1 00:00:00,080 --> 00:00:03,800 Speaker 1: I'm off my game today. No, you're not. People are 2 00:00:03,800 --> 00:00:05,640 Speaker 1: going to have to start making better content. I think 3 00:00:05,640 --> 00:00:07,160 Speaker 1: we're gonna be talking about this for a long time. 4 00:00:07,240 --> 00:00:10,200 Speaker 1: When you program for everyone, you program for no one. 5 00:00:10,240 --> 00:00:12,319 Speaker 1: I think it's that we're purpose driven platform, like we're 6 00:00:12,320 --> 00:00:15,600 Speaker 1: trying to get to substance. How was that? Are you 7 00:00:15,680 --> 00:00:19,200 Speaker 1: happy with that? This is marketing therapy right now? It 8 00:00:19,480 --> 00:00:29,560 Speaker 1: really is? What's up? I'm Laura Coarntia and I'm Alexa Kristen. 9 00:00:29,600 --> 00:00:34,320 Speaker 1: Welcome back to at Landia. So today onto Ganska from Notch. 10 00:00:34,440 --> 00:00:37,240 Speaker 1: She's the co founder and CEO. So she really kicks 11 00:00:37,280 --> 00:00:41,080 Speaker 1: us off into a conversation that we've been having. I think, 12 00:00:41,200 --> 00:00:43,239 Speaker 1: in a lot of ways more implied. And Laura and 13 00:00:43,240 --> 00:00:45,360 Speaker 1: I have been talking a lot about dipping our toe 14 00:00:45,440 --> 00:00:49,960 Speaker 1: into shark infested waters, maybe just jumping into shark infested waters. 15 00:00:50,000 --> 00:00:52,800 Speaker 1: Around the data and analytics topic and what it really 16 00:00:52,840 --> 00:00:55,160 Speaker 1: means for marketers, what it means for media, what it 17 00:00:55,240 --> 00:00:59,279 Speaker 1: means around insights forward and data back. Because we're so 18 00:00:59,360 --> 00:01:03,639 Speaker 1: focused on benchmarks that don't mean anything today, we've created 19 00:01:03,760 --> 00:01:07,880 Speaker 1: kind of these false indicators. Yeah, I think it's interesting, 20 00:01:07,920 --> 00:01:12,039 Speaker 1: you know, as data becomes democratized or commoditized, and everybody 21 00:01:12,120 --> 00:01:15,039 Speaker 1: has the first party set, the third party set, how 22 00:01:15,080 --> 00:01:19,720 Speaker 1: they're translating that into how they're translating that into dashboards 23 00:01:19,720 --> 00:01:22,080 Speaker 1: and insights. And you know, what's the right talent that 24 00:01:22,120 --> 00:01:24,200 Speaker 1: you should have at the table? Are they analysts? Are 25 00:01:24,240 --> 00:01:28,399 Speaker 1: they scientists? Are they engineers? You know, it's interesting. We've 26 00:01:28,400 --> 00:01:33,560 Speaker 1: had everybody from brands to publishers, um to agency executives 27 00:01:33,600 --> 00:01:37,600 Speaker 1: on this show talking about what data means to their business. 28 00:01:37,600 --> 00:01:39,480 Speaker 1: And I think what's interesting and what you were just 29 00:01:39,520 --> 00:01:42,559 Speaker 1: alluding to is that we're an industry that continually looks 30 00:01:42,600 --> 00:01:47,800 Speaker 1: for the standardization why why? And I think what we're 31 00:01:47,800 --> 00:01:50,120 Speaker 1: going to hear from Onda is that there isn't actually 32 00:01:50,120 --> 00:01:54,320 Speaker 1: a one size fits all solution and that the KPI 33 00:01:54,360 --> 00:01:56,920 Speaker 1: s are indicators from the brands that she's working with, 34 00:01:57,520 --> 00:02:00,920 Speaker 1: are very indicative of the fact that maybe there shouldn't 35 00:02:00,960 --> 00:02:04,720 Speaker 1: be And so what works for your business? How do 36 00:02:04,760 --> 00:02:08,359 Speaker 1: you move the bottom line? How do you understand your consumer? 37 00:02:08,840 --> 00:02:12,480 Speaker 1: And should you be looking for the same consumer persona 38 00:02:12,639 --> 00:02:16,399 Speaker 1: profile that your competitor is. She might suggest probably not, 39 00:02:16,600 --> 00:02:20,359 Speaker 1: and she may suggest that your competitor may create this content. 40 00:02:20,639 --> 00:02:23,840 Speaker 1: Actually you should be over here on the complete other side, 41 00:02:24,120 --> 00:02:27,359 Speaker 1: creating another piece of content or whole content program. Yeah. 42 00:02:27,360 --> 00:02:28,799 Speaker 1: And I think as an industry, we we talk a 43 00:02:28,840 --> 00:02:31,079 Speaker 1: lot obviously as data as an output. And I think 44 00:02:31,080 --> 00:02:34,320 Speaker 1: what Ada is going to talk a bit about today 45 00:02:34,360 --> 00:02:37,920 Speaker 1: is where where it becomes an input, right and how 46 00:02:38,240 --> 00:02:41,360 Speaker 1: understanding as she'll call it system one versus system two, 47 00:02:41,400 --> 00:02:43,760 Speaker 1: which is a very articulate way that she'll talk about 48 00:02:44,280 --> 00:02:48,280 Speaker 1: UM emotion versus the analytical side. UM is a really 49 00:02:48,280 --> 00:02:51,160 Speaker 1: interesting filter for I think about how we try to 50 00:02:51,240 --> 00:02:54,959 Speaker 1: jam everything together, but really the importance of allowing them 51 00:02:55,000 --> 00:02:58,440 Speaker 1: to live independently to come to direction or a decision 52 00:02:58,520 --> 00:03:01,720 Speaker 1: in your marketing. So put on your Scoopa suit. We'll 53 00:03:01,760 --> 00:03:16,239 Speaker 1: be back with Onto Gonska from Notch and we're back 54 00:03:16,240 --> 00:03:22,119 Speaker 1: in the studio with the founder and CEO of Notch ANDASA. 55 00:03:22,400 --> 00:03:25,680 Speaker 1: Welcome and thank you so much. Thank you for pronouncing 56 00:03:25,680 --> 00:03:29,680 Speaker 1: my name right. So you're from Transylvania. Yes, I am 57 00:03:30,080 --> 00:03:33,600 Speaker 1: a very lovely place, a lot more, a lot friendlier 58 00:03:33,600 --> 00:03:35,720 Speaker 1: than you would think. Brn and Rays grew up there, 59 00:03:35,920 --> 00:03:38,640 Speaker 1: became very good at math early on, kind of like 60 00:03:38,680 --> 00:03:41,960 Speaker 1: a celebration of all Eastern European stereotypes. I was really 61 00:03:41,960 --> 00:03:44,480 Speaker 1: really good at math and a very good ballroom dancer. 62 00:03:44,560 --> 00:03:46,360 Speaker 1: So from Eastern Europe, right, you're supposed to be a 63 00:03:46,360 --> 00:03:49,120 Speaker 1: ballerina and a computer scientist. So I'm just saying I 64 00:03:49,200 --> 00:03:51,080 Speaker 1: kind of fit into the stereotype. How do you have 65 00:03:51,120 --> 00:03:54,000 Speaker 1: time to do anything else? Well, now I don't dance anymore. 66 00:03:54,040 --> 00:03:55,920 Speaker 1: I just do the nerd stuff. So on to tell 67 00:03:56,000 --> 00:03:59,600 Speaker 1: us about Notch, it's about four years old. Yes, how 68 00:03:59,600 --> 00:04:01,960 Speaker 1: did you get started? What was the farthest through the 69 00:04:02,000 --> 00:04:05,440 Speaker 1: trees that you saw? And how is that company evolved? Yeah, 70 00:04:05,440 --> 00:04:08,360 Speaker 1: so we got started by not being at all focused 71 00:04:08,400 --> 00:04:12,200 Speaker 1: on marketing. I initially was really obsessed with figuring out 72 00:04:12,240 --> 00:04:14,800 Speaker 1: how people felt in real time about something. I was 73 00:04:14,840 --> 00:04:18,120 Speaker 1: trying to figure out what are the kind of big 74 00:04:18,160 --> 00:04:21,760 Speaker 1: missing data sets about humans online? And I realized that 75 00:04:21,760 --> 00:04:25,159 Speaker 1: there wasn't a very good way to collect feedback. UM 76 00:04:25,200 --> 00:04:27,880 Speaker 1: surveys were really crappy consumer products. No one wanted to 77 00:04:27,880 --> 00:04:29,960 Speaker 1: engage with them, and as such, there was this big 78 00:04:29,960 --> 00:04:32,359 Speaker 1: missing data set of how people actually felt about stuff. 79 00:04:32,400 --> 00:04:34,679 Speaker 1: And so I set out initially to build a consumer 80 00:04:34,720 --> 00:04:37,040 Speaker 1: product that people would want to engage with, something that 81 00:04:37,120 --> 00:04:40,800 Speaker 1: was elegant, simple, frictionless, UM an action that you could 82 00:04:40,800 --> 00:04:43,279 Speaker 1: do within your attention span, which is about six seconds 83 00:04:43,880 --> 00:04:46,719 Speaker 1: um and something that you'd really enjoy engaging with, because 84 00:04:46,800 --> 00:04:49,960 Speaker 1: you know, human beings are rage atmental creatures. We have opinions. 85 00:04:50,000 --> 00:04:52,320 Speaker 1: It's just a matter of having the right product to 86 00:04:52,360 --> 00:04:56,000 Speaker 1: get them to to share that opinion. So we built 87 00:04:56,040 --> 00:04:59,080 Speaker 1: something it really worked. Um you know, surveys get around 88 00:04:59,080 --> 00:05:01,040 Speaker 1: a zero point zero one sent response rate, and we 89 00:05:01,040 --> 00:05:05,039 Speaker 1: were getting on average, and the exponential increase in response 90 00:05:05,120 --> 00:05:07,200 Speaker 1: rate meant that we could get a lot of data 91 00:05:07,279 --> 00:05:10,600 Speaker 1: at scale about how people feel, which then we started 92 00:05:10,600 --> 00:05:12,440 Speaker 1: thinking who cares about this data? And we had all 93 00:05:12,480 --> 00:05:15,159 Speaker 1: sorts of assumptions around it. At the time, we were 94 00:05:15,200 --> 00:05:17,560 Speaker 1: in Silicon Valley still, which is where we got started, 95 00:05:18,040 --> 00:05:20,120 Speaker 1: and I started traveling to New York meeting with a 96 00:05:20,120 --> 00:05:22,680 Speaker 1: bunch of different people. And one of the first marketers 97 00:05:22,680 --> 00:05:25,040 Speaker 1: I've ever met was someone that Alexa knows really well, 98 00:05:25,120 --> 00:05:27,440 Speaker 1: Beth Comstock. And thank god I met with her because 99 00:05:27,640 --> 00:05:30,680 Speaker 1: you know, as we all know, she's this incredible, you know, 100 00:05:31,000 --> 00:05:34,839 Speaker 1: legend marketer in our space who sees innovation and manages 101 00:05:34,880 --> 00:05:37,839 Speaker 1: to kind of pinpoint and had lifted up for everyone 102 00:05:37,880 --> 00:05:40,000 Speaker 1: else to see. And that's exactly what she did for us. 103 00:05:40,560 --> 00:05:43,040 Speaker 1: So with time, UM, we kind of went from just 104 00:05:43,200 --> 00:05:46,320 Speaker 1: tracking how people felt, too, then using our technology to 105 00:05:46,400 --> 00:05:50,880 Speaker 1: track everything else about a person, behavior, demographic data, literally everything. 106 00:05:50,920 --> 00:05:54,679 Speaker 1: So we kind of blended what someone says about themselves 107 00:05:54,720 --> 00:05:57,600 Speaker 1: and what their behavior says about them um into kind 108 00:05:57,600 --> 00:06:00,880 Speaker 1: of a more holistic version of themselves. UM. And then 109 00:06:00,880 --> 00:06:03,560 Speaker 1: we started working with you know, all the big financial brands, 110 00:06:03,600 --> 00:06:05,880 Speaker 1: all the big auto brands, all the big telco brands, 111 00:06:05,880 --> 00:06:08,240 Speaker 1: and we kind of are on our way to becoming 112 00:06:08,279 --> 00:06:12,240 Speaker 1: a standard for how content, in particular r o I 113 00:06:12,640 --> 00:06:15,679 Speaker 1: is measured. So we when you started, you weren't working 114 00:06:15,680 --> 00:06:18,520 Speaker 1: with brands. What were you measuring in terms of people's 115 00:06:18,800 --> 00:06:21,840 Speaker 1: feelings and perceptions of things? Great question. So we were 116 00:06:21,839 --> 00:06:24,560 Speaker 1: working with editorial teams. We were measuring, Yeah, we were 117 00:06:24,560 --> 00:06:28,799 Speaker 1: measuring the response of people two topics that editorial content 118 00:06:28,839 --> 00:06:31,920 Speaker 1: was about. Um. But as you can imagine, we realized 119 00:06:31,960 --> 00:06:35,200 Speaker 1: that one, you know, by large, creators don't love being 120 00:06:35,240 --> 00:06:38,799 Speaker 1: told what data data says. Right, and this is a 121 00:06:38,839 --> 00:06:41,640 Speaker 1: generalization that might not be true today, but it definitely 122 00:06:41,720 --> 00:06:44,560 Speaker 1: was about three years ago. UM. And too, there wasn't 123 00:06:44,560 --> 00:06:48,160 Speaker 1: a lot of money and selling creative sentiment type of 124 00:06:48,240 --> 00:06:51,200 Speaker 1: data to creators, and so we started taking a step 125 00:06:51,200 --> 00:06:52,960 Speaker 1: back and thinking what's the best go to market here? 126 00:06:52,960 --> 00:06:55,800 Speaker 1: And we realized that marketers were becoming content creators a k. 127 00:06:55,920 --> 00:06:58,880 Speaker 1: Content marketing was on the rise still is, and that 128 00:06:58,920 --> 00:07:01,240 Speaker 1: they needed a better way to measure it because all 129 00:07:01,279 --> 00:07:04,960 Speaker 1: the standards for display advertising like mode and integral ad science, 130 00:07:05,440 --> 00:07:08,160 Speaker 1: we're not really doing the job. So I don't think 131 00:07:08,160 --> 00:07:11,280 Speaker 1: it's a surprise actually that you have an artistic creative 132 00:07:11,320 --> 00:07:14,880 Speaker 1: side as a former ballroom deal because one of the 133 00:07:14,920 --> 00:07:17,760 Speaker 1: things that I have always been impressed by your product 134 00:07:18,280 --> 00:07:21,960 Speaker 1: is the aesthetic of this online survey, if you will, 135 00:07:22,000 --> 00:07:27,680 Speaker 1: and the sort of creativity that went into that engagement 136 00:07:27,800 --> 00:07:31,280 Speaker 1: rate output. Right, So can you talk about how you 137 00:07:31,360 --> 00:07:35,480 Speaker 1: blended art and science to create an online survey that 138 00:07:35,560 --> 00:07:39,320 Speaker 1: could engage consumers in a way that isn't just a 139 00:07:39,840 --> 00:07:42,800 Speaker 1: backs out totally. So we kind of left off from 140 00:07:42,840 --> 00:07:46,920 Speaker 1: the idea that UM surveys were not very engaging consumer 141 00:07:46,960 --> 00:07:49,880 Speaker 1: products and started asking ourselves why. We realized that there's 142 00:07:49,920 --> 00:07:53,040 Speaker 1: two issues. The first was the context. Usually surveys are 143 00:07:53,360 --> 00:07:55,680 Speaker 1: targeted to you in a disruptive way. They kind of 144 00:07:55,720 --> 00:07:58,600 Speaker 1: pop up or they're sent to you via email two 145 00:07:58,720 --> 00:08:01,080 Speaker 1: days later. By then, you know, in today's world there 146 00:08:01,120 --> 00:08:02,440 Speaker 1: was so a d D that you have no idea 147 00:08:02,480 --> 00:08:04,520 Speaker 1: what happened two days ago. So it's really kind of 148 00:08:04,560 --> 00:08:07,920 Speaker 1: a disruptive context in both cases. Right, So we realize 149 00:08:07,920 --> 00:08:09,800 Speaker 1: that it has to be at the end of an experience, 150 00:08:09,840 --> 00:08:12,520 Speaker 1: and ideally and immersive experience, not a transactional and like 151 00:08:12,520 --> 00:08:15,520 Speaker 1: a display at And therefore we realized that content was 152 00:08:15,720 --> 00:08:18,880 Speaker 1: not only a winning mechanism for brands to tell their 153 00:08:18,920 --> 00:08:21,160 Speaker 1: story and engage with audiences, but also a really good 154 00:08:21,160 --> 00:08:23,280 Speaker 1: way to get someone to share their feedback. The second 155 00:08:23,320 --> 00:08:25,640 Speaker 1: piece was the methodology. As it turns out, you know, 156 00:08:25,680 --> 00:08:27,400 Speaker 1: if you if you believe that the brain has kind 157 00:08:27,440 --> 00:08:30,080 Speaker 1: of like system one system too, As it turns out, 158 00:08:30,120 --> 00:08:32,440 Speaker 1: you know, System one is where we have kind of 159 00:08:32,480 --> 00:08:35,480 Speaker 1: store feelings or analyzed feelings. When you smile, I know 160 00:08:35,559 --> 00:08:38,480 Speaker 1: exactly how you're feeling because you know it's in system one. 161 00:08:38,480 --> 00:08:39,959 Speaker 1: I don't have to think about it. But if you 162 00:08:40,000 --> 00:08:41,959 Speaker 1: ask me how much is seven times pretty seven? I 163 00:08:42,000 --> 00:08:44,880 Speaker 1: go into system two, which is the nalytical brain now surveys. 164 00:08:44,920 --> 00:08:46,720 Speaker 1: What they do is they ask you to convert something 165 00:08:46,760 --> 00:08:48,920 Speaker 1: that lives in system one, which is a feeling into 166 00:08:49,120 --> 00:08:52,280 Speaker 1: a percentage or a number or multiple choice, which automatically 167 00:08:52,320 --> 00:08:54,960 Speaker 1: send you in system too. Therefore, there's a massive friction 168 00:08:54,960 --> 00:08:57,160 Speaker 1: and people don't tend to express that feeling. So what 169 00:08:57,280 --> 00:08:59,959 Speaker 1: we did is we basically said, let's create a really 170 00:09:00,000 --> 00:09:03,480 Speaker 1: the intuitive design. Um, let's take a universal metaphor. And 171 00:09:03,480 --> 00:09:04,920 Speaker 1: this is I think where we really kind of hit 172 00:09:04,920 --> 00:09:07,960 Speaker 1: the nail on the head. Because universal metaphors or something 173 00:09:08,000 --> 00:09:10,320 Speaker 1: you learn between zero and FOD regardless of whether you're 174 00:09:10,360 --> 00:09:14,680 Speaker 1: from Transylvania or from California or Nebraska. There you go. Um, 175 00:09:14,760 --> 00:09:16,640 Speaker 1: and we realize that temperature is one of them. So 176 00:09:16,679 --> 00:09:19,600 Speaker 1: if you present someone with a temperature scale, they automatically 177 00:09:19,640 --> 00:09:21,559 Speaker 1: know what to do with it without having to think 178 00:09:21,559 --> 00:09:26,080 Speaker 1: about it. Visual like a visual temperature temperature I like, 179 00:09:26,160 --> 00:09:28,599 Speaker 1: I don't like a slider. Yes, And we realize that 180 00:09:28,640 --> 00:09:30,800 Speaker 1: it also gives us, as like data nurse, it gives 181 00:09:30,840 --> 00:09:33,080 Speaker 1: us the ability to create granularity because you could have 182 00:09:33,080 --> 00:09:35,400 Speaker 1: as many skills to the temperature as you want, so 183 00:09:35,440 --> 00:09:38,079 Speaker 1: you could have really granular feeling. You could enable people 184 00:09:38,120 --> 00:09:40,640 Speaker 1: to quickly answer a question without thinking about it, and 185 00:09:40,720 --> 00:09:42,800 Speaker 1: you could enable them to have a really positive experience 186 00:09:42,840 --> 00:09:45,640 Speaker 1: as opposed to making them feel like they've had to 187 00:09:45,760 --> 00:09:48,840 Speaker 1: spend minutes figuring out how they actually feel. You just 188 00:09:48,880 --> 00:09:52,160 Speaker 1: give them are really easy and um and rewarding way 189 00:09:52,200 --> 00:09:54,679 Speaker 1: to share that feeling like they want to actually share 190 00:09:54,679 --> 00:09:57,079 Speaker 1: their feedback they do. Yeah, I mean this is kind 191 00:09:57,080 --> 00:09:58,800 Speaker 1: of the premise on which re build the companies that 192 00:09:59,240 --> 00:10:01,760 Speaker 1: human beings and nice say judgmental, but you know, and 193 00:10:01,760 --> 00:10:04,040 Speaker 1: like its scientifically speaking, we wouldn't be able to survive 194 00:10:04,120 --> 00:10:06,800 Speaker 1: unless we made judgments all the time. So you know, 195 00:10:06,960 --> 00:10:09,320 Speaker 1: I do believe that we're very opinionated and we do 196 00:10:09,400 --> 00:10:11,720 Speaker 1: want to share that feeling. But the methods in which 197 00:10:11,840 --> 00:10:15,560 Speaker 1: we are given that opportunity today or what Twitter and Facebook? 198 00:10:15,559 --> 00:10:17,400 Speaker 1: We have to sit there and write a hundred forty characters. 199 00:10:17,400 --> 00:10:18,920 Speaker 1: It takes me like an hour to do that every 200 00:10:18,960 --> 00:10:21,120 Speaker 1: time I try to do it. So yeah, we gave 201 00:10:21,120 --> 00:10:23,360 Speaker 1: people a much more frictionless, easy way to do it. 202 00:10:23,640 --> 00:10:26,280 Speaker 1: And what kind of surveys or what kind of questions 203 00:10:26,320 --> 00:10:29,120 Speaker 1: are you asking for brands? Is it you know, around 204 00:10:30,080 --> 00:10:34,360 Speaker 1: brand sentiment like that high level? Is it around products? Well? 205 00:10:34,400 --> 00:10:36,560 Speaker 1: What are the types of things that you're actually going 206 00:10:36,559 --> 00:10:39,400 Speaker 1: out there for brands and asking consumers? So taking just 207 00:10:39,480 --> 00:10:42,000 Speaker 1: a quick step back, I think the bigger question is 208 00:10:42,040 --> 00:10:45,239 Speaker 1: how do brands think of content marketing success. I realized 209 00:10:45,280 --> 00:10:47,880 Speaker 1: that when it comes to display um I mean not 210 00:10:47,920 --> 00:10:50,600 Speaker 1: to discredit any of the products that have done so 211 00:10:50,640 --> 00:10:53,160 Speaker 1: well in the market, but measuring displays a little bit 212 00:10:53,200 --> 00:10:55,679 Speaker 1: more binary. The ad has either been seen or not 213 00:10:55,760 --> 00:10:57,320 Speaker 1: or clicked on or not right. But when it comes 214 00:10:57,320 --> 00:11:00,880 Speaker 1: to content, people just have very different definitions of success um, 215 00:11:00,960 --> 00:11:04,520 Speaker 1: but they tend to gravitate towards five big categories sentiments 216 00:11:04,559 --> 00:11:08,640 Speaker 1: one of them, or attitude rather, engagement, impressions, social, and conversion. 217 00:11:09,080 --> 00:11:11,400 Speaker 1: So what we do now is we capture all those 218 00:11:11,440 --> 00:11:13,560 Speaker 1: five categories and we try to work with the brand 219 00:11:13,559 --> 00:11:16,600 Speaker 1: to understand what their custom version of successes and then 220 00:11:16,640 --> 00:11:19,000 Speaker 1: create custom scores that help them kind of force rank 221 00:11:19,040 --> 00:11:21,520 Speaker 1: every single content investment to see what's performing the best 222 00:11:21,520 --> 00:11:23,800 Speaker 1: in real time. But when it comes to the kind 223 00:11:23,800 --> 00:11:26,280 Speaker 1: of upper funnel goals, because I've kind of described upper 224 00:11:26,280 --> 00:11:28,480 Speaker 1: funnel to lower front of right UM, I would say 225 00:11:28,520 --> 00:11:30,320 Speaker 1: it also kind of depends on what the brand is 226 00:11:30,320 --> 00:11:32,520 Speaker 1: really after. So we can ask any question whether it's 227 00:11:32,640 --> 00:11:35,520 Speaker 1: intend to purchase or brand loyalty or perception of brand innovation, 228 00:11:35,679 --> 00:11:38,640 Speaker 1: or brand love brand awareness. I mean, it really kind 229 00:11:38,640 --> 00:11:41,480 Speaker 1: of depends. And that's the beauty of this universal metaphor 230 00:11:41,600 --> 00:11:44,640 Speaker 1: that it is that it kind of enables you to 231 00:11:44,640 --> 00:11:47,160 Speaker 1: to ask any type of question, as opposed to emojis 232 00:11:47,240 --> 00:11:49,800 Speaker 1: or the thumbs up thumbs down, which only really gives 233 00:11:49,800 --> 00:11:53,000 Speaker 1: you a limited range of responses. If brands aren't using notch, 234 00:11:53,040 --> 00:11:56,600 Speaker 1: what are they using? So uh quickly kind of leads 235 00:11:56,600 --> 00:11:58,400 Speaker 1: me to the other thing that really stand for, which 236 00:11:58,400 --> 00:12:01,240 Speaker 1: is transparency. Um, we realize when we came into the 237 00:12:01,240 --> 00:12:04,080 Speaker 1: market that when brands work with agencies, agencies then kind 238 00:12:04,120 --> 00:12:06,959 Speaker 1: of pay let's say, ten different distribution channels, and then 239 00:12:06,960 --> 00:12:09,280 Speaker 1: those distribution channels are actually the ones that pay data 240 00:12:09,320 --> 00:12:11,600 Speaker 1: companies to measure their own performance. So say at the 241 00:12:11,640 --> 00:12:14,240 Speaker 1: level of the distribution channel, you find a combination of 242 00:12:14,320 --> 00:12:17,920 Speaker 1: a few different companies mode you know, Google Analytics sometimes Um, 243 00:12:18,040 --> 00:12:21,440 Speaker 1: Simple Reach, Nudge. There's a bunch of different analytics companies 244 00:12:21,480 --> 00:12:24,480 Speaker 1: that sell to distribution channels. But the problem is this 245 00:12:24,600 --> 00:12:27,520 Speaker 1: is click attribution still though in a lot of ways, right, 246 00:12:27,559 --> 00:12:30,520 Speaker 1: I mean then but like it's front end, yeah, yeah, 247 00:12:30,520 --> 00:12:33,840 Speaker 1: I mean, there's not a lot of depth or variety 248 00:12:33,880 --> 00:12:35,720 Speaker 1: in the type of data that is collected. But the 249 00:12:35,800 --> 00:12:38,160 Speaker 1: problem is two fold. If you're allowing as a brand, 250 00:12:38,200 --> 00:12:39,679 Speaker 1: I mean, you used to be the head of meter, right, 251 00:12:39,720 --> 00:12:42,560 Speaker 1: So um, if you're allowing your distribution channels to grade 252 00:12:42,559 --> 00:12:44,480 Speaker 1: their own homework, they're not going to give themselves a B. 253 00:12:44,840 --> 00:12:46,839 Speaker 1: So you know, you end up as a head of media. 254 00:12:46,920 --> 00:12:48,559 Speaker 1: Not only do you get good news by the time 255 00:12:48,559 --> 00:12:50,320 Speaker 1: it gets to it's like, oh my god, you've done great. 256 00:12:50,440 --> 00:12:52,920 Speaker 1: Keeps any one with us. But the other portion of 257 00:12:53,160 --> 00:12:56,720 Speaker 1: the other problem is that you're allowing the distribution channels 258 00:12:56,800 --> 00:12:59,760 Speaker 1: to spend all this money that you gave them on 259 00:13:00,080 --> 00:13:03,120 Speaker 1: duplicate measurement. So what we came to brands and said was, 260 00:13:03,280 --> 00:13:05,640 Speaker 1: we're not going to monetize from any distribution channel because 261 00:13:05,679 --> 00:13:08,240 Speaker 1: we want to be you know, Switzerland. We want you 262 00:13:08,280 --> 00:13:10,160 Speaker 1: to be our master, and we're going to represent your 263 00:13:10,200 --> 00:13:12,959 Speaker 1: interests when we go and measure on your behalf across 264 00:13:13,000 --> 00:13:17,200 Speaker 1: all these different channels. So we kind of centralized that data, um, 265 00:13:17,240 --> 00:13:20,160 Speaker 1: the data collection essentially, not just the data reporting. And 266 00:13:20,200 --> 00:13:22,400 Speaker 1: as such, I don't think, you know, there's a lot 267 00:13:22,440 --> 00:13:24,319 Speaker 1: of competition in that space. I haven't heard of any 268 00:13:24,360 --> 00:13:26,480 Speaker 1: startup that said I will actively try not to make 269 00:13:26,520 --> 00:13:28,800 Speaker 1: money from two thirds of the market. You know, it's 270 00:13:28,840 --> 00:13:30,719 Speaker 1: kind of like a pretty crazy bold thing to do 271 00:13:31,120 --> 00:13:33,640 Speaker 1: on the how as an industry do we need to 272 00:13:33,640 --> 00:13:35,880 Speaker 1: shake things up when when it comes to data? There 273 00:13:35,880 --> 00:13:38,480 Speaker 1: have been standards of metrics that have been in place 274 00:13:38,640 --> 00:13:41,440 Speaker 1: since I've been in the industry that don't seem to 275 00:13:41,559 --> 00:13:44,760 Speaker 1: keep pace with the change of technology, with the types 276 00:13:44,800 --> 00:13:48,320 Speaker 1: of advertising or non advertising that brands are going to 277 00:13:48,400 --> 00:13:50,880 Speaker 1: market with. You know, laugh a lot about this idea 278 00:13:50,920 --> 00:13:54,600 Speaker 1: of having to duct tape together measurement reports because there 279 00:13:54,640 --> 00:13:56,760 Speaker 1: isn't a one size fits all solution and if there's 280 00:13:56,760 --> 00:14:00,360 Speaker 1: nothing that allows me to see front to back. But yeah, 281 00:14:00,400 --> 00:14:02,640 Speaker 1: we kind of chase to the lowest common denomina and 282 00:14:02,679 --> 00:14:05,360 Speaker 1: say this was a success because X number of people 283 00:14:05,400 --> 00:14:09,600 Speaker 1: engaged with something. Um, what's your long term dream vision 284 00:14:09,720 --> 00:14:12,400 Speaker 1: for how we piece those things together? So I think 285 00:14:12,440 --> 00:14:15,239 Speaker 1: the biggest shift that needs to happen is that cmos 286 00:14:15,360 --> 00:14:18,040 Speaker 1: and marketing teams need to understand the power that they 287 00:14:18,080 --> 00:14:21,720 Speaker 1: have in demanding that they are able to collect first 288 00:14:21,760 --> 00:14:24,520 Speaker 1: party data across every single spend that they do. I 289 00:14:24,520 --> 00:14:26,640 Speaker 1: don't care if it's offline, online, I don't care if 290 00:14:26,680 --> 00:14:31,000 Speaker 1: it's podcast or digital marketing display advertising, it doesn't matter. 291 00:14:31,320 --> 00:14:34,760 Speaker 1: They should understand that they are able to request that 292 00:14:34,800 --> 00:14:38,280 Speaker 1: they get independent data reporting. Now, what this will do 293 00:14:38,560 --> 00:14:41,880 Speaker 1: is to fold. First, it's going to declutter all the 294 00:14:42,000 --> 00:14:44,800 Speaker 1: messs happening right now with all these different distribution channels 295 00:14:44,800 --> 00:14:48,040 Speaker 1: measuring their own performance. It's also going to hopefully remove 296 00:14:48,080 --> 00:14:50,680 Speaker 1: the need for these massive data aggregators like you have, 297 00:14:50,840 --> 00:14:53,360 Speaker 1: including data Ama right that got acquired by Salesforce. They 298 00:14:53,400 --> 00:14:56,360 Speaker 1: are an amazing platform and they do incredible work with 299 00:14:56,400 --> 00:14:58,880 Speaker 1: AI and putting together all these different data sets. But 300 00:14:59,040 --> 00:15:02,160 Speaker 1: why do we allow people to report their own data sets? 301 00:15:02,200 --> 00:15:04,440 Speaker 1: Like why do we allow people to bring an Apple 302 00:15:04,480 --> 00:15:06,440 Speaker 1: and a Draff and ask them data Rama to put 303 00:15:06,440 --> 00:15:08,560 Speaker 1: the two together and make sense of them. But imagine 304 00:15:08,560 --> 00:15:10,680 Speaker 1: if you had ten million dollars and you put a 305 00:15:10,720 --> 00:15:13,320 Speaker 1: million across ten different channels, and you said, guess what, 306 00:15:13,360 --> 00:15:15,120 Speaker 1: I'm going to put my own pixel on all of this, 307 00:15:15,240 --> 00:15:17,040 Speaker 1: and I'm going to get the data in the format 308 00:15:17,080 --> 00:15:19,880 Speaker 1: that I want, on the timeline that I want, and 309 00:15:19,920 --> 00:15:21,520 Speaker 1: I'm going to make sense of it because now I 310 00:15:21,560 --> 00:15:24,240 Speaker 1: can control the kind of collection of it. Right, So 311 00:15:24,400 --> 00:15:26,280 Speaker 1: when you control the collection and the formatting of it, 312 00:15:26,760 --> 00:15:28,640 Speaker 1: making sense of it is really easy. Like as a 313 00:15:28,680 --> 00:15:30,960 Speaker 1: data scientist, I can tell you of the job of 314 00:15:30,960 --> 00:15:33,240 Speaker 1: a data scientist is cleaning data sets and making them 315 00:15:33,280 --> 00:15:36,200 Speaker 1: talk to each other. But should they hold on? But 316 00:15:36,320 --> 00:15:39,360 Speaker 1: should it be? That's a problem in the industry right now. 317 00:15:39,400 --> 00:15:41,240 Speaker 1: By the way, the problem, one of the biggest problems 318 00:15:41,240 --> 00:15:44,720 Speaker 1: in the industry in my opinion, is that data scientists 319 00:15:44,920 --> 00:15:47,480 Speaker 1: are actually people who need to be innovating and be 320 00:15:47,560 --> 00:15:51,080 Speaker 1: more like data entrepreneurs and be actually getting more from 321 00:15:51,360 --> 00:15:55,640 Speaker 1: insight and be evaluating insight versus cleaning data. Right now, 322 00:15:55,800 --> 00:16:00,280 Speaker 1: we have built this system that is all about data prep. Yeah, 323 00:16:00,320 --> 00:16:02,360 Speaker 1: and it's because people are freaked out and I don't 324 00:16:02,360 --> 00:16:04,360 Speaker 1: even know is this the right question? Are we asking 325 00:16:04,400 --> 00:16:08,040 Speaker 1: the right question around just gathering data? To me, it's 326 00:16:08,400 --> 00:16:11,720 Speaker 1: are we gathering the right insight? Do we actually right? 327 00:16:11,880 --> 00:16:15,080 Speaker 1: Do we can? We evaluate the right insight? But if 328 00:16:15,120 --> 00:16:18,160 Speaker 1: we agree that their time is spent on cleaning data sets, 329 00:16:18,160 --> 00:16:20,080 Speaker 1: then we need to figure out how to remove that 330 00:16:20,160 --> 00:16:24,680 Speaker 1: time so they can spend time on insights and strategies. 331 00:16:24,680 --> 00:16:27,280 Speaker 1: So my proposition is that there's gonna be really bold 332 00:16:27,280 --> 00:16:29,280 Speaker 1: brands like Jimpy, Morgan Chase. We're going to go in 333 00:16:29,360 --> 00:16:32,840 Speaker 1: and say notch will be my data collection practice. Right, 334 00:16:32,920 --> 00:16:35,160 Speaker 1: my data collection tag picks whatever you want to call it, 335 00:16:35,480 --> 00:16:38,680 Speaker 1: and we collect all that data. It's already clean. So literally, 336 00:16:38,880 --> 00:16:41,720 Speaker 1: our data science team, all they do is find insights. 337 00:16:41,840 --> 00:16:43,920 Speaker 1: All they do is get on the phone with Jamie 338 00:16:43,920 --> 00:16:46,640 Speaker 1: Morgan and their agency and say, put more money behind here. 339 00:16:47,000 --> 00:16:49,920 Speaker 1: Decrease the importance of that that audience is not responding well, 340 00:16:49,960 --> 00:16:52,320 Speaker 1: because we don't waste any time trying to clean up 341 00:16:52,360 --> 00:16:55,560 Speaker 1: stuff for can we trust this data? No, it's all 342 00:16:55,600 --> 00:16:58,040 Speaker 1: first party, it's all transparent, and we can, you know, 343 00:16:58,120 --> 00:17:01,560 Speaker 1: go ahead and truly analyze it. So my big outlook 344 00:17:01,640 --> 00:17:05,159 Speaker 1: is CMOS basically going to Facebook and YouTube and Twitter 345 00:17:05,280 --> 00:17:08,240 Speaker 1: and Amazon and everyone and saying you want my money, great, 346 00:17:08,480 --> 00:17:10,520 Speaker 1: put my pixel on it. I need to collect data 347 00:17:10,520 --> 00:17:13,280 Speaker 1: about my audience and how they're responding to my advertising. 348 00:17:13,320 --> 00:17:15,439 Speaker 1: And they're doing that through not they're doing that well. 349 00:17:15,760 --> 00:17:19,080 Speaker 1: So you have an agreement with any third party site 350 00:17:19,080 --> 00:17:23,240 Speaker 1: out there. I assume right that if someone is interacting 351 00:17:23,320 --> 00:17:27,920 Speaker 1: in a notch ui that you have rights to all 352 00:17:27,960 --> 00:17:30,919 Speaker 1: of that data. So that's how it works. So the 353 00:17:31,000 --> 00:17:33,000 Speaker 1: data belongs to the brand, it doesn't belong to us. 354 00:17:33,119 --> 00:17:36,040 Speaker 1: We're literally just a conduit for data collection, and we 355 00:17:36,160 --> 00:17:38,920 Speaker 1: reserve the right to create benchmarks and aggregate insights. You 356 00:17:38,960 --> 00:17:41,320 Speaker 1: would do work with you know, publisher X. We would 357 00:17:41,640 --> 00:17:45,040 Speaker 1: embed in that content piece, and any data that comes, 358 00:17:45,080 --> 00:17:47,480 Speaker 1: like the audience that came to that audience, to that 359 00:17:47,520 --> 00:17:50,040 Speaker 1: content pies, the type of audience, the way they engaged, 360 00:17:50,080 --> 00:17:53,080 Speaker 1: the way they felt, all of that would be passed 361 00:17:53,080 --> 00:17:55,840 Speaker 1: on immediately to you and the publisher and the agency. 362 00:17:55,920 --> 00:17:59,000 Speaker 1: So complete transparency. But you would own the data, and 363 00:17:59,040 --> 00:18:02,680 Speaker 1: we would aggregate data from publisher X, publisher, why, publisher whatever, 364 00:18:02,920 --> 00:18:04,239 Speaker 1: and you all of a sudden you would be left 365 00:18:04,280 --> 00:18:07,080 Speaker 1: with this first party data pool that you could then 366 00:18:07,119 --> 00:18:09,680 Speaker 1: put into your CRM, put into your DVP, etcetera. And 367 00:18:10,119 --> 00:18:14,919 Speaker 1: what you just said, no one's doing. But let's like no, 368 00:18:15,040 --> 00:18:18,520 Speaker 1: but let's let's like like explain that you are saying 369 00:18:18,720 --> 00:18:23,320 Speaker 1: that a brand could actually own data from a publisher, 370 00:18:23,359 --> 00:18:26,520 Speaker 1: from an audience, from audiences they came to a specific 371 00:18:26,560 --> 00:18:31,960 Speaker 1: publisher versus a publisher giving you the data they want 372 00:18:31,960 --> 00:18:36,879 Speaker 1: you to have. Yeah, exactly. Hello, And and also the 373 00:18:36,920 --> 00:18:39,719 Speaker 1: reason why publishers are okay with this, just for a second, 374 00:18:39,960 --> 00:18:42,960 Speaker 1: there's some publishers out there that are definitely pushing back 375 00:18:43,000 --> 00:18:46,320 Speaker 1: and have against transparency on this idea, but publishers are 376 00:18:46,320 --> 00:18:48,639 Speaker 1: actually waking up to realize the more they embrace the 377 00:18:48,640 --> 00:18:52,119 Speaker 1: transparency rhetoric and approach, the better it is for them, 378 00:18:52,119 --> 00:18:54,720 Speaker 1: because it's coming anyways, right, and if they truly trust 379 00:18:54,720 --> 00:18:57,160 Speaker 1: their engagement, it makes sense for them to do this. 380 00:18:57,359 --> 00:19:00,359 Speaker 1: The question they ask us is can you please make 381 00:19:00,400 --> 00:19:01,960 Speaker 1: sure that this data that we're going to give to 382 00:19:02,080 --> 00:19:04,280 Speaker 1: j Imraan Chase is not going to be put into 383 00:19:04,840 --> 00:19:08,040 Speaker 1: a audience extension platform, Like they don't want their data 384 00:19:08,160 --> 00:19:10,399 Speaker 1: or their audience to be monetized. And yes, we do that. 385 00:19:10,440 --> 00:19:13,040 Speaker 1: We promise them that because the data that we capture, 386 00:19:13,119 --> 00:19:14,920 Speaker 1: we don't have a data exchange, We don't sell that 387 00:19:15,000 --> 00:19:16,919 Speaker 1: data to competitors, we don't do anything with it. It 388 00:19:16,960 --> 00:19:19,120 Speaker 1: just belongs to the brand. So what the brand could 389 00:19:19,160 --> 00:19:22,040 Speaker 1: do something with it? They if they put it into 390 00:19:22,040 --> 00:19:24,800 Speaker 1: their d MP. By that point it's completely anonymized. I mean, 391 00:19:24,800 --> 00:19:27,880 Speaker 1: we're GDP are complying across every single state of America 392 00:19:27,920 --> 00:19:31,080 Speaker 1: and Europe, right, and it doesn't really lead back to 393 00:19:31,119 --> 00:19:34,360 Speaker 1: the publisher. An interesting m article that just came out 394 00:19:34,400 --> 00:19:38,040 Speaker 1: from saraficher Um inter Axios Trends Reports that is calling 395 00:19:38,080 --> 00:19:42,399 Speaker 1: this the summer of ad tech UM acquisitions. Right, So 396 00:19:42,440 --> 00:19:46,320 Speaker 1: you alluded to obviously data Rama being purchased by Salesforce 397 00:19:46,359 --> 00:19:52,880 Speaker 1: for over a hundred million dollars. You've got Medium IPG 398 00:19:53,160 --> 00:19:57,080 Speaker 1: obviously one of the largest holding company groups, acquiring Axiom 399 00:19:57,359 --> 00:20:01,760 Speaker 1: for over two billion dollars. This obvious is going to continue. 400 00:20:02,040 --> 00:20:04,520 Speaker 1: As you're talking on and you're describing your kind of 401 00:20:04,560 --> 00:20:07,080 Speaker 1: thought and vision is what's happening in the the industry is 402 00:20:07,119 --> 00:20:12,440 Speaker 1: where you're positions and knowing that agencies UM you need 403 00:20:12,520 --> 00:20:16,560 Speaker 1: this support. Do you see not moving to a data 404 00:20:16,640 --> 00:20:20,400 Speaker 1: agency that happens to make products. We currently have kind 405 00:20:20,400 --> 00:20:23,880 Speaker 1: of a customer success data and our team that does that, 406 00:20:23,960 --> 00:20:27,399 Speaker 1: and they've worked with your teams and hopefully helped interpret 407 00:20:27,480 --> 00:20:29,399 Speaker 1: some data. But we're thinking a lot about how do 408 00:20:29,440 --> 00:20:31,760 Speaker 1: you scale that, how do you actually even bload that 409 00:20:31,880 --> 00:20:34,000 Speaker 1: up to take it to the next level UM, And 410 00:20:34,040 --> 00:20:37,080 Speaker 1: so we're thinking about UM either building our own high 411 00:20:37,080 --> 00:20:41,159 Speaker 1: touch consultancy around UH, you know, analyzing data not just 412 00:20:41,240 --> 00:20:43,199 Speaker 1: our data, but maybe other first party data that we 413 00:20:43,240 --> 00:20:47,320 Speaker 1: can bring into our fold UM or creating an ecosystem 414 00:20:47,359 --> 00:20:50,119 Speaker 1: around notch and just saying hey, guys, here's our data. 415 00:20:50,560 --> 00:20:52,280 Speaker 1: You know, do you do you want to go analyze it? 416 00:20:52,280 --> 00:20:55,479 Speaker 1: And maybe we work with Canada or a center, you know, 417 00:20:55,520 --> 00:20:58,879 Speaker 1: the kind of more services heavy players um. And so 418 00:20:58,920 --> 00:21:01,560 Speaker 1: that's something that I'm very actively thinking about and we'll 419 00:21:01,600 --> 00:21:04,040 Speaker 1: probably you'll see something from us over the next six months. 420 00:21:04,240 --> 00:21:08,240 Speaker 1: What what is the industry missing? Like? Where are we 421 00:21:08,440 --> 00:21:11,959 Speaker 1: We're so data obsessed to the point of just complete saturation. 422 00:21:12,000 --> 00:21:13,400 Speaker 1: I mean we have people come and talk about data 423 00:21:13,400 --> 00:21:16,439 Speaker 1: in all different types of ways on this show. What 424 00:21:16,600 --> 00:21:18,840 Speaker 1: is the thing that we're not seeing? I mean, you're 425 00:21:18,920 --> 00:21:21,240 Speaker 1: from your vantage point. I always say, you know, there's 426 00:21:21,800 --> 00:21:24,480 Speaker 1: there's this weird paradox where you have too much data 427 00:21:24,680 --> 00:21:28,480 Speaker 1: and very little insight. And I think the what's missing 428 00:21:28,560 --> 00:21:31,040 Speaker 1: is that we're just taking data sets that have already 429 00:21:31,040 --> 00:21:33,320 Speaker 1: been collected and banging our head against the will trying 430 00:21:33,359 --> 00:21:35,840 Speaker 1: to make them, you know, talk to each other. When 431 00:21:35,840 --> 00:21:38,640 Speaker 1: the truth is, things are changing so fast online that 432 00:21:38,840 --> 00:21:41,200 Speaker 1: if you're not collecting data in real time and using 433 00:21:41,240 --> 00:21:43,639 Speaker 1: it for predictive insights, then you're already behind. Like if 434 00:21:43,640 --> 00:21:45,840 Speaker 1: you're still trying to figure out how the data sets 435 00:21:45,880 --> 00:21:48,359 Speaker 1: of six months ago fit with each other, and you know, 436 00:21:48,400 --> 00:21:51,120 Speaker 1: forget it. So I think what's the point that we're 437 00:21:51,160 --> 00:21:54,040 Speaker 1: missing is kind of forget the data sets you've already collected. 438 00:21:54,080 --> 00:21:56,520 Speaker 1: Just try to focus on how you streamline data collections. 439 00:21:56,600 --> 00:21:58,520 Speaker 1: You can action on the stuff that the audience is 440 00:21:58,520 --> 00:22:00,600 Speaker 1: trying to tell you. Now, that's I was just going 441 00:22:00,640 --> 00:22:04,679 Speaker 1: to say to you, this is about insight forward, go back, right, 442 00:22:04,800 --> 00:22:08,719 Speaker 1: it's data back. It's insight forward, which to me means analytics. Right. 443 00:22:08,840 --> 00:22:11,639 Speaker 1: It to me means actually going back to your roots. 444 00:22:12,040 --> 00:22:15,879 Speaker 1: It's math. Yeah, I think it's actually marrying research analytics 445 00:22:15,880 --> 00:22:18,600 Speaker 1: in real time, which has not really been possible, and 446 00:22:18,680 --> 00:22:20,919 Speaker 1: we're trying to obviously do that through what we do. 447 00:22:21,000 --> 00:22:22,600 Speaker 1: And by the way, this is the other thing that 448 00:22:22,920 --> 00:22:24,680 Speaker 1: I don't even know if it's going to blow up 449 00:22:24,880 --> 00:22:29,199 Speaker 1: the Internet, probably not. But I don't really believe in 450 00:22:29,440 --> 00:22:33,280 Speaker 1: benchmarks and indexes in today's world. I've just seen brands 451 00:22:33,320 --> 00:22:37,120 Speaker 1: define their notions of success so radically differently, and content 452 00:22:37,320 --> 00:22:40,400 Speaker 1: is not only valuable, but as I mentioned, it's multivariate 453 00:22:40,440 --> 00:22:42,920 Speaker 1: and its success. I feel like people should just kind 454 00:22:42,920 --> 00:22:45,280 Speaker 1: of forget that and focus on who we are, what 455 00:22:45,400 --> 00:22:47,080 Speaker 1: is our bottom line, and how do we get there 456 00:22:47,080 --> 00:22:53,760 Speaker 1: through marketing? Right, Because everyone's talking about the single source 457 00:22:53,800 --> 00:22:56,840 Speaker 1: of truth, and what you're saying is there is no 458 00:22:57,000 --> 00:23:00,920 Speaker 1: single source of truth. You have to start going forward 459 00:23:01,000 --> 00:23:06,560 Speaker 1: into insights versus worrying about these made up benchmarks well 460 00:23:06,600 --> 00:23:11,040 Speaker 1: that we've created that aren't really actually benchmarks. Right. Data 461 00:23:11,080 --> 00:23:16,840 Speaker 1: is directional. Of course, people have made data their strategy. Yeah, 462 00:23:17,080 --> 00:23:19,199 Speaker 1: you know, and people are like this says this, so 463 00:23:19,280 --> 00:23:22,880 Speaker 1: we must do that. And he's opposed to like, where 464 00:23:22,880 --> 00:23:25,639 Speaker 1: where Why aren't we critically thinking about these things in 465 00:23:25,760 --> 00:23:29,479 Speaker 1: aggregate and then borrowing from culture and borrowing from you know, 466 00:23:29,640 --> 00:23:33,479 Speaker 1: influence and being able to kind of create as opposed 467 00:23:33,520 --> 00:23:35,600 Speaker 1: to dictate. I think it's very different. I also think 468 00:23:35,640 --> 00:23:39,120 Speaker 1: it changes the mindset and the organization of a marketing 469 00:23:39,160 --> 00:23:42,240 Speaker 1: team all of a sudden, puts you into like thought 470 00:23:42,359 --> 00:23:47,800 Speaker 1: sprints almost versus these long drawn out plans that make 471 00:23:47,880 --> 00:23:50,720 Speaker 1: no sense. Data decks that are over a hundred pages. 472 00:23:50,880 --> 00:23:53,360 Speaker 1: Give me the executive summary, what is the negative information? 473 00:23:53,400 --> 00:23:55,240 Speaker 1: I need to move on? Let's go. I think data 474 00:23:55,280 --> 00:23:58,159 Speaker 1: has two functions. The first one is you put it 475 00:23:58,240 --> 00:24:01,520 Speaker 1: to use immediately through plant forms that can harness it 476 00:24:01,560 --> 00:24:05,520 Speaker 1: to better target audiences both with creative and distribute media. Right, 477 00:24:06,080 --> 00:24:09,760 Speaker 1: or the second is its surfaces three to five main 478 00:24:09,800 --> 00:24:12,480 Speaker 1: insights that you can go and think about and act 479 00:24:12,520 --> 00:24:15,719 Speaker 1: on in your next campaign or next strategy. But anything 480 00:24:15,720 --> 00:24:18,120 Speaker 1: in between. If you're talking about you know, even tempage 481 00:24:18,160 --> 00:24:20,800 Speaker 1: data decks, I don't think it's you know, I don't 482 00:24:20,800 --> 00:24:25,480 Speaker 1: think it's realistic. Are you only digital in terms of UM, 483 00:24:25,520 --> 00:24:30,359 Speaker 1: Like online media is notch? Can you notch work on broadcast? 484 00:24:30,840 --> 00:24:37,000 Speaker 1: You know, linear, other screens, vot T voice, those types 485 00:24:37,040 --> 00:24:39,399 Speaker 1: of places. So now we're going to get to like 486 00:24:39,440 --> 00:24:44,200 Speaker 1: how we take over the world answer. So we started 487 00:24:44,240 --> 00:24:48,040 Speaker 1: off with literally just paid custom content. We then expanded 488 00:24:48,119 --> 00:24:52,520 Speaker 1: into content syndication, then expanded into own content, then social 489 00:24:52,600 --> 00:24:55,080 Speaker 1: so we actually can capture first pretty data on a 490 00:24:55,119 --> 00:24:57,920 Speaker 1: couple of the big walled curtains, which is crazy UM. 491 00:24:58,119 --> 00:25:02,000 Speaker 1: Then expanded into email, and now we're expanding into being 492 00:25:02,000 --> 00:25:04,720 Speaker 1: able to measure podcasts as well. And so we're building 493 00:25:04,800 --> 00:25:07,240 Speaker 1: towards understanding what is being said in a podcast and 494 00:25:07,280 --> 00:25:09,480 Speaker 1: then trying to basically figure out how do we meet 495 00:25:09,760 --> 00:25:11,920 Speaker 1: the customer or the audience in a way to ask 496 00:25:11,920 --> 00:25:14,480 Speaker 1: them for their actual feedback but also study their behavior. 497 00:25:14,560 --> 00:25:17,080 Speaker 1: So very much working on that very much, thinking about 498 00:25:17,119 --> 00:25:18,800 Speaker 1: O T T I think there will be a day 499 00:25:18,840 --> 00:25:21,240 Speaker 1: when we figure out how to do offline as well. UM. 500 00:25:21,280 --> 00:25:23,560 Speaker 1: But yeah, the entire philosophy behind it is do it 501 00:25:23,640 --> 00:25:26,240 Speaker 1: in a way that has the CMOS kind of interest 502 00:25:26,320 --> 00:25:28,119 Speaker 1: in mind, and do it in a way that collects 503 00:25:28,160 --> 00:25:30,439 Speaker 1: first party data only. I would love to see the 504 00:25:30,480 --> 00:25:34,040 Speaker 1: temperature scale on this show. You'll be the first customer 505 00:25:34,080 --> 00:25:38,359 Speaker 1: the beta test. I don't know how Atlantia feels about that, 506 00:25:38,400 --> 00:25:41,959 Speaker 1: but like it's coming at you Atlandia. So and uh, 507 00:25:42,200 --> 00:25:45,680 Speaker 1: we're gonna flip to our game that we play. It's 508 00:25:45,720 --> 00:25:48,680 Speaker 1: called kill by d I Y what would you kill? 509 00:25:49,320 --> 00:25:51,520 Speaker 1: What would you kill? What would you buy? What would 510 00:25:51,560 --> 00:25:56,200 Speaker 1: you do yourself? Not? Not something else? So I would 511 00:25:56,400 --> 00:26:00,840 Speaker 1: kill any form of transactional advertising, to be honest ist, UM, 512 00:26:00,880 --> 00:26:03,280 Speaker 1: that would be mine, and that includes display, but it 513 00:26:03,320 --> 00:26:06,200 Speaker 1: includes a few others. UM. I absolutely hate being met 514 00:26:06,280 --> 00:26:11,000 Speaker 1: with the um the really impersonal advertising of a brand. 515 00:26:11,000 --> 00:26:13,600 Speaker 1: So I persume a massive believer in content, I would 516 00:26:13,680 --> 00:26:19,560 Speaker 1: probably buy most of our competitors and create a larger 517 00:26:19,600 --> 00:26:21,960 Speaker 1: consortium that has a lot of power to compete with 518 00:26:22,000 --> 00:26:24,959 Speaker 1: the Oracles and Adobes and salesforces of the world. And 519 00:26:24,960 --> 00:26:28,000 Speaker 1: I'm actually actively thinking about that so hopefully they can 520 00:26:28,000 --> 00:26:30,440 Speaker 1: hear me say that. Um, And what was the last one? 521 00:26:30,560 --> 00:26:35,840 Speaker 1: What would you do yourself outside of notch? Oh? Well, 522 00:26:35,840 --> 00:26:38,199 Speaker 1: can I answer something radically different that's not at all 523 00:26:38,240 --> 00:26:40,920 Speaker 1: in this space. That's what we like. Well, So I've 524 00:26:40,960 --> 00:26:44,440 Speaker 1: been thinking a lot about how professional women need UM 525 00:26:44,760 --> 00:26:48,080 Speaker 1: a brand that cuters, like a clothing brand that cuters 526 00:26:48,119 --> 00:26:50,679 Speaker 1: to them, UM in a way that you know, enables 527 00:26:50,720 --> 00:26:53,720 Speaker 1: them to look amazing and high powered and sassy and 528 00:26:53,960 --> 00:26:57,840 Speaker 1: awesome from seven am to the eleven PM when that 529 00:26:57,920 --> 00:26:59,600 Speaker 1: dinner ends. And so I've been thinking a lot about 530 00:26:59,600 --> 00:27:01,679 Speaker 1: the fact that I don't really have those types of clothes. 531 00:27:01,680 --> 00:27:04,040 Speaker 1: I have to go like our shoes that are comfortable 532 00:27:04,119 --> 00:27:07,439 Speaker 1: but also kind of show my very spunky personality. So 533 00:27:07,480 --> 00:27:09,400 Speaker 1: I've been thinking a lot about like what brand would 534 00:27:09,440 --> 00:27:11,560 Speaker 1: do that? And I would probably do it myself. And 535 00:27:11,920 --> 00:27:13,840 Speaker 1: thank you so much for coming, Thank you for having 536 00:27:14,040 --> 00:27:16,000 Speaker 1: this was so much fun. If people want to get 537 00:27:16,040 --> 00:27:18,280 Speaker 1: in touch with you by your product, get more of 538 00:27:18,280 --> 00:27:21,800 Speaker 1: your insight, yeah, reach and at k n otc H 539 00:27:21,880 --> 00:27:28,000 Speaker 1: dot com. And it's been awesome much thank you, Thank 540 00:27:28,040 --> 00:27:30,520 Speaker 1: you on a fun conversation, and there's so much more 541 00:27:30,600 --> 00:27:32,399 Speaker 1: we like just hit the tip of the iceberg. So 542 00:27:32,400 --> 00:27:34,720 Speaker 1: we're definitely going to have you back. I think we 543 00:27:34,760 --> 00:27:37,840 Speaker 1: should do like a much bigger round table too with 544 00:27:37,920 --> 00:27:40,879 Speaker 1: some of these folks who are thinking differently, talking differently 545 00:27:40,920 --> 00:27:43,240 Speaker 1: about data. And I love that she said she wants 546 00:27:43,240 --> 00:27:46,440 Speaker 1: to put together a consortium of ad tack that could 547 00:27:46,440 --> 00:27:49,480 Speaker 1: go and compete against the oracles. I think that actually 548 00:27:49,640 --> 00:27:53,480 Speaker 1: is the new attack, that meaningful attack, and we've got 549 00:27:53,480 --> 00:27:58,160 Speaker 1: to like strip away this like old skin of ad 550 00:27:58,200 --> 00:28:01,200 Speaker 1: tech and actually kind of raise the people up who 551 00:28:01,200 --> 00:28:03,359 Speaker 1: are thinking like Ada and who are doing things that 552 00:28:03,520 --> 00:28:06,440 Speaker 1: actually make a difference and have a vision around what 553 00:28:06,960 --> 00:28:09,400 Speaker 1: marketing of the future is going to look like and 554 00:28:09,440 --> 00:28:13,280 Speaker 1: how it's going to speak and get back to their 555 00:28:13,280 --> 00:28:15,320 Speaker 1: consumer needs the way we need to get back to 556 00:28:15,359 --> 00:28:17,680 Speaker 1: our consumer needs. Yeah, I love the idea of creative 557 00:28:17,680 --> 00:28:20,639 Speaker 1: and strategic application of data. I mean, I think, you know, 558 00:28:21,160 --> 00:28:24,560 Speaker 1: data for face value in the numbers is certainly valuable 559 00:28:24,600 --> 00:28:26,600 Speaker 1: and I think we will continue to need it, But 560 00:28:27,040 --> 00:28:30,080 Speaker 1: the interpretation and how we use it is obviously where 561 00:28:30,320 --> 00:28:32,359 Speaker 1: I think we all collectively agree to it needs to 562 00:28:32,400 --> 00:28:35,440 Speaker 1: go so big. Thanks to our friends and family at Panoply, Dana, 563 00:28:35,680 --> 00:28:39,800 Speaker 1: our producer Matt Turk Andy Bowers, Jacob Weissberg, and all 564 00:28:39,840 --> 00:28:42,280 Speaker 1: of our friends and family in Atlantia. We'll be back 565 00:28:42,280 --> 00:28:48,680 Speaker 1: in two weeks. Full Disclosure. Our opinions are our own