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