1 00:00:00,880 --> 00:00:04,720 Speaker 1: You guys remember Madman. Do you guys ever watch mad Ben? Oh? Vividly? Oh, 2 00:00:04,760 --> 00:00:06,640 Speaker 1: I've seen an apport two. I don't think I've actually 3 00:00:06,720 --> 00:00:08,240 Speaker 1: made it to the end, though, doesn't he like join 4 00:00:08,320 --> 00:00:10,320 Speaker 1: a a rum or something like that or become a 5 00:00:10,560 --> 00:00:14,680 Speaker 1: whoa whoa spoilers. I think we've passed the statute of 6 00:00:14,720 --> 00:00:18,400 Speaker 1: limitations for spoiling mad Hey, some people haven't had time yet, Okay, okay, 7 00:00:18,920 --> 00:00:22,400 Speaker 1: like me, I mean, I mean, you guys know how 8 00:00:22,440 --> 00:00:25,120 Speaker 1: I am. As soon as I figured out that there 9 00:00:25,160 --> 00:00:29,400 Speaker 1: weren't any monsters, there wasn't any time travel or anything supernatural, 10 00:00:29,800 --> 00:00:32,440 Speaker 1: I just thought, I'll ask I'll ask someone else for 11 00:00:32,479 --> 00:00:38,280 Speaker 1: the recap. Uh And it was playground. Yeah, and Ben, 12 00:00:38,360 --> 00:00:41,720 Speaker 1: the monster's capitalism. Everybody knows that. That's a good point. 13 00:00:41,760 --> 00:00:45,680 Speaker 1: That's a fair point. Yeah. This this episode is about 14 00:00:45,720 --> 00:00:50,879 Speaker 1: avers high scenes about what's called neuro marketing, the idea 15 00:00:51,040 --> 00:00:56,840 Speaker 1: that we can, through the arcade application of data, nudge 16 00:00:57,080 --> 00:01:01,560 Speaker 1: a user's subconscious mind. No, folks, we did this one. 17 00:01:02,320 --> 00:01:06,080 Speaker 1: We did this one a long time ago. And it's 18 00:01:06,080 --> 00:01:08,039 Speaker 1: weird to see that so much of the stuff we 19 00:01:08,040 --> 00:01:10,600 Speaker 1: talked about is still in play today. But it's even 20 00:01:10,680 --> 00:01:14,119 Speaker 1: stranger to see how much of it is increasingly openly 21 00:01:14,160 --> 00:01:17,199 Speaker 1: applied to everything from you know, like a new car 22 00:01:17,440 --> 00:01:23,520 Speaker 1: to uh soda or what are those pogo sticks? I 23 00:01:23,560 --> 00:01:29,240 Speaker 1: guess those come back? Yes, remember those Nickelodeon moon boots 24 00:01:29,560 --> 00:01:34,000 Speaker 1: that were like bouncy boots. Um. And I'm just gonna 25 00:01:34,000 --> 00:01:37,080 Speaker 1: put this out there. We made a video series on 26 00:01:37,120 --> 00:01:41,800 Speaker 1: this topic, and if you have not watched on YouTube 27 00:01:41,800 --> 00:01:45,800 Speaker 1: dot com slash conspiracy stuff a video called neuromarketing and 28 00:01:45,959 --> 00:01:49,840 Speaker 1: you stop what you're doing watch that video. This is 29 00:01:49,840 --> 00:01:53,200 Speaker 1: one of my favorite things that that we created back 30 00:01:53,240 --> 00:01:55,960 Speaker 1: in the day. It only has forty three thousand views, 31 00:01:56,040 --> 00:01:59,200 Speaker 1: which is pretty low for our channel. It is one 32 00:01:59,200 --> 00:02:01,280 Speaker 1: of the silliest and most wonderful things you'll ever see, 33 00:02:01,280 --> 00:02:05,200 Speaker 1: I promise, Ah, yes, Okay, no spoilers, but that one 34 00:02:05,320 --> 00:02:08,240 Speaker 1: is worth your time. And the uh, the folks who 35 00:02:08,240 --> 00:02:10,320 Speaker 1: are listening, I've already seeing that. You guys, you folks 36 00:02:10,320 --> 00:02:15,400 Speaker 1: know what we're talking about. But without further ado, uh 37 00:02:15,440 --> 00:02:20,880 Speaker 1: the dangerous, disturbing science of manipulating you into thinking you're 38 00:02:20,960 --> 00:02:26,360 Speaker 1: making your own decisions from UFOs two, ghosts and government 39 00:02:26,400 --> 00:02:30,160 Speaker 1: cover ups. History is wailed with unexplained events. You can 40 00:02:30,200 --> 00:02:33,000 Speaker 1: turn back now or learn the stuff they don't want 41 00:02:33,040 --> 00:02:41,359 Speaker 1: you to now. Hello, everyone, my name is Matt and Ben, 42 00:02:41,480 --> 00:02:43,680 Speaker 1: and this is stuff they don't want you to know. 43 00:02:44,280 --> 00:02:47,560 Speaker 1: Matt a couple of questions for you to start the show. 44 00:02:48,240 --> 00:02:51,639 Speaker 1: One and you don't have to answer if you don't 45 00:02:51,680 --> 00:02:54,240 Speaker 1: want to. Look, I'm gonna answer. Okay, what was the 46 00:02:54,360 --> 00:03:00,760 Speaker 1: last thing you bought? Beatles rock band or the three sixty? 47 00:03:01,520 --> 00:03:04,280 Speaker 1: That was literally last thing you bought today? Oh? No, 48 00:03:04,520 --> 00:03:07,400 Speaker 1: the last thing I bought today was food? Okay, something 49 00:03:07,560 --> 00:03:12,560 Speaker 1: but yes, something that is not was not marketed to me? Okay, okay, 50 00:03:12,600 --> 00:03:16,240 Speaker 1: so you're sure it wasn't marketed to you? Oh you 51 00:03:16,240 --> 00:03:19,720 Speaker 1: know what, perhaps it was. There is a sign, there 52 00:03:19,840 --> 00:03:23,120 Speaker 1: is a sign downstairs in our lobby that, yeah, is 53 00:03:23,200 --> 00:03:26,160 Speaker 1: promoting their food. I guess. Yeah. We have a place 54 00:03:26,560 --> 00:03:30,680 Speaker 1: downstairs in our building where all the podcasters have been 55 00:03:30,720 --> 00:03:35,440 Speaker 1: at least once, even the vegetarians and vegans in the crowd. Uh. 56 00:03:35,720 --> 00:03:41,840 Speaker 1: Wherein they sell you know, sandwiches and wraps and yogurt 57 00:03:41,880 --> 00:03:44,040 Speaker 1: and granola, and they have a salad bar and a 58 00:03:44,040 --> 00:03:47,520 Speaker 1: hot bar. And you know, if you you feel very 59 00:03:47,640 --> 00:03:52,120 Speaker 1: uh very American and in poor health. Sometimes if you 60 00:03:52,160 --> 00:03:56,080 Speaker 1: shop at the hot food bar because they charge by 61 00:03:56,080 --> 00:04:00,640 Speaker 1: the pound, yeah, which is a quick indicator of uh, 62 00:04:00,800 --> 00:04:03,360 Speaker 1: people who eat too much and no offense to carrying. 63 00:04:03,400 --> 00:04:06,680 Speaker 1: The people that run it, hopefully, they Yeah, they're wonderful people. 64 00:04:06,800 --> 00:04:08,720 Speaker 1: That's why I go there actually, because the food is 65 00:04:08,720 --> 00:04:11,040 Speaker 1: pretty good. The food is great. But man, yeah, they 66 00:04:11,120 --> 00:04:15,720 Speaker 1: charge more than uh, let's say, their competitors around the area, right, 67 00:04:15,760 --> 00:04:18,719 Speaker 1: But we're also paying for the convenience. Before we get 68 00:04:18,760 --> 00:04:22,120 Speaker 1: too derailed, the reason I ask you you already clocked it, Matt, 69 00:04:22,400 --> 00:04:30,200 Speaker 1: is because it is strange and frightening how oblivious most 70 00:04:30,279 --> 00:04:34,640 Speaker 1: people are to the sheer amounts. This your magnitude of 71 00:04:34,680 --> 00:04:38,800 Speaker 1: advertising that plagues us throughout a day. And maybe plague 72 00:04:38,839 --> 00:04:42,200 Speaker 1: isn't a fair word, how about inning dates our existence. 73 00:04:42,279 --> 00:04:44,919 Speaker 1: I think we have to become somewhat blind to some 74 00:04:45,000 --> 00:04:48,560 Speaker 1: of it, because literally, wherever you look, there's some logo, 75 00:04:48,680 --> 00:04:53,200 Speaker 1: there's something selling you something. And oh yeah, well, looking 76 00:04:53,240 --> 00:04:55,960 Speaker 1: around this room, it's not so bad. It's mostly soundproofing 77 00:04:56,040 --> 00:05:00,200 Speaker 1: and tiling. Yeah. Look at those rocket speakers though, Look 78 00:05:00,200 --> 00:05:06,479 Speaker 1: at that apple. Oh man, uh Dixie cup, Oh no, 79 00:05:06,680 --> 00:05:09,320 Speaker 1: they got us. So, just in the space of looking around, 80 00:05:09,320 --> 00:05:13,160 Speaker 1: we've already seen several logos, and if we count logos 81 00:05:13,200 --> 00:05:18,280 Speaker 1: as advertisement, then we quickly realize that this stuff is everywhere. 82 00:05:18,760 --> 00:05:23,880 Speaker 1: I guess the fancy word for it is ubiquitous. However, however, listeners, 83 00:05:24,600 --> 00:05:27,880 Speaker 1: you probably have already heard Matt and I talked about 84 00:05:27,960 --> 00:05:33,600 Speaker 1: Edward Burnet's and other other things in advertising, so you 85 00:05:33,680 --> 00:05:38,480 Speaker 1: were already relatively aware. I would say they're relatively aware, right, Matt? Yeah? Sure. 86 00:05:38,480 --> 00:05:41,120 Speaker 1: And if you ever listened to George Carlin or you 87 00:05:41,120 --> 00:05:44,680 Speaker 1: know a lot of these other comedians, or watched I 88 00:05:44,680 --> 00:05:49,560 Speaker 1: don't know anything on NPR's website or PBS with Bill Moyers, 89 00:05:49,600 --> 00:05:52,159 Speaker 1: maybe you know more about this than we do. Or 90 00:05:52,320 --> 00:05:57,320 Speaker 1: maybe you have taken a survey somewhere at a mall 91 00:05:57,440 --> 00:06:01,320 Speaker 1: or in a grocery store and tit you. Oh, these 92 00:06:01,360 --> 00:06:04,320 Speaker 1: people are taking the questions they're asking me and trying 93 00:06:04,360 --> 00:06:07,520 Speaker 1: to build something that I will be much more likely 94 00:06:07,560 --> 00:06:12,599 Speaker 1: to buy. But do you know how far this science 95 00:06:12,640 --> 00:06:16,240 Speaker 1: has evolved in very short span of time. Uh, we 96 00:06:16,279 --> 00:06:19,479 Speaker 1: would like to today introduce you to the concept of 97 00:06:19,640 --> 00:06:25,760 Speaker 1: neuro marketing. Neuro marketing, Uh so, Matt, lay it on, mean, man, 98 00:06:25,839 --> 00:06:28,880 Speaker 1: I want some stolen cold definition here? All right, here 99 00:06:28,920 --> 00:06:31,400 Speaker 1: we go So this is the definition of neuro marketing. 100 00:06:31,800 --> 00:06:35,440 Speaker 1: It's the process of researching the brain patterns of consumers 101 00:06:35,480 --> 00:06:38,760 Speaker 1: to reveal their responses to a particular advertisement or a 102 00:06:38,839 --> 00:06:43,320 Speaker 1: brand or product, and use that to develop new advertising 103 00:06:43,360 --> 00:06:48,960 Speaker 1: campaigns and branding techniques to better influence the your consumers, 104 00:06:49,000 --> 00:06:52,960 Speaker 1: the human beings. Yeah weird. Huh. So this means that 105 00:06:53,000 --> 00:06:56,040 Speaker 1: they're using things like m r s and CT scans, 106 00:06:56,360 --> 00:06:59,760 Speaker 1: even measuring the amount of moisture or sweat your body 107 00:06:59,760 --> 00:07:03,960 Speaker 1: and it's uh. The people doing this, who call themselves neuromarketers, 108 00:07:04,000 --> 00:07:08,960 Speaker 1: claim that they can interpret this data and interpret it 109 00:07:09,000 --> 00:07:11,840 Speaker 1: in such a way that they are able to make 110 00:07:11,960 --> 00:07:15,520 Speaker 1: predictions about what consumers should buy and what kind of 111 00:07:15,560 --> 00:07:18,000 Speaker 1: products they want to see. Now, here's a good thing 112 00:07:18,040 --> 00:07:21,000 Speaker 1: and the bad thing. But it's still debatable whether or 113 00:07:21,040 --> 00:07:23,560 Speaker 1: not this is effective, or whether it works or how 114 00:07:23,600 --> 00:07:27,360 Speaker 1: effective it is spoiler alert. Yeah, um, but but it 115 00:07:27,440 --> 00:07:31,800 Speaker 1: is kind of an emerging um science. I guess y um. 116 00:07:33,080 --> 00:07:36,000 Speaker 1: We do know that there are there are there are 117 00:07:36,040 --> 00:07:38,720 Speaker 1: several pieces of the puzzle here that we do know. Yeah, 118 00:07:38,800 --> 00:07:42,640 Speaker 1: oh yeah, we know that For instance, uh, the brain 119 00:07:43,000 --> 00:07:48,880 Speaker 1: unconsciously prepares your decision, so before you or I Matt 120 00:07:49,000 --> 00:07:54,120 Speaker 1: or you listener would consciously decide to go left or 121 00:07:54,240 --> 00:07:57,040 Speaker 1: right somewhere. You know, So you're walking around a trash 122 00:07:57,080 --> 00:08:00,640 Speaker 1: can or an obstacle or something. Before you conscious realized 123 00:08:00,680 --> 00:08:02,760 Speaker 1: that you're gonna go right or you're gonna go left, 124 00:08:02,920 --> 00:08:06,200 Speaker 1: your brain has already made the decision. There's a bit 125 00:08:06,280 --> 00:08:09,160 Speaker 1: of a lag time, like you're in a crappy computer 126 00:08:09,520 --> 00:08:13,400 Speaker 1: connection or something. So in two thousand eight, some scientists 127 00:08:13,400 --> 00:08:16,320 Speaker 1: in Germany published a study showing this, and here's what 128 00:08:16,360 --> 00:08:20,880 Speaker 1: they did. Uh, they measured somebody using their left or 129 00:08:20,960 --> 00:08:23,840 Speaker 1: right hand for a task, and they found that several 130 00:08:23,960 --> 00:08:27,800 Speaker 1: seconds before human being consciously decides what they're going to do, 131 00:08:28,240 --> 00:08:32,840 Speaker 1: the outcome can be predicted by looking at unconscious activity 132 00:08:32,960 --> 00:08:35,240 Speaker 1: in the brain. So they have them wired up to 133 00:08:35,360 --> 00:08:39,280 Speaker 1: an e g. A real one and academic lab level, 134 00:08:39,480 --> 00:08:43,000 Speaker 1: not the commercial stuff as we'll find. And they saw 135 00:08:43,120 --> 00:08:45,480 Speaker 1: that some part of their brain a couple of seconds 136 00:08:45,520 --> 00:08:48,760 Speaker 1: before they said left, some part of their brain spiked 137 00:08:48,840 --> 00:08:52,000 Speaker 1: up and the decision was already made. Spooky stuff that 138 00:08:52,040 --> 00:08:55,880 Speaker 1: comes from an article in the Telegraph by Alex Hannaford. So, 139 00:08:55,880 --> 00:08:59,920 Speaker 1: so what happens in our brains when we're watching tell 140 00:09:00,080 --> 00:09:03,240 Speaker 1: vision commercial about a sad one about puppies and how 141 00:09:03,320 --> 00:09:07,120 Speaker 1: they need to be adopted. I mean, well, there there's 142 00:09:07,160 --> 00:09:10,200 Speaker 1: something that happens you. You might tear up, you might um, 143 00:09:10,240 --> 00:09:13,520 Speaker 1: you might laugh hilarious, like something you just find so funny, 144 00:09:14,240 --> 00:09:16,960 Speaker 1: maybe a beer commercial. So what's happening in our brains 145 00:09:16,960 --> 00:09:19,079 Speaker 1: when when we're watching these things. Well, one thing is 146 00:09:19,120 --> 00:09:22,120 Speaker 1: for certain, there there are brain waves that are correlated 147 00:09:22,160 --> 00:09:28,079 Speaker 1: to these heightened uh states of attention and emotional reactions. 148 00:09:28,640 --> 00:09:30,800 Speaker 1: So according to the researchers who are studying these e 149 00:09:30,880 --> 00:09:34,160 Speaker 1: g s, they're really saying that when you're watching something 150 00:09:34,200 --> 00:09:37,880 Speaker 1: that's compelling like this or causing an emotional response, uh, 151 00:09:37,960 --> 00:09:41,480 Speaker 1: the the electrical responses in your brain are hyper focused 152 00:09:41,640 --> 00:09:44,840 Speaker 1: on some of these areas and then the other um, 153 00:09:44,880 --> 00:09:48,880 Speaker 1: the other activity kind of subsides. So you're there's less 154 00:09:48,920 --> 00:09:51,960 Speaker 1: thinking going on, less activity going on in other places 155 00:09:51,960 --> 00:09:57,880 Speaker 1: besides that focused fear or um um, some finding something's 156 00:09:57,880 --> 00:10:01,600 Speaker 1: funny or romantic or where whatever emotion they're going for. 157 00:10:02,280 --> 00:10:05,000 Speaker 1: I see, So they're kind of hacking past the critical 158 00:10:05,080 --> 00:10:08,160 Speaker 1: thinking aspect of the brain to see what triggers that 159 00:10:08,240 --> 00:10:13,520 Speaker 1: immediate emotional response. So let's think of every advertisement, every 160 00:10:13,520 --> 00:10:17,840 Speaker 1: logo as essentially a pitch or proposition, right why you 161 00:10:17,840 --> 00:10:21,400 Speaker 1: should buy this? If these pitches are going to succeed, 162 00:10:21,480 --> 00:10:24,280 Speaker 1: then they have to reach that subconscious level of the 163 00:10:24,320 --> 00:10:27,480 Speaker 1: brain where you develop initial interest in the product, the 164 00:10:27,480 --> 00:10:30,720 Speaker 1: inclination to buy it, where you have that brand loyalty. 165 00:10:30,800 --> 00:10:33,959 Speaker 1: At least that's according to a guy named A. K. 166 00:10:34,280 --> 00:10:37,280 Speaker 1: Pre Deep, the founder and chief executive of a company 167 00:10:37,320 --> 00:10:42,400 Speaker 1: called neuro Focus. Now, neuro Focus being a neuromarketing firm, 168 00:10:42,440 --> 00:10:44,880 Speaker 1: clearly as some bias, they're not gonna They're gonna be 169 00:10:44,920 --> 00:10:47,320 Speaker 1: the ones on the side of the argument that says 170 00:10:47,360 --> 00:10:51,160 Speaker 1: neuro marketing does work well because that's their product, right, 171 00:10:51,280 --> 00:10:54,400 Speaker 1: their brand. And to me that okay, I just want 172 00:10:54,440 --> 00:10:57,320 Speaker 1: to pause really fast. Yeah, that's a fascinating thing to me, 173 00:10:57,360 --> 00:10:59,120 Speaker 1: and I know we're going to talk about it later, 174 00:10:59,360 --> 00:11:04,080 Speaker 1: but that the idea of selling a brand is it's 175 00:11:04,120 --> 00:11:06,800 Speaker 1: in their interests to sell their brand just as much 176 00:11:06,840 --> 00:11:09,000 Speaker 1: as they are looking at how these other brands are 177 00:11:09,000 --> 00:11:12,080 Speaker 1: selling their brand, and that will come into play later. 178 00:11:12,320 --> 00:11:14,720 Speaker 1: Makes you wonder, Yeah, it's a meta pitch for sure. 179 00:11:15,080 --> 00:11:19,040 Speaker 1: So we know that the neuro Focused company has done 180 00:11:19,040 --> 00:11:22,120 Speaker 1: marketing tests where they take volunteers. They give them an 181 00:11:22,120 --> 00:11:25,200 Speaker 1: e G. Sensor cap and an eye tracking device and 182 00:11:25,200 --> 00:11:28,480 Speaker 1: then just have them watch commercials or you know, use 183 00:11:28,760 --> 00:11:32,120 Speaker 1: the internet and visit a website or even watch the trailer, 184 00:11:32,679 --> 00:11:36,319 Speaker 1: and the researchers are able to watch the volunteers brain 185 00:11:36,400 --> 00:11:41,800 Speaker 1: patterns and them the eye movements, so they know exactly 186 00:11:41,920 --> 00:11:45,600 Speaker 1: what's firing when they look at this stuff. Um, and 187 00:11:45,640 --> 00:11:48,120 Speaker 1: you can. You can go online and find videos and 188 00:11:48,200 --> 00:11:52,480 Speaker 1: look at the brain patterns and eye movements for particular commercials. 189 00:11:52,480 --> 00:11:54,400 Speaker 1: There are several places. If you just go to YouTube 190 00:11:54,440 --> 00:11:57,640 Speaker 1: and check out neuromarketing to do a search, you'll be 191 00:11:57,679 --> 00:12:00,160 Speaker 1: able to see exactly what the readout looks like for 192 00:12:00,280 --> 00:12:04,080 Speaker 1: these scientists. And so people who believe that this does 193 00:12:04,120 --> 00:12:07,040 Speaker 1: work say stuff very similar to what Dr Predep said, 194 00:12:07,080 --> 00:12:10,679 Speaker 1: which is, by measuring brain waves were able to measure attention, emotion, 195 00:12:11,080 --> 00:12:17,240 Speaker 1: and memory. So computing the subconscious response to stimuli got 196 00:12:17,280 --> 00:12:19,880 Speaker 1: a little poetic at the interire. If you add all 197 00:12:19,920 --> 00:12:24,160 Speaker 1: these electric patterns together, you find it represents the whispers 198 00:12:24,200 --> 00:12:29,920 Speaker 1: of your brain. I mean, yeah, buy more soup, man, 199 00:12:30,040 --> 00:12:33,240 Speaker 1: That soup looks so good soup. You know they reduced 200 00:12:33,280 --> 00:12:36,760 Speaker 1: the sodium in that soup. You can tell because it's 201 00:12:36,760 --> 00:12:41,760 Speaker 1: on the label. And this weird whispering thing we're doing 202 00:12:41,920 --> 00:12:44,320 Speaker 1: is a true story. It's an introduction to one of 203 00:12:44,360 --> 00:12:51,200 Speaker 1: our first examples of neuromarketing, which involves D. D. Campbell's soup. Yeah, 204 00:12:51,320 --> 00:12:55,040 Speaker 1: true story, you guys, Campbell's soup. The company hired some 205 00:12:55,360 --> 00:13:01,600 Speaker 1: researchers to study microscopic changes in skin moisture, heart rate, 206 00:13:01,880 --> 00:13:05,880 Speaker 1: and other stuff to see how consumers react to everything 207 00:13:05,920 --> 00:13:08,560 Speaker 1: from like pictures bowls of soup to the design of 208 00:13:08,600 --> 00:13:13,520 Speaker 1: the logo. And this neural marketing approach is kind of 209 00:13:13,520 --> 00:13:17,280 Speaker 1: a fresh attempt among these consumer goods companies to to 210 00:13:17,360 --> 00:13:20,600 Speaker 1: kind of understand how consumers really respond to their marketing 211 00:13:21,000 --> 00:13:23,400 Speaker 1: and you know, all the money that they're spending on 212 00:13:23,480 --> 00:13:26,880 Speaker 1: marketing and advertising. Yeah, and it's getting cheaper and cheaper 213 00:13:26,920 --> 00:13:31,000 Speaker 1: and faster and faster. Uh. There are people like Robert 214 00:13:31,280 --> 00:13:36,959 Speaker 1: Barocci from the Advertising Research Foundation who says that this 215 00:13:37,120 --> 00:13:41,960 Speaker 1: allows us to build a bridge between the raw data 216 00:13:42,040 --> 00:13:47,000 Speaker 1: of brain activity to uh, the generation of meaning, you know, 217 00:13:47,120 --> 00:13:50,800 Speaker 1: so that we're able to somehow divine and interpret these signs. 218 00:13:51,360 --> 00:13:54,160 Speaker 1: It's crazy, yeah, it is, uh, you know, because for 219 00:13:54,280 --> 00:13:57,720 Speaker 1: years Campbell's was doing the same thing that everybody else 220 00:13:57,800 --> 00:13:59,880 Speaker 1: was doing in the game. They would, you know, you 221 00:14:00,080 --> 00:14:03,120 Speaker 1: go to a marketing research thing. I don't know if 222 00:14:03,160 --> 00:14:05,720 Speaker 1: you ever did this A little sidebar here, I have, actually, 223 00:14:06,080 --> 00:14:09,000 Speaker 1: not with Campbell's in particularly, I've done this, not with Campbell's, 224 00:14:09,040 --> 00:14:11,120 Speaker 1: but it's a great way to make a little extra 225 00:14:11,240 --> 00:14:13,800 Speaker 1: scratch on the side, you know. So I've done it 226 00:14:13,840 --> 00:14:17,760 Speaker 1: for a number of things. Um, this market research stuff 227 00:14:17,840 --> 00:14:19,480 Speaker 1: is something you can look into if you want a 228 00:14:19,480 --> 00:14:24,440 Speaker 1: little extra sporadic money, and they'll ask you some ridiculous questions. Anyway, 229 00:14:24,520 --> 00:14:26,640 Speaker 1: I digress. Here's how it goes. They sit you down 230 00:14:26,680 --> 00:14:29,440 Speaker 1: the room, Matt, you know about this, They show you 231 00:14:29,520 --> 00:14:32,760 Speaker 1: some things, they ask you some questions, and then they 232 00:14:32,840 --> 00:14:37,440 Speaker 1: also ask you how you feel about stuff. So Campbell's 233 00:14:37,480 --> 00:14:39,960 Speaker 1: was doing this just like pretty much any other company 234 00:14:40,040 --> 00:14:43,880 Speaker 1: under the sun. And they were showing people ads and saying, Okay, 235 00:14:43,920 --> 00:14:47,240 Speaker 1: would this ad make you more likely to buy soup? 236 00:14:48,360 --> 00:14:54,240 Speaker 1: And they they realize over time that even the ads 237 00:14:54,280 --> 00:14:57,480 Speaker 1: that their marketing research folks told them we're surefire, can't 238 00:14:57,560 --> 00:15:01,000 Speaker 1: miss full proof hits uh didn't only seemed to affect 239 00:15:01,040 --> 00:15:03,400 Speaker 1: sales one way or the other. You know, soup sales 240 00:15:03,480 --> 00:15:07,920 Speaker 1: still went up in colder months down in hotter months. Yeah, well, 241 00:15:07,960 --> 00:15:09,920 Speaker 1: you know been the thing I did that with was 242 00:15:10,000 --> 00:15:13,520 Speaker 1: movie trailers, or they would show you three different edits 243 00:15:13,680 --> 00:15:16,200 Speaker 1: of a movie trailer, and I have you answer questions 244 00:15:16,240 --> 00:15:17,920 Speaker 1: about each of them, like what do you think this 245 00:15:18,440 --> 00:15:20,080 Speaker 1: the plot of this movie is about? Who do you 246 00:15:20,120 --> 00:15:23,160 Speaker 1: think the main characters are? Do you want to watch 247 00:15:23,200 --> 00:15:26,760 Speaker 1: this movie because you saw this person more it was? 248 00:15:27,800 --> 00:15:32,640 Speaker 1: It's pretty horrifying that then that that marketing research becomes 249 00:15:32,680 --> 00:15:36,000 Speaker 1: what a film that's you know, either trying to make 250 00:15:36,000 --> 00:15:38,880 Speaker 1: a statement or just making some money off the back 251 00:15:38,920 --> 00:15:43,600 Speaker 1: of In this case, Uh, let's I don't want to 252 00:15:44,080 --> 00:15:46,160 Speaker 1: I want to see what movie was. It was a 253 00:15:46,160 --> 00:15:48,000 Speaker 1: bad it was a terrible movie. Tell me what movie 254 00:15:48,000 --> 00:15:51,360 Speaker 1: it was? Please? It was The Love Guru, The Love Guru? 255 00:15:51,480 --> 00:15:55,560 Speaker 1: Okay with Mike Myers. Yes, oh man, so you're responsible, 256 00:15:55,640 --> 00:16:00,440 Speaker 1: huh partly? One thing. Let's side by this for a 257 00:16:00,480 --> 00:16:03,080 Speaker 1: second again. One thing that gets me and I want 258 00:16:03,080 --> 00:16:05,800 Speaker 1: to ask you about is have you noticed the tendency 259 00:16:05,840 --> 00:16:09,800 Speaker 1: of film trailers to become longer and longer and tell 260 00:16:09,880 --> 00:16:13,880 Speaker 1: most of the story It waits, it waxes and waynes okay, 261 00:16:13,880 --> 00:16:16,120 Speaker 1: And I would say it depends on the how much 262 00:16:16,160 --> 00:16:19,080 Speaker 1: control the director has, because the director doesn't want to 263 00:16:19,080 --> 00:16:21,560 Speaker 1: give away all the secrets of the show. Yeah, the 264 00:16:21,600 --> 00:16:24,320 Speaker 1: director wants you to come and watch his story, or 265 00:16:24,320 --> 00:16:26,960 Speaker 1: you know, maybe the writer or in some cases of 266 00:16:26,960 --> 00:16:30,120 Speaker 1: the producer. Um. But yeah, usually they want you to 267 00:16:30,160 --> 00:16:31,720 Speaker 1: come and watch their whole story, and they want to 268 00:16:31,760 --> 00:16:34,960 Speaker 1: tease you to get there. I see. But the the 269 00:16:34,960 --> 00:16:37,320 Speaker 1: the money guys just want to show you whatever is 270 00:16:37,400 --> 00:16:39,120 Speaker 1: whatever they have to show you to get you in 271 00:16:39,160 --> 00:16:44,840 Speaker 1: that theater. And that is one of the things that 272 00:16:45,040 --> 00:16:49,520 Speaker 1: I think separates neural marketing from other conventional market research. 273 00:16:49,600 --> 00:16:52,200 Speaker 1: Going back to our Campbell's story, this guy, Robert Woodard 274 00:16:52,600 --> 00:16:56,280 Speaker 1: uh he was Campbell's vice president of Global Consumer Sales 275 00:16:56,360 --> 00:17:00,000 Speaker 1: and Customer Insights, and he said that their regular interview 276 00:17:00,120 --> 00:17:04,480 Speaker 1: style wasn't that useful because people weren't able to fully 277 00:17:04,560 --> 00:17:09,760 Speaker 1: articulate their unconscious responses. So they needed Campbell's being. They 278 00:17:09,800 --> 00:17:15,280 Speaker 1: they needed to figure out the neurological and bodily underpinnings 279 00:17:15,359 --> 00:17:18,200 Speaker 1: to an ad. They needed to see how people were 280 00:17:18,320 --> 00:17:23,440 Speaker 1: unconsciously reacting rather than see how people thought they were reacting. Yeah, 281 00:17:23,480 --> 00:17:26,720 Speaker 1: because you don't know, man, it's unconscious. So by two 282 00:17:26,720 --> 00:17:29,879 Speaker 1: thousand eight, Mr Woodard, he settled on the biometric tools 283 00:17:29,880 --> 00:17:33,200 Speaker 1: combined with a different type of deep interview. And I'd 284 00:17:33,280 --> 00:17:35,080 Speaker 1: like to know more about that. Oh, I bet it 285 00:17:35,119 --> 00:17:42,560 Speaker 1: was the Blade Runner. What's your address? I wish I 286 00:17:42,560 --> 00:17:44,520 Speaker 1: could do that whole thing. I know the other guys 287 00:17:44,560 --> 00:17:47,480 Speaker 1: here can do that, but yeah, he they decided to 288 00:17:47,520 --> 00:17:51,160 Speaker 1: use this deep interview to more accurately gauge which consumer 289 00:17:51,200 --> 00:17:56,119 Speaker 1: communications worked best, so U Campbell. Campbell's then hired on 290 00:17:56,400 --> 00:18:00,680 Speaker 1: Interscope Research Incorporated, which is a Boston company, and they 291 00:18:00,720 --> 00:18:04,639 Speaker 1: do the same thing. They measure bodily responses, and they 292 00:18:04,680 --> 00:18:07,200 Speaker 1: also hired a couple other firms to help conduct this 293 00:18:07,200 --> 00:18:09,840 Speaker 1: this kind of research where they're looking at it's so 294 00:18:09,880 --> 00:18:13,840 Speaker 1: weird to me, bodily responses to marketing, that suit makes 295 00:18:13,840 --> 00:18:17,480 Speaker 1: people too sweaty. Put it in a blue can. Uh 296 00:18:17,640 --> 00:18:21,080 Speaker 1: the you know, I am joking around. I don't know 297 00:18:21,119 --> 00:18:24,640 Speaker 1: if anybody did that. But the Campbell's story is pretty fascinating, 298 00:18:24,800 --> 00:18:27,080 Speaker 1: and you can read about it more in depth than 299 00:18:27,160 --> 00:18:29,439 Speaker 1: an excellent article by The New York Times and a 300 00:18:29,440 --> 00:18:32,920 Speaker 1: few other publications. I'd say try the Times first. And 301 00:18:33,000 --> 00:18:37,480 Speaker 1: speaking of huge companies. Campbell's is not the only company 302 00:18:37,520 --> 00:18:41,480 Speaker 1: in the game, right, No, oh, no, freedo lay. Uh. 303 00:18:41,520 --> 00:18:44,160 Speaker 1: They've been studying female brains to learn how to better 304 00:18:44,240 --> 00:18:47,479 Speaker 1: appeal to women, and their findings show that the company 305 00:18:47,520 --> 00:18:51,719 Speaker 1: could avoid pitches related to guilt and guilt free and 306 00:18:51,880 --> 00:18:56,359 Speaker 1: uh and instead play up healthy associations like Friedo's for 307 00:18:56,520 --> 00:19:01,600 Speaker 1: your health, yes, okay uh. And Microsoft is looking at 308 00:19:01,600 --> 00:19:06,240 Speaker 1: E e G data to understand how users interact with computers, 309 00:19:06,560 --> 00:19:12,680 Speaker 1: specifically three feelings. This is so weird, surprise, satisfaction, and frustration. 310 00:19:13,320 --> 00:19:19,000 Speaker 1: I just don't understand. Uh, I don't understand, ben alright, whatever, 311 00:19:19,119 --> 00:19:22,280 Speaker 1: I'm satisfied by that. But Microsoft isn't the only tech 312 00:19:22,359 --> 00:19:27,359 Speaker 1: company getting into this racket, oh gosh, racket. Okay. So 313 00:19:27,480 --> 00:19:29,439 Speaker 1: Google made some waves when it partnered it up with 314 00:19:29,480 --> 00:19:33,600 Speaker 1: this company called Media Vest and they they're using biomet 315 00:19:33,760 --> 00:19:37,840 Speaker 1: biometrics to study and measure the effectiveness of YouTube overlay 316 00:19:37,920 --> 00:19:41,200 Speaker 1: ads as well as pre roll ads and kind of 317 00:19:41,480 --> 00:19:45,400 Speaker 1: what's the effectiveness with the pre roll versus overlay. Well, 318 00:19:45,440 --> 00:19:49,320 Speaker 1: here's the result. Overlays were much more effective on the 319 00:19:49,359 --> 00:19:53,119 Speaker 1: subjects because, as you all know, what happens when you 320 00:19:53,160 --> 00:19:55,800 Speaker 1: see a pre roll ad. You wait for that five 321 00:19:55,840 --> 00:19:58,239 Speaker 1: seconds to pass, and you click it as quickly as 322 00:19:58,240 --> 00:20:02,080 Speaker 1: you can. Yeah, and and that makes sense. I hope 323 00:20:02,119 --> 00:20:04,200 Speaker 1: they didn't pay too much money for that. I know 324 00:20:04,760 --> 00:20:08,560 Speaker 1: we could have told them another company, Daimler. Right, it 325 00:20:08,640 --> 00:20:10,960 Speaker 1: might be familiar with people if you've heard Chrysler, diame 326 00:20:11,040 --> 00:20:13,639 Speaker 1: Ler and Diamlar Chrysler. They employed F M R I 327 00:20:13,800 --> 00:20:19,120 Speaker 1: research to inform a campaign featuring car headlights and they 328 00:20:19,160 --> 00:20:22,560 Speaker 1: found that it hits the rewards center of the brain 329 00:20:22,920 --> 00:20:26,800 Speaker 1: when the car's headlights help it look like a human face. Wow, 330 00:20:26,960 --> 00:20:30,199 Speaker 1: that's some people buy cars. I yeah, I mean, how 331 00:20:30,200 --> 00:20:32,600 Speaker 1: do you think? Was it the bug? The bug was 332 00:20:32,760 --> 00:20:35,240 Speaker 1: hugely popular back in the day. I bet that's why 333 00:20:35,240 --> 00:20:38,639 Speaker 1: I've been And one more example, maybe before we go 334 00:20:38,800 --> 00:20:42,720 Speaker 1: to commercial, yes, bend the Weather Channel uses and they 335 00:20:42,840 --> 00:20:45,960 Speaker 1: used e g. Eye tracking and skin response techniques to 336 00:20:46,000 --> 00:20:50,480 Speaker 1: measure viewer reactions to three different promotional pitches for this 337 00:20:50,560 --> 00:20:53,320 Speaker 1: popular series they were putting on. And I don't know 338 00:20:53,359 --> 00:20:56,119 Speaker 1: exactly what they found, but just knowing that even the 339 00:20:56,119 --> 00:20:59,679 Speaker 1: Weather Channel is going, Okay, this is a new thing. 340 00:20:59,680 --> 00:21:01,919 Speaker 1: We need to get on this. Yeah, how how do 341 00:21:02,000 --> 00:21:07,400 Speaker 1: we how do we bring uh sexy back to tornadoes. Yeah, head, 342 00:21:07,920 --> 00:21:10,639 Speaker 1: I don't they don't talk that way. I hope they 343 00:21:10,640 --> 00:21:13,080 Speaker 1: don't talk that way. Maybe it's a different show. We 344 00:21:13,119 --> 00:21:15,800 Speaker 1: can dig in and get more information on that. But 345 00:21:16,000 --> 00:21:18,800 Speaker 1: now we are going to take a brief break and 346 00:21:19,119 --> 00:21:22,840 Speaker 1: we will return very soon after a word from our sponsor. 347 00:21:29,560 --> 00:21:33,840 Speaker 1: The conscious and intelligent manipulation of the organized habits and 348 00:21:33,880 --> 00:21:38,560 Speaker 1: opinions of the masses is an important element in democratic society. 349 00:21:39,040 --> 00:21:43,199 Speaker 1: Those who manipulate this unseen mechanism of society constitute an 350 00:21:43,200 --> 00:21:48,280 Speaker 1: invisible government, which is the true ruling power of our country. 351 00:21:48,680 --> 00:21:53,040 Speaker 1: We are governed, our minds are molded, our tastes formed, 352 00:21:53,320 --> 00:21:57,679 Speaker 1: our ideas suggested largely by men we have never heard of. 353 00:21:58,520 --> 00:22:01,040 Speaker 1: This is a logical result of the way in which 354 00:22:01,040 --> 00:22:05,720 Speaker 1: our democratic society is organized. Vast numbers of human beings 355 00:22:05,800 --> 00:22:08,520 Speaker 1: must cooperate in this manner if they are to live 356 00:22:08,560 --> 00:22:16,560 Speaker 1: together as a smoothly functioning society. Aside from propaganda by 357 00:22:16,760 --> 00:22:21,960 Speaker 1: Edward Burnet's to learn more about public relations, advertising and 358 00:22:22,119 --> 00:22:26,439 Speaker 1: the human brain, read Propaganta. This announcement paid for as 359 00:22:26,440 --> 00:22:29,200 Speaker 1: a result of ongoing litigation on the part of Illumination 360 00:22:29,200 --> 00:22:32,480 Speaker 1: Global Unlimited. This advertisement in no way reflects the personal 361 00:22:32,560 --> 00:22:36,800 Speaker 1: opinion of Illumination Global Unlimited, its shareholders, corporate overlords, or 362 00:22:36,880 --> 00:22:39,840 Speaker 1: secret society leaders. This advertisement is in no way of 363 00:22:39,920 --> 00:22:43,399 Speaker 1: denial nor confirmation of contact with extra dimensional entities. This 364 00:22:43,480 --> 00:22:45,800 Speaker 1: advertisement is in no way an emission of guilt nor 365 00:22:45,880 --> 00:22:48,840 Speaker 1: task and implication that you are being controlled. This advertisement 366 00:22:48,840 --> 00:22:57,120 Speaker 1: and no way relates to from Saigon to Jakarta until 367 00:22:57,160 --> 00:23:01,040 Speaker 1: the fire was contained, Illumination Global Unlimit, It turn on 368 00:23:01,080 --> 00:23:11,080 Speaker 1: the light. Alright, Ben, we're back. Um. That one. That 369 00:23:11,200 --> 00:23:15,240 Speaker 1: was an interesting outro. There seemed to like some serious 370 00:23:15,280 --> 00:23:17,480 Speaker 1: litigation going on with our sponsors. I mean, I guess 371 00:23:17,520 --> 00:23:21,040 Speaker 1: they're just now required to tell people about propaganda by 372 00:23:21,160 --> 00:23:23,880 Speaker 1: Edward Bernes, which is a good book. We've recommended it. 373 00:23:24,000 --> 00:23:26,000 Speaker 1: Did you did you notice that part at the end 374 00:23:26,000 --> 00:23:28,760 Speaker 1: where they were talking about extra dimensional entities. I'm just 375 00:23:28,800 --> 00:23:33,399 Speaker 1: glad we have a sponsor, man, don't dig too deep. Alright, alright, 376 00:23:33,440 --> 00:23:35,560 Speaker 1: I guess you're right. The money is good. So how 377 00:23:35,640 --> 00:23:38,360 Speaker 1: are you getting paid? Well, I mean our shows. Fill on, 378 00:23:38,520 --> 00:23:41,160 Speaker 1: I'll sleep in my car, man, Well, yeah, we both 379 00:23:41,160 --> 00:23:43,480 Speaker 1: sleep in our cars. But the lights are on in 380 00:23:43,520 --> 00:23:47,359 Speaker 1: the studio and the mics are working. Yeah yeah, But 381 00:23:47,520 --> 00:23:51,320 Speaker 1: speaking of working, there's a big question about neuromarketing, and 382 00:23:51,359 --> 00:23:55,879 Speaker 1: that is this does it work? Does it work? Skeptics 383 00:23:55,880 --> 00:23:59,520 Speaker 1: aren't convinced that there's any proven correlation between brain activity 384 00:23:59,600 --> 00:24:02,800 Speaker 1: and utimate predictions of what someone will buy. So just 385 00:24:03,000 --> 00:24:06,400 Speaker 1: because some part of your brain is lighting up as 386 00:24:06,440 --> 00:24:09,600 Speaker 1: seen by an e G doesn't mean that we have 387 00:24:09,760 --> 00:24:13,119 Speaker 1: solved the secret of what makes people buy Raisin brand. 388 00:24:13,240 --> 00:24:15,560 Speaker 1: For instance. Speaking of e g s, remember we were 389 00:24:15,560 --> 00:24:19,080 Speaker 1: talking about earlier how those researchers were using the legit 390 00:24:19,560 --> 00:24:22,040 Speaker 1: e G s, or at least the ones we are 391 00:24:22,040 --> 00:24:26,200 Speaker 1: speaking of earlier. Well, e g s can vary widely 392 00:24:26,480 --> 00:24:29,840 Speaker 1: in their effectiveness because there are you know, like any 393 00:24:29,840 --> 00:24:32,720 Speaker 1: product you can get the top of the line jagua 394 00:24:33,400 --> 00:24:36,440 Speaker 1: or you know, maybe someone's rolling around, I don't want 395 00:24:36,440 --> 00:24:40,800 Speaker 1: to use another, uh, like the geo metro of e G. 396 00:24:41,320 --> 00:24:44,240 Speaker 1: There you go, there's a geometro of e G s, 397 00:24:44,320 --> 00:24:47,480 Speaker 1: and it can just it's just not as good unfortunately. 398 00:24:47,880 --> 00:24:50,760 Speaker 1: And another thing that we need to remember is that 399 00:24:51,200 --> 00:24:54,480 Speaker 1: e e g s need to be applied by professionals 400 00:24:54,480 --> 00:24:57,480 Speaker 1: who know what they're doing. And there's a reason that 401 00:24:57,640 --> 00:25:01,320 Speaker 1: in an actual study or in like a medical study, 402 00:25:01,400 --> 00:25:05,440 Speaker 1: or a clinical psychology study. The people applying the e 403 00:25:05,520 --> 00:25:09,680 Speaker 1: G spend a long time putting them on correctly. So 404 00:25:09,800 --> 00:25:14,520 Speaker 1: there's also a question of how how high quality or 405 00:25:14,560 --> 00:25:17,800 Speaker 1: low quality the data is. Yeah, you have that's something 406 00:25:17,840 --> 00:25:19,639 Speaker 1: you have to be trained for a long time on 407 00:25:19,680 --> 00:25:23,040 Speaker 1: how to do it effectively because really you're just getting numbers, 408 00:25:23,080 --> 00:25:25,679 Speaker 1: outputs and graphs, and yeah, if you don't know how 409 00:25:25,760 --> 00:25:28,879 Speaker 1: to do that. And for the companies they're doing this, 410 00:25:28,960 --> 00:25:33,200 Speaker 1: the private companies, whatever you do or don't think about them, uh, 411 00:25:33,720 --> 00:25:38,240 Speaker 1: we know that their primary selling point is their methods 412 00:25:38,280 --> 00:25:42,000 Speaker 1: of interpretation. That's what differentiates them from one another. That's 413 00:25:42,000 --> 00:25:46,359 Speaker 1: what lets company A get better results in company B. Right, supposedly, Yeah, 414 00:25:46,400 --> 00:25:50,720 Speaker 1: here's the data. Were you collected it scientifically? And uh, 415 00:25:51,119 --> 00:25:54,200 Speaker 1: enjoy It's it's real until you ask, hey, how does 416 00:25:54,240 --> 00:25:58,080 Speaker 1: that work? Because that is one of the most difficult 417 00:25:58,080 --> 00:26:03,400 Speaker 1: things for skeptics about neuromarketing, which is that the companies 418 00:26:03,840 --> 00:26:08,520 Speaker 1: are required to keep their methods secret, you know, kind 419 00:26:08,520 --> 00:26:10,840 Speaker 1: of like a trade secret. The way that Coca Cola's 420 00:26:11,240 --> 00:26:14,639 Speaker 1: actual recipe is still a big secret, so they can't 421 00:26:14,680 --> 00:26:17,960 Speaker 1: reveal their methods, which means there's no way for somebody 422 00:26:18,000 --> 00:26:23,240 Speaker 1: to independently verify the results, which means that we can't 423 00:26:23,280 --> 00:26:27,280 Speaker 1: really know because reproducing result is part of what makes 424 00:26:27,280 --> 00:26:30,560 Speaker 1: something real. Science. Yeah, that's how you test the scientific method. 425 00:26:31,000 --> 00:26:36,360 Speaker 1: Well okay, Ben, Now let's really let's really get down 426 00:26:36,359 --> 00:26:39,800 Speaker 1: to brass tacks here. Okay, brass tacking. What does all 427 00:26:39,800 --> 00:26:43,000 Speaker 1: of this mean, the studying of our brains to even 428 00:26:43,080 --> 00:26:48,240 Speaker 1: further convince us that we need to buy products? What? 429 00:26:48,240 --> 00:26:52,399 Speaker 1: What is neuro marketing? What's it gonna do overall to 430 00:26:52,560 --> 00:26:56,600 Speaker 1: advertising and marketing? Okay? Do you want to ProCon this? 431 00:26:57,000 --> 00:26:59,600 Speaker 1: I do? Okay? Uh no, if we could have some 432 00:27:00,040 --> 00:27:07,119 Speaker 1: utopian music, okay. So one of the first things is 433 00:27:07,200 --> 00:27:10,200 Speaker 1: that there will be better advertising. You won't see as 434 00:27:10,200 --> 00:27:12,920 Speaker 1: many ads that don't apply to you. Guys, your days 435 00:27:12,920 --> 00:27:16,080 Speaker 1: of sitting through ads for feminine hygiene products are probably 436 00:27:16,119 --> 00:27:21,479 Speaker 1: over unless your your female partner, mothers, whoever it is, 437 00:27:21,480 --> 00:27:23,640 Speaker 1: is sitting next to you. Ah yeah, but then it's 438 00:27:23,760 --> 00:27:27,040 Speaker 1: kind of aimed at them. So there will be better advertising. Uh. 439 00:27:27,080 --> 00:27:29,560 Speaker 1: There will be this use of predictive data, and it 440 00:27:29,600 --> 00:27:32,639 Speaker 1: will also be easier to learn about things you might enjoy. 441 00:27:32,760 --> 00:27:36,640 Speaker 1: So if you like, uh, if you like a film, 442 00:27:36,720 --> 00:27:38,960 Speaker 1: or you would like a film or a book that 443 00:27:39,040 --> 00:27:42,280 Speaker 1: you would not ordinarily have heard of. Then having this 444 00:27:42,440 --> 00:27:46,639 Speaker 1: data will enable these companies to tell you about this 445 00:27:46,720 --> 00:27:49,720 Speaker 1: amazing thing that you miss. I wanna jump on this 446 00:27:49,760 --> 00:27:52,960 Speaker 1: point really fast, this utopian thing. I think this might 447 00:27:53,080 --> 00:27:57,400 Speaker 1: be something that we have to go towards because there 448 00:27:57,480 --> 00:28:01,120 Speaker 1: is so much media being oenerated, there are so many 449 00:28:01,160 --> 00:28:05,440 Speaker 1: products being created. If we in this specialization is at 450 00:28:05,440 --> 00:28:08,400 Speaker 1: the point now where if we don't get targeted by 451 00:28:08,440 --> 00:28:10,760 Speaker 1: these things, we will just like you said, we will 452 00:28:10,800 --> 00:28:14,439 Speaker 1: be inundated even further by just products being thrown at 453 00:28:14,560 --> 00:28:18,680 Speaker 1: us and films being shown in television shows and YouTube shows. 454 00:28:19,280 --> 00:28:21,000 Speaker 1: That's a really good point, Matt, and I'm glad you 455 00:28:21,040 --> 00:28:25,800 Speaker 1: say it because something interesting this is not completely related 456 00:28:25,840 --> 00:28:29,879 Speaker 1: to neuromarketing, but something interesting about this is that you know, 457 00:28:30,040 --> 00:28:33,520 Speaker 1: in the days of old, before information was so cheap 458 00:28:33,560 --> 00:28:37,240 Speaker 1: to move around the world, the way that people kept 459 00:28:37,320 --> 00:28:43,000 Speaker 1: a secret was by reducing the amount of content in 460 00:28:43,080 --> 00:28:45,680 Speaker 1: the field. So you wanted something secret, you locked the 461 00:28:45,720 --> 00:28:48,960 Speaker 1: file away, right, and you don't tell anybody. Maybe you 462 00:28:49,040 --> 00:28:51,680 Speaker 1: kill somebody if they know about it. And now that 463 00:28:51,800 --> 00:28:55,840 Speaker 1: switch is flipped. Now instead of slowing things down to 464 00:28:55,880 --> 00:29:00,320 Speaker 1: a trickle, or even stopping the trickle. We've opened the floodgates. Yeah, 465 00:29:00,480 --> 00:29:04,560 Speaker 1: and it turns out that in some ways it's equally 466 00:29:04,600 --> 00:29:08,480 Speaker 1: difficult to find the needle in the haystack, um than 467 00:29:08,560 --> 00:29:11,719 Speaker 1: it is, you know, as opposed to searching in in 468 00:29:11,760 --> 00:29:15,080 Speaker 1: the dark for something that doesn't exist. So I now 469 00:29:15,840 --> 00:29:25,600 Speaker 1: I think it's time for some dystopian music, right, what's 470 00:29:25,640 --> 00:29:29,440 Speaker 1: the bad side? All right, here's the dystopian angle everybody, 471 00:29:30,120 --> 00:29:35,880 Speaker 1: So constant advertising and predictive data and lack of privacy, 472 00:29:36,400 --> 00:29:43,239 Speaker 1: uninformed consumers, possible predictive mistakes. How does this interact with 473 00:29:43,320 --> 00:29:46,560 Speaker 1: other predictive data that's being collected about you right now? 474 00:29:47,120 --> 00:29:51,240 Speaker 1: And what are the applications outside of the ad world? Oh, 475 00:29:51,280 --> 00:29:55,920 Speaker 1: I can tell you what war? War? Yeah? Yeah, I remember. 476 00:29:56,080 --> 00:30:00,440 Speaker 1: In our stuff on Edward Burnet's we explored how he 477 00:30:00,680 --> 00:30:05,240 Speaker 1: used the same advertising techniques that made bacon and breakfast 478 00:30:05,280 --> 00:30:09,200 Speaker 1: food true story, made women smoke, lucky strikes true story, 479 00:30:09,560 --> 00:30:13,920 Speaker 1: and then he transferred those skills to win support for 480 00:30:14,000 --> 00:30:18,880 Speaker 1: an invasion of Guatemala. Okay, we talked about this before, 481 00:30:19,320 --> 00:30:21,160 Speaker 1: and I don't want to belabor the point too much, 482 00:30:21,200 --> 00:30:25,400 Speaker 1: but uh, the United States didn't really have a compelling 483 00:30:25,560 --> 00:30:28,680 Speaker 1: interest to go into Guatemala for the good of either 484 00:30:28,680 --> 00:30:32,360 Speaker 1: Guatemala's citizens or the average Jane and Joe public In 485 00:30:32,400 --> 00:30:36,480 Speaker 1: the United States, it was for the advantage of the 486 00:30:36,560 --> 00:30:41,640 Speaker 1: United Fruit Company, and he was able to convince people 487 00:30:42,080 --> 00:30:47,520 Speaker 1: using these marketing techniques that this was a stand against um. 488 00:30:47,600 --> 00:30:50,440 Speaker 1: What was it was the boogeyman at the time, Communism. 489 00:30:50,480 --> 00:30:53,680 Speaker 1: It's usually the boogeyman. I can't recall. Yeah, I believe 490 00:30:53,680 --> 00:30:57,440 Speaker 1: it was Communism ninety four. This invasion happened because a 491 00:30:57,600 --> 00:31:01,160 Speaker 1: very convincing admin was on it. So with the use 492 00:31:01,360 --> 00:31:09,240 Speaker 1: of neuromarketing, it may be possible to hack somebody's brain 493 00:31:09,600 --> 00:31:14,480 Speaker 1: tapped directly into the ideas of fear and anger and 494 00:31:14,680 --> 00:31:20,360 Speaker 1: boom another war. When I see it's oh, man, I 495 00:31:20,360 --> 00:31:23,080 Speaker 1: completely I completely see where you're going there. It's just 496 00:31:23,160 --> 00:31:29,120 Speaker 1: so tough. I don't know unless they're really good, I 497 00:31:29,120 --> 00:31:31,440 Speaker 1: don't know how they're going to convince people our age 498 00:31:31,520 --> 00:31:34,960 Speaker 1: and younger than us that we're still the way to go. 499 00:31:35,600 --> 00:31:38,760 Speaker 1: I feel like we've become we're becoming much more and 500 00:31:38,840 --> 00:31:43,240 Speaker 1: more skeptical of how the warring nature of at least 501 00:31:43,280 --> 00:31:47,200 Speaker 1: our country in the United States right well, people may 502 00:31:47,200 --> 00:31:52,320 Speaker 1: be more cynical, for sure. Also, there might be a 503 00:31:52,320 --> 00:31:58,400 Speaker 1: little bit of fatigue amid the people who would ordinarily 504 00:31:58,480 --> 00:32:01,360 Speaker 1: go to war. Um, the people who would actually serve 505 00:32:01,560 --> 00:32:08,200 Speaker 1: in the military may feel that they they've been betrayed 506 00:32:08,240 --> 00:32:11,960 Speaker 1: in some ways by the leadership. But also people may 507 00:32:12,000 --> 00:32:16,160 Speaker 1: feel that it is their duty regardless. You know, I 508 00:32:16,160 --> 00:32:19,160 Speaker 1: can't remember the old the old poem, Ours is not 509 00:32:19,320 --> 00:32:23,200 Speaker 1: the reason why ours is but to do and die? Um, 510 00:32:23,200 --> 00:32:25,840 Speaker 1: it's pretty pretty bleak. Yeah, I can't remember the name 511 00:32:25,840 --> 00:32:27,719 Speaker 1: of the poem, but I do remember that line sticking 512 00:32:27,760 --> 00:32:30,000 Speaker 1: with me. So I guess it just depends ben on 513 00:32:30,040 --> 00:32:35,120 Speaker 1: how well this stuff works. And if if they can 514 00:32:35,120 --> 00:32:38,160 Speaker 1: convince me that we need to go to war, then 515 00:32:38,160 --> 00:32:41,719 Speaker 1: it's working. Okay, Yeah, and I'll report back. I'll let 516 00:32:41,720 --> 00:32:46,120 Speaker 1: you know. You're not a big fan of war, who is? Well, yeah, 517 00:32:46,520 --> 00:32:48,080 Speaker 1: there aren't a lot of people that are. But I 518 00:32:48,080 --> 00:32:52,320 Speaker 1: think especially you and I after digging into so much 519 00:32:52,360 --> 00:32:55,680 Speaker 1: of this information and just start to realize the the 520 00:32:55,760 --> 00:32:59,160 Speaker 1: external reasons for war, and it's not the ones that 521 00:32:59,200 --> 00:33:04,520 Speaker 1: they're telling you. M Yeah. Historically, the reasons that a 522 00:33:04,560 --> 00:33:06,479 Speaker 1: group of people are told to go to war by 523 00:33:06,520 --> 00:33:12,240 Speaker 1: their leaders are often not entirely the I'm not saying 524 00:33:12,240 --> 00:33:15,880 Speaker 1: they're untrue, but they're not the entire truth. Uh. There's 525 00:33:15,920 --> 00:33:18,000 Speaker 1: another thing we should talk about here, which would be 526 00:33:18,040 --> 00:33:23,680 Speaker 1: possible mistakes, predictive mistakes. So we know that in neuro 527 00:33:23,920 --> 00:33:29,440 Speaker 1: marketing again, if this actually works, they'll be able to 528 00:33:30,760 --> 00:33:35,520 Speaker 1: market towards people based on their past uh data. So 529 00:33:35,680 --> 00:33:38,240 Speaker 1: everything from your Facebook likes to where your phone goes 530 00:33:38,360 --> 00:33:43,920 Speaker 1: via GPS, plus what parts of your brain fire when 531 00:33:44,000 --> 00:33:48,520 Speaker 1: you see, you know, an xbox. And here's where it 532 00:33:48,560 --> 00:33:52,120 Speaker 1: gets strange. What if they get it wrong? You know 533 00:33:52,160 --> 00:33:55,720 Speaker 1: what happens if there's some faulty gear or maybe when 534 00:33:55,720 --> 00:33:59,040 Speaker 1: they read your data you're having an off day, then 535 00:33:59,080 --> 00:34:02,960 Speaker 1: you just spend your time doing what like constantly being 536 00:34:03,080 --> 00:34:06,479 Speaker 1: bombarded by you know, let's say you're hungover. When you 537 00:34:06,760 --> 00:34:10,200 Speaker 1: somehow get your data entered, then to what what happens? Then? 538 00:34:10,239 --> 00:34:12,839 Speaker 1: Do you just for the rest of your time get 539 00:34:12,880 --> 00:34:16,480 Speaker 1: bombarded by iyberproof and ads or whatever. Here here's the 540 00:34:16,480 --> 00:34:22,279 Speaker 1: messed up thing. It's probably I'm assuming that it's once 541 00:34:22,320 --> 00:34:25,879 Speaker 1: it comes into full swing and your television or your 542 00:34:26,040 --> 00:34:30,160 Speaker 1: Xbox or your PS four is monitoring you at all 543 00:34:30,200 --> 00:34:32,879 Speaker 1: times when you're sitting in front of your screen. It's 544 00:34:32,880 --> 00:34:36,400 Speaker 1: going to see that one day as possibly an anomaly 545 00:34:36,719 --> 00:34:38,960 Speaker 1: and blind. It will still take I'm assuming this is 546 00:34:39,000 --> 00:34:40,960 Speaker 1: messed up, but I'm I'm assuming it will still take 547 00:34:41,000 --> 00:34:45,040 Speaker 1: that into consideration. That this guy gets drunk maybe on 548 00:34:45,440 --> 00:34:50,040 Speaker 1: this certain day, depending yeah, maybe advil is something to 549 00:34:50,040 --> 00:34:54,240 Speaker 1: this person. Saturday night will advertise some sort of beer, 550 00:34:54,719 --> 00:34:59,880 Speaker 1: and Sunday morning will advertise So it's it's and it 551 00:35:00,080 --> 00:35:01,880 Speaker 1: you look out in the future, you can really see. 552 00:35:02,320 --> 00:35:05,120 Speaker 1: I don't know that dystopian view hits home with me. 553 00:35:05,440 --> 00:35:09,279 Speaker 1: Here's a here's another concern about neural marketing. UM. The 554 00:35:09,320 --> 00:35:14,000 Speaker 1: idea that this would be continual monitoring, which you do 555 00:35:14,120 --> 00:35:17,920 Speaker 1: bring up continual biometric monitoring, which could happen is ways 556 00:35:17,920 --> 00:35:19,759 Speaker 1: a way for now, but it will probably happen in 557 00:35:19,760 --> 00:35:24,280 Speaker 1: our lifetimes. It'll at least be possible for large groups 558 00:35:24,280 --> 00:35:28,759 Speaker 1: of people to be followed that way. But one other 559 00:35:29,040 --> 00:35:33,000 Speaker 1: question that I have here is is how predictive can 560 00:35:33,120 --> 00:35:36,040 Speaker 1: this data be? To what degree does it predict and 561 00:35:36,120 --> 00:35:42,760 Speaker 1: what if any uh legal rights or do do average 562 00:35:42,800 --> 00:35:46,080 Speaker 1: consumers have Because here's one of the worst things. If 563 00:35:46,120 --> 00:35:49,080 Speaker 1: we're going for the dystopian view, and that is that 564 00:35:49,880 --> 00:35:53,200 Speaker 1: with neural marketing. If somebody has found the silver bullet, 565 00:35:53,680 --> 00:35:57,360 Speaker 1: then what they're doing is taking advantage of people without 566 00:35:57,400 --> 00:36:01,919 Speaker 1: their knowledge and without their consent. Um. My next question, then, 567 00:36:02,040 --> 00:36:06,520 Speaker 1: going back to the predictive data idea, is you know, 568 00:36:07,239 --> 00:36:12,120 Speaker 1: how does this how does this coalesce with privacy or 569 00:36:12,160 --> 00:36:14,800 Speaker 1: with other things? Like there was a rumor for a 570 00:36:14,800 --> 00:36:17,759 Speaker 1: while in a in a worry that I think is 571 00:36:17,800 --> 00:36:22,360 Speaker 1: well founded when people were on Facebook said, you know 572 00:36:23,360 --> 00:36:28,120 Speaker 1: what happens if Facebook works with credit reporting agencies and 573 00:36:28,160 --> 00:36:30,799 Speaker 1: then gives me a score of some sort based on 574 00:36:30,920 --> 00:36:34,720 Speaker 1: not my credit score, but the aggregate of the people 575 00:36:34,760 --> 00:36:38,719 Speaker 1: who know me. And does that and and could that 576 00:36:39,200 --> 00:36:43,880 Speaker 1: make the banks in turn decide to give me a 577 00:36:43,920 --> 00:36:48,120 Speaker 1: lower or a higher interest rate? Could it affect my insurance? 578 00:36:48,760 --> 00:36:54,080 Speaker 1: That's interesting? So looking at the your closest associates to 579 00:36:54,200 --> 00:36:58,399 Speaker 1: see how to judge you building a network. You know. Oh, 580 00:36:59,080 --> 00:37:02,719 Speaker 1: when I first found out about it, I went through. Um, 581 00:37:02,800 --> 00:37:05,080 Speaker 1: when I saw that Facebook account, I went through and 582 00:37:05,600 --> 00:37:09,960 Speaker 1: liked a bunch of investment banks and you know, uh 583 00:37:10,160 --> 00:37:16,680 Speaker 1: wealth equity management stuff. Uh and high end cars. Oh yeah, sure, 584 00:37:17,400 --> 00:37:22,560 Speaker 1: changed all my movies over to the Wall Street movies. Uh. Yeah. Yeah, 585 00:37:22,600 --> 00:37:25,040 Speaker 1: that's a great idea and the only the only groups 586 00:37:25,040 --> 00:37:28,279 Speaker 1: I like our humanitarian right. We have one other thing 587 00:37:28,320 --> 00:37:30,239 Speaker 1: to do before we get out of here, uh, and 588 00:37:30,320 --> 00:37:35,640 Speaker 1: that is to issue a correction the resort we talked 589 00:37:35,680 --> 00:37:40,440 Speaker 1: about in our podcast on deep underground military bases. We 590 00:37:40,520 --> 00:37:43,759 Speaker 1: said it was in Virginia. That is incorrect. It is 591 00:37:43,800 --> 00:37:47,719 Speaker 1: in West Virginia. And no offense to the people of 592 00:37:47,800 --> 00:37:50,759 Speaker 1: Virginia or the people of West Virginia. And thank you 593 00:37:50,880 --> 00:37:53,440 Speaker 1: to the great folks who wrote in to let us 594 00:37:53,480 --> 00:37:55,600 Speaker 1: know about that correction. Yeah, if you ever hear anything 595 00:37:55,640 --> 00:37:58,239 Speaker 1: like that, um, that we got wrong, just let us know. 596 00:37:58,560 --> 00:38:00,680 Speaker 1: We're not perfect, but we tried to be. Yeah, and 597 00:38:00,680 --> 00:38:02,600 Speaker 1: I had a couple of shout outs that we're supposed 598 00:38:02,600 --> 00:38:05,040 Speaker 1: to give, but I'm going to hold those over for 599 00:38:05,080 --> 00:38:07,480 Speaker 1: the next day because I think we're pretty good on 600 00:38:07,520 --> 00:38:10,480 Speaker 1: our time here, Matt. So, so what we're going to 601 00:38:10,560 --> 00:38:13,160 Speaker 1: do is send it to you. Guys. What do you 602 00:38:13,200 --> 00:38:17,040 Speaker 1: think what's the solution to neural marketing? Is it okay 603 00:38:17,120 --> 00:38:19,279 Speaker 1: or you're all right with it? Do you want to 604 00:38:19,320 --> 00:38:22,560 Speaker 1: have your advertising more targeted towards you? Is that a 605 00:38:22,560 --> 00:38:25,399 Speaker 1: good thing? Or should we all just turn off our 606 00:38:25,440 --> 00:38:28,400 Speaker 1: TVs and get rid of the Internet and go outside 607 00:38:28,400 --> 00:38:31,400 Speaker 1: and plant an orange tree. I I don't know. I 608 00:38:31,440 --> 00:38:33,480 Speaker 1: don't have the answers, but I want to hear what 609 00:38:33,560 --> 00:38:36,680 Speaker 1: you think, so write to us on Facebook we are 610 00:38:36,960 --> 00:38:40,080 Speaker 1: conspiracy stuff. On Twitter, we are at conspiracy stuff you 611 00:38:40,120 --> 00:38:43,120 Speaker 1: can find And that's the end of this classic episode. 612 00:38:43,200 --> 00:38:47,040 Speaker 1: If you have any thoughts or questions about this episode, 613 00:38:47,400 --> 00:38:49,479 Speaker 1: you can get into contact with us in a number 614 00:38:49,480 --> 00:38:51,480 Speaker 1: of different ways. One of the best is to give 615 00:38:51,560 --> 00:38:54,120 Speaker 1: us a call. 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