1238: How Marketers Can Leverage AI to Address Data Analytics Challenges w/ Tim Burke

WEBVTT 1 00:00:05.599 --> 00:00:09.710 Welcome back to beb growth on Logan lyles with sweet fish media. Today I'm 2 00:00:09.710 --> 00:00:13.429 joined by Tim Burke. He's the CEO over at a Fineo. Tim, 3 00:00:13.630 --> 00:00:17.190 how's it going today, sir, great, great to talk today. Absolutely, 4 00:00:17.309 --> 00:00:19.510 man. I am excited to dive in. We're going to be talking 5 00:00:19.510 --> 00:00:25.460 about the changing data landscape and what that means for marketers, both regulatory changes 6 00:00:25.539 --> 00:00:29.100 that we're all kind of familiar with, looking at how we manage all that 7 00:00:29.140 --> 00:00:33.539 data and what do we do about this changing landscape. For some context, 8 00:00:33.659 --> 00:00:37.049 why you're the guy sharing about this today. Gives a little bit of background 9 00:00:37.049 --> 00:00:39.570 on yourself, Tim, and what you in the Afineo team are up to 10 00:00:39.649 --> 00:00:42.570 these days. Yeah, all this great. Appreciate the time, Logan. 11 00:00:42.649 --> 00:00:46.929 So my name is Tim Burke, CEO of Afineo. We built the company 12 00:00:47.009 --> 00:00:50.679 but seven years ago, and just little bit of background on a fine itself. 13 00:00:51.079 --> 00:00:55.600 We're an augmented analytics platform and we're built on a custom graph technology, 14 00:00:55.679 --> 00:01:00.320 and what that fundamentally means is that we analyzed data in a little different way 15 00:01:00.399 --> 00:01:04.189 than many people were, looking for specifically the connections in data and trying to 16 00:01:04.269 --> 00:01:11.069 identify those common behaviors and affinities across massive consumer data sets and then, once 17 00:01:11.189 --> 00:01:15.709 we unlock those, we able to provide that into an insight layer that's easy 18 00:01:15.790 --> 00:01:19.859 to use and visualize so that the marketing teams can generate data gerven strategies and 19 00:01:19.980 --> 00:01:26.980 targeted octivations. Ultimately, so helping teams do everything from content ideation and strategy 20 00:01:26.140 --> 00:01:30.459 to hyper targeted in person last content. I love it. I always say, 21 00:01:30.500 --> 00:01:34.530 you know, we're just swimming in data these days, but we I 22 00:01:34.609 --> 00:01:38.530 mean you hit on two things that are so key to actually making that actual. 23 00:01:38.810 --> 00:01:41.810 How can we analyze it, analyze it quickly, and how can we 24 00:01:41.930 --> 00:01:45.730 visualize it so that we can see the trends and then take appropriate action? 25 00:01:45.930 --> 00:01:49.760 So we wouldn't be able to talk about data without talking about some of the 26 00:01:49.840 --> 00:01:55.079 regulatory changes going on. Everyone you know what was up in arms and my 27 00:01:55.239 --> 00:01:59.280 linkedin feed was full for about a month on gear our last year. But 28 00:01:59.599 --> 00:02:01.549 tell us a little bit about some of the things that have changed over the 29 00:02:01.629 --> 00:02:05.870 last year or so for some context. Then we'll get into you know, 30 00:02:06.109 --> 00:02:08.389 what can marketers do about it? But I think that stage setting still a 31 00:02:08.430 --> 00:02:13.629 little bit appropriate, though I think everybody listening to this has heard of gdpr 32 00:02:13.669 --> 00:02:15.620 at least. So, yeah, right, no, I mean I think 33 00:02:15.659 --> 00:02:21.539 we live in really interesting times. Right. It's with the things like Gdpr, 34 00:02:21.819 --> 00:02:25.780 CCPA that are obviously emerging from a privacy perspective. I think consumers as 35 00:02:25.819 --> 00:02:30.330 a whole have a whole new perspective exactly of what kind of data is being 36 00:02:30.370 --> 00:02:34.169 collected us on a regular basis, how that data is being used and want, 37 00:02:34.289 --> 00:02:37.569 you know, have opinions and want to know exactly how that day is 38 00:02:37.610 --> 00:02:40.849 being used. And as a result, that's transition being master changes in the 39 00:02:40.930 --> 00:02:46.879 marketplace relative to third party data, third party did exchanges, not the least 40 00:02:46.879 --> 00:02:50.400 of which, you know, most recently, or street Google's announcements of third 41 00:02:50.439 --> 00:02:53.120 party cookie, you know, not being sort of supported within the chrome browser. 42 00:02:53.759 --> 00:02:57.719 So we've seen the trends, you know, emerging, I would say, 43 00:02:57.759 --> 00:03:00.189 over the last couple of years. I think we will continue to see 44 00:03:00.629 --> 00:03:06.469 privacy be at the forefront and ultimately, what that means to, you know, 45 00:03:06.990 --> 00:03:09.750 today's marketers, for me anyway, is that I look at it from 46 00:03:09.870 --> 00:03:14.699 that changing landscape of being, you know, parties and groups and teams being 47 00:03:14.740 --> 00:03:20.099 able to rely heavily on sort of insights derived from segments and third party data 48 00:03:20.139 --> 00:03:23.659 or being able to use, you know, oftentimes the ad targeting platforms of 49 00:03:24.060 --> 00:03:29.210 the likes sort of facebook and Google as from the foundational starting point for insights 50 00:03:29.610 --> 00:03:35.250 from marketing. Now we're transitioning into a phase where everybody has to collect first 51 00:03:35.289 --> 00:03:38.409 party data. That's obvious and clear. But, more importantly, have to 52 00:03:38.569 --> 00:03:43.560 do more with that first party data and I think that's kind of the interesting 53 00:03:43.599 --> 00:03:46.599 trend that we're starting to see in the market. I tell people that, 54 00:03:46.159 --> 00:03:50.840 you know, we've been storing and you know, enterprises have been storing first 55 00:03:50.840 --> 00:03:53.680 party data for years. I think we're starting to see the gap in the 56 00:03:53.800 --> 00:03:57.509 market place is and how you store and manage that date. It's how you 57 00:03:57.669 --> 00:04:02.069 make it meaningful and actionable and put insights and actual insights in the fingertips of 58 00:04:02.189 --> 00:04:08.110 the entire organization. That becomes now the challenge that has to get addressed by 59 00:04:08.110 --> 00:04:11.780 the enterprise, and so I think it's interesting. I think the you know, 60 00:04:11.979 --> 00:04:15.100 the all the way down to the emergence of things like be to see 61 00:04:15.300 --> 00:04:18.819 brands that are, you know, trumping, you know, the the old 62 00:04:18.939 --> 00:04:23.620 enterprises in a lot of their own businesses, and often times it's a boat 63 00:04:23.980 --> 00:04:28.889 how they're leveraging their data faster and more nimble, and that translates into better 64 00:04:28.930 --> 00:04:32.970 strategies and winning in categories that you know haven't moved or having changed in a 65 00:04:33.050 --> 00:04:36.329 long time. Yeah, absolutely, and you talk about, you know, 66 00:04:36.410 --> 00:04:40.759 kind of the first step when it comes to data management is where's it coming 67 00:04:40.759 --> 00:04:44.040 from? You talked a little bit about first party versus third party. One 68 00:04:44.079 --> 00:04:47.240 of the other challenges that you guys have been seeing, Tim correct me if 69 00:04:47.279 --> 00:04:51.560 I'm wrong, is how that data is being handled internally, because you have 70 00:04:51.759 --> 00:04:57.230 to be careful about who's touching that, what teams have access. It can 71 00:04:57.350 --> 00:05:00.670 create some real bottlenecks for marketing teams to be able to leverage that date, 72 00:05:00.750 --> 00:05:04.029 even when they've got it from appropriate places, right, and that's good. 73 00:05:04.069 --> 00:05:08.139 That's absolutely correct. I would say that, you know, some of the 74 00:05:08.259 --> 00:05:11.300 you know, the number one pain point we here directly in Mark I is 75 00:05:12.019 --> 00:05:15.300 the reliance on oftentimes sort of the data science or analyst teams, who are 76 00:05:15.339 --> 00:05:20.689 simply just overwhelmed, right. I mean they oftentimes have higher level data security 77 00:05:20.889 --> 00:05:26.649 and authorization, so they end up being the teams who can actually access the 78 00:05:26.730 --> 00:05:30.050 raw data and the PII. That's, you know, tends to sort of 79 00:05:30.089 --> 00:05:32.810 reside within the data warehouse or within the seat Ram. So they sort of 80 00:05:32.850 --> 00:05:36.360 become sort of this fun, you know, the single point of contact you 81 00:05:36.560 --> 00:05:41.279 to the raw data set. And as a result, those teams are becoming 82 00:05:41.560 --> 00:05:46.079 backlogged with just requests from all across that, you know, all across the 83 00:05:46.199 --> 00:05:48.160 enterprise and marketing. He's just one of those groups, right. I mean 84 00:05:48.199 --> 00:05:54.149 they're handling and managing request from sales, from operations, from Yo HR, 85 00:05:54.389 --> 00:05:59.069 everywhere. So I think the the CH challenge is that you with the with 86 00:05:59.310 --> 00:06:04.420 certainly the increased sensitivity around privacy, which is completely appropriate in terms of, 87 00:06:05.019 --> 00:06:09.459 you know, the concerns for the enterprise. The solution to date, or 88 00:06:09.579 --> 00:06:13.019 are you know what I would deem the state of the art, is employing 89 00:06:13.060 --> 00:06:16.139 large data science teams to try to find insights and try to scale that within 90 00:06:16.180 --> 00:06:21.050 an organization. But I mean the reality is that those teams simply aren't scalable. 91 00:06:21.089 --> 00:06:27.449 Are the data volumes are increasing almost faster than those teams can manage it, 92 00:06:27.610 --> 00:06:30.649 and not only that, the requests that are coming in down to those 93 00:06:30.689 --> 00:06:34.480 teams. He's growing as similarly an exponential rate, and so for us, 94 00:06:34.519 --> 00:06:39.600 I think we're sort of hitting a tipping point that to manage privacy means, 95 00:06:39.839 --> 00:06:44.279 you know, not allowing everybody in your organization to be able to reach private 96 00:06:44.319 --> 00:06:46.629 data and Pii data, and that's the right decision. I think the the 97 00:06:47.069 --> 00:06:51.589 approach at that most organizations are trying to take the to overcome that challenge. 98 00:06:51.910 --> 00:06:56.670 He's just simply not scalable now and I think we're starting to see that come 99 00:06:56.790 --> 00:07:00.269 to light in a lot of organizations who are simply challenged by growing their data 100 00:07:00.350 --> 00:07:04.259 science or analyst teams fast enough to address the need within the market. And 101 00:07:04.740 --> 00:07:10.500 the default, unfortunately, within many of these enterprises and organizations then tends to 102 00:07:10.579 --> 00:07:14.740 be, you know, those marketers and sales people and those who actually the 103 00:07:14.779 --> 00:07:18.050 advertisers who actually need those insights, you know, are suck sort of doing 104 00:07:18.209 --> 00:07:25.170 best guesses and, you know, using traditional techniques and pulling on old static 105 00:07:25.250 --> 00:07:28.009 data from a survey from a year ago. Right. It mean, it's 106 00:07:28.170 --> 00:07:31.360 it's pretty remarkable. What we have to default to is pretty rudimentary. Can, 107 00:07:31.519 --> 00:07:35.399 given what the power of that data reside sort of in those private data 108 00:07:35.439 --> 00:07:39.920 warehouses and, you know, in the private cloud, could actually unlock for 109 00:07:39.959 --> 00:07:43.360 those people if it was feasible. Yeah, it's really interesting the way those 110 00:07:43.399 --> 00:07:47.230 dynamics unfold and what the realities actually are. Relying on old data, taking 111 00:07:47.350 --> 00:07:50.470 guesses. It's like we're stepping backwards quite a bit. You know, you 112 00:07:50.629 --> 00:07:55.990 talked about the volume of data, the volume of request that data science teams 113 00:07:56.269 --> 00:08:00.259 are seeing, not only from marketing but from other functional areas like hr and 114 00:08:00.339 --> 00:08:03.500 it seems like every time in these sorts of conversations, when it comes to 115 00:08:03.899 --> 00:08:09.180 we've just got so much volume of information that needs to be processed, the 116 00:08:09.259 --> 00:08:15.209 conversation turns to Ai and in your opinion, Tim there is some opportunity there, 117 00:08:15.290 --> 00:08:18.889 but also kind of proceed with caution. Tell us some of the ways 118 00:08:18.930 --> 00:08:24.290 you see teams leveraging ai effectively and where they're going, but not going, 119 00:08:24.810 --> 00:08:28.600 in leveraging that sort of technology to solve this problem. Well, I think 120 00:08:28.600 --> 00:08:33.080 it's sort of the holy grail, right. I mean I think in market 121 00:08:33.279 --> 00:08:37.840 and certainly for the end users, the promise of AI is nothing new, 122 00:08:37.960 --> 00:08:41.360 right. We've heard about if we've heard about the growth and emergence. There's 123 00:08:41.399 --> 00:08:45.950 been massive investments. Many of the biggest players in sort of the MARTECH space 124 00:08:46.110 --> 00:08:50.470 are basically doing heavy investments in terms of their own applications in AI. Although 125 00:08:50.549 --> 00:08:54.110 I think some of that is simply at to date has fallen short, and 126 00:08:54.350 --> 00:09:00.500 I think that's for a number of reasons. I think oftentimes the application of 127 00:09:00.700 --> 00:09:05.500 sort of generic models of a I just don't align with workflows. I think 128 00:09:05.860 --> 00:09:11.769 oftentimes the interpretation of the results are questionable and therefore it's sort of your people 129 00:09:11.850 --> 00:09:16.129 lose faith in in emerging models fairly rapidly and have to get sort of comfortable 130 00:09:16.210 --> 00:09:20.250 with how they come you know, how those models are actually drived and what's 131 00:09:20.250 --> 00:09:24.840 it? What's valid and real information versus what isn't? But Net net. 132 00:09:24.879 --> 00:09:28.759 I think the the marketers as a whole, I think are are sort of 133 00:09:28.879 --> 00:09:31.679 waiting for, you know, the actual outcome that's been promised of the AI 134 00:09:31.840 --> 00:09:37.320 engine to come true, and I think we're starting to see some levels of 135 00:09:37.399 --> 00:09:41.350 that, but I would say it's still mason and I think it's probably growing, 136 00:09:41.669 --> 00:09:45.029 you know, in emerging slower than many of us anticipated. I mean, 137 00:09:45.110 --> 00:09:48.029 I'm personally sort of blown away still to this day that we have autonomous 138 00:09:48.029 --> 00:09:52.899 vehicles who can navigate, you know, any areas of North America with these 139 00:09:54.220 --> 00:09:58.379 except simple questions of a marketer about like who is our customer and what makes 140 00:09:58.379 --> 00:10:03.460 them unique? Goes unsolved within many organizations. Rightly, it's he's just doesn't 141 00:10:03.500 --> 00:10:05.539 it doesn't seem to make sense that we've unlocked, you know, some of 142 00:10:05.580 --> 00:10:09.690 the more challenging aspects and yet the more trivial ones we can't, we can't 143 00:10:09.690 --> 00:10:16.409 leverage in the same way. Today's Gross Story is about active PDF, the 144 00:10:16.490 --> 00:10:22.320 leading global provider of Server side pdf automation and digital transformation tools. Active PDF 145 00:10:22.440 --> 00:10:26.960 gives teams the tools they need to create, convert, modified, view, 146 00:10:26.120 --> 00:10:33.279 extract and automate data to and from PDF files. Active PDF relies on product 147 00:10:33.399 --> 00:10:37.190 trial sign ups for leads to drive new business, but they faced a challenge 148 00:10:37.230 --> 00:10:41.230 when low quality sign ups were driving up the cost of product trials. 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Maybe there are some 162 00:11:45.470 --> 00:11:48.259 folks listening to this that have had a similar experience or they're thinking about this 163 00:11:48.379 --> 00:11:54.220 application and looking ahead to maybe they can for see some row of blocks that 164 00:11:54.299 --> 00:11:58.379 that you guys have encountered or some of your customers have encountered in that area 165 00:11:58.460 --> 00:12:01.450 that you mentioned there. Yeah, I think the I think the the most 166 00:12:01.529 --> 00:12:05.850 significant challenge with respect to AI applications in most businesses is the fact that, 167 00:12:05.889 --> 00:12:11.289 although we sort of the envision ai is being this fully automated, just throw 168 00:12:11.330 --> 00:12:15.529 the model that you know, throw the data at the model and it will 169 00:12:15.570 --> 00:12:18.840 solve kind of anything. From most cases that's just not real. You know, 170 00:12:18.960 --> 00:12:24.600 models are built for certain specific purposes they you know, and even in 171 00:12:24.720 --> 00:12:28.480 terms of our underlying graph technology. Right, it's very good at doing very 172 00:12:28.559 --> 00:12:35.029 specific things like community detection, like anomaly detection, right, like identifying common 173 00:12:35.070 --> 00:12:37.590 affinities between, you know, and patterns across mass data sets, but it 174 00:12:37.669 --> 00:12:41.350 doesn't answer everything, right. So anybody who sort of, you know, 175 00:12:41.549 --> 00:12:46.299 is expecting, you know, a single single source or single code to basically 176 00:12:46.340 --> 00:12:50.419 unlock all answers across all date, I think is unreal what I think you'll 177 00:12:50.419 --> 00:12:54.940 start to see emerging, and this is my expectation over the next three to 178 00:12:54.100 --> 00:12:58.250 five years, is you will start to see sort of very purposefully built ai 179 00:12:58.970 --> 00:13:05.210 solutions that basically addresses specific workflow and a repeatable request, right. And so 180 00:13:05.490 --> 00:13:09.769 for US personally, the sort of number one request we hear from marketers, 181 00:13:09.970 --> 00:13:13.159 you know, against sort of the their consumer data, is things like, 182 00:13:13.399 --> 00:13:18.440 you know, find me common signals across certain buyer behaviors. Right. What 183 00:13:18.559 --> 00:13:22.120 are sort of the data driven behaviors that we can find a cont you know, 184 00:13:22.240 --> 00:13:26.120 commonly a cross in our entire customer base, and what did those segments 185 00:13:26.200 --> 00:13:30.710 look like and how do they differ between segment of segments? So they're unlocking 186 00:13:30.870 --> 00:13:33.389 to me is you know, the the unlockinged power of AI at the foundation 187 00:13:33.669 --> 00:13:41.779 starts with unlocking those repeatable business requests and sort of aligning ai and specific algorithms 188 00:13:41.820 --> 00:13:46.740 to solve those repeatable requests. And that, I think, is going to 189 00:13:46.779 --> 00:13:50.899 be how you sort of see ai emerge within the marketing and advertising as advertising 190 00:13:50.940 --> 00:13:54.539 space, as sort of it has been predicted today. Yeah, that makes 191 00:13:54.580 --> 00:13:56.970 a lot of sense. So, you know, for marketers listening to this, 192 00:13:58.450 --> 00:14:01.450 you might want to take those promises of broad applications of AI. Just 193 00:14:01.610 --> 00:14:05.289 dump everything in, as you were saying, in all sorts of insights will 194 00:14:05.330 --> 00:14:09.200 come out. Take those with a grain assault. Instead, look for where 195 00:14:09.399 --> 00:14:15.799 they're very Nige applications of AI to identify, you know, repeatable patterns. 196 00:14:15.840 --> 00:14:18.600 Where are those things that we know kind of what needs to go in and 197 00:14:18.960 --> 00:14:22.000 what needs to come out, because, you know, it's it's an equation, 198 00:14:22.240 --> 00:14:26.830 it's an algorithm, that's exactly and so you have to know what's the 199 00:14:26.870 --> 00:14:30.029 starting point, what's The endpoint? But oftentimes we can figure those out. 200 00:14:30.070 --> 00:14:33.710 We can with some human intelligence. Right, let's talk about human intelligence a 201 00:14:33.750 --> 00:14:37.029 little bit. The AI is only going to be, you know, as 202 00:14:37.110 --> 00:14:39.659 smart as we train it to be. In there has to be a repeatable 203 00:14:39.860 --> 00:14:43.340 pattern so that we can train it. You talked a little bit earlier about 204 00:14:43.419 --> 00:14:50.419 kind of democratizing data science, training it marketers other functional roles, as you 205 00:14:50.500 --> 00:14:54.450 know, quasi data scientists. Took a little bit about that and other steps 206 00:14:54.490 --> 00:15:00.529 that you think teams can take tim to address some of these problems that we've 207 00:15:00.529 --> 00:15:03.769 spent a good portion talking down. I want to give people some some hopes, 208 00:15:03.809 --> 00:15:05.330 some tactical takeaways. What can they do today? What are some of 209 00:15:05.370 --> 00:15:09.039 the steps they should be taking in the light of all this? Yeah, 210 00:15:09.120 --> 00:15:11.320 no, and I think it's first and foremost. I'll clarify that. You 211 00:15:11.360 --> 00:15:16.240 know, for me, to marketizing data science doesn't mean getting a rid of 212 00:15:16.240 --> 00:15:18.759 the data science teams. I think the investment of those teams is super valuable. 213 00:15:18.799 --> 00:15:24.429 I both and I actually think that they're sort of underutilized, primarily because 214 00:15:24.509 --> 00:15:28.509 the twenty rule. I we talking with a lot of these teams, we 215 00:15:28.710 --> 00:15:31.549 find that many of the like the eighty percent of their workload tends to be 216 00:15:33.070 --> 00:15:37.899 around simple request that are these repeatable ones that should be being satisfied by an 217 00:15:37.940 --> 00:15:41.860 AI based algorithm. Right, I mean, and so when we speak to 218 00:15:41.940 --> 00:15:45.340 those teams, tends to be more so around the fact of like the you 219 00:15:45.419 --> 00:15:50.169 know, the the Ai Engines in the opportunity that that unlocks is essentially to 220 00:15:50.330 --> 00:15:54.649 offload from the data science teams those repeatable requests in a format that those can 221 00:15:54.730 --> 00:15:58.370 now be, you know, self served based on request from the marketers, 222 00:15:58.409 --> 00:16:02.409 who can get their own, you know, their own answers directly from the 223 00:16:02.529 --> 00:16:06.080 data, you know, through this algorithm and through the machine learning algorithms that 224 00:16:06.120 --> 00:16:08.559 are being built. Meanwhile, those data science teams can then be put to 225 00:16:08.679 --> 00:16:12.480 use against much, much harder, nonrepeatable request type of work. Right. 226 00:16:12.519 --> 00:16:17.279 So I think it's sort of unlocking and building efficiencies for that team. Is 227 00:16:17.360 --> 00:16:19.190 How we envision it. But for us, I think there's, you know, 228 00:16:19.389 --> 00:16:22.710 the format for the marketers in the power that we're starting to see a 229 00:16:22.870 --> 00:16:26.509 lock of markets. Certainly a big focus for us to ourselves is that is, 230 00:16:26.629 --> 00:16:30.629 you know, this type of affinity based insight becomes some of the most 231 00:16:30.750 --> 00:16:34.340 compelling that a marketer can use at their fingertips. Right, those types of 232 00:16:34.419 --> 00:16:38.460 request around, like what's the difference between the person who purchased this product from 233 00:16:38.500 --> 00:16:42.500 us last week versus those who purchase this week? We are data show the 234 00:16:42.820 --> 00:16:48.289 change in pattern between those people who have churned versus those who we've retained. 235 00:16:48.450 --> 00:16:51.330 Right, what are those affinities of those two different groups that we can then 236 00:16:51.409 --> 00:16:55.730 leverage and often times to you know, for us it's those signals at the 237 00:16:55.889 --> 00:17:00.409 core that the marketer who has their own hypothesis. It's not, you know, 238 00:17:00.529 --> 00:17:03.600 this isn't a boat sort of discovery, you know, from from raw 239 00:17:03.679 --> 00:17:06.400 datas as as, to your point, Logan. It's it's not about just 240 00:17:06.519 --> 00:17:10.039 throwing data and having it tell you the answer. Most marketers already have a 241 00:17:10.160 --> 00:17:12.519 thesis, you know, and hypothesis at the core in terms like what's going 242 00:17:12.640 --> 00:17:15.990 on, the means with which, in the speed with which they can basically 243 00:17:15.990 --> 00:17:21.349 get insights and answers to those, you know, that justify those hypothesis. 244 00:17:21.430 --> 00:17:25.589 Is What we see is the emerging opportunity right so at your fingertips. If 245 00:17:25.630 --> 00:17:29.630 they can make simple requests or make simple comparisons between, you know, people 246 00:17:29.670 --> 00:17:33.619 who clicked on this campaign versus those who clicked on previous campaign, those types 247 00:17:33.660 --> 00:17:38.940 of things become then the signals where optimization and, to your point, human 248 00:17:38.980 --> 00:17:44.450 intelligence can then get purely applied, right and more effectively applied, and so 249 00:17:44.569 --> 00:17:51.130 we move away from simple guesswork and static personas and sort of static surveyors too, 250 00:17:51.170 --> 00:17:55.690 subtlely, a far more dynamic enterprise. Where for us, and this 251 00:17:55.849 --> 00:17:57.880 is what we sort of think is the most propelling part, is that your 252 00:17:57.920 --> 00:18:03.839 leverage in the creativity of the marketer, because they themselves have become data driven 253 00:18:03.000 --> 00:18:07.279 in this mechanism, right, and I think that's where the future stal lies 254 00:18:07.400 --> 00:18:11.400 within the enterprise and within the sort of format and formula of what you know, 255 00:18:11.519 --> 00:18:14.750 the Ai Promise actually has always been. Yeah, I love that. 256 00:18:14.869 --> 00:18:18.230 I am always kind of there's there's truth in the middle, typically sort of 257 00:18:18.309 --> 00:18:22.390 guy, and it's like I picture this pendulum of, like you said, 258 00:18:22.470 --> 00:18:25.230 going back to our old ways and just like okay, we've got to rely 259 00:18:25.349 --> 00:18:27.740 on static data and like the promise of Ai is just, you know, 260 00:18:27.819 --> 00:18:32.220 it's too far fetched. That's on one end. The other is just believing 261 00:18:32.299 --> 00:18:37.220 that we can just dump data into AI applications without any rhyme or reason and 262 00:18:37.380 --> 00:18:41.180 just get back all the answers like a crystal ball. But what you're saying 263 00:18:41.220 --> 00:18:44.490 is, you know, somewhere in the middle. If we tamper our expectations 264 00:18:44.730 --> 00:18:48.089 and we go into it with human creativity, then we can take some steps 265 00:18:48.250 --> 00:18:55.089 forward, we can advance in the way that we apply ai to the problems 266 00:18:55.130 --> 00:18:56.880 that we're trying to solve in marketing. I really like that and that's kind 267 00:18:56.920 --> 00:19:00.720 of the visual that I have in my head as you were talking, tim 268 00:19:00.160 --> 00:19:03.000 as we wrap to day, Tim is, there are there any other final 269 00:19:03.119 --> 00:19:07.680 thoughts action steps that you want to share with listeners before we round out today's 270 00:19:07.680 --> 00:19:11.829 conversation? I think the you know, just enclosing, I think the opportunity 271 00:19:12.069 --> 00:19:17.390 ultimately is is leveraging the brilliance of creativity of marketers at the core. That's 272 00:19:17.430 --> 00:19:19.390 what, you know, the power of a marketing team is always been. 273 00:19:19.430 --> 00:19:25.460 I think the what we'll see emerge is you'll sort of through Ai and through 274 00:19:25.579 --> 00:19:29.619 rich visualization, and it is sort of just an enhancement to that creativity, 275 00:19:29.660 --> 00:19:32.339 and that's what I'm excited about. I love it. Going back to that, 276 00:19:32.660 --> 00:19:36.980 you know, artificial intelligence and human intelligence coming together, not just bluely 277 00:19:37.059 --> 00:19:41.569 AI is going to replace us, let's just you know, become mindless moving 278 00:19:41.690 --> 00:19:45.009 cogs. I love that. Well, Tim this has been a great conversation. 279 00:19:45.049 --> 00:19:47.890 If anybody listening to this would like to learn more about what you and 280 00:19:47.970 --> 00:19:49.849 your team are up to, or just stay connected with you personally? What's 281 00:19:49.890 --> 00:19:52.519 the best way for them to take next steps there? Yeah, certainly. 282 00:19:52.559 --> 00:19:57.039 You can reach me directly on my email. It is tim at a Fineocom, 283 00:19:57.559 --> 00:20:03.440 or you can follow me on Linkedin or twitter. It's t the number 284 00:20:03.440 --> 00:20:07.470 one MV rkae. So look forward to connecting with people. I love it. 285 00:20:07.549 --> 00:20:10.910 Make it nice and easy, Tim. Thank you so much for joining 286 00:20:10.950 --> 00:20:15.789 us on the show today. Great thank you for the time. I hate 287 00:20:15.829 --> 00:20:21.069 it when podcasts incessantly ask their listeners for reviews, but I get why they 288 00:20:21.109 --> 00:20:23.980 do it, because reviews are enormously helpful when you're trying to grow a podcast 289 00:20:23.980 --> 00:20:27.339 audience. So here's what we decided to do. If you leave a review 290 00:20:27.380 --> 00:20:32.059 for me to be growth and apple podcasts and email me a screenshot of the 291 00:20:32.099 --> 00:20:36.450 review to James At sweetfish Mediacom, I'll send you a signed copy of my 292 00:20:36.529 --> 00:20:40.450 new book, content based networking, how to instantly connect with anyone you want 293 00:20:40.450 --> 00:20:42.450 to know. We get a review, you get a free book. We 294 00:20:42.609 --> 00:20:44.009 both win.