1: and memory. So computing the subconscious response to stimuli. UM 159 00:09:53,679 --> 00:09:56,320 Speaker 1: get a little poetic at the interview. If you add 160 00:09:56,360 --> 00:10:00,040 Speaker 1: all these electric patterns together, you find it represents the 161 00:10:00,120 --> 00:10:06,520 Speaker 1: whispers of your brain. I mean, yeah, buy more. Soup man, 162 00:10:06,640 --> 00:10:09,920 Speaker 1: that soup looks so good, so you know they reduced 163 00:10:09,920 --> 00:10:13,360 Speaker 1: the sodium in that soup. You can tell because it's 164 00:10:13,400 --> 00:10:16,920 Speaker 1: on the label. And this weird whispering thing we're doing 165 00:10:17,080 --> 00:10:19,520 Speaker 1: is a true story. It's an introduction to one of 166 00:10:19,520 --> 00:10:23,920 Speaker 1: our first examples of neuro marketing, which involves dunt da 167 00:10:24,600 --> 00:10:29,040 Speaker 1: Campbell's soup. Yeah, true story, you guys, Campbell's soup. The 168 00:10:29,080 --> 00:10:35,720 Speaker 1: company hired some researchers to study microscopic changes in skin moisture, 169 00:10:36,160 --> 00:10:39,400 Speaker 1: heart rate, and other stuff to see how consumers react 170 00:10:39,520 --> 00:10:43,160 Speaker 1: to everything from like pictures bowls of soup to the 171 00:10:43,200 --> 00:10:48,120 Speaker 1: design of the logo. And this neural marketing approach is 172 00:10:48,360 --> 00:10:52,120 Speaker 1: kind of a fresh attempt among these consumer goods companies 173 00:10:52,120 --> 00:10:55,080 Speaker 1: to to kind of understand how consumers really respond to 174 00:10:55,120 --> 00:10:57,640 Speaker 1: their marketing. And you know, all the money that they're 175 00:10:57,679 --> 00:11:01,440 Speaker 1: spending on marketing and advertising, and it's getting cheaper and 176 00:11:01,559 --> 00:11:05,640 Speaker 1: cheaper and faster and faster. Uh. There are people like 177 00:11:05,760 --> 00:11:11,679 Speaker 1: Robert Barocci from the Advertising Research Foundation who says that 178 00:11:11,840 --> 00:11:16,640 Speaker 1: this allows us to build a bridge between the raw 179 00:11:16,800 --> 00:11:21,600 Speaker 1: data of brain activity to uh, the generation of meaning, 180 00:11:21,920 --> 00:11:24,440 Speaker 1: you know, so that we're able to somehow divine and 181 00:11:24,559 --> 00:11:28,959 Speaker 1: interpret these signs. Yeah, it is, uh, you know, because 182 00:11:29,200 --> 00:11:32,640 Speaker 1: for years Campbell's was doing the same thing that everybody 183 00:11:32,640 --> 00:11:34,920 Speaker 1: else was doing in the game. They would, you know, 184 00:11:35,040 --> 00:11:38,120 Speaker 1: you go to a marketing research thing. I don't know 185 00:11:38,160 --> 00:11:40,200 Speaker 1: if you ever did this a little sidebar here, I have, 186 00:11:40,480 --> 00:11:43,400 Speaker 1: actually not with Campbell's in particularly, I've done this, not 187 00:11:43,480 --> 00:11:45,560 Speaker 1: with Campbell's, but it's a great way to make a 188 00:11:45,559 --> 00:11:48,640 Speaker 1: little extra scratch on the side, you know. So I've 189 00:11:48,679 --> 00:11:51,800 Speaker 1: done it for a number of things. Um, this market 190 00:11:51,840 --> 00:11:54,400 Speaker 1: research stuff is something you can look into if you 191 00:11:54,400 --> 00:11:57,800 Speaker 1: want a little extra sporadic money, and they'll ask you 192 00:11:57,840 --> 00:12:01,120 Speaker 1: some ridiculous questions. Anyway, I digress. Here's how it goes. 193 00:12:01,160 --> 00:12:03,439 Speaker 1: They sit you down the room, Matt, you know about this, 194 00:12:03,840 --> 00:12:06,719 Speaker 1: They show you some things, they ask you some questions, 195 00:12:07,240 --> 00:12:11,400 Speaker 1: and then they also ask you how you feel about stuff. 196 00:12:11,920 --> 00:12:14,560 Speaker 1: So Campbell's was doing this just like pretty much any 197 00:12:14,559 --> 00:12:17,920 Speaker 1: other company under the sun. And they were showing people 198 00:12:17,960 --> 00:12:20,440 Speaker 1: ads and saying, Okay, would this ad make you more 199 00:12:20,520 --> 00:12:27,480 Speaker 1: likely to buy soup? And they they realize over time 200 00:12:27,760 --> 00:12:31,200 Speaker 1: that even the ads that their marketing research folks told 201 00:12:31,200 --> 00:12:34,640 Speaker 1: them were sure fire, can't miss, full proof hits. Uh 202 00:12:34,800 --> 00:12:37,600 Speaker 1: didn't really seem to affect sales one way or the other. 203 00:12:37,679 --> 00:12:40,560 Speaker 1: You know, soup sales still went up in colder months 204 00:12:40,600 --> 00:12:43,960 Speaker 1: down in hotter months. Uh, well, you know. Been The 205 00:12:44,080 --> 00:12:47,040 Speaker 1: thing I did that with was movie trailers, or they 206 00:12:47,040 --> 00:12:49,800 Speaker 1: would show you three different edits of a movie trailer, 207 00:12:49,840 --> 00:12:52,200 Speaker 1: and I have you answer questions about each of them, 208 00:12:52,240 --> 00:12:54,280 Speaker 1: like what do you think this the plot of this 209 00:12:54,320 --> 00:12:56,600 Speaker 1: movie is about? Who do you think the main characters are? 210 00:12:57,600 --> 00:12:59,480 Speaker 1: Do you want to watch this movie? Because you saw 211 00:12:59,600 --> 00:13:04,040 Speaker 1: this per some more it was. It's pretty horrifying that 212 00:13:04,080 --> 00:13:09,439 Speaker 1: then that that marketing research becomes what a film that's 213 00:13:09,600 --> 00:13:12,040 Speaker 1: you know, either trying to make a statement or just 214 00:13:12,160 --> 00:13:16,679 Speaker 1: making some money off the back of In this case, um, 215 00:13:17,880 --> 00:13:19,800 Speaker 1: let's I don't want to I don't want to see 216 00:13:19,840 --> 00:13:21,800 Speaker 1: what movie was? It was a bad it was a 217 00:13:21,880 --> 00:13:24,480 Speaker 1: terrible movie. Tell me what movie it was? Please? It 218 00:13:24,520 --> 00:13:27,800 Speaker 1: was The Love Guru, The Love Guru? Okay with Mike Myers. 219 00:13:28,559 --> 00:13:34,880 Speaker 1: Oh man, so you're responsible? Huh? Partly? One thing. Let's 220 00:13:34,920 --> 00:13:37,040 Speaker 1: side by this for a second again. One thing that 221 00:13:37,120 --> 00:13:39,240 Speaker 1: gets me now I want to ask you about is 222 00:13:39,440 --> 00:13:42,720 Speaker 1: have you noticed the tendency of film trailers to become 223 00:13:42,800 --> 00:13:47,000 Speaker 1: longer and longer and tell most of the story. It waits, 224 00:13:47,080 --> 00:13:49,760 Speaker 1: it waxes in waynes okay. And I would say it 225 00:13:49,800 --> 00:13:53,200 Speaker 1: depends on the how much control the director has, because 226 00:13:53,200 --> 00:13:55,960 Speaker 1: the director doesn't want to give away all the secrets 227 00:13:55,960 --> 00:13:57,760 Speaker 1: of the show. Yeah, the director wants you to come 228 00:13:57,760 --> 00:14:00,200 Speaker 1: and watch his story, or you know, maybe be the 229 00:14:00,240 --> 00:14:04,160 Speaker 1: writer or in some cases of the producer. Um. But yeah, 230 00:14:04,280 --> 00:14:06,440 Speaker 1: usually they want you to come and watch their whole story, 231 00:14:06,440 --> 00:14:08,480 Speaker 1: and they want to tease you to get there. I see. 232 00:14:08,559 --> 00:14:11,840 Speaker 1: But the the the money guys just want to show 233 00:14:11,880 --> 00:14:13,640 Speaker 1: you whatever is whatever they have to show you to 234 00:14:13,679 --> 00:14:18,960 Speaker 1: get you in that theater. And that is one of 235 00:14:19,000 --> 00:14:23,160 Speaker 1: the things that I think separates neural marketing from other 236 00:14:23,280 --> 00:14:26,160 Speaker 1: conventional market research. Going back to our Campbell's story, this 237 00:14:26,200 --> 00:14:29,800 Speaker 1: guy Robert Woodard uh he was Campbell's vice president of 238 00:14:29,840 --> 00:14:33,960 Speaker 1: Global Consumer Sales and Customer Insights, and he said that 239 00:14:34,000 --> 00:14:38,760 Speaker 1: their regular interview style wasn't that useful because people weren't 240 00:14:38,800 --> 00:14:43,560 Speaker 1: able to fully articulate their unconscious responses. So they needed 241 00:14:43,680 --> 00:14:48,240 Speaker 1: Campbell's being. They they needed to figure out the neurological 242 00:14:48,400 --> 00:14:52,720 Speaker 1: and bodily underpinnings to an ad. They needed to see 243 00:14:52,720 --> 00:14:56,760 Speaker 1: how people were unconsciously reacting rather than see how people 244 00:14:56,880 --> 00:14:59,760 Speaker 1: thought they were reacting. Yeah, because you don't know, man, 245 00:14:59,840 --> 00:15:03,600 Speaker 1: it's unconscious. So by two thousand eight, Mr Woodard, he 246 00:15:03,640 --> 00:15:06,440 Speaker 1: settled on the biometric tools combined with a different type 247 00:15:06,480 --> 00:15:09,640 Speaker 1: of deep interview. And I'd like to know more about that. 248 00:15:10,160 --> 00:15:17,640 Speaker 1: It was the blade Runner what's your address? I wish 249 00:15:17,640 --> 00:15:19,360 Speaker 1: I could do that whole thing. I know the other 250 00:15:19,440 --> 00:15:22,520 Speaker 1: guys here could do that, but yeah, he they decided 251 00:15:22,560 --> 00:15:25,840 Speaker 1: to use this deep interview to more accurately gauge which 252 00:15:25,880 --> 00:15:31,160 Speaker 1: consumer communications worked best, so Um Campbell. Campbell's then hired 253 00:15:31,200 --> 00:15:35,520 Speaker 1: on Interscope Research Incorporated, which is a Boston company, and 254 00:15:35,680 --> 00:15:39,360 Speaker 1: they do the same thing. They measure bodily responses, and 255 00:15:39,680 --> 00:15:41,920 Speaker 1: they also hired a couple other firms to help conduct 256 00:15:41,920 --> 00:15:44,840 Speaker 1: this this kind of research where they're looking at it's 257 00:15:44,880 --> 00:15:48,720 Speaker 1: so weird to me bodily responses to marketing, that suit 258 00:15:48,760 --> 00:15:51,320 Speaker 1: makes people too sweaty. Put it in a blue can. 259 00:15:52,160 --> 00:15:56,200 Speaker 1: Uh the you know, I am joking around. I don't 260 00:15:56,200 --> 00:15:58,560 Speaker 1: know if anybody did that. But the Campbell story is 261 00:15:58,640 --> 00:16:01,840 Speaker 1: pretty fascinating, and you can read about it more in 262 00:16:01,880 --> 00:16:04,360 Speaker 1: depth than an excellent article by The New York Times 263 00:16:04,400 --> 00:16:07,640 Speaker 1: and a few other publications. I'd say try The Times first. 264 00:16:07,880 --> 00:16:12,200 Speaker 1: And speaking of huge companies, Campbell's is not the only 265 00:16:12,240 --> 00:16:16,680 Speaker 1: company in the game, right no, oh no, freedo lay Uh. 266 00:16:16,720 --> 00:16:19,360 Speaker 1: They've been studying female brains to learn how to better 267 00:16:19,400 --> 00:16:22,640 Speaker 1: appeal to women, and their findings show that the company 268 00:16:22,680 --> 00:16:26,920 Speaker 1: could avoid pitches related to guilt and guilt free and 269 00:16:27,040 --> 00:16:31,520 Speaker 1: uh and instead play up healthy associations like Friedo's for 270 00:16:31,680 --> 00:16:36,760 Speaker 1: your health, yes, okay uh. And Microsoft is looking at 271 00:16:36,800 --> 00:16:41,400 Speaker 1: E e G data to understand how users interact with computers, 272 00:16:41,760 --> 00:16:47,840 Speaker 1: specifically three feelings. This is so weird, surprise, satisfaction, and frustration. 273 00:16:48,520 --> 00:16:54,200 Speaker 1: I just don't understand. Uh, I don't understand, Ben alright, whatever, 274 00:16:54,280 --> 00:16:57,480 Speaker 1: I'm satisfied by that. But Microsoft isn't the only tech 275 00:16:57,520 --> 00:17:03,000 Speaker 1: company getting into this racket. Oh gosh. Okay. So Google 276 00:17:03,000 --> 00:17:05,240 Speaker 1: made some waves when it partnered up with this company 277 00:17:05,280 --> 00:17:09,800 Speaker 1: called Media Vest and they they're using biomet biometrics to 278 00:17:09,880 --> 00:17:13,800 Speaker 1: study and measure the effectiveness of YouTube overlay ads as 279 00:17:13,840 --> 00:17:17,040 Speaker 1: well as pre roll ads and kind of what's the 280 00:17:17,080 --> 00:17:21,320 Speaker 1: effectiveness with the pre roll versus overlay. Well, here's the result. 281 00:17:21,960 --> 00:17:26,520 Speaker 1: Overlays were much more effective on the subjects because, as 282 00:17:26,520 --> 00:17:28,720 Speaker 1: you all know what happens when you see a pre 283 00:17:28,880 --> 00:17:31,840 Speaker 1: roll ad. You wait for that five seconds to pass 284 00:17:32,000 --> 00:17:34,720 Speaker 1: and you click it as quickly as you can. Yeah, 285 00:17:34,960 --> 00:17:38,080 Speaker 1: and that makes sense. I hope they didn't pay too 286 00:17:38,119 --> 00:17:40,680 Speaker 1: much money for that. I know we could have told 287 00:17:40,680 --> 00:17:44,480 Speaker 1: them another company, diam Ler. Right, it might be familiar 288 00:17:44,480 --> 00:17:47,280 Speaker 1: with people if you've heard Chrysler. Diame learned Diamlar Chrysler. 289 00:17:47,480 --> 00:17:50,199 Speaker 1: They employed F M R I research to inform a 290 00:17:50,320 --> 00:17:56,240 Speaker 1: campaign featuring car headlights, and they found that it hits 291 00:17:56,280 --> 00:17:59,320 Speaker 1: the rewards center of the brain when the car's headlights 292 00:17:59,320 --> 00:18:03,160 Speaker 1: help it look like human face. Wow, that's some people 293 00:18:03,160 --> 00:18:05,840 Speaker 1: buy cars. I yeah, I mean, how do you think? 294 00:18:06,080 --> 00:18:09,000 Speaker 1: Was it the bug? The bug was hugely popular back 295 00:18:09,000 --> 00:18:11,480 Speaker 1: in the day. I bet that's why I've been And 296 00:18:11,720 --> 00:18:15,240 Speaker 1: one more example, maybe before we go to commercial, Yes, 297 00:18:15,320 --> 00:18:18,800 Speaker 1: bend the Weather Channel uses and they used e g. 298 00:18:19,080 --> 00:18:22,440 Speaker 1: Eye tracking and skin response techniques to measure viewer reactions 299 00:18:22,480 --> 00:18:26,639 Speaker 1: to three different promotional pitches for this popular series they 300 00:18:26,640 --> 00:18:29,639 Speaker 1: were putting on. And I don't know exactly what they found, 301 00:18:30,080 --> 00:18:34,120 Speaker 1: but just knowing that even the Weather Channel is going, Okay, 302 00:18:34,160 --> 00:18:36,400 Speaker 1: this is a new thing. We need to get on this. Yeah, 303 00:18:36,400 --> 00:18:39,440 Speaker 1: how how do we how do we bring uh sexy 304 00:18:39,520 --> 00:18:44,720 Speaker 1: back to tornadoes? Yeah, I hope they don't talk that way. 305 00:18:45,280 --> 00:18:47,600 Speaker 1: I hope they don't talk that way. Maybe it's a 306 00:18:47,640 --> 00:18:49,800 Speaker 1: different show. We can dig in and get more information 307 00:18:49,880 --> 00:18:52,760 Speaker 1: on that. But now we are going to take a 308 00:18:52,760 --> 00:18:57,000 Speaker 1: brief break and we will return very soon after a 309 00:18:57,000 --> 00:19:07,760 Speaker 1: word from our sponsor. The conscious and intelligent manipulation of 310 00:19:07,800 --> 00:19:11,000 Speaker 1: the organized habits and opinions of the masses is an 311 00:19:11,040 --> 00:19:16,080 Speaker 1: important element in democratic society. Those who manipulate this unseen 312 00:19:16,119 --> 00:19:20,320 Speaker 1: mechanism of society constitute an invisible government, which is the 313 00:19:20,400 --> 00:19:25,400 Speaker 1: true ruling power of our country. We are governed, our 314 00:19:25,520 --> 00:19:31,000 Speaker 1: minds are molded, our tastes formed, our ideas suggested largely 315 00:19:31,040 --> 00:19:34,080 Speaker 1: by men we have never heard of. This is a 316 00:19:34,119 --> 00:19:37,600 Speaker 1: logical result of the way in which our democratic society 317 00:19:37,680 --> 00:19:41,919 Speaker 1: is organized. Vast numbers of human beings must cooperate in 318 00:19:41,960 --> 00:19:44,800 Speaker 1: this manner if they are to live together as a 319 00:19:44,880 --> 00:19:53,920 Speaker 1: smoothly functioning society. From propaganda by Edward Burnet's to learn 320 00:19:53,960 --> 00:19:59,840 Speaker 1: more about public relations, advertising and human brain read propaganda. 321 00:20:00,320 --> 00:20:03,160 Speaker 1: This announcement paid for as a result of ongoing litigation 322 00:20:03,200 --> 00:20:06,399 Speaker 1: on the part of Illumination Global Unlimited. This advertisement in 323 00:20:06,440 --> 00:20:09,960 Speaker 1: no way reflects the personal opinion of Illumination Global Unlimited, 324 00:20:10,000 --> 00:20:14,280 Speaker 1: its shareholders, corporate overlords, or secret society leaders. This advertisement 325 00:20:14,400 --> 00:20:16,879 Speaker 1: is in no way a denial nor confirmation of contact 326 00:20:16,960 --> 00:20:19,920 Speaker 1: with extra dimensional entities. This advertisement is in no way 327 00:20:19,920 --> 00:20:22,199 Speaker 1: an emission of guilt nor test and implication that you 328 00:20:22,200 --> 00:20:24,919 Speaker 1: are being controlled. This advertisement and no way relates to 329 00:20:30,800 --> 00:20:34,320 Speaker 1: from Saigon to Jakarta until the fire was contained. Illumination 330 00:20:34,359 --> 00:20:42,120 Speaker 1: Global Unlimited, Turn on the light. All right, Ben, we're 331 00:20:42,119 --> 00:20:48,160 Speaker 1: back um that one. That was an interesting pultro. Thereel 332 00:20:48,160 --> 00:20:50,760 Speaker 1: like some serious litigation going on with our sponsors. I mean, 333 00:20:50,800 --> 00:20:53,560 Speaker 1: I guess they're just now required to tell people about 334 00:20:53,760 --> 00:20:56,720 Speaker 1: propaganda by Edward Burns, which is a good book. We've 335 00:20:56,760 --> 00:20:59,439 Speaker 1: recommended it. Did you did you notice that part at 336 00:20:59,440 --> 00:21:02,320 Speaker 1: the end where their talking about extra dimensional entities. I'm 337 00:21:02,320 --> 00:21:07,080 Speaker 1: just glad we have a sponsor. Man, don't dig too deep, Okay, alright, alright, 338 00:21:07,119 --> 00:21:09,239 Speaker 1: I guess you're right. The money is good. So how 339 00:21:09,320 --> 00:21:11,560 Speaker 1: are you getting paid? Well, I mean our show is 340 00:21:11,600 --> 00:21:14,280 Speaker 1: still on. I'll sleep in my car, man. Well, yeah, 341 00:21:14,400 --> 00:21:16,960 Speaker 1: we both sleep in our cars. But the lights are 342 00:21:16,960 --> 00:21:20,919 Speaker 1: on in the studio and the mics are working. Yeah. Yeah. 343 00:21:20,960 --> 00:21:24,880 Speaker 1: But speaking of working, there's a big question about neuromarketing, 344 00:21:24,880 --> 00:21:27,720 Speaker 1: and that is this does it work? Does it work? 345 00:21:29,040 --> 00:21:32,600 Speaker 1: Skeptics aren't convinced that there's any proven correlation between brain 346 00:21:32,680 --> 00:21:36,160 Speaker 1: activity and ultimate predictions of what someone will buy. So 347 00:21:36,359 --> 00:21:39,760 Speaker 1: just because some part of your brain is lighting up 348 00:21:39,880 --> 00:21:43,080 Speaker 1: as seen by an e G doesn't mean that we 349 00:21:43,160 --> 00:21:46,800 Speaker 1: have solved the secret of what makes people buy Raisin brand. 350 00:21:46,920 --> 00:21:49,240 Speaker 1: For instance. Speaking of e g s, remember we were 351 00:21:49,240 --> 00:21:52,760 Speaker 1: talking about earlier how those researchers were using the legit 352 00:21:53,240 --> 00:21:55,719 Speaker 1: e g s, or at least the ones we are 353 00:21:55,720 --> 00:21:59,840 Speaker 1: speaking of earlier. Well, e g s can vary widely 354 00:22:00,160 --> 00:22:03,440 Speaker 1: in their effectiveness because there are you know, like any 355 00:22:03,520 --> 00:22:06,359 Speaker 1: product you can get the top of the line jagua 356 00:22:07,080 --> 00:22:10,080 Speaker 1: or you know, maybe someone's rolling around, I don't want 357 00:22:10,119 --> 00:22:15,000 Speaker 1: to use another, uh, like the geo metro of the 358 00:22:15,000 --> 00:22:17,919 Speaker 1: there you go, there's a geo metro of e G s, 359 00:22:18,000 --> 00:22:21,160 Speaker 1: and it can just it's just not as good unfortunately. 360 00:22:21,560 --> 00:22:24,400 Speaker 1: And another thing that we need to remember is that 361 00:22:24,880 --> 00:22:28,120 Speaker 1: e e g s need to be applied by professionals 362 00:22:28,119 --> 00:22:31,120 Speaker 1: who know what they're doing. And there's a reason that 363 00:22:31,320 --> 00:22:34,560 Speaker 1: in an actual study, you know, in like a medical 364 00:22:34,640 --> 00:22:38,840 Speaker 1: study or a clinical psychology study. The people applying the 365 00:22:38,880 --> 00:22:42,640 Speaker 1: e G spent a long time putting them on correctly. 366 00:22:43,080 --> 00:22:48,080 Speaker 1: So there's also a question of how how high quality 367 00:22:48,119 --> 00:22:51,200 Speaker 1: or low quality the data is. Yeah, you have, that's 368 00:22:51,240 --> 00:22:53,200 Speaker 1: something you have to be trained for a long time 369 00:22:53,200 --> 00:22:55,520 Speaker 1: on how to do it effectively because really you're just 370 00:22:55,560 --> 00:22:59,240 Speaker 1: getting numbers outputs in graphs and if you don't know 371 00:22:59,280 --> 00:23:02,520 Speaker 1: how to do that. And for the companies they're doing this, 372 00:23:02,640 --> 00:23:06,840 Speaker 1: the private companies, whatever you do or don't think about them, uh, 373 00:23:07,400 --> 00:23:11,920 Speaker 1: we know that their primary selling point is their methods 374 00:23:11,960 --> 00:23:15,680 Speaker 1: of interpretation. That's what differentiates them from one another. That's 375 00:23:15,680 --> 00:23:19,960 Speaker 1: what lets company A get better results in company B. Right, supposedly, Yeah, 376 00:23:20,040 --> 00:23:25,119 Speaker 1: here's the data, we collected it scientifically, and uh, enjoy 377 00:23:25,320 --> 00:23:28,320 Speaker 1: it's it's real until you ask, hey, how does that work? 378 00:23:28,760 --> 00:23:32,320 Speaker 1: Because that is one of the most difficult things for 379 00:23:32,440 --> 00:23:38,360 Speaker 1: skeptics about neuromarketing, which is that the companies are required 380 00:23:38,480 --> 00:23:42,359 Speaker 1: to keep their methods secret, you know, kind of like 381 00:23:42,400 --> 00:23:45,880 Speaker 1: a trade secret. The way that Coca Cola's actual recipe 382 00:23:45,920 --> 00:23:49,359 Speaker 1: is still a big secret, so they can't reveal their methods, 383 00:23:49,400 --> 00:23:53,080 Speaker 1: which means there's no way for somebody to independently verify 384 00:23:53,200 --> 00:23:57,879 Speaker 1: the results, which means that we can't really know because 385 00:23:58,119 --> 00:24:02,160 Speaker 1: reproducing result is part of what makes something real. Science. Yeah, 386 00:24:02,200 --> 00:24:05,440 Speaker 1: that's how you test the scientific method. Well okay, Ben, 387 00:24:06,320 --> 00:24:11,439 Speaker 1: Now let's really let's really get down to brass tacks here. Okay, 388 00:24:11,480 --> 00:24:15,080 Speaker 1: brass tacking. What does all of this mean, the studying 389 00:24:15,119 --> 00:24:19,800 Speaker 1: of our brains to even further convince us that we 390 00:24:19,880 --> 00:24:24,440 Speaker 1: need to buy products? What? What is neuro marketing? What's 391 00:24:24,480 --> 00:24:29,119 Speaker 1: it gonna do overall to advertising and marketing? Okay? Do 392 00:24:29,160 --> 00:24:32,200 Speaker 1: you want to ProCon this? I do? Okay? Uh no, 393 00:24:32,440 --> 00:24:39,600 Speaker 1: if we could have some utopian music, okay. So one 394 00:24:39,640 --> 00:24:43,080 Speaker 1: of the first things is that there will be better advertising. 395 00:24:43,160 --> 00:24:46,159 Speaker 1: You won't see as many ads that don't apply to you. Guys, 396 00:24:46,160 --> 00:24:49,080 Speaker 1: your days of sitting through ads for feminine hygiene products 397 00:24:49,080 --> 00:24:54,879 Speaker 1: are probably over unless your your female partner, mothers, whoever 398 00:24:54,880 --> 00:24:56,919 Speaker 1: it is, is sitting next to you. Ah yeah, but 399 00:24:56,960 --> 00:24:59,120 Speaker 1: then it's kind of aimed at them. So there will 400 00:24:59,160 --> 00:25:01,679 Speaker 1: be better advertise. Uh. There will be this use of 401 00:25:01,720 --> 00:25:04,920 Speaker 1: predictive data, and it will also be easier to learn 402 00:25:04,960 --> 00:25:08,680 Speaker 1: about things you might enjoy. So if you like, uh, 403 00:25:09,119 --> 00:25:11,119 Speaker 1: if you like a film or you would like a 404 00:25:11,160 --> 00:25:14,280 Speaker 1: film or a book that you would not ordinarily have 405 00:25:14,320 --> 00:25:18,919 Speaker 1: heard of. Then having this data will enable these companies 406 00:25:19,160 --> 00:25:21,560 Speaker 1: to tell you about this amazing thing that you miss. 407 00:25:22,240 --> 00:25:24,399 Speaker 1: And I wanna jump on this point really fast, this 408 00:25:24,520 --> 00:25:28,040 Speaker 1: utopian thing. I think this might be something that we 409 00:25:29,359 --> 00:25:33,119 Speaker 1: have to go towards because there is so much media 410 00:25:33,240 --> 00:25:36,600 Speaker 1: being generated, there are so many products being created. If 411 00:25:36,640 --> 00:25:39,959 Speaker 1: we this specialization is at the point now where if 412 00:25:40,000 --> 00:25:43,359 Speaker 1: we don't get targeted by these things, we will just 413 00:25:43,440 --> 00:25:46,480 Speaker 1: like you said, we will be inundated even further by 414 00:25:46,560 --> 00:25:49,960 Speaker 1: just products being thrown at us and films being shown 415 00:25:49,960 --> 00:25:54,080 Speaker 1: in television shows and YouTube shows. That's a really good point, Matt, 416 00:25:54,080 --> 00:25:57,920 Speaker 1: and I'm glad you say it because something interesting this 417 00:25:58,000 --> 00:26:01,480 Speaker 1: is not completely related to neuro marketing, but something interesting 418 00:26:01,520 --> 00:26:05,040 Speaker 1: about this is that you know, in the days of old, 419 00:26:05,119 --> 00:26:08,359 Speaker 1: before information was so cheap to move around the world, 420 00:26:09,680 --> 00:26:14,399 Speaker 1: the way that people kept a secret was by reducing 421 00:26:14,480 --> 00:26:17,840 Speaker 1: the amount of content in the field. So you wanted 422 00:26:17,880 --> 00:26:21,040 Speaker 1: something secret, you locked the file away, right, and you 423 00:26:21,080 --> 00:26:23,639 Speaker 1: don't tell anybody. Maybe he kills somebody if they know 424 00:26:23,680 --> 00:26:27,720 Speaker 1: about it. And now that switch is flipped. Now instead 425 00:26:27,840 --> 00:26:30,840 Speaker 1: of slowing things down to a trickle, or even stopping 426 00:26:30,840 --> 00:26:35,080 Speaker 1: the trickle. We've opened the floodgates, and it turns out 427 00:26:35,400 --> 00:26:39,720 Speaker 1: that in some ways it's equally difficult to find the 428 00:26:39,800 --> 00:26:43,159 Speaker 1: needle in the haystack. Um, then it is, you know, 429 00:26:43,200 --> 00:26:46,399 Speaker 1: as opposed to searching in in the dark for something 430 00:26:46,440 --> 00:26:50,160 Speaker 1: that doesn't exist. So I now I think it's time 431 00:26:50,200 --> 00:27:01,040 Speaker 1: for some dystopian music, right, what's the bats? All? Right, 432 00:27:01,119 --> 00:27:07,199 Speaker 1: here's the dystopian angle everybody. So constant advertising and predictive 433 00:27:07,320 --> 00:27:14,200 Speaker 1: data and lack of privacy, uninformed consumers, possible predictive mistakes. 434 00:27:14,760 --> 00:27:18,520 Speaker 1: How does this interact with other predictive data that's being 435 00:27:18,520 --> 00:27:23,080 Speaker 1: collected about you right now? And what are the applications 436 00:27:23,080 --> 00:27:25,560 Speaker 1: outside of the ad world? Oh, I can tell you 437 00:27:26,000 --> 00:27:31,560 Speaker 1: what war? War? Yeah? Yeah, I remember. In our stuff 438 00:27:31,600 --> 00:27:35,399 Speaker 1: on Edward Burnet's we explored how he used the same 439 00:27:35,480 --> 00:27:40,080 Speaker 1: advertising techniques that made bacon and breakfast food true story, 440 00:27:40,280 --> 00:27:43,960 Speaker 1: made women smoke, lucky strikes true story, and then he 441 00:27:44,000 --> 00:27:51,000 Speaker 1: transferred those skills to win support for an invasion of Guatemala. Okay, 442 00:27:51,040 --> 00:27:53,600 Speaker 1: we talked about this before, and I don't want to 443 00:27:53,600 --> 00:27:56,520 Speaker 1: belabor the point too much, but uh, the United States 444 00:27:56,600 --> 00:28:00,960 Speaker 1: didn't really have a compelling interest to go into Guatemala 445 00:28:01,080 --> 00:28:04,760 Speaker 1: for the good of either Guatemala's citizens or the average 446 00:28:04,880 --> 00:28:07,840 Speaker 1: Jane and Joe public In the United States, it was 447 00:28:08,000 --> 00:28:13,480 Speaker 1: for the advantage of the United Fruit Company, and he 448 00:28:13,560 --> 00:28:18,080 Speaker 1: was able to convince people using these marketing techniques that 449 00:28:18,200 --> 00:28:21,960 Speaker 1: this was a stand against um. What was it was 450 00:28:21,960 --> 00:28:25,320 Speaker 1: the boogeyman at the time, Communism. It's usually the boogeyman. 451 00:28:25,359 --> 00:28:29,240 Speaker 1: I can't recall. Yeah, I believe it was Communism. Four, 452 00:28:29,359 --> 00:28:33,119 Speaker 1: This invasion happened because a very convincing admin was on it. 453 00:28:33,320 --> 00:28:40,200 Speaker 1: So with the use of neuromarketing, it may be possible 454 00:28:40,680 --> 00:28:46,040 Speaker 1: to hack somebody's brain tapped directly into the ideas of 455 00:28:46,080 --> 00:28:52,000 Speaker 1: fear and anger and boom in another war. When I 456 00:28:52,080 --> 00:28:55,400 Speaker 1: see it's, oh man, I completely I completely see where 457 00:28:55,400 --> 00:29:00,479 Speaker 1: you're going there. It's just so tough. I don't know, 458 00:29:01,080 --> 00:29:03,560 Speaker 1: unless they're really good, I don't know how they're going 459 00:29:03,600 --> 00:29:06,920 Speaker 1: to convince people our age and younger than us that 460 00:29:07,440 --> 00:29:09,960 Speaker 1: we're still the way to go. I feel like we've 461 00:29:10,000 --> 00:29:14,360 Speaker 1: become we're becoming much more and more skeptical of how 462 00:29:14,480 --> 00:29:17,760 Speaker 1: the warring nature of at least our country in the 463 00:29:17,840 --> 00:29:23,880 Speaker 1: United States right well, people may be more cynical for sure. Also, 464 00:29:25,200 --> 00:29:29,960 Speaker 1: there might be a little bit of fatigue amid the 465 00:29:30,080 --> 00:29:33,920 Speaker 1: people who would ordinally go to war. Um, the people 466 00:29:33,960 --> 00:29:38,960 Speaker 1: who would actually serve in the military may feel that 467 00:29:40,240 --> 00:29:43,440 Speaker 1: they they've been betrayed in some ways by the leadership. 468 00:29:43,840 --> 00:29:48,920 Speaker 1: But also people may feel that it is their duty regardless. 469 00:29:49,080 --> 00:29:52,160 Speaker 1: You know, I can't remember the old the old poem. 470 00:29:52,160 --> 00:29:54,680 Speaker 1: Ours is not the reason why ours is but to 471 00:29:54,720 --> 00:29:58,680 Speaker 1: do and die? Um, it's pretty pretty bleak. Yeah, I 472 00:29:58,680 --> 00:30:00,200 Speaker 1: can't remember the name of the poem, but I do 473 00:30:00,280 --> 00:30:02,440 Speaker 1: remember that line sticking with me. So I guess it 474 00:30:02,560 --> 00:30:05,800 Speaker 1: just depends ben on how well this stuff works. And 475 00:30:07,000 --> 00:30:10,480 Speaker 1: if if they can convince me that we need to 476 00:30:10,480 --> 00:30:14,520 Speaker 1: go to war, then it's working. Okay, Yeah, and I'll 477 00:30:14,560 --> 00:30:17,080 Speaker 1: report back. I'll let you know. You're not a big 478 00:30:17,120 --> 00:30:20,640 Speaker 1: fan of war, who is? Well, yeah, there aren't a 479 00:30:20,640 --> 00:30:23,120 Speaker 1: lot of people that are. But I think especially you 480 00:30:23,160 --> 00:30:26,880 Speaker 1: and I after digging into so much of this information 481 00:30:26,920 --> 00:30:31,600 Speaker 1: and just start to realize the the external reasons for war, 482 00:30:31,840 --> 00:30:35,840 Speaker 1: and it's not the ones that they're telling you. Yeah. Historically, 483 00:30:36,320 --> 00:30:39,480 Speaker 1: the reasons that a group of people are told to 484 00:30:39,520 --> 00:30:44,280 Speaker 1: go to war by their leaders are often not entirely 485 00:30:44,720 --> 00:30:47,360 Speaker 1: the I'm not saying they're untrue, but they're not the 486 00:30:47,520 --> 00:30:50,960 Speaker 1: entire truth. Uh. There's another thing we should talk about here, 487 00:30:51,120 --> 00:30:55,560 Speaker 1: which would be possible mistakes, predictive mistakes. So we know 488 00:30:55,720 --> 00:31:02,360 Speaker 1: that in neual marketing. Again, if this actually works, they'll 489 00:31:02,400 --> 00:31:07,800 Speaker 1: be able to market towards people based on their past. 490 00:31:08,400 --> 00:31:11,160 Speaker 1: Uh data, So everything from your Facebook likes to where 491 00:31:11,160 --> 00:31:16,200 Speaker 1: your phone goes via GPS, plus what parts of your 492 00:31:16,240 --> 00:31:20,280 Speaker 1: brain fire when you see, you know, an xbox. And 493 00:31:21,280 --> 00:31:24,840 Speaker 1: here's where it gets strange. What if they get it wrong? 494 00:31:25,600 --> 00:31:28,840 Speaker 1: You know what happens if there's some faulty gear or 495 00:31:28,880 --> 00:31:32,360 Speaker 1: maybe when they read your data you're having an off day, 496 00:31:32,520 --> 00:31:36,320 Speaker 1: then you just spend your time doing what like constantly 497 00:31:36,400 --> 00:31:40,000 Speaker 1: being bombarded by you know, let's see, you're hungover. When 498 00:31:40,040 --> 00:31:43,880 Speaker 1: you somehow get your data entered, then for what what happens? Then? 499 00:31:43,920 --> 00:31:46,480 Speaker 1: Do you just for the rest of your time get 500 00:31:46,560 --> 00:31:50,160 Speaker 1: bombarded by iberproof and ads or whatever. Here here's the 501 00:31:50,160 --> 00:31:55,960 Speaker 1: messed up thing. It's probably I'm assuming that it's once 502 00:31:56,000 --> 00:31:59,560 Speaker 1: it comes into full swing and your television or your 503 00:31:59,680 --> 00:32:03,640 Speaker 1: xbo X or your PS four is monitoring you at 504 00:32:03,680 --> 00:32:05,680 Speaker 1: all times when you're sitting in front of your screen. 505 00:32:06,360 --> 00:32:10,040 Speaker 1: It's going to see that one day as possibly an anomaly, 506 00:32:10,400 --> 00:32:12,640 Speaker 1: and but it will still take I'm assuming this is 507 00:32:12,680 --> 00:32:14,640 Speaker 1: messed up, but I'm I'm assuming it will still take 508 00:32:14,640 --> 00:32:18,720 Speaker 1: that into consideration. That this guy gets drunk maybe on 509 00:32:19,120 --> 00:32:24,160 Speaker 1: this certain day, Yeah, maybe advil is something to this person. 510 00:32:24,600 --> 00:32:29,200 Speaker 1: Saturday night will advertise some sort of beer, and Sunday 511 00:32:29,240 --> 00:32:34,080 Speaker 1: morning will advertise it's it's And if you look out 512 00:32:34,120 --> 00:32:36,360 Speaker 1: in the future, you can really see. I don't know 513 00:32:36,400 --> 00:32:39,840 Speaker 1: that dystopian view hits home with me. Here's a here's 514 00:32:39,880 --> 00:32:44,880 Speaker 1: another concern about neural marketing. Um. The idea that this 515 00:32:44,920 --> 00:32:48,680 Speaker 1: would be continual monitoring, which you do bring up continual 516 00:32:48,720 --> 00:32:52,240 Speaker 1: biometric monitoring, which could happen is ways away for now, 517 00:32:52,280 --> 00:32:54,880 Speaker 1: but it will probably happen in our lifetimes. It'll at 518 00:32:54,960 --> 00:32:58,840 Speaker 1: least be possible for large groups of people to be 519 00:32:58,920 --> 00:33:03,640 Speaker 1: followed that way. But one other question that I have 520 00:33:03,800 --> 00:33:07,680 Speaker 1: here is is how predictive can this data be? To 521 00:33:07,720 --> 00:33:12,280 Speaker 1: what degree does it predict and what if any uh 522 00:33:12,520 --> 00:33:17,560 Speaker 1: legal rights or do do average consumers have Because here's 523 00:33:17,600 --> 00:33:20,400 Speaker 1: one of the worst things if we're going for the 524 00:33:20,480 --> 00:33:24,800 Speaker 1: dystopian view, and that is that with neural marketing, if 525 00:33:24,840 --> 00:33:28,800 Speaker 1: somebody has found the silver bullet, then what they're doing 526 00:33:29,160 --> 00:33:32,400 Speaker 1: is taking advantage of people without their knowledge and without 527 00:33:32,440 --> 00:33:36,280 Speaker 1: their consent. Um. My next question, then, going back to 528 00:33:36,320 --> 00:33:41,880 Speaker 1: the predictive data idea, is you know, how does this 529 00:33:42,560 --> 00:33:47,200 Speaker 1: how does this coalesce with privacy or with other things? 530 00:33:47,240 --> 00:33:49,040 Speaker 1: Like there was a rumor for a while and a 531 00:33:49,200 --> 00:33:53,280 Speaker 1: and a worry that I think is well founded when 532 00:33:53,760 --> 00:33:58,280 Speaker 1: people were on Facebook said, you know what happens if 533 00:33:58,760 --> 00:34:02,600 Speaker 1: Facebook works with out reporting agencies and then gives me 534 00:34:02,800 --> 00:34:05,800 Speaker 1: a score of some sort based on not my credit score, 535 00:34:06,120 --> 00:34:10,440 Speaker 1: but the aggregate of the people who know me? And 536 00:34:10,560 --> 00:34:14,440 Speaker 1: does that and and could that make the banks in 537 00:34:14,520 --> 00:34:18,760 Speaker 1: turn decide to give me a lower or a higher 538 00:34:18,760 --> 00:34:23,600 Speaker 1: interest rate? Could it affect my insurance? That's interesting? So 539 00:34:23,719 --> 00:34:28,960 Speaker 1: looking at the your closest associates to see how to 540 00:34:29,120 --> 00:34:33,040 Speaker 1: judge you building a network. You know. Oh, when I 541 00:34:33,080 --> 00:34:36,640 Speaker 1: first found out about it, I went through, Um, when 542 00:34:36,680 --> 00:34:39,520 Speaker 1: I saw that Facebook account, I went through and liked 543 00:34:39,520 --> 00:34:44,160 Speaker 1: a bunch of investment banks and you know, uh wealth 544 00:34:44,360 --> 00:34:50,360 Speaker 1: equity management stuff. Uh and high end cars. Oh yeah, sure. 545 00:34:51,080 --> 00:34:54,040 Speaker 1: Changed all my movies over to the Wall Street movies. 546 00:34:54,440 --> 00:34:58,000 Speaker 1: Uh yeah, Yeah, that's a great idea. And the only 547 00:34:58,080 --> 00:35:01,160 Speaker 1: the only groups I like our humanitarian right. We have 548 00:35:01,280 --> 00:35:03,640 Speaker 1: one other thing to do before we get out of here, uh, 549 00:35:03,680 --> 00:35:08,960 Speaker 1: and that is to issue a correction the resort we 550 00:35:09,000 --> 00:35:13,680 Speaker 1: talked about in our podcasts on deep underground military bases. 551 00:35:14,000 --> 00:35:17,200 Speaker 1: We said it was in Virginia. That is incorrect. It 552 00:35:17,320 --> 00:35:21,320 Speaker 1: is in West Virginia. And no offense to the people 553 00:35:21,320 --> 00:35:24,200 Speaker 1: of Virginia or the people of West Virginia. And thank 554 00:35:24,280 --> 00:35:26,920 Speaker 1: you to the great folks who wrote in to let 555 00:35:27,000 --> 00:35:28,879 Speaker 1: us know about that correction. Yeah, if you ever hear 556 00:35:28,880 --> 00:35:31,640 Speaker 1: anything like that, um, that we got wrong, just let 557 00:35:31,680 --> 00:35:34,279 Speaker 1: us know. We're not perfect, but we try to be. Yeah. 558 00:35:34,280 --> 00:35:35,879 Speaker 1: And I had a couple of shout outs that we're 559 00:35:35,880 --> 00:35:38,600 Speaker 1: supposed to give, but I'm going to hold those over 560 00:35:38,640 --> 00:35:41,000 Speaker 1: for the next day because I think we're pretty good 561 00:35:41,080 --> 00:35:44,200 Speaker 1: on our time here, Matt. So, so what we're gonna 562 00:35:44,239 --> 00:35:46,799 Speaker 1: do is send it to you. Guys. What do you 563 00:35:46,840 --> 00:35:50,720 Speaker 1: think what's the solution to neural marketing? Is it okay 564 00:35:50,800 --> 00:35:52,960 Speaker 1: or you're all right with it? Do you want to 565 00:35:53,000 --> 00:35:56,200 Speaker 1: have your advertising more targeted towards you? Is that a 566 00:35:56,239 --> 00:35:59,040 Speaker 1: good thing? Or should we all just turn off our 567 00:35:59,080 --> 00:36:02,040 Speaker 1: TVs and get rid of the internet. And go outside 568 00:36:02,080 --> 00:36:05,080 Speaker 1: and plant an orange tree. I I don't know, I 569 00:36:05,120 --> 00:36:07,160 Speaker 1: don't have the answers, but I want to hear what 570 00:36:07,200 --> 00:36:10,319 Speaker 1: you think, So write to us on Facebook. We are 571 00:36:10,600 --> 00:36:13,799 Speaker 1: conspiracy Stuff. On Twitter, we are at conspiracy Stuff. You 572 00:36:13,800 --> 00:36:15,800 Speaker 1: can find us on Stuff they Don't want you to 573 00:36:15,880 --> 00:36:18,080 Speaker 1: Know dot com. That's our home base. You can get 574 00:36:18,120 --> 00:36:20,560 Speaker 1: all of these audio podcasts there. If you're not already 575 00:36:20,600 --> 00:36:23,040 Speaker 1: listening to it on there. Thank you if you are. 576 00:36:23,520 --> 00:36:24,960 Speaker 1: And if you don't like any of that stuff and 577 00:36:25,000 --> 00:36:27,000 Speaker 1: you just want to send us an email, you can 578 00:36:27,040 --> 00:36:34,799 Speaker 1: do that. We are conspiracy at Discovery dot com. From 579 00:36:34,800 --> 00:36:38,080 Speaker 1: more on this topic and now they're unexplained phenomenon. Visit 580 00:36:38,160 --> 00:36:42,040 Speaker 1: test tube dot com slash conspiracy stuff. You can also 581 00:36:42,080 --> 00:36:45,560 Speaker 1: get in touch on Twitter at the handle at conspiracy stuff.