1 00:00:04,240 --> 00:00:07,240 Speaker 1: Welcome to tex Stuff, a production of I Heart Radios, 2 00:00:07,320 --> 00:00:13,920 Speaker 1: How Stuff Works. Hey there, and welcome to tex Stuff. 3 00:00:13,960 --> 00:00:17,119 Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with 4 00:00:17,120 --> 00:00:18,880 Speaker 1: How Stuff Works and I Heart Radio and I love 5 00:00:18,920 --> 00:00:22,720 Speaker 1: all things tech and today we're having another classic episode 6 00:00:22,720 --> 00:00:26,239 Speaker 1: of tech Stuff. This episode originally published on October twenty four, 7 00:00:26,480 --> 00:00:29,920 Speaker 1: two thousand and twelve. It's called tech Stuff has Clout 8 00:00:30,320 --> 00:00:34,440 Speaker 1: and spoiler alert, cloud is no longer a thing, but 9 00:00:34,479 --> 00:00:38,080 Speaker 1: when we recorded this episode, it was a thing. Clout, 10 00:00:38,360 --> 00:00:42,479 Speaker 1: as you will learn, was all about social media influence 11 00:00:42,600 --> 00:00:46,159 Speaker 1: and reach, and depending on your point of view, it 12 00:00:46,280 --> 00:00:49,440 Speaker 1: was either an incredibly useful tool, a tool to turn 13 00:00:49,520 --> 00:00:53,960 Speaker 1: people into narcissists, or something even more sinister than that. 14 00:00:55,080 --> 00:00:57,360 Speaker 1: But spoiler alert again, it's all moot now because it's 15 00:00:57,360 --> 00:01:01,160 Speaker 1: no longer a thing. Anyway, enjoy this lastic episode as 16 00:01:01,200 --> 00:01:04,760 Speaker 1: Chris and I tout our cloud. So today we wanted 17 00:01:04,800 --> 00:01:08,400 Speaker 1: to talk about clout and whether whether or not you 18 00:01:08,440 --> 00:01:12,160 Speaker 1: have any uh and maybe you personally. You know that 19 00:01:12,240 --> 00:01:14,840 Speaker 1: guy over there, he's got no cloud. Apparently I apparently 20 00:01:14,840 --> 00:01:19,640 Speaker 1: I have some. Um the it's a not insignificant amount, 21 00:01:19,760 --> 00:01:22,520 Speaker 1: though not nearly as much. As you know, like Justin Bieber. 22 00:01:23,080 --> 00:01:26,520 Speaker 1: Uh so, what is Cloud? So? Cloud is this company 23 00:01:26,640 --> 00:01:30,120 Speaker 1: out of San Francisco that was founded by Joe Fernandez 24 00:01:30,959 --> 00:01:33,320 Speaker 1: back in two thousand and eight. He started to build 25 00:01:33,319 --> 00:01:37,160 Speaker 1: it when he couldn't talk. Did you know about that? 26 00:01:38,640 --> 00:01:41,280 Speaker 1: There's an article and Wired and I actually revisited it. 27 00:01:41,280 --> 00:01:43,880 Speaker 1: It's it's called what Your Cloud Score Really Means. It's 28 00:01:43,920 --> 00:01:46,959 Speaker 1: from Seth Stevenson. Actually read it in the print edition, 29 00:01:46,959 --> 00:01:48,720 Speaker 1: but I went back and revisited it so I could 30 00:01:48,720 --> 00:01:53,920 Speaker 1: throw in my electronic notes. Yeah. He uh, this is 31 00:01:53,960 --> 00:01:57,960 Speaker 1: really really funny. He um had had had to have 32 00:01:58,040 --> 00:02:01,160 Speaker 1: his jaw wired shut. That that's not funny. I don't 33 00:02:01,200 --> 00:02:06,440 Speaker 1: think that's funny. But because he couldn't talk, he started, Uh, 34 00:02:06,560 --> 00:02:09,960 Speaker 1: he started tweeting and using Facebook to communicate with people 35 00:02:10,000 --> 00:02:12,160 Speaker 1: because it was driving him nuts that he couldn't talk 36 00:02:12,200 --> 00:02:14,799 Speaker 1: to people, he said, And in fact he said he 37 00:02:14,840 --> 00:02:20,280 Speaker 1: couldn't his mom didn't even understand what he was Yeah. Really, 38 00:02:21,080 --> 00:02:24,680 Speaker 1: uh so, I mean, thankfully he's apparently all better now. 39 00:02:24,720 --> 00:02:26,720 Speaker 1: But I've seen I've seen interviews with him where he 40 00:02:26,760 --> 00:02:29,720 Speaker 1: was not wired shut and he was talking. Yes, so 41 00:02:29,760 --> 00:02:34,040 Speaker 1: he actually started uh, he actually started talking about or tweeting, 42 00:02:34,120 --> 00:02:38,040 Speaker 1: tweeting about this, all sorts of different things about um 43 00:02:38,440 --> 00:02:42,000 Speaker 1: restaurants that he had been to and making recommendations, and 44 00:02:42,040 --> 00:02:44,560 Speaker 1: you know, again kind of iron as they point out 45 00:02:44,560 --> 00:02:47,520 Speaker 1: the art kind of ironic. He couldn't open his mouth 46 00:02:47,560 --> 00:02:51,880 Speaker 1: and go eat there, but basically product recommendations. He started thinking, 47 00:02:51,880 --> 00:02:54,600 Speaker 1: because he had had some experience with with statistics, and 48 00:02:54,639 --> 00:02:58,000 Speaker 1: started thinking, you know, how many people am I talking to? 49 00:02:58,120 --> 00:03:01,359 Speaker 1: And and to who are they talking and how much 50 00:03:01,400 --> 00:03:05,840 Speaker 1: influence do they have over people who buy stuff or 51 00:03:06,160 --> 00:03:09,919 Speaker 1: go shopping or go out to eat with these different people. 52 00:03:10,120 --> 00:03:14,119 Speaker 1: And that's when he started. He started making some rudimentary calculations, 53 00:03:14,200 --> 00:03:18,480 Speaker 1: rudimentary by the terms of clouds algorithms today, and started 54 00:03:18,520 --> 00:03:21,720 Speaker 1: putting together this idea of you know, how much influence 55 00:03:22,000 --> 00:03:27,400 Speaker 1: do people have in the social media? So that's this 56 00:03:27,480 --> 00:03:31,880 Speaker 1: is really an interesting concept to me because some people 57 00:03:31,880 --> 00:03:36,560 Speaker 1: would have you believe that influence can be measured solely 58 00:03:36,600 --> 00:03:39,440 Speaker 1: by things like the number of followers you happen to 59 00:03:39,520 --> 00:03:43,960 Speaker 1: have on social networks. But Fernandez's point is that's not 60 00:03:44,080 --> 00:03:48,040 Speaker 1: necessarily the case. You could have lots of people following you, 61 00:03:48,120 --> 00:03:50,000 Speaker 1: but if no one's really paying attention to what you 62 00:03:50,080 --> 00:03:53,280 Speaker 1: have to say, and they're following you anyway, then your 63 00:03:53,320 --> 00:03:56,000 Speaker 1: influence isn't that great. You know, there's a lot of 64 00:03:56,040 --> 00:03:58,360 Speaker 1: people who could potentially hear you, but no one's really 65 00:03:58,400 --> 00:04:01,200 Speaker 1: listening to you, and that's a problem problem. So you 66 00:04:01,200 --> 00:04:05,800 Speaker 1: could you could conversely have relatively few followers compared to 67 00:04:05,840 --> 00:04:08,680 Speaker 1: someone who has millions, like you know, Justin Bieber's got 68 00:04:08,960 --> 00:04:11,840 Speaker 1: millions of followers on Twitter. Now he happens to be 69 00:04:11,960 --> 00:04:15,360 Speaker 1: very influential on Twitter. So that's I've heard that. It's 70 00:04:15,400 --> 00:04:17,200 Speaker 1: kind of a bad example. But let's say let's say 71 00:04:17,200 --> 00:04:19,520 Speaker 1: you've got someone who's got over example. Let's say the 72 00:04:19,560 --> 00:04:21,640 Speaker 1: same You've got someone who's got over a million followers, 73 00:04:21,800 --> 00:04:24,039 Speaker 1: but for some reason, their cloud score is not that high. 74 00:04:24,360 --> 00:04:26,600 Speaker 1: The reason for that might be that perhaps a lot 75 00:04:26,600 --> 00:04:29,679 Speaker 1: of those followers aren't real people. They might be boughts, 76 00:04:30,279 --> 00:04:35,120 Speaker 1: So that could be one factor. But what Fernandez wanted 77 00:04:35,120 --> 00:04:38,960 Speaker 1: to do was measure how much impact a person has 78 00:04:39,040 --> 00:04:43,040 Speaker 1: on their various social circles and on the web in general. Uh, 79 00:04:43,200 --> 00:04:45,839 Speaker 1: not just how many people are following them and those 80 00:04:45,839 --> 00:04:49,120 Speaker 1: are those are two different things. So he built this, 81 00:04:49,320 --> 00:04:54,039 Speaker 1: uh this company and this algorithm helped to Yeah, the 82 00:04:54,400 --> 00:04:57,680 Speaker 1: His side, of course, is called cloud with a k UM, 83 00:04:57,920 --> 00:05:02,440 Speaker 1: and since its inception there have been other imitators. I 84 00:05:02,480 --> 00:05:04,960 Speaker 1: don't know some of them were actually out beforehand. And 85 00:05:05,000 --> 00:05:09,279 Speaker 1: Twitter itself is working on its own implementation of a 86 00:05:09,320 --> 00:05:12,120 Speaker 1: similar software that would allow you to see, like when 87 00:05:12,160 --> 00:05:14,640 Speaker 1: you tweet, how how much of your audience are you 88 00:05:14,720 --> 00:05:17,440 Speaker 1: actually reaching when you send out a Twitter message. And 89 00:05:17,520 --> 00:05:19,840 Speaker 1: this really applies to people who have lots and lots 90 00:05:19,839 --> 00:05:23,120 Speaker 1: of followers. So for example, if you take me, I 91 00:05:23,160 --> 00:05:26,920 Speaker 1: don't have you know, thousands and thousands of I've got 92 00:05:26,960 --> 00:05:29,560 Speaker 1: like four thousand followers, which that's that's a that's a 93 00:05:29,600 --> 00:05:31,720 Speaker 1: lot for me because I'm like, well, I'm just a guy, 94 00:05:31,839 --> 00:05:34,320 Speaker 1: you know, I'm a guy that four thousand people follow 95 00:05:34,400 --> 00:05:37,400 Speaker 1: I hope that I'm interesting enough to justify that. But 96 00:05:37,440 --> 00:05:40,120 Speaker 1: there are other people out there who have millions of followers, 97 00:05:40,160 --> 00:05:42,760 Speaker 1: but they don't know necessarily how many of those people 98 00:05:42,760 --> 00:05:45,240 Speaker 1: they're reaching when they send out a message. Yeah, just 99 00:05:45,240 --> 00:05:48,280 Speaker 1: just for independent confirmation, I am sitting here directly across 100 00:05:48,279 --> 00:05:53,600 Speaker 1: from from Jonathan and he is in fact just a guy. Yes, um, 101 00:05:53,640 --> 00:05:55,480 Speaker 1: I'm just a guy with a sixty five cloud score 102 00:05:55,520 --> 00:05:58,400 Speaker 1: though that's true. And then and part of the reason 103 00:05:58,480 --> 00:06:00,960 Speaker 1: that that Jonathan does have a five Cloud score, and 104 00:06:00,960 --> 00:06:03,760 Speaker 1: that's of the day we're recording this. These things do change. 105 00:06:03,800 --> 00:06:05,480 Speaker 1: It could be, it could be it could be in 106 00:06:05,480 --> 00:06:07,599 Speaker 1: the low forties. By the fame of this Podcas goes out, 107 00:06:08,200 --> 00:06:11,400 Speaker 1: part of the reason that that Jonathan has a cloud 108 00:06:11,440 --> 00:06:13,880 Speaker 1: score of sixty five is because there are a lot 109 00:06:13,880 --> 00:06:17,400 Speaker 1: of people following him. But he's also well networked with 110 00:06:17,520 --> 00:06:20,599 Speaker 1: people also in the tech world. So there are people 111 00:06:20,640 --> 00:06:25,359 Speaker 1: who share his tweets with other people. That's part of 112 00:06:25,360 --> 00:06:28,040 Speaker 1: the score. There there are people who who talk back 113 00:06:28,080 --> 00:06:30,640 Speaker 1: to him who also, boy do they talk back to me? 114 00:06:31,640 --> 00:06:35,080 Speaker 1: Shut up? You? Uh see what I did there? Yes? 115 00:06:35,160 --> 00:06:37,720 Speaker 1: I do. Who There are people who have conversations with 116 00:06:37,800 --> 00:06:40,360 Speaker 1: him on Twitter, let's go with that, who are also 117 00:06:40,440 --> 00:06:45,920 Speaker 1: influential in the Twitter sphere and on cloud and therefore, uh, 118 00:06:46,120 --> 00:06:51,600 Speaker 1: that boosts his reputation um and his his ability to 119 00:06:51,839 --> 00:06:54,600 Speaker 1: to network with other people, and so therefore he has 120 00:06:54,839 --> 00:06:57,400 Speaker 1: a higher score than than maybe some other people with 121 00:06:57,440 --> 00:06:59,960 Speaker 1: more followers. This might sound really familiar to you, especially 122 00:07:00,000 --> 00:07:03,320 Speaker 1: if you are familiar with like Google's page rank algorithms 123 00:07:03,480 --> 00:07:05,719 Speaker 1: what used to be called page rank. Anyway, Uh, the 124 00:07:05,800 --> 00:07:09,960 Speaker 1: idea that Google would use this complex formula to determine 125 00:07:10,280 --> 00:07:15,000 Speaker 1: how well various sites would rank and search for any 126 00:07:15,040 --> 00:07:19,400 Speaker 1: particular term that you're searching for. So when you're searching for, 127 00:07:20,200 --> 00:07:24,480 Speaker 1: I don't know, uh, cool tech gifts for the holidays, 128 00:07:24,880 --> 00:07:28,280 Speaker 1: then Google would look through its use its algorithm and 129 00:07:28,360 --> 00:07:31,200 Speaker 1: rank all these pages that relate to those search terms 130 00:07:31,520 --> 00:07:34,560 Speaker 1: and give you the highest ranked ones. And that ranking 131 00:07:34,640 --> 00:07:37,160 Speaker 1: is based on lots of different factors, including how many 132 00:07:37,160 --> 00:07:42,560 Speaker 1: websites link into it, and how old that particular website 133 00:07:42,560 --> 00:07:45,120 Speaker 1: that you're looking at is, and and other factors as well. 134 00:07:45,160 --> 00:07:47,360 Speaker 1: There's a lot of stuff that goes into it. This 135 00:07:47,440 --> 00:07:50,280 Speaker 1: is the same idea except for people instead of pages. 136 00:07:50,320 --> 00:07:53,360 Speaker 1: It's it's how well a person ranks, partly due to 137 00:07:53,400 --> 00:07:56,760 Speaker 1: their behavior online, partly due to their network that's around them, 138 00:07:56,800 --> 00:07:59,080 Speaker 1: partly due to how many people are following them. There 139 00:07:59,120 --> 00:08:01,559 Speaker 1: are a lot of different actors at play. There's actually 140 00:08:01,600 --> 00:08:04,440 Speaker 1: a web page on cloud site that goes into the 141 00:08:04,480 --> 00:08:07,160 Speaker 1: different factors that that they will admit to. Now, they 142 00:08:07,160 --> 00:08:11,120 Speaker 1: don't give you the nuts and bolts. It's a secret 143 00:08:11,160 --> 00:08:13,520 Speaker 1: sauce if you will. Clearly, you wouldn't want to be 144 00:08:13,600 --> 00:08:16,400 Speaker 1: able to give out all the information, because then people 145 00:08:16,440 --> 00:08:18,800 Speaker 1: would game the system like crazy just to get their 146 00:08:18,840 --> 00:08:21,480 Speaker 1: cloud score up, which is not the purpose of the 147 00:08:21,760 --> 00:08:24,080 Speaker 1: of the site at all, or to to start a 148 00:08:24,080 --> 00:08:28,240 Speaker 1: competitor to another good point. Um, yeah, let's let's talk. 149 00:08:28,640 --> 00:08:32,160 Speaker 1: I'll mention a little bit more about what what they say, Uh, 150 00:08:32,280 --> 00:08:37,360 Speaker 1: competitors are critics, I should say, actually criticize cloud because 151 00:08:37,400 --> 00:08:41,040 Speaker 1: it's usually the comparison I've seen is to a credit score, 152 00:08:41,679 --> 00:08:47,440 Speaker 1: because the credit reporting agencies don't tell consumers what's actually 153 00:08:47,480 --> 00:08:50,920 Speaker 1: going into your score. Um. And in the in the 154 00:08:50,960 --> 00:08:53,120 Speaker 1: case of credit, it has to do with you know 155 00:08:53,440 --> 00:08:57,719 Speaker 1: how much you spend, how quickly you pay it back? Um, 156 00:08:57,800 --> 00:08:59,800 Speaker 1: you know how many cards credit cards you have? Are 157 00:09:00,000 --> 00:09:03,480 Speaker 1: it accounts, lines of credit that you have opened, and 158 00:09:03,600 --> 00:09:06,200 Speaker 1: lots of different little proprietary things, and each of them 159 00:09:06,240 --> 00:09:09,120 Speaker 1: has a little different mix. Well, the cloud is sort 160 00:09:09,120 --> 00:09:11,480 Speaker 1: of the same way. They tell you roughly what goes 161 00:09:11,520 --> 00:09:13,319 Speaker 1: into it, but they don't tell you exactly how much 162 00:09:13,320 --> 00:09:16,640 Speaker 1: weight each one carries. Um. It's based primarily on on 163 00:09:16,679 --> 00:09:19,480 Speaker 1: Twitter and Facebook. Um, this is not really much of 164 00:09:19,480 --> 00:09:25,400 Speaker 1: a secret. Things like mentions of your name and Facebook posts. Um. 165 00:09:25,600 --> 00:09:30,000 Speaker 1: People who who are engaged with a lot of brands 166 00:09:30,120 --> 00:09:32,280 Speaker 1: on there, the likes that you have, the TV shows 167 00:09:32,280 --> 00:09:36,120 Speaker 1: you like, UM, the the UH stores that you like 168 00:09:36,320 --> 00:09:39,960 Speaker 1: on Facebook, UM, the number of comments you have, people 169 00:09:39,960 --> 00:09:43,960 Speaker 1: who subscribe to you, uh, the number of friends. Obviously 170 00:09:44,000 --> 00:09:46,120 Speaker 1: that's a metric and obvious metric. You know, how many 171 00:09:46,160 --> 00:09:48,160 Speaker 1: people follow you on Twitter, how many people follow you 172 00:09:48,200 --> 00:09:51,439 Speaker 1: on Facebook, people who mention you on on Twitter, or 173 00:09:51,480 --> 00:09:55,280 Speaker 1: people who um people you reply to and people who 174 00:09:55,320 --> 00:09:59,080 Speaker 1: reply to you shows your engagement with that network. They've 175 00:09:59,120 --> 00:10:05,600 Speaker 1: also extended it you to Google Plus, LinkedIn, four Square, Wikipedia, UM, 176 00:10:05,640 --> 00:10:09,920 Speaker 1: things on on LinkedIn. UM they go by your reported title. 177 00:10:10,160 --> 00:10:12,440 Speaker 1: So if you say you're CEO of the World then 178 00:10:12,480 --> 00:10:15,160 Speaker 1: and guess that that would probably help your cloud score. Yeah. So, 179 00:10:15,400 --> 00:10:18,360 Speaker 1: although I think that's probably hard to verify. These are 180 00:10:18,400 --> 00:10:22,920 Speaker 1: relatively new in the in the cloud world. These these 181 00:10:23,000 --> 00:10:26,400 Speaker 1: UM these items, because before what it was really looking 182 00:10:26,440 --> 00:10:29,040 Speaker 1: at was Twitter and Facebook mainly, First it was Twitter, 183 00:10:29,080 --> 00:10:31,280 Speaker 1: and then it was there in Facebook then and it 184 00:10:31,400 --> 00:10:35,480 Speaker 1: also linked You could choose to link stuff to your 185 00:10:35,640 --> 00:10:38,760 Speaker 1: your cloud. If you create a cloud account, you could 186 00:10:38,840 --> 00:10:42,440 Speaker 1: choose to link things like Google Plus, Facebook, Twitter and 187 00:10:42,480 --> 00:10:45,280 Speaker 1: other platforms as well. Now it looks at these other 188 00:10:45,440 --> 00:10:48,360 Speaker 1: elements like Wikipedia, or if you are mentioned in something 189 00:10:48,400 --> 00:10:51,960 Speaker 1: like the New York Times, so that it can determine 190 00:10:52,160 --> 00:10:56,800 Speaker 1: all right, well, um, you know you're getting press, people 191 00:10:56,800 --> 00:10:59,319 Speaker 1: are writing about you, so then that means you are 192 00:10:59,360 --> 00:11:01,760 Speaker 1: more in Floyd Childen someone who is not getting pressed, 193 00:11:01,800 --> 00:11:04,959 Speaker 1: So that would impact your score as well. Yeah, if 194 00:11:04,960 --> 00:11:08,000 Speaker 1: you want to sign up for Cloud, you would sign 195 00:11:08,080 --> 00:11:11,520 Speaker 1: up using your Twitter or Facebook information. Basically, once you're 196 00:11:11,520 --> 00:11:14,360 Speaker 1: logged in, you can you can uh sign up for 197 00:11:14,400 --> 00:11:17,319 Speaker 1: Cloud and it will access your account um and it 198 00:11:17,360 --> 00:11:20,520 Speaker 1: will say, okay, do you want to add your other accounts? 199 00:11:20,520 --> 00:11:21,880 Speaker 1: Do you want to add these other accounts? And they're 200 00:11:21,880 --> 00:11:24,280 Speaker 1: even more than they're mentioned here, things like Flicker and 201 00:11:24,640 --> 00:11:27,680 Speaker 1: other accounts and things um and so yeah, what what 202 00:11:27,760 --> 00:11:31,239 Speaker 1: Jonathan said is is true. They're also measuring this information 203 00:11:31,400 --> 00:11:34,280 Speaker 1: whether or not you sign up for Cloud. Yeah, because 204 00:11:34,320 --> 00:11:37,040 Speaker 1: you're you're if you have a Twitter account and it's 205 00:11:37,080 --> 00:11:41,160 Speaker 1: public and you are sending out messages, that's public record. 206 00:11:41,320 --> 00:11:44,360 Speaker 1: That's stuff that anyone can access. So what Cloud does 207 00:11:44,480 --> 00:11:48,000 Speaker 1: is it will automatically start to index you and look 208 00:11:48,080 --> 00:11:51,320 Speaker 1: at how other people are responding to you. So whether 209 00:11:51,400 --> 00:11:54,000 Speaker 1: you have signed up for Cloud or not, you may 210 00:11:54,040 --> 00:11:58,400 Speaker 1: have a Cloud score and uh, you know the we 211 00:11:58,840 --> 00:12:01,880 Speaker 1: have we mentioned what the range of scores is. It's 212 00:12:01,960 --> 00:12:05,720 Speaker 1: between one and one hundred. So the higher your cloud score, 213 00:12:05,800 --> 00:12:08,040 Speaker 1: the more the more cloud you have, and the more 214 00:12:08,120 --> 00:12:13,520 Speaker 1: influence you would theoretically have over the web based upon uh, 215 00:12:13,720 --> 00:12:19,080 Speaker 1: you know, the algorithms assessment of you. Most people are 216 00:12:19,200 --> 00:12:21,520 Speaker 1: not like you would think the average would be fifty, 217 00:12:21,520 --> 00:12:24,600 Speaker 1: but the average is really closer to the twenties range. 218 00:12:24,960 --> 00:12:28,600 Speaker 1: And it's that the people who are fifty or higher 219 00:12:28,640 --> 00:12:34,480 Speaker 1: are are assumed to be more influential in whatever fields 220 00:12:34,520 --> 00:12:38,520 Speaker 1: they've been identified as being experts in. Yeah, I mean, 221 00:12:38,840 --> 00:12:41,679 Speaker 1: I'm sorry. I was gonna say, if you sign up, 222 00:12:41,720 --> 00:12:43,840 Speaker 1: you'll find out that they ask you, hey, what are 223 00:12:43,880 --> 00:12:46,040 Speaker 1: you influential in? Yeah, And it turns out like if 224 00:12:46,040 --> 00:12:47,920 Speaker 1: I log into my cloud and I'd look to see 225 00:12:47,960 --> 00:12:50,319 Speaker 1: what I'm influential in. There's some things you would expect, 226 00:12:50,320 --> 00:12:53,200 Speaker 1: you know, like technology is up there, which that that 227 00:12:53,280 --> 00:12:56,400 Speaker 1: makes sense. I do a tech podcast, I write tech 228 00:12:56,520 --> 00:12:59,760 Speaker 1: articles for a website. Uh, that makes sense. There are 229 00:12:59,760 --> 00:13:03,400 Speaker 1: other things like Atlanta restaurants. Huh, I didn't know that 230 00:13:03,440 --> 00:13:06,480 Speaker 1: I was that influential with Atlanta restaurants. I should get 231 00:13:06,480 --> 00:13:09,920 Speaker 1: better tables darn it. Um. But yeah, there there are 232 00:13:09,960 --> 00:13:12,000 Speaker 1: other things that I look at on that people have 233 00:13:12,080 --> 00:13:15,520 Speaker 1: identified me as being influential, and some of them maybe jokes, 234 00:13:17,320 --> 00:13:19,800 Speaker 1: but that's that's kind of interesting to me. Yeah, And 235 00:13:19,840 --> 00:13:22,640 Speaker 1: I think I think here is a good point to 236 00:13:22,760 --> 00:13:25,680 Speaker 1: point out. And then there are critics and their proponents 237 00:13:25,679 --> 00:13:28,840 Speaker 1: of cloud, just like anything else. But um, I think 238 00:13:28,920 --> 00:13:30,800 Speaker 1: it's a good point to go right down the middle 239 00:13:30,840 --> 00:13:34,400 Speaker 1: and say, look, this is what one person and his 240 00:13:34,520 --> 00:13:37,560 Speaker 1: company have done, you know, as as as Joe built 241 00:13:37,600 --> 00:13:41,040 Speaker 1: this company up, this is how they chose to measure people. 242 00:13:41,480 --> 00:13:45,400 Speaker 1: And it's not it's it's based on one system, so 243 00:13:45,440 --> 00:13:47,920 Speaker 1: it's not really I think, Uh, it was a couple 244 00:13:47,920 --> 00:13:49,520 Speaker 1: of years ago, or was a couple of years ago 245 00:13:49,720 --> 00:13:53,080 Speaker 1: last year where they where they rejiggered the algorithm, and 246 00:13:53,440 --> 00:13:55,679 Speaker 1: was this year? It was this year. It was late 247 00:13:56,920 --> 00:14:01,160 Speaker 1: some earlier this year when they changed it because my score, 248 00:14:01,600 --> 00:14:08,280 Speaker 1: my score skyrocketed. I had been around sixty two and uh, 249 00:14:08,600 --> 00:14:11,240 Speaker 1: and then I jumped all the way up to sixty nine. 250 00:14:11,360 --> 00:14:14,079 Speaker 1: I was almost seventy. It was very close to seventy, 251 00:14:14,080 --> 00:14:17,480 Speaker 1: and I was thinking, whoa, how did that happen? Holy cow? 252 00:14:17,520 --> 00:14:20,040 Speaker 1: I'm more and I started comparing myself because it cars. 253 00:14:20,120 --> 00:14:23,880 Speaker 1: You know, I'm a bit of an egomaniac, you know, 254 00:14:24,040 --> 00:14:26,120 Speaker 1: I admit this, and so I was like, let me 255 00:14:26,160 --> 00:14:28,960 Speaker 1: see what my good friend I as actar of this 256 00:14:29,000 --> 00:14:31,400 Speaker 1: weekend tech Let me on tech news today. Let me 257 00:14:31,400 --> 00:14:35,400 Speaker 1: see what how he ranks. Oh, he's he's at sixty five. 258 00:14:35,520 --> 00:14:38,960 Speaker 1: He's lower than I am. Gosh I as and of 259 00:14:38,960 --> 00:14:41,960 Speaker 1: course now I think he I think he ranks rightfully 260 00:14:42,040 --> 00:14:45,960 Speaker 1: so above me, because these things, do you know? It 261 00:14:46,040 --> 00:14:48,400 Speaker 1: was it was like an initial shock because people's scores 262 00:14:48,480 --> 00:14:50,880 Speaker 1: changed pretty dramatically. Some went up quite a bit, some 263 00:14:50,920 --> 00:14:56,200 Speaker 1: went down, and then it's been slowly kind of readjusting 264 00:14:56,240 --> 00:14:58,120 Speaker 1: to where it needs to be based on all this 265 00:14:58,200 --> 00:15:01,760 Speaker 1: new information. Um, and you know this, this could be 266 00:15:01,760 --> 00:15:04,360 Speaker 1: really challenging because you're building an algorithm that needs to 267 00:15:04,400 --> 00:15:07,800 Speaker 1: identify people, and it needs to be able to make 268 00:15:07,800 --> 00:15:09,880 Speaker 1: sure it links to the right person, because, as it 269 00:15:09,880 --> 00:15:13,320 Speaker 1: turns out, there's some folks out there who share the 270 00:15:13,360 --> 00:15:16,560 Speaker 1: same name as other people, and so you have a 271 00:15:16,600 --> 00:15:18,840 Speaker 1: lot of false positives. For instance, if you were to 272 00:15:18,880 --> 00:15:22,160 Speaker 1: search Jonathan Strickland in the metro Atlanta area, Uh, some 273 00:15:22,240 --> 00:15:24,160 Speaker 1: mug shots to be popping up and none of them 274 00:15:24,200 --> 00:15:29,040 Speaker 1: are me today today. Okay, but let's be fair. Today, 275 00:15:29,080 --> 00:15:31,320 Speaker 1: here's the recording of this podcast of those mug shots 276 00:15:31,320 --> 00:15:35,680 Speaker 1: would be me. Who knows what tomorrow may brain. But 277 00:15:35,680 --> 00:15:37,200 Speaker 1: but that's the thing is that I could end up 278 00:15:37,240 --> 00:15:39,120 Speaker 1: identifying this stuff like, oh, John, this is being written 279 00:15:39,120 --> 00:15:43,720 Speaker 1: about in criminal newsletters. That's odd. But I guess that 280 00:15:43,800 --> 00:15:46,480 Speaker 1: ad just his cloud score, Like, no, that's not me. 281 00:15:46,680 --> 00:15:50,040 Speaker 1: That's another Jonathan Strickland and the same spelling and everything. 282 00:15:50,120 --> 00:15:52,400 Speaker 1: But you know, so the algorithm has to take that 283 00:15:52,400 --> 00:15:55,440 Speaker 1: stuff into account. The other thing that's kind of a 284 00:15:55,480 --> 00:15:59,840 Speaker 1: controversy with clout is how companies are using clout in 285 00:16:00,040 --> 00:16:04,200 Speaker 1: various ways. Guys, it's Jonathan from two thousand nineteena interrupting 286 00:16:04,200 --> 00:16:07,760 Speaker 1: this classical episode because I don't yet have quite enough 287 00:16:07,800 --> 00:16:10,640 Speaker 1: clout to get away with this without taking a break 288 00:16:10,760 --> 00:16:21,920 Speaker 1: to thank our sponsor. So what is cloud other than 289 00:16:21,920 --> 00:16:25,480 Speaker 1: other than influence? A measure of influence in the social 290 00:16:25,520 --> 00:16:28,960 Speaker 1: media world. An idea of of that, what does cloud 291 00:16:29,040 --> 00:16:33,240 Speaker 1: get you? Very little or or al or a lot, 292 00:16:33,640 --> 00:16:37,600 Speaker 1: depending on how high your cloud already is. Um, yeah, 293 00:16:37,720 --> 00:16:40,960 Speaker 1: it's cloud is a weird thing. In some cases, all 294 00:16:41,000 --> 00:16:43,240 Speaker 1: it might get you is bragging rights in the sense 295 00:16:43,280 --> 00:16:46,200 Speaker 1: of you know you, you measure your cloud against your buddy, 296 00:16:46,280 --> 00:16:49,640 Speaker 1: like your buddy i azactar of this weekend tech news today, 297 00:16:50,000 --> 00:16:52,440 Speaker 1: and then you say, hey, look at that, or you 298 00:16:52,520 --> 00:16:55,600 Speaker 1: may say cloud scores going up because you mentioned in 299 00:16:56,040 --> 00:16:59,240 Speaker 1: very well and deservedly. So the man does good work. 300 00:17:00,240 --> 00:17:02,760 Speaker 1: As much as I joke. He he is an actual 301 00:17:02,800 --> 00:17:06,040 Speaker 1: friend of mine. Uh so, maybe not after this podcast, 302 00:17:06,359 --> 00:17:10,919 Speaker 1: but he. You know, the these these numbers for a 303 00:17:11,000 --> 00:17:13,280 Speaker 1: large part of the population may not mean much, although 304 00:17:13,320 --> 00:17:17,840 Speaker 1: it can tell you how effective you are online. So 305 00:17:17,880 --> 00:17:20,520 Speaker 1: in a in a sense, it's doing what it's supposed 306 00:17:20,520 --> 00:17:22,359 Speaker 1: to do. It's telling you what your impact is. And 307 00:17:22,400 --> 00:17:25,520 Speaker 1: if your impact is low, you might say, you know what, 308 00:17:26,280 --> 00:17:27,960 Speaker 1: you might be cool with it. You might be saying, oh, 309 00:17:28,040 --> 00:17:30,000 Speaker 1: you know, I just use this for fun, to stay 310 00:17:30,000 --> 00:17:31,639 Speaker 1: in touch with my friends. That's all I use it 311 00:17:31,680 --> 00:17:34,040 Speaker 1: for that. I don't care. But if you're trying to 312 00:17:34,040 --> 00:17:37,120 Speaker 1: get a message out there, then what it's telling you 313 00:17:37,240 --> 00:17:40,720 Speaker 1: is your message is not being heard. It's not it's 314 00:17:40,720 --> 00:17:44,640 Speaker 1: not punishing you. This number is related back to that algorithm, 315 00:17:44,920 --> 00:17:46,680 Speaker 1: and if you want to make sure your message is 316 00:17:46,720 --> 00:17:49,320 Speaker 1: heard by more people, you need to look into ways 317 00:17:49,600 --> 00:17:52,480 Speaker 1: of delivering that message in in a in a way 318 00:17:52,480 --> 00:17:55,359 Speaker 1: that that interests people, that gets other people talking, that 319 00:17:55,880 --> 00:17:59,040 Speaker 1: goes beyond just your immediate circle of followers. And if 320 00:17:59,080 --> 00:18:01,280 Speaker 1: you're able to crack that code. And by the way, 321 00:18:01,400 --> 00:18:04,000 Speaker 1: I do not have the secret sauce for that. There 322 00:18:04,000 --> 00:18:06,600 Speaker 1: are some messages I send out there that go bonkers 323 00:18:06,880 --> 00:18:09,000 Speaker 1: and I think, Wow, that's amazing, and then I and 324 00:18:09,040 --> 00:18:11,320 Speaker 1: then I'll sit there and I'll craft what I think 325 00:18:11,359 --> 00:18:16,480 Speaker 1: is the most brilliant, funny, uh timely tweet of all time, 326 00:18:16,480 --> 00:18:18,840 Speaker 1: and I send it out and no one replies, no 327 00:18:18,880 --> 00:18:22,280 Speaker 1: one favorites, no one retweets. So I mean, I don't 328 00:18:22,280 --> 00:18:24,320 Speaker 1: have that secret sauce. Definitely you need to ask people 329 00:18:24,359 --> 00:18:26,120 Speaker 1: who have a much higher cloth score than I do. 330 00:18:26,440 --> 00:18:28,600 Speaker 1: But if your score is low, that could be an 331 00:18:28,600 --> 00:18:31,000 Speaker 1: indicator that, hey, you need to change things so that 332 00:18:31,080 --> 00:18:33,440 Speaker 1: you can make sure that whatever message you're sending out 333 00:18:33,440 --> 00:18:35,960 Speaker 1: there is being heard by the right people. Yeah, yeah, 334 00:18:36,000 --> 00:18:39,359 Speaker 1: and so and and for somebody like um, you know, 335 00:18:39,480 --> 00:18:43,479 Speaker 1: for politicians for example, yeah, um, this is incredibly important. 336 00:18:43,720 --> 00:18:47,040 Speaker 1: Or entrepreneurs want who get their name out. And then 337 00:18:47,080 --> 00:18:50,680 Speaker 1: there's the the other group of people who are are 338 00:18:51,119 --> 00:18:53,800 Speaker 1: short of in between there there, they're doing this shorter 339 00:18:53,800 --> 00:18:56,200 Speaker 1: because they want to, and then they might be getting 340 00:18:56,920 --> 00:19:02,520 Speaker 1: perks are cloud perks. So if you are an influential person, 341 00:19:03,200 --> 00:19:07,160 Speaker 1: then you are an attractive person for certain brands. If 342 00:19:07,240 --> 00:19:11,080 Speaker 1: you are influential in a particular field. Let's say with me, 343 00:19:11,160 --> 00:19:15,840 Speaker 1: I'm apparently I'm influential one technology and Atlanta restaurants. Then 344 00:19:15,920 --> 00:19:18,640 Speaker 1: let's say an Atlanta restaurant opens up and they are 345 00:19:19,200 --> 00:19:24,040 Speaker 1: cloud savvy, they understand how cloud works. They and they know, Hey, 346 00:19:24,240 --> 00:19:27,719 Speaker 1: if we convinced this guy to come into our restaurant 347 00:19:28,359 --> 00:19:31,159 Speaker 1: and eat our food, and we really pull out all 348 00:19:31,160 --> 00:19:33,359 Speaker 1: the stops, he's gonna go out and he's going to 349 00:19:33,440 --> 00:19:36,960 Speaker 1: talk about this incredible experience he had. And because his 350 00:19:37,080 --> 00:19:40,160 Speaker 1: influence is high in this area, that means we could 351 00:19:40,200 --> 00:19:43,920 Speaker 1: see a return on that investment. We could see people 352 00:19:44,000 --> 00:19:47,920 Speaker 1: come in because we tapped the right person to talk 353 00:19:47,960 --> 00:19:51,320 Speaker 1: about this, and that can go across all sorts of industries. 354 00:19:51,359 --> 00:19:52,919 Speaker 1: So that being said, I'll come out and say it 355 00:19:53,040 --> 00:19:55,919 Speaker 1: right now. I have not received any cloud perks. I 356 00:19:55,960 --> 00:19:58,679 Speaker 1: am not I don't use cloud to get cloud perks. 357 00:19:58,720 --> 00:20:02,120 Speaker 1: I'm not saying that I would ever use one. If 358 00:20:02,160 --> 00:20:05,800 Speaker 1: one came along that was just unbelievable, and I'm like, hey, 359 00:20:05,840 --> 00:20:08,199 Speaker 1: this is exactly why I need and exactly how I 360 00:20:08,240 --> 00:20:10,680 Speaker 1: need it. I would probably do it, but I've never 361 00:20:10,760 --> 00:20:13,800 Speaker 1: had that. I've never had that happen. Yeah. The companies, 362 00:20:13,920 --> 00:20:17,120 Speaker 1: the companies involved in cloud perks are generally people who 363 00:20:17,200 --> 00:20:21,720 Speaker 1: want to do some promotion. That's it's pretty simple. Um. 364 00:20:21,800 --> 00:20:25,119 Speaker 1: And so once you get your cloud score up to 365 00:20:25,200 --> 00:20:27,719 Speaker 1: a certain level, and it could it depends on what 366 00:20:27,760 --> 00:20:29,919 Speaker 1: it is. Yeah, it depends on the company. Like some 367 00:20:29,960 --> 00:20:33,600 Speaker 1: companies will say, Okay, anyone who's below a seventy five, 368 00:20:33,800 --> 00:20:36,440 Speaker 1: we're not interested in because that's not that's not worth 369 00:20:36,520 --> 00:20:38,960 Speaker 1: the time it would take to do this. Yeah, but 370 00:20:39,000 --> 00:20:42,720 Speaker 1: you might, um, you might pass let's say thirty, and 371 00:20:42,760 --> 00:20:46,560 Speaker 1: they'll say, hey, people with cloud score of thirty, we'll 372 00:20:46,560 --> 00:20:49,480 Speaker 1: give you a coupon for a free UM six pack 373 00:20:49,520 --> 00:20:52,960 Speaker 1: of our new soda flavor. Go ahead and redeem it 374 00:20:52,960 --> 00:20:54,840 Speaker 1: at your local store, you know, tell us where to 375 00:20:54,880 --> 00:20:57,520 Speaker 1: mail the coupon. So they mail the coupon and you 376 00:20:57,560 --> 00:20:59,120 Speaker 1: get the beverage and you try and you go, hey, 377 00:20:59,119 --> 00:21:01,800 Speaker 1: this stuff is pretty good what they're hoping. But there 378 00:21:01,840 --> 00:21:04,400 Speaker 1: is no requirement to do what they're hoping is you're 379 00:21:04,440 --> 00:21:07,119 Speaker 1: going to share that experience with their friends. Hey, I 380 00:21:07,200 --> 00:21:09,480 Speaker 1: was Uncloud. They sent me a coupon. I tried this stuff. 381 00:21:09,640 --> 00:21:12,200 Speaker 1: It tastes like a combination of mangoes and feet, But 382 00:21:12,240 --> 00:21:14,080 Speaker 1: I happen to like the taste of mangoes and feet. 383 00:21:14,480 --> 00:21:17,520 Speaker 1: So I'm gonna, you know, tweet this on Facebook and 384 00:21:17,560 --> 00:21:20,200 Speaker 1: Twitter and put it on my Google Plus account and 385 00:21:20,480 --> 00:21:23,720 Speaker 1: mentioned it on LinkedIn. That you just did exactly what 386 00:21:23,760 --> 00:21:28,080 Speaker 1: the promoters were what apparently my choice of mangoes and 387 00:21:28,119 --> 00:21:31,800 Speaker 1: feet was not just having I just want to do 388 00:21:31,960 --> 00:21:34,680 Speaker 1: something so trying not to think about that taste right now. 389 00:21:35,760 --> 00:21:38,199 Speaker 1: I just wanted to say something silly. I don't like 390 00:21:38,320 --> 00:21:42,119 Speaker 1: mangoes and I really don't like feet. But yeah, okay, 391 00:21:42,160 --> 00:21:45,480 Speaker 1: so um so that's essentially what they're hoping that that 392 00:21:45,600 --> 00:21:50,159 Speaker 1: will say that clout for it for its uh, you know, 393 00:21:50,280 --> 00:21:53,440 Speaker 1: just to to promote its own credibility here. And I 394 00:21:53,480 --> 00:21:55,680 Speaker 1: feel like it's fair to mention this Cloud says there's 395 00:21:55,720 --> 00:22:01,200 Speaker 1: absolutely no obligation on your part as a cloud participant. 396 00:22:01,720 --> 00:22:03,960 Speaker 1: If you get a perk to promote it in any 397 00:22:03,960 --> 00:22:06,280 Speaker 1: way at all, you can take them up on the perk. 398 00:22:06,320 --> 00:22:09,240 Speaker 1: You can decline the perk. You can tweet about it 399 00:22:09,359 --> 00:22:13,040 Speaker 1: or not. Um and uh, and you know it's up 400 00:22:13,040 --> 00:22:15,399 Speaker 1: to you whether you you could say, hey, this stuff 401 00:22:15,440 --> 00:22:17,640 Speaker 1: is nasty. I don't ever want to see it again. 402 00:22:17,680 --> 00:22:19,520 Speaker 1: I hope they pull it from the market fast before 403 00:22:19,560 --> 00:22:23,400 Speaker 1: I sue them. UM, you know that that is completely 404 00:22:23,480 --> 00:22:25,760 Speaker 1: up to you. But it it depends on partially on 405 00:22:25,760 --> 00:22:29,000 Speaker 1: your cloud score, and it depends partially on generally, and 406 00:22:29,040 --> 00:22:33,800 Speaker 1: it depends partially generally on what areas you are influential. 407 00:22:33,880 --> 00:22:37,320 Speaker 1: And I would assume that uh, President Obama and Justin 408 00:22:37,359 --> 00:22:40,960 Speaker 1: Bieber probably you know, get all the cloud perks they want. 409 00:22:40,960 --> 00:22:43,160 Speaker 1: They probably can't. Don't take anyone up on them. Yeah, 410 00:22:43,160 --> 00:22:46,440 Speaker 1: there's also we should also point out that, UM, with 411 00:22:46,560 --> 00:22:51,400 Speaker 1: perks comes the responsibility of revealing the fact that you 412 00:22:51,520 --> 00:22:56,280 Speaker 1: have received this as a perk, because, uh, there's an 413 00:22:56,320 --> 00:22:59,600 Speaker 1: ethical problem if you don't do that, and a legal 414 00:22:59,600 --> 00:23:03,040 Speaker 1: problem too in the United States. Um, you know the 415 00:23:03,200 --> 00:23:08,080 Speaker 1: the quote unquote mommy bloggers, um without cloud this was 416 00:23:08,119 --> 00:23:10,400 Speaker 1: going on for a while and they were not revealing 417 00:23:10,400 --> 00:23:13,280 Speaker 1: that they had received this as again to its credit cloud. 418 00:23:13,520 --> 00:23:18,760 Speaker 1: UH encourages you to remember, if you're going to post this, 419 00:23:18,840 --> 00:23:21,920 Speaker 1: say hey, I got it free because I was here. Yeah. 420 00:23:22,320 --> 00:23:27,400 Speaker 1: So yeah, yes, the clouds not condoning unethical behavior, which 421 00:23:27,520 --> 00:23:31,000 Speaker 1: you know that's important to remember because their critics who 422 00:23:31,040 --> 00:23:34,520 Speaker 1: kind of overlook that, UM, And you know, that's why 423 00:23:34,520 --> 00:23:36,359 Speaker 1: I wanted to mention that what you get for it 424 00:23:36,440 --> 00:23:38,760 Speaker 1: first before we got to the critics. Yeah, so it's 425 00:23:38,800 --> 00:23:43,359 Speaker 1: it's gotten. It's become a great tool for UH companies 426 00:23:43,400 --> 00:23:47,399 Speaker 1: to try and court influencers. Chris and I have a 427 00:23:47,480 --> 00:23:49,760 Speaker 1: little bit more to say about clout, but before we 428 00:23:49,800 --> 00:23:59,960 Speaker 1: get to that, let's take another quick break. In some case, 429 00:24:00,000 --> 00:24:02,399 Speaker 1: says it may be that a company says, hey, this 430 00:24:02,440 --> 00:24:05,080 Speaker 1: person is really influential in the field that we're involved in. 431 00:24:05,600 --> 00:24:07,960 Speaker 1: Let's not just give him a perk, Let's hire them 432 00:24:08,480 --> 00:24:11,200 Speaker 1: to do something for us. So in that case, you're 433 00:24:11,240 --> 00:24:13,480 Speaker 1: talking about, like you might have a freelancer out there 434 00:24:13,520 --> 00:24:17,600 Speaker 1: who is just an incredible influencer in a particular industry, 435 00:24:17,680 --> 00:24:19,800 Speaker 1: and then this company says, this is who we need 436 00:24:19,840 --> 00:24:23,600 Speaker 1: to get to represent our brand or to message out 437 00:24:23,720 --> 00:24:26,600 Speaker 1: something about our brand, UH, and we're going to actually 438 00:24:26,680 --> 00:24:29,720 Speaker 1: hire them. Maybe it's a contract for a certain number 439 00:24:29,760 --> 00:24:32,760 Speaker 1: of appearances, or maybe we actually give them a job 440 00:24:32,800 --> 00:24:36,159 Speaker 1: offer and that's another way that people are using cloud 441 00:24:36,520 --> 00:24:42,120 Speaker 1: Some companies, particularly advertising firms, marketing firms, places where uh 442 00:24:42,200 --> 00:24:45,199 Speaker 1: your ability to get a message out is clearly an 443 00:24:45,200 --> 00:24:48,200 Speaker 1: important part of your job, they are looking at cloud 444 00:24:48,240 --> 00:24:51,760 Speaker 1: scores because if you if you're going into an ad 445 00:24:51,800 --> 00:24:54,720 Speaker 1: agency and you're saying, hey, I know how to message 446 00:24:54,720 --> 00:24:58,679 Speaker 1: out information. I am. I am the guy that you 447 00:24:58,760 --> 00:25:01,520 Speaker 1: need or the lady you need to hire for this 448 00:25:01,560 --> 00:25:04,239 Speaker 1: position because I know what I'm doing, And then they 449 00:25:04,240 --> 00:25:06,640 Speaker 1: look up your cloud score and it's like twenty two. 450 00:25:06,720 --> 00:25:10,600 Speaker 1: They'll say, hey, um, can you show us a better 451 00:25:10,640 --> 00:25:12,879 Speaker 1: example than your cloud score, because according to this, you 452 00:25:12,920 --> 00:25:17,439 Speaker 1: are not getting your message heard, and and that's you know, 453 00:25:17,640 --> 00:25:20,280 Speaker 1: that's also drawn some criticism. People have said, well, how 454 00:25:20,359 --> 00:25:24,119 Speaker 1: is that fair? And the counter argument is to say, well, 455 00:25:24,320 --> 00:25:27,760 Speaker 1: if the algorithm is measuring how well a message is 456 00:25:27,800 --> 00:25:31,320 Speaker 1: going out across networks and the and the score is 457 00:25:31,359 --> 00:25:33,840 Speaker 1: really low, how would it be fair to this company 458 00:25:33,880 --> 00:25:37,280 Speaker 1: to hire someone who is not as good as a 459 00:25:37,400 --> 00:25:41,320 Speaker 1: different uh potential employee who has a much higher cloud score, 460 00:25:41,320 --> 00:25:43,199 Speaker 1: who has proven that they are able to get a 461 00:25:43,240 --> 00:25:46,720 Speaker 1: message out across networks? Yeah? Yeah, the the cloud score 462 00:25:46,760 --> 00:25:53,159 Speaker 1: has come into hiring practices recently, I've seen articles written 463 00:25:53,200 --> 00:25:56,480 Speaker 1: on it. And you know, people who were unaware of 464 00:25:56,680 --> 00:25:59,080 Speaker 1: the cloud score, you know, they hadn't signed up for it, 465 00:25:59,119 --> 00:26:01,840 Speaker 1: didn't know it existed. Uh, sort of taken aback by 466 00:26:01,840 --> 00:26:03,879 Speaker 1: the fact that there is a cloud score. Yes, you 467 00:26:03,920 --> 00:26:07,480 Speaker 1: are being measured. Um, that's a little uh, that's a 468 00:26:07,480 --> 00:26:10,520 Speaker 1: little weird, I think for some people, um and people 469 00:26:10,920 --> 00:26:14,479 Speaker 1: without knowing. And it's again the reason they compare the 470 00:26:14,520 --> 00:26:18,840 Speaker 1: cloud score to the credit credit score is that without 471 00:26:18,920 --> 00:26:22,120 Speaker 1: knowing exactly what you're being measured on, it's difficult to 472 00:26:22,160 --> 00:26:24,520 Speaker 1: improve on it, um and be a bit of a 473 00:26:24,560 --> 00:26:27,240 Speaker 1: treadmill to to constantly try to figure out what to 474 00:26:27,280 --> 00:26:29,919 Speaker 1: say next and how often to say it, exactly what 475 00:26:30,040 --> 00:26:33,080 Speaker 1: way to fit in, what time to say it, because 476 00:26:33,280 --> 00:26:35,840 Speaker 1: there are you know those are that That's something I 477 00:26:35,880 --> 00:26:39,280 Speaker 1: actually take into consideration. You see, I, Chris and I 478 00:26:39,359 --> 00:26:43,840 Speaker 1: we don't just uh, interface with social networks for ourselves. 479 00:26:43,880 --> 00:26:45,920 Speaker 1: Sometimes we have to. We do it for our text 480 00:26:46,000 --> 00:26:49,240 Speaker 1: off brand, right, because because we want we want people 481 00:26:49,280 --> 00:26:51,439 Speaker 1: to know what we are doing as tech stuff. We 482 00:26:51,480 --> 00:26:53,840 Speaker 1: want to stay engaged with our audience. We want to 483 00:26:53,840 --> 00:26:57,320 Speaker 1: know what they're thinking. So uh, but there comes a 484 00:26:57,359 --> 00:27:00,359 Speaker 1: time where you're like, should I post this now because 485 00:27:00,840 --> 00:27:04,280 Speaker 1: my audience might be asleep, you know, and then you think, well, 486 00:27:04,600 --> 00:27:07,760 Speaker 1: considering how many of us are tuned into the social 487 00:27:07,800 --> 00:27:11,560 Speaker 1: networking crazes out there, a message that was posted four 488 00:27:11,600 --> 00:27:15,080 Speaker 1: hours ago may never be seen. You know. If I 489 00:27:15,160 --> 00:27:17,960 Speaker 1: log into Twitter and there's a message that was posted 490 00:27:17,960 --> 00:27:20,000 Speaker 1: about four hours ago that I think would have been amazing, 491 00:27:20,359 --> 00:27:23,879 Speaker 1: I might never get to that because too page back 492 00:27:23,960 --> 00:27:28,720 Speaker 1: through that many tweets, uh, is a daunting task. I'd 493 00:27:28,720 --> 00:27:30,879 Speaker 1: have to clear out a half hour five minutes of 494 00:27:30,920 --> 00:27:33,960 Speaker 1: my schedule just to do that. So you know, that 495 00:27:34,040 --> 00:27:36,840 Speaker 1: also comes into place when you should post if you're 496 00:27:36,880 --> 00:27:38,920 Speaker 1: trying to if you're if you're really trying to use 497 00:27:38,960 --> 00:27:42,560 Speaker 1: social networking to get a message out. You know, if 498 00:27:42,560 --> 00:27:45,879 Speaker 1: you're just using social networking to say I watched that 499 00:27:45,960 --> 00:27:49,160 Speaker 1: episode of Blah Blah blah last night and it was phenomenal, 500 00:27:49,359 --> 00:27:52,439 Speaker 1: it may not matter that you posted at two in 501 00:27:52,480 --> 00:27:55,680 Speaker 1: the morning or eight am or four pm. It may 502 00:27:55,680 --> 00:27:59,080 Speaker 1: not have any impact there, and that might be fine 503 00:27:59,119 --> 00:28:02,000 Speaker 1: for you. There's nothing wrong with that. Um. The other 504 00:28:02,040 --> 00:28:05,480 Speaker 1: element of this is, unlike the credit score, the cloud 505 00:28:05,480 --> 00:28:07,119 Speaker 1: score is something that I think a lot of people 506 00:28:07,119 --> 00:28:11,119 Speaker 1: are not aware of and that's the problem, you know, 507 00:28:11,240 --> 00:28:14,440 Speaker 1: because they could be judged on something that they themselves 508 00:28:14,560 --> 00:28:19,760 Speaker 1: had no knowledge of going into, you know, a job interview. Yeah. 509 00:28:19,760 --> 00:28:23,560 Speaker 1: I also found an article by a Manachum Wecker about 510 00:28:23,600 --> 00:28:27,280 Speaker 1: a professor at Florida State University who was using cloud 511 00:28:27,359 --> 00:28:31,359 Speaker 1: to grade his students in class. Now it was for 512 00:28:31,560 --> 00:28:36,320 Speaker 1: a marketing class. It was, you know, for an electronic 513 00:28:36,400 --> 00:28:39,840 Speaker 1: marketing class. So people were upset about it. But it 514 00:28:39,920 --> 00:28:42,960 Speaker 1: kind of makes me think, well, if this is specifically 515 00:28:43,400 --> 00:28:47,360 Speaker 1: a metric for measuring your ability to market yourself in 516 00:28:47,440 --> 00:28:51,400 Speaker 1: electronic media, maybe clouds. I don't know that I would 517 00:28:51,400 --> 00:28:54,360 Speaker 1: make it the soul thing that you grade someone on, 518 00:28:54,480 --> 00:28:57,440 Speaker 1: but it probably at least belongs in part of it. 519 00:28:58,040 --> 00:29:00,680 Speaker 1: Um or should build an aware that the clash should 520 00:29:00,680 --> 00:29:04,080 Speaker 1: build an awareness of its prominence in in today's society 521 00:29:04,080 --> 00:29:06,760 Speaker 1: for that. But I wanted to mention to one thing 522 00:29:06,800 --> 00:29:09,480 Speaker 1: that where it doesn't compare to a credit score, however, 523 00:29:09,560 --> 00:29:12,680 Speaker 1: is there are more than one agency developing credit scores 524 00:29:12,800 --> 00:29:16,800 Speaker 1: in the United States. Cloud is not alone, but it 525 00:29:16,960 --> 00:29:21,200 Speaker 1: is an unusual prominence and it's a cloud, it turns out, 526 00:29:21,400 --> 00:29:25,280 Speaker 1: you might say that. So, yeah, it's if we If 527 00:29:25,320 --> 00:29:33,160 Speaker 1: we assume that the cloud algorithm is looking at things 528 00:29:33,520 --> 00:29:40,000 Speaker 1: in a very methodical and measured way, and that it is, 529 00:29:40,520 --> 00:29:43,320 Speaker 1: in the end of the day, looking at the how 530 00:29:43,360 --> 00:29:47,560 Speaker 1: how influential people are based upon how widely their message 531 00:29:47,560 --> 00:29:50,320 Speaker 1: has heard and how many people are writing about that person, 532 00:29:51,640 --> 00:29:55,600 Speaker 1: then cloud and of itself is an interesting and I 533 00:29:55,640 --> 00:29:58,640 Speaker 1: think valuable service for people who are trying to get 534 00:29:58,640 --> 00:30:01,520 Speaker 1: a message out. It's it's not it's not necessarily a 535 00:30:01,560 --> 00:30:05,160 Speaker 1: good thing or a bad thing. It's a useful tool. Um. 536 00:30:05,200 --> 00:30:08,000 Speaker 1: The way other people are using this tool might be, 537 00:30:08,040 --> 00:30:11,400 Speaker 1: you know, might make you upset or it might throw 538 00:30:11,440 --> 00:30:15,120 Speaker 1: you depending on your cloud score. Um, but that's you 539 00:30:15,160 --> 00:30:17,440 Speaker 1: know again, you can't blame the tool for the way 540 00:30:17,480 --> 00:30:20,120 Speaker 1: people are using it, right. I mean the same thing 541 00:30:20,120 --> 00:30:22,440 Speaker 1: we could say about Twitter. Twitter. When it was launched, 542 00:30:22,960 --> 00:30:25,040 Speaker 1: people were talking about Twitter being a way that you 543 00:30:25,040 --> 00:30:27,880 Speaker 1: would send out a message to a group of friends. Right. 544 00:30:27,960 --> 00:30:30,600 Speaker 1: That was the main use case of Twitter. That is 545 00:30:30,680 --> 00:30:34,960 Speaker 1: totally changed since Twitter launched. And one of these days 546 00:30:35,000 --> 00:30:36,920 Speaker 1: we are going to do an episode or at least 547 00:30:37,080 --> 00:30:39,080 Speaker 1: at least one episode about the Twitter will probably a 548 00:30:39,160 --> 00:30:41,680 Speaker 1: two parter, but the story of Twitter, to really talk 549 00:30:41,680 --> 00:30:44,360 Speaker 1: about the foundation of it, what it was originally meant for, 550 00:30:44,440 --> 00:30:47,480 Speaker 1: and what it has turned into, especially in light of 551 00:30:47,520 --> 00:30:50,760 Speaker 1: the controversy of how Twitter has handled the whole API 552 00:30:51,000 --> 00:30:55,239 Speaker 1: approach and third party developers, because that's definitely caused a 553 00:30:55,240 --> 00:30:58,840 Speaker 1: lot of frustration in the tech world. Um, and you know, 554 00:30:58,880 --> 00:31:03,320 Speaker 1: I've got companies like Cloud that are very much leveraging 555 00:31:03,360 --> 00:31:10,320 Speaker 1: Twitter to get, you know, to to become a valuable service. So, um, 556 00:31:10,400 --> 00:31:12,320 Speaker 1: at the end of the day, does cloud really matter? 557 00:31:12,680 --> 00:31:16,240 Speaker 1: I guess it depends on the industry you're in. And Uh, 558 00:31:16,720 --> 00:31:20,040 Speaker 1: the nice thing about the Cloud score is it's not static. 559 00:31:20,800 --> 00:31:24,720 Speaker 1: It will change over time, and if you are dedicated 560 00:31:24,760 --> 00:31:27,880 Speaker 1: to increasing that Cloud score, you can do it in 561 00:31:28,000 --> 00:31:32,200 Speaker 1: legitimate ways. Hopefully, the cloud algorithm will be able to 562 00:31:32,360 --> 00:31:36,719 Speaker 1: take into account things like dummy Twitter accounts or dummy 563 00:31:36,720 --> 00:31:40,000 Speaker 1: Facebook accounts, so that if you were to get a 564 00:31:40,080 --> 00:31:43,320 Speaker 1: huge number of followers immediately, it would not automatically adjust 565 00:31:43,400 --> 00:31:48,120 Speaker 1: your score because obviously you could gain that system. I mean, 566 00:31:48,120 --> 00:31:51,800 Speaker 1: there are companies out there already that only exists to 567 00:31:51,880 --> 00:31:56,720 Speaker 1: provide people fake essentially fake accounts. Uh. There are even 568 00:31:56,760 --> 00:31:59,080 Speaker 1: other ones that will try and get real accounts to 569 00:31:59,120 --> 00:32:04,640 Speaker 1: follow you. There's this whole um, I scratch your back, 570 00:32:04,720 --> 00:32:07,400 Speaker 1: you scratch mine approach where you like, if you follow 571 00:32:07,440 --> 00:32:09,760 Speaker 1: these twenty people, these twenty people will follow you back. 572 00:32:10,400 --> 00:32:13,120 Speaker 1: You know. That's kind of you know, almost like a 573 00:32:13,160 --> 00:32:15,760 Speaker 1: pyramid scheme of Twitter followers. I would hope that the 574 00:32:15,800 --> 00:32:19,240 Speaker 1: cloud algorithm takes that into account and again really concentrates 575 00:32:19,240 --> 00:32:25,680 Speaker 1: more on the impact and hopefully avoids counting false positives. 576 00:32:25,760 --> 00:32:28,840 Speaker 1: And that wraps up this classic episode about cloud. The 577 00:32:29,160 --> 00:32:33,520 Speaker 1: cloud went belly up. Originally it started to kind of 578 00:32:33,600 --> 00:32:37,480 Speaker 1: get away from the whole scoring system, and then eventually 579 00:32:37,520 --> 00:32:41,800 Speaker 1: it went away entirely. It was an interesting idea, but 580 00:32:41,960 --> 00:32:46,160 Speaker 1: one that was almost guaranteed to go sour pretty quickly. 581 00:32:46,680 --> 00:32:50,440 Speaker 1: If we ever assigned scores for anything, people will start 582 00:32:50,480 --> 00:32:52,600 Speaker 1: trying to figure out how to game the system, because 583 00:32:52,640 --> 00:32:56,760 Speaker 1: of course, getting a high score means winning well. If 584 00:32:56,800 --> 00:32:59,360 Speaker 1: you have any suggestions for future episodes of tech Stuff, 585 00:32:59,480 --> 00:33:02,080 Speaker 1: send me an email the addresses tech Stuff at how 586 00:33:02,120 --> 00:33:04,800 Speaker 1: stuff works dot com, or pop on over to tech 587 00:33:04,840 --> 00:33:07,640 Speaker 1: stuff podcast dot com. You're gonna find archive of all 588 00:33:07,640 --> 00:33:09,960 Speaker 1: of our past shows. There. You will also find links 589 00:33:10,000 --> 00:33:12,280 Speaker 1: to where we are in social media and a link 590 00:33:12,320 --> 00:33:14,760 Speaker 1: to our online store, where every purchase you make goes 591 00:33:14,800 --> 00:33:17,000 Speaker 1: to help the show, and we greatly appreciate it, and 592 00:33:17,040 --> 00:33:24,720 Speaker 1: I'll talk to you again really soon. Y Text Stuff 593 00:33:24,760 --> 00:33:27,080 Speaker 1: is a production of I heart Radio's How Stuff Works. 594 00:33:27,240 --> 00:33:30,080 Speaker 1: For more podcasts from my heart Radio, visit the i 595 00:33:30,160 --> 00:33:33,400 Speaker 1: heart Radio app, Apple Podcasts, or wherever you listen to 596 00:33:33,440 --> 00:33:34,400 Speaker 1: your favorite shows.