Big Data (& How Higher Ed Marketers Can Use It!)

WEBVTT 1 00:00:00.040 --> 00:00:04.519 There's so much data available on every user, every consumer, and it's not 2 00:00:04.599 --> 00:00:10.470 as a anonymous value. It's more of an opportunity for individuals and marketers to 3 00:00:11.349 --> 00:00:15.949 be able to promote services are products that are in line with the interest level 4 00:00:15.990 --> 00:00:22.899 of that particular prospect. You are listening to the Higher Ed Marketer, a 5 00:00:23.019 --> 00:00:28.300 podcast geared towards marketing professionals in higher education. This show will tackle all sorts 6 00:00:28.339 --> 00:00:33.140 of questions related to student recruitment, don'tor relations, marketing trends, new technologies 7 00:00:33.299 --> 00:00:37.329 and so much more. If you are looking for conversation centered around where the 8 00:00:37.369 --> 00:00:41.530 industry is going, this podcast is for you. Let's get into the show. 9 00:00:46.049 --> 00:00:49.570 Welcome to the hired marketer podcast. My name is troy singer and I'm 10 00:00:49.649 --> 00:00:53.960 here with my cohost, Bark Taylor, where each and every week we interview 11 00:00:54.399 --> 00:01:00.640 higher ed marketing professionals or surface providers for the betterment of Higher Ed marketers. 12 00:01:00.880 --> 00:01:04.430 Today we're going to talk to Roosevelt Smith from e Web data on how Higher 13 00:01:04.469 --> 00:01:10.150 Ed marketers can best utilize big data. I know that this is something that 14 00:01:10.269 --> 00:01:12.989 you've been talking about for the last few months and you really wanted to get 15 00:01:12.989 --> 00:01:19.579 the message out there for marketers to better utilize the data that is available to 16 00:01:19.700 --> 00:01:22.060 them. Yeah, Troy, I think this is this is a really good 17 00:01:22.060 --> 00:01:26.099 conversation. I think Roosevelt been doing this for several years and you know, 18 00:01:26.180 --> 00:01:29.859 Big Dad has been around all all long. You know, we'll talk during 19 00:01:29.900 --> 00:01:33.250 the podcast a little bit about what it is and where it came from and 20 00:01:33.650 --> 00:01:38.290 how it's proceeding, especially in light of the Internet and with with some different 21 00:01:38.329 --> 00:01:40.450 things that are going on. But I think it's one of those things that 22 00:01:40.969 --> 00:01:42.049 it's going to be a really good episode. I want you to kind of 23 00:01:42.090 --> 00:01:47.200 really listen to it because there's a lot to learn about big data and how 24 00:01:47.319 --> 00:01:51.159 that can be leveraged for Higher Ed Marketing and how it's already being leveraged in 25 00:01:51.239 --> 00:01:53.719 a lot of other places in higher in marketing in general in general, and 26 00:01:55.120 --> 00:01:57.200 a lot of times I tell people, and those of you that are in 27 00:01:57.280 --> 00:02:01.590 highed marketing, especially if you've been in other marketing fields, if you've transitioned 28 00:02:01.629 --> 00:02:07.269 into higher Ed, sometimes higher ed can be a little bit behind in marketing 29 00:02:07.310 --> 00:02:13.310 techniques and in marketing applications, and Big Dad is probably a really good example 30 00:02:13.389 --> 00:02:16.780 of an area that a lot of other places are already doing and doing well 31 00:02:17.060 --> 00:02:21.419 and that High Ed could kind of pick up the pace and learn from so 32 00:02:21.780 --> 00:02:23.659 I hope that this is going to be a good conversation. Here is our 33 00:02:23.740 --> 00:02:30.810 conversation with Roosevelt Smith. Everyone, it's my pleasure to welcome Roosevelt Smith from 34 00:02:30.969 --> 00:02:35.849 e Web data to the Higher Ed Marketer podcast. And before we get into 35 00:02:35.930 --> 00:02:39.330 our interesting conversation about using big data, Roosevelt, if you could tell everyone 36 00:02:39.409 --> 00:02:44.840 a little bit about you roll, let you web data and what the company 37 00:02:45.039 --> 00:02:49.159 does. Yeah, absolutely, Try. Thanks for the introductions. So I've 38 00:02:49.159 --> 00:02:52.759 been the president of the web data nowly since early two thousand and fourteen. 39 00:02:53.159 --> 00:02:58.000 We started out as a data modeling company and also into a course of website 40 00:02:58.000 --> 00:03:02.550 design. We've now kind of bridge the gap and big data analytics and data 41 00:03:02.590 --> 00:03:08.310 augmentation. We have a couple of platforms in the space that works with big 42 00:03:08.349 --> 00:03:16.020 data analytics and and basically helping customers uncover their website traffic. So, in 43 00:03:16.139 --> 00:03:21.060 a nutshell and thirtyzero foot view, that's who we are. Great. Thanks, 44 00:03:21.099 --> 00:03:23.419 Roosevelt. It's good to have you on the on the podcast. To 45 00:03:23.500 --> 00:03:27.409 know we've we met probably close to about a year ago through some common friends, 46 00:03:27.449 --> 00:03:30.370 and so it's good to have you here and in all transparency, of 47 00:03:30.610 --> 00:03:36.650 Taylor Solutions Leverages and uses some of you web data's technology, but I found 48 00:03:36.650 --> 00:03:39.729 it fascinating that I wanted to at least have the conversation about what what big 49 00:03:39.770 --> 00:03:44.120 data is and how it can be used in marketing, how it is being 50 00:03:44.199 --> 00:03:46.759 used in marketing. And so we've been talking about big data and throwing around 51 00:03:46.800 --> 00:03:50.599 the name here the last couple of minutes and people are probably like, okay, 52 00:03:50.680 --> 00:03:52.759 tell me what it is. So can you just give us kind of 53 00:03:52.759 --> 00:03:54.990 an idea of what big data is and how it's how it's a part of 54 00:03:55.030 --> 00:03:59.430 our lives? Yeah, right, I think you know, big data can 55 00:03:59.430 --> 00:04:03.310 be comprised of different explanations. To me, what big data means is an 56 00:04:03.349 --> 00:04:09.270 aggregation of a lot of data, a lot of information that is utilize every 57 00:04:09.270 --> 00:04:16.740 day through marketing channels for everything from real estate to e commerce to automotive dealerships, 58 00:04:17.180 --> 00:04:19.699 you name it, big data's used, you know, every day. 59 00:04:19.939 --> 00:04:25.610 So that to me is kind of what they eat explanation is, and I 60 00:04:25.689 --> 00:04:29.810 guess how that correlates to you know, et Web dadas. We utilize that 61 00:04:29.889 --> 00:04:34.569 information to make the, you know, the best marketing decisions. In real 62 00:04:34.649 --> 00:04:40.240 time through severe our plot that's great. I was introduced to big data probably 63 00:04:40.279 --> 00:04:44.079 in one thousand nine hundred and ninety four, probably about the same time that 64 00:04:44.160 --> 00:04:47.279 I did my first website. I was a young buck of a designer, 65 00:04:47.560 --> 00:04:51.160 couple years out of college and I was working for a design firm and date 66 00:04:51.240 --> 00:04:55.069 in Ohio and one of our clients was in CR and I remember they had 67 00:04:55.230 --> 00:04:59.230 this huge server system that they would sell to retailers and when their biggest client 68 00:04:59.230 --> 00:05:00.910 at the time was Walmart, and I remember having to do a brochure that 69 00:05:00.949 --> 00:05:06.300 it was explaining how, whether the whether the customer paid via credit card or 70 00:05:06.339 --> 00:05:11.740 check or, you know, in various ways, that they could take that 71 00:05:11.860 --> 00:05:15.939 that connection to that to that check or that credit card number and they could 72 00:05:15.939 --> 00:05:20.420 aggregate all the customers behavior at the stores to be able to put together a 73 00:05:20.529 --> 00:05:27.089 profile of what the customer purchased. I mean they could store this for years 74 00:05:27.170 --> 00:05:30.889 and and start to develop a bit of a profile to say this particular customer 75 00:05:30.930 --> 00:05:35.360 or this particular family has a tendency to buy these brands, has a tendency 76 00:05:35.399 --> 00:05:40.319 to do these types of things, to purchase on these days, and they 77 00:05:40.360 --> 00:05:43.199 could start using that date in a lot of different ways, whether it would 78 00:05:43.199 --> 00:05:46.600 be how to, how to, you know, put the store together, 79 00:05:46.680 --> 00:05:47.720 how they could, you know, do traffic in the store, all the 80 00:05:47.759 --> 00:05:50.589 way to marketing, and so I was really fascinated, even at that time, 81 00:05:51.189 --> 00:05:54.949 you know, this is thirty, thirty five years ago, how these 82 00:05:54.990 --> 00:05:59.430 progressive companies were using all this data that we were every day just using. 83 00:05:59.470 --> 00:06:01.829 I mean, this was before the Internet too, and so I have to 84 00:06:01.910 --> 00:06:05.220 believe now that even this idea of big data, I mean we leave such 85 00:06:05.259 --> 00:06:11.500 a trail in our wake of just information about us, whether we're going to 86 00:06:11.620 --> 00:06:15.100 a website and you always, you know, asking about cookies, or whether 87 00:06:15.139 --> 00:06:18.209 we're, you know, clicking on a link or, you know, using 88 00:06:18.250 --> 00:06:21.889 our mobile phone, using a you know, from home. The idea of 89 00:06:21.930 --> 00:06:26.009 big data, I guess, Roosevelt, is the idea that we're just being 90 00:06:26.209 --> 00:06:28.649 you know, that, instead of being in the S and being able to 91 00:06:28.689 --> 00:06:30.810 track a credit card number and a check to apply that to the fact that 92 00:06:30.889 --> 00:06:35.240 you just bought, you know, apples at Walmart, now really contract so 93 00:06:35.279 --> 00:06:38.920 much other parts of our life, especially when it comes to the Internet. 94 00:06:39.120 --> 00:06:44.839 I totally agree with you, bar that that is a very good understanding of 95 00:06:44.920 --> 00:06:47.990 it and from yes, from where we are today and Modern Day technology, 96 00:06:48.389 --> 00:06:53.750 with the capability of being able to, you know, look in sight of 97 00:06:53.870 --> 00:07:00.910 understanding who your consumer is and how they are progressively adapting to your ecosystem or 98 00:07:00.149 --> 00:07:06.100 or, you know, basically kind of working within your your ecosystem, that 99 00:07:06.300 --> 00:07:11.939 it is just become fascinating and just a just in itself. And there's so 100 00:07:12.060 --> 00:07:15.649 much data available on every user, every consumer, and it's not as a 101 00:07:15.730 --> 00:07:23.810 anonymous value. It's more of an oportunity for individuals and marketers to be able 102 00:07:23.850 --> 00:07:28.569 to promote services are products that are in line with the interest level of that 103 00:07:28.689 --> 00:07:33.680 particular prospect yeah, I was recently. We recently moved my family and we 104 00:07:33.879 --> 00:07:38.120 do all of our we cut the cord on cable a few years ago and 105 00:07:38.199 --> 00:07:41.680 so we use all of our streaming devices and it was interesting to me when 106 00:07:41.680 --> 00:07:46.230 we moved that we would be watching like the hallmark channel or some other channel 107 00:07:46.310 --> 00:07:49.230 on sling or on the Hulu or something, and obviously you have the streaming 108 00:07:49.230 --> 00:07:53.910 ads that still come across and you can't fast forward through some of those. 109 00:07:53.990 --> 00:07:57.149 You have to watch those. But I was fascinated with the fact that the 110 00:07:57.230 --> 00:08:01.740 ADS that we're getting served up were things about gutters and home, a lot 111 00:08:01.819 --> 00:08:05.019 of home stuff. A lot of home stuff. There were some things about 112 00:08:05.300 --> 00:08:09.740 retired physicians and different things like that. Couple other things about, you know, 113 00:08:11.500 --> 00:08:13.889 have parents of teenagers and things like that, and I remember sitting with 114 00:08:13.930 --> 00:08:18.209 my wife one time as I started kind of realizing that these ads were not 115 00:08:18.329 --> 00:08:20.889 just ads that I would have gotten, you know, two years ago if 116 00:08:20.930 --> 00:08:22.889 I was watching the colts game and, you know, there was a bud 117 00:08:24.009 --> 00:08:26.170 ad and the you know, the next ad would be, you know, 118 00:08:26.410 --> 00:08:28.319 a state farm and that type of thing. These ads were a lot more 119 00:08:28.600 --> 00:08:33.879 specialized to what was going on in my life at that time. You know, 120 00:08:33.960 --> 00:08:35.159 I had an older house, I was going to have to replace some 121 00:08:35.279 --> 00:08:39.159 gutters, I had an older house, I had to do some roofing and 122 00:08:39.279 --> 00:08:41.710 I realized, and I was talking to my wife. My Wife's a retired 123 00:08:41.750 --> 00:08:43.590 physician and I was talking to her and I said, I think all these 124 00:08:43.629 --> 00:08:46.750 ads are based on who we are. They know who we are and their 125 00:08:46.789 --> 00:08:50.350 serve these ads up based on that side. A little research and came across 126 00:08:50.830 --> 00:08:56.340 the concept of overthetop advertising, Ott Advertising, which all the streaming channels leverage, 127 00:08:56.340 --> 00:09:01.659 and it's leveraging this big data that we talked about, that they know 128 00:09:01.580 --> 00:09:05.899 that. You know my house. The people in that House have these habits, 129 00:09:05.940 --> 00:09:09.529 they've done these things, they have children that are high high school students, 130 00:09:09.889 --> 00:09:13.450 and so they were serving up ads that was customized to the Kaylor family 131 00:09:13.690 --> 00:09:16.769 and not just thrown out there for anybody's it is. That what you're seeing 132 00:09:16.809 --> 00:09:24.169 to absolutely you know the the power behind retargeted ads. Today's is, you 133 00:09:24.250 --> 00:09:26.480 know, far better than what it was even two, three, four years 134 00:09:26.480 --> 00:09:30.799 ago. You know, the enhancement value of technology has come so far in 135 00:09:30.840 --> 00:09:35.000 advance and I am, you know, primarily happy and proud to say that 136 00:09:35.039 --> 00:09:39.509 we are on the cutting edge as well in regards to yeah, you know 137 00:09:39.629 --> 00:09:43.509 that that data formula. It's all about capturing the information, first, understanding 138 00:09:43.750 --> 00:09:48.389 what data is available on the the particular prospect and then being able to serve 139 00:09:48.470 --> 00:09:50.950 that information, like I said, in retrospect to what you've actively been looking 140 00:09:50.990 --> 00:09:54.820 for. So if you've completed a google search for, you know, gut 141 00:09:54.899 --> 00:10:00.340 or installation or roofing, new roofing installation in the past seventy two hours, 142 00:10:00.419 --> 00:10:05.860 and information is tracked and there are thousands of marketers out there or big data 143 00:10:05.860 --> 00:10:09.970 aggregators like ourselves, like you, web data, who are attracting that information, 144 00:10:09.250 --> 00:10:13.409 and we try to provide you with the best, you know, promoted 145 00:10:13.490 --> 00:10:16.809 product or service available and an almost real time you know, because I think 146 00:10:16.809 --> 00:10:22.200 that window is what's very important to it. You know, as a it 147 00:10:22.320 --> 00:10:26.440 may be a longer shot in a rooping estimate because you are considering, you 148 00:10:26.519 --> 00:10:31.399 know, thousands of dollars versus a, you know, a purchase of organic 149 00:10:31.440 --> 00:10:37.269 apples versus farm rates. But there's there's there's some coexistence in your purchase behavior, 150 00:10:37.509 --> 00:10:39.389 but goes along in that. That's great. And so obviously big date 151 00:10:39.429 --> 00:10:41.309 has been around for a while. I mean we talked about that, you 152 00:10:41.350 --> 00:10:45.110 know, from NCR back in the s and then, you know, there 153 00:10:45.149 --> 00:10:46.309 was a book that I read a few years ago, the power of habit, 154 00:10:46.350 --> 00:10:50.779 by Charles doing and and he brings a he talks about target and some 155 00:10:50.460 --> 00:10:54.059 uses an illustration of story about, you know, how target aggregates data and 156 00:10:54.179 --> 00:11:00.539 can actually predict when women are pregnant and start marketing to them based on on 157 00:11:01.059 --> 00:11:03.179 the concept that they know that they're pregnant because of their purchasing habits in the 158 00:11:03.220 --> 00:11:05.809 way that they do that. And so I kind of understand that big date 159 00:11:05.929 --> 00:11:09.649 is just a big part of our lives, but when I bring it up 160 00:11:09.690 --> 00:11:11.889 in the higher head marketing circles it's kind of all the sudden it's kind of 161 00:11:11.889 --> 00:11:16.009 like, well, what's that and why should we do that and how's that 162 00:11:16.090 --> 00:11:20.159 work and is that legal? Is that ethical? And so I'm curious because 163 00:11:20.480 --> 00:11:22.279 I think there's a lot of people out there that are doing it. They 164 00:11:22.320 --> 00:11:24.879 might be calling it different things. I know that. I know I've got 165 00:11:24.919 --> 00:11:28.480 some competitors that have some names for it, that it's basically the same thing. 166 00:11:28.519 --> 00:11:31.840 I tend to call it data harvesting because it's the idea of, you 167 00:11:31.909 --> 00:11:35.950 know, being able to know who's coming to your website, being able to 168 00:11:37.029 --> 00:11:39.789 capture that data and then being able to turn that data and actionable items and 169 00:11:39.830 --> 00:11:43.269 actionable, you know, contact information, and we'll get into that in a 170 00:11:43.269 --> 00:11:46.950 second. I de Troy has some questions about that, but I think that 171 00:11:46.110 --> 00:11:50.860 my bigger question is is that for Higher Ed marketers this could be a big 172 00:11:50.980 --> 00:11:56.299 change because we do a lot of paper click advertising and with especially with traditional 173 00:11:56.340 --> 00:12:01.929 Undergrad there's been years and years of buying lists for search campaigns where, you 174 00:12:03.009 --> 00:12:07.090 know, we'll go to the sat scores or the act scores and and purchase. 175 00:12:07.169 --> 00:12:09.769 The kids that are taking those tests will buy their names and then we'll 176 00:12:11.250 --> 00:12:13.730 mark it to them based on the demographics of you know, they're in the 177 00:12:13.809 --> 00:12:16.600 stay, they have this GPA, and so we've been doing that for years 178 00:12:16.600 --> 00:12:20.360 as High Ed marketers. But now, with test optional, with the pandemic, 179 00:12:20.440 --> 00:12:24.279 less kids are taking the test, the less schools are acquiring the tests, 180 00:12:24.600 --> 00:12:26.480 and so all of a sudden that where the well that we're going to 181 00:12:26.679 --> 00:12:31.070 is going smaller. And then not even to think about the fact that the 182 00:12:31.149 --> 00:12:35.389 largest part of Higher Ed Marketing is in adult and graduate studies. I mean 183 00:12:35.429 --> 00:12:39.190 there's there's there's a lot more students in the graduate space and then the online 184 00:12:39.230 --> 00:12:43.149 space than there are in the traditional Undergrad but you know, they're not taking 185 00:12:43.269 --> 00:12:46.419 tests. We can't buy list necessarily and so we've got to come up with 186 00:12:46.580 --> 00:12:52.460 other ways to generate leads, and so I'm guessing that that this big data 187 00:12:52.539 --> 00:12:56.659 idea is a way that we can do that. That is absolutely correct. 188 00:12:56.740 --> 00:13:03.129 Part, you know, being able to segmentize your search based upon behavior and 189 00:13:03.330 --> 00:13:11.490 breaking down the formula is what I call the winning and winning solution. I 190 00:13:11.570 --> 00:13:15.200 think in the our x space it's A. It's a commonly known, you 191 00:13:15.240 --> 00:13:20.080 know, Opportunity for that. So yeah, I totally I totally agree with 192 00:13:20.200 --> 00:13:24.240 you and segment in that process. Roosevelt, could you give us an idea 193 00:13:24.279 --> 00:13:30.629 of how you're capturing this data in order to serve it and enable high ed 194 00:13:30.710 --> 00:13:33.590 marketers, or any market for that matter, to best utilize it? Yeah, 195 00:13:33.669 --> 00:13:37.549 well, I'm formerly used, we used what we call super cookies, 196 00:13:37.789 --> 00:13:43.740 which was a combination of cookie based tracking technology, and that was used for 197 00:13:43.980 --> 00:13:50.100 the last few years within our platform. And basically we were we were counterparts 198 00:13:50.419 --> 00:13:56.690 of other website users and companies and they allowed us to install our cookies to 199 00:13:56.769 --> 00:13:58.610 be able to track data and then, of course, we provided them with, 200 00:13:58.850 --> 00:14:03.409 you know, intelligent information on their consumers and their prospects. But of 201 00:14:03.490 --> 00:14:07.690 course, now, with the recent changes in technology, with Google and apple 202 00:14:07.929 --> 00:14:09.639 and kind of facebook now coming forward saying they're, you know, they're going 203 00:14:09.679 --> 00:14:13.720 to be doing a way with cookies, we have now switched to smart Pixel 204 00:14:13.759 --> 00:14:18.960 Technology. So we're now using a combination of some some some old tech with 205 00:14:18.120 --> 00:14:24.230 some new school tech foundations. And the data capture process is always been the 206 00:14:24.309 --> 00:14:28.029 same. You know, you capture the user at the IP level and didn't 207 00:14:28.029 --> 00:14:33.909 you utilize several algorithms and augmentation to figure out who they are, and in 208 00:14:33.990 --> 00:14:37.149 a real time basis. So that's kind of our the basis of our technology 209 00:14:37.190 --> 00:14:41.539 today. So just to clarify with everyone, I just want to make sure 210 00:14:41.539 --> 00:14:43.460 that, I mean we're getting kind of technical. What's an IP address? 211 00:14:43.500 --> 00:14:46.700 I mean, you know, I've got an idea, but maybe resevelt, 212 00:14:46.740 --> 00:14:50.259 you can tell you no problem. So your stationery Ip address is going to 213 00:14:50.299 --> 00:14:56.529 be basically the unique identifier that's provided from your ISP, which is your Internet 214 00:14:56.570 --> 00:15:00.330 service provider. So comcast, exfinity, verizon, you know, those are 215 00:15:00.370 --> 00:15:03.409 all of your ips. They're going to give you a stationary Ip address and 216 00:15:03.490 --> 00:15:07.639 that's what you use as your navigation tool you surf the web. Think of 217 00:15:07.720 --> 00:15:09.200 it as a vehicle and that you hop into when you're when you go to 218 00:15:09.279 --> 00:15:13.039 the Google search engine and you plug in, you know, Nike, Golf 219 00:15:13.080 --> 00:15:16.399 Clubs and you travel a big sporting goods. So that Ip addresses is your 220 00:15:16.879 --> 00:15:20.200 your vehicle to travel from website to website. So it's kind of like having 221 00:15:20.200 --> 00:15:22.950 your license plate. You know, I can I can see your license plate, 222 00:15:24.029 --> 00:15:26.149 I can track your license plate and I know where your license plates been, 223 00:15:26.230 --> 00:15:28.269 and so that's a little bit more of what that Ip address is. 224 00:15:28.429 --> 00:15:31.429 Great, great, great, example. Yes, part you got it. 225 00:15:31.590 --> 00:15:35.669 Okay, Great. So once you kind of capture that license plate, I 226 00:15:35.740 --> 00:15:37.620 mean you're basically running it against the database as if, you know, if 227 00:15:37.659 --> 00:15:43.860 you're the state trooper, you know, drive by it ninety five miles an 228 00:15:43.899 --> 00:15:48.179 hour and he sees my license plate, he's going to then take that license 229 00:15:48.220 --> 00:15:52.490 plate, go to a database and be able to download information about me. 230 00:15:52.610 --> 00:15:54.529 Is is that basically the same thing? Absolutely yeah. So, instead of 231 00:15:56.289 --> 00:15:58.889 writing you a citation for overspeeding, we're just going to serve you an add 232 00:16:00.009 --> 00:16:03.929 directly into your your ad feed for those golf clubs that you search for and 233 00:16:03.129 --> 00:16:07.080 kind of ever reverse method. And you know, the think of the speed 234 00:16:07.159 --> 00:16:11.919 limit on that highway is the flag that is used by the client client it. 235 00:16:12.000 --> 00:16:15.000 Maybe our says, Hey, we want to know everyone in the last 236 00:16:15.080 --> 00:16:21.110 seventy two hours in the centurianapolist that have searched a set of Nike Golf Clubs 237 00:16:21.350 --> 00:16:23.549 on the big sporting goods website. And so when you pass, you know, 238 00:16:23.629 --> 00:16:29.110 when you pass by that that speed trap and you know we pull out 239 00:16:29.110 --> 00:16:30.669 to get you. You know, we're serving you an add in regards to 240 00:16:32.029 --> 00:16:36.019 that information. Okay, that's really helpful to know. And so the idea 241 00:16:36.100 --> 00:16:38.940 then, is that not only do you know that I was searching for that 242 00:16:40.259 --> 00:16:42.980 those golf clubs, but you also can pull up, just like with the 243 00:16:44.340 --> 00:16:48.370 you know, going to our license plate and speeding analogy, they can also 244 00:16:48.450 --> 00:16:51.690 pull up, you know, maybe some other history of me. My Name, 245 00:16:51.850 --> 00:16:56.169 might address, my email, contact information. I mean, I'm guessing 246 00:16:56.210 --> 00:17:00.169 that because of the big data and and I I remember you telling me one 247 00:17:00.250 --> 00:17:03.240 time too that, you know, Dick Sporting goods might trade data with my 248 00:17:03.359 --> 00:17:07.400 utility company, and these different places are trading data so that there's like a, 249 00:17:07.720 --> 00:17:11.759 you know, set of Pokemon cards that everybody, you know, trades 250 00:17:11.839 --> 00:17:14.839 to be able to get a full profile. What I mean, what kind 251 00:17:14.839 --> 00:17:18.430 of data is out there on me? Just about everything, I mean everything 252 00:17:18.509 --> 00:17:22.829 outside of Your Pii, which is your personal informations. You know, first 253 00:17:22.829 --> 00:17:26.630 name, last name, physical address, you knows, there's mobile connecting IDs 254 00:17:26.950 --> 00:17:32.900 like your facebook ID, your instagram handle, twitter account information and then, 255 00:17:32.940 --> 00:17:36.059 of course, demographical data. We know that you are a, you know, 256 00:17:36.220 --> 00:17:41.420 a middle aged African American or Caucasian mail that is interested in Golf, 257 00:17:41.779 --> 00:17:45.569 you know, we know that you have, and then they're shared e commerce 258 00:17:45.609 --> 00:17:48.250 data. That which is which is highly, highly impactful as well. You 259 00:17:48.329 --> 00:17:52.690 know, Amazon be in the root of a lot of this information is valuable, 260 00:17:52.529 --> 00:17:56.410 which stiy'll share with you. Know, big data providers out there, 261 00:17:56.490 --> 00:17:59.880 if you acts for a connection to their to their database, to their big 262 00:17:59.880 --> 00:18:03.359 data portal. So we know that you've got a set of those golf clubs 263 00:18:03.359 --> 00:18:07.880 in your shopping cart already at Amazoncom and you're just kind of price shopping. 264 00:18:07.920 --> 00:18:11.799 You're looking for the best shipping methods or same day shipping, you know. 265 00:18:11.920 --> 00:18:15.269 So Dick to sporting goods is now going to put twenty percent off with free 266 00:18:15.349 --> 00:18:18.109 pick up at the store that's two point eight miles away from your house. 267 00:18:18.630 --> 00:18:22.670 And so that's how the aggregation process works. And just think about all that 268 00:18:22.869 --> 00:18:27.099 being done in real time. That's the significant value behind, I think, 269 00:18:27.180 --> 00:18:30.140 big data today, and so you had told me before, I think there's 270 00:18:30.140 --> 00:18:36.059 like thirt thirty two points of data that you can do everything from household income 271 00:18:36.220 --> 00:18:38.380 to, you know, whether you have children under eighteen in the house. 272 00:18:38.420 --> 00:18:42.289 I mean there's just all kinds of things, and I mean and and I 273 00:18:42.369 --> 00:18:47.289 guess that's one of the things when we talk about high read marketing, there's 274 00:18:47.329 --> 00:18:49.250 a lot of things that we could we could, you know, purchase, 275 00:18:49.410 --> 00:18:52.049 I mean, and essence, we're kind of purchasing our own search list of 276 00:18:52.089 --> 00:18:56.279 people who've been to our website, who we already know show some pro you 277 00:18:56.319 --> 00:19:00.920 know, some some sense of leaning toward what we have to sell them, 278 00:19:02.119 --> 00:19:04.559 what we have to offer them as a service, in this case higher education. 279 00:19:06.279 --> 00:19:07.759 So being able to actually know that, okay, they already have. 280 00:19:08.160 --> 00:19:11.750 They've already arrived at our website. So, you know, maybe a paper 281 00:19:11.829 --> 00:19:15.750 click campaign or Seo or something else drove in there for whatever reason they chose 282 00:19:15.789 --> 00:19:19.950 not to fill out a form. But I can actually purchase their names because 283 00:19:19.990 --> 00:19:23.299 of this pixel because of this big data, and then I can create a 284 00:19:23.859 --> 00:19:29.099 campaign of whether it's email marketing or direct mail marketing, I can start to 285 00:19:29.259 --> 00:19:33.859 create a campaign that that probably needs to be sensitively created. I mean, 286 00:19:33.859 --> 00:19:36.180 I don't want to say, Hey, we noticed you were on the website 287 00:19:36.220 --> 00:19:40.130 and you're looking at this program and we just new didn't for you forgot to 288 00:19:40.170 --> 00:19:41.529 fill out your name and address, so we took it upon ourselves to find 289 00:19:41.569 --> 00:19:45.569 we're not doing that, but we're we're going to do it in such a 290 00:19:45.609 --> 00:19:48.009 way that's just like a purchase name, as if it were a purchase named 291 00:19:48.009 --> 00:19:51.569 for an Undergrad, and we're going to go and we're going to do a 292 00:19:51.650 --> 00:19:55.279 brand awareness campaign and start to help them understand so that, you know, 293 00:19:55.440 --> 00:19:57.440 not only the retargeting adds that they see because they went to the website and 294 00:19:57.519 --> 00:20:00.640 they're starting to see the retargeting adds, but hey, they just got a 295 00:20:00.720 --> 00:20:04.200 postcard about this and and Oh, they just got an email about it. 296 00:20:04.559 --> 00:20:07.789 Wow, it's everybody and this this school's everywhere. Wow, I need to 297 00:20:07.869 --> 00:20:11.869 check more about this. And so I'm guessing that's a little bit of the 298 00:20:11.910 --> 00:20:14.309 way that some of that data can be used. Absolutely, Barry, get 299 00:20:14.309 --> 00:20:17.589 the nail on the head there. And I want to address one I think 300 00:20:17.670 --> 00:20:22.619 critical point would just the educational process with big DDAS that they're still regulations around 301 00:20:22.660 --> 00:20:26.980 it. You know, it's not the complete wall ball blast with even big 302 00:20:26.980 --> 00:20:32.460 data providers or aggregators like like myself and my team, there are limb it's 303 00:20:32.500 --> 00:20:36.289 within what information we can capture and when information we are, you know, 304 00:20:36.769 --> 00:20:41.329 able to give out to prospects or client to use for marketing purposes. You 305 00:20:41.410 --> 00:20:45.569 know that the first step is clearly consent. You know you have to gather 306 00:20:45.769 --> 00:20:51.480 and capture consent from the user first before you start the augmentation process. And 307 00:20:51.559 --> 00:20:55.200 then there's a second layer of what I like to call the federal hygiene, 308 00:20:55.359 --> 00:21:00.119 which the government stepped in a couple of years back when facebook and Cambridge analytic 309 00:21:00.119 --> 00:21:03.160 could, you know, have their whole deal with a government said okay, 310 00:21:03.160 --> 00:21:06.309 well, now everyone's going to play on the same in the same sandbox, 311 00:21:06.589 --> 00:21:08.549 whether your Google or whether your e Web data, and so they created this 312 00:21:08.750 --> 00:21:14.470 this this hyper database, which basically means that now every all the information we 313 00:21:14.509 --> 00:21:18.819 capture on that user we have to send through the national and state level of 314 00:21:18.859 --> 00:21:21.819 DNC, which is to do not call, the Dne list, which is 315 00:21:21.819 --> 00:21:25.539 do not email and do not mail list as well, just to make sure 316 00:21:25.660 --> 00:21:29.500 that any of those users are prospect that are on those protected list. We're 317 00:21:29.539 --> 00:21:33.329 not able to capture or relay that data. So there is a level of 318 00:21:33.490 --> 00:21:37.650 protection and privacy that we uphold, you know, as a very high standard 319 00:21:37.690 --> 00:21:41.690 within our ecosystem and we really try to tease that to all of our clients 320 00:21:41.769 --> 00:21:45.890 to make sure that you know, everything is done the right way, because 321 00:21:45.890 --> 00:21:49.799 no one wants to be solicited with information they haven't requested and that's that's never 322 00:21:49.920 --> 00:21:52.720 the goal. Okay, that's good to know because I think that, you 323 00:21:52.839 --> 00:21:56.480 know, I mentioned to you before the before we started recording, is it, 324 00:21:56.720 --> 00:22:00.549 you know, and again, transparency. I'm level ridging Roosevelt any web 325 00:22:00.589 --> 00:22:03.710 data on some of the projects that we do. But you know, I 326 00:22:03.789 --> 00:22:07.269 had a client where we were doing a paper click campaign and we were going 327 00:22:07.309 --> 00:22:10.910 to leverage the Pixel to be able to harvest some of the people that did 328 00:22:10.950 --> 00:22:14.789 not fill out the form but actually click through, but they didn't understand it 329 00:22:14.910 --> 00:22:17.099 and and really, at the end of the day, did not want to 330 00:22:17.140 --> 00:22:21.819 understand it and really kind of caused a lot of caused a lot of consternation 331 00:22:21.980 --> 00:22:25.099 for the client because they felt like, Oh, this was somehow, you 332 00:22:25.180 --> 00:22:29.180 know, shady or dark. And I think that the what you just said 333 00:22:29.259 --> 00:22:32.410 is a really good point, is that there's there's laws in place, there's 334 00:22:32.410 --> 00:22:36.450 a lot of things in place already. There's privacy that we are taking into 335 00:22:36.450 --> 00:22:41.130 account. There's consent and we've we are, we are consenting to this, 336 00:22:41.690 --> 00:22:44.480 whether we realize it or not, by clicking on the cookies and the other 337 00:22:44.720 --> 00:22:48.200 car and there's already this is happening in every aspect of our life, and 338 00:22:48.440 --> 00:22:52.680 so you know you are being you know this, this is happening. I 339 00:22:52.759 --> 00:22:56.359 mean you can certainly go in and unhooked from the Internet and and you know, 340 00:22:56.559 --> 00:22:59.029 do a lot of extra work to do this, but at the end 341 00:22:59.029 --> 00:23:00.750 of the day, if you really want to be able to take advantage of 342 00:23:00.789 --> 00:23:06.230 the Amazon recommendations, this is part of it. And so so I think 343 00:23:06.269 --> 00:23:08.990 that, you know, educating everyone and helping everybody understand that this is a 344 00:23:10.109 --> 00:23:12.619 part of our live, this is a part of the way that e commerce 345 00:23:12.740 --> 00:23:15.700 works, this is the part of the way the Internet works, and we 346 00:23:15.819 --> 00:23:19.859 can either bury our heads in the sand and keep doing the way the marketing 347 00:23:19.900 --> 00:23:22.740 that we've been doing the same way, or we can lean into it, 348 00:23:22.859 --> 00:23:29.410 educate ourselves, understand what it is, partner with trusted resources and really make 349 00:23:29.450 --> 00:23:32.450 a difference in the way that we're impact in the world with our missions of 350 00:23:32.970 --> 00:23:36.930 extending higher education to those who are looking for it. And so I really 351 00:23:37.490 --> 00:23:40.009 part of it. I guess I'm just kind of making a statement. It's 352 00:23:40.009 --> 00:23:42.759 probably not a very good interviewer technique, just the idea of being able to 353 00:23:42.880 --> 00:23:47.240 say, you know, would you agree with that? Roosevelt, the idea 354 00:23:47.319 --> 00:23:51.440 that you know, we've got to we've got to educate ourselves and educate within 355 00:23:51.519 --> 00:23:55.910 higher education what this is really all about. Absolutely that that is purely the 356 00:23:55.990 --> 00:24:02.069 focus point of understanding. You know how to utilize this data every day and 357 00:24:02.150 --> 00:24:06.390 that's always been the challenge. We can provide the best data formula in the 358 00:24:06.390 --> 00:24:10.259 best data profile out there, in my opinion, and and just about a 359 00:24:10.299 --> 00:24:12.859 real time basis for for any you know, you know Higher Ed, but 360 00:24:14.140 --> 00:24:18.500 you know it's all about how you actionably use that information and you know I 361 00:24:18.980 --> 00:24:22.420 we have create a partnerships with with other companies to give you a road map 362 00:24:22.740 --> 00:24:27.049 and and to give you that that that modeling, you know, guidance and 363 00:24:27.609 --> 00:24:32.089 on how to use the data effectively so that you get the maximum result and 364 00:24:32.210 --> 00:24:37.170 that you not only do you gain access to your website traffic and you now 365 00:24:37.210 --> 00:24:41.000 you're understanding who these people are and what they're doing and why they're doing this 366 00:24:41.079 --> 00:24:45.559 on the website, but you're building a relationship. You know your you really 367 00:24:45.599 --> 00:24:49.880 step away from a pure transactional model and you become a relationship model based solution. 368 00:24:51.440 --> 00:24:56.109 And you know, proof is in the pudding with years of recommended studies 369 00:24:56.109 --> 00:25:00.109 are out there. The more relationship cure you create with any sort of consumer 370 00:25:00.309 --> 00:25:04.230 prospect, the more they're going to do with you. And and whether that's, 371 00:25:04.470 --> 00:25:07.339 you know, subscribing to your platform or, you know, spending more 372 00:25:07.380 --> 00:25:11.220 money with the monthly subscription, it goes hand in hand. That's great, 373 00:25:11.259 --> 00:25:14.460 and I I love the fact too, that it's I mean, at the 374 00:25:14.460 --> 00:25:17.339 end of the day, it comes down to business tell business intelligence. It's 375 00:25:17.460 --> 00:25:21.490 great. How smart will we be with the information that we have accessible to 376 00:25:21.529 --> 00:25:25.369 ourselves? And I'll be honest with you, hired marketing does this every day 377 00:25:25.369 --> 00:25:27.529 already. I mean we have campus visits. We have people sign up for 378 00:25:27.609 --> 00:25:30.849 campus visits. As soon as they sign up, we look in the database 379 00:25:30.890 --> 00:25:34.569 and see who they are. Oh Look, they mom and dad went here 380 00:25:34.650 --> 00:25:37.400 to so they're a legacy student. So I know that and I'm going to 381 00:25:37.559 --> 00:25:41.119 I'm going to talk about that when we're on the tour. Oh look, 382 00:25:41.160 --> 00:25:44.519 they said that they're interested in psychology. Well, I'm going to take them 383 00:25:44.640 --> 00:25:48.119 by the psychology department. I'm going to introduce them to Dr Jones, because 384 00:25:48.119 --> 00:25:52.470 he's a great, dynamic speaker. So I'm using another piece of data that 385 00:25:52.549 --> 00:25:55.950 I know about them and I'm going to market to them and then I'm also 386 00:25:55.950 --> 00:25:59.069 going to make sure that the student guide I see that they're from Ohio. 387 00:25:59.150 --> 00:26:00.630 So I'm going to pull one of my students who's in the office, the 388 00:26:00.750 --> 00:26:03.910 one from Ohio. I'm going to say, why don't you take them, 389 00:26:03.190 --> 00:26:06.700 because I know that you might be able to connect with them and build that 390 00:26:06.900 --> 00:26:10.220 relationship. And so I think that what we need to understand as high end 391 00:26:10.259 --> 00:26:14.059 marketing is you're already doing this. You already doing this in other aspects of 392 00:26:14.140 --> 00:26:18.049 the enrollment and the development cycle. I mean, I know development utilizes tools 393 00:26:18.089 --> 00:26:22.970 like razors edge and you can do a study to say what's the proclivity of 394 00:26:22.049 --> 00:26:26.609 somebody giving a large gift, and you can do that data research. You're 395 00:26:26.650 --> 00:26:30.609 already doing that as a school. So do not be afraid of something that's 396 00:26:30.609 --> 00:26:33.440 a little new and a little different. Thank you, Bart Roosevelt. As 397 00:26:33.519 --> 00:26:38.720 we wind up our episode, what advice would you give a marketer, either 398 00:26:38.759 --> 00:26:45.279 an overall marketing executive at an institution or maybe an enrollment leader? Their first 399 00:26:45.319 --> 00:26:48.269 step, if they're intrigued, how could they utilize big data or what would 400 00:26:48.309 --> 00:26:52.670 be the first thing that you would recommend them doing. I think, you 401 00:26:52.789 --> 00:26:59.950 know, the best recommendation is to develop a action plan of how you intend 402 00:27:00.150 --> 00:27:03.500 on using that data. That can be through a series of marketing channels, 403 00:27:03.779 --> 00:27:07.940 that can be a marketing plan put together with several of your colleagues. But 404 00:27:08.380 --> 00:27:11.980 you know, I think the common thing that we see most often, and 405 00:27:12.019 --> 00:27:15.890 this is with calls that I have on a daily basis, it's okay, 406 00:27:15.809 --> 00:27:19.130 you know, we get this common quote. Your data is awesome, but 407 00:27:19.250 --> 00:27:26.890 how do we use it? And so I think it's truly the missing piece 408 00:27:26.970 --> 00:27:30.039 to this puzzle is, okay, how do we plan to utilize this information? 409 00:27:30.440 --> 00:27:33.640 And there are organizations that are out there that can help you plan to 410 00:27:33.759 --> 00:27:37.480 use big data every day and and for it to be highly effective. And 411 00:27:37.839 --> 00:27:41.920 you know, I think that is that is truly the best advice that I 412 00:27:41.000 --> 00:27:45.390 could give to any marketing executive or anyone in so in it, in anyone 413 00:27:45.950 --> 00:27:51.069 within a roll of marketing in the Higher d space, is to put together 414 00:27:51.069 --> 00:27:53.829 a really effective plan before you buy a bunch of data. Thank you, 415 00:27:53.869 --> 00:28:00.059 Roosevelt, and I will also add that Barts Organization, Roosevelt's organization, or 416 00:28:00.259 --> 00:28:06.700 think patent died, who I work for all offer something similar and we all 417 00:28:06.779 --> 00:28:11.420 would love to help anyone take that first step or develop that action plan. 418 00:28:11.900 --> 00:28:15.769 But if you wanted to talk to Roosevelt, Roosevelt, what would be the 419 00:28:15.849 --> 00:28:18.809 best ways someone could reach out in contact you? Yeah, I think the 420 00:28:18.930 --> 00:28:22.730 best way, you know, you can reach us on our website, probably 421 00:28:22.769 --> 00:28:25.690 the most effective way. You know, I'm carbon copy on just about every 422 00:28:26.130 --> 00:28:30.279 reach out portal there, or you'll feel free to email me directly. He 423 00:28:30.359 --> 00:28:34.559 It's our Smith at Web Datacom, and the same thing for our support team 424 00:28:34.599 --> 00:28:38.359 as well, support at e Web Datacom. We're more than happy to talk 425 00:28:38.519 --> 00:28:42.750 and to you know, research and help you develop a plan. That's our 426 00:28:42.789 --> 00:28:45.509 goal. We necessarily don't want to sell you data if you're not ready to 427 00:28:45.589 --> 00:28:49.630 use it, and so you know, ultimately we're looking to build a relationship 428 00:28:51.069 --> 00:28:55.630 and to leverage that relationship for better results. Thank you, Roosevelt. Thank 429 00:28:55.670 --> 00:28:59.660 you for helping us get this message out. This is a topic that both 430 00:28:59.900 --> 00:29:04.180 Bart and I feel that institutions should leverage more. So thank you for enabling 431 00:29:04.220 --> 00:29:07.940 us to get that message out there. But do you have any closing comments 432 00:29:08.019 --> 00:29:11.609 for us? Yeah, just a very brief one. I think that this 433 00:29:11.730 --> 00:29:15.490 is one of those things that it's new and a lot of times new and 434 00:29:15.609 --> 00:29:19.970 change is difficult. I remember when we first started utilizing the ad people are 435 00:29:21.009 --> 00:29:22.890 like, Oh, I don't know about that thing, we don't I don't 436 00:29:22.890 --> 00:29:25.640 know if we want to ever do anything with that. Well, it's going 437 00:29:25.680 --> 00:29:27.799 to be a part of our lives and so I would just encourage everyone to 438 00:29:27.880 --> 00:29:30.559 really take a take a hard look at it. You know, reach out 439 00:29:30.559 --> 00:29:34.599 to Roosevelt, reach out to troy or myself and have some conversations and just 440 00:29:36.039 --> 00:29:40.269 learn more about it. You can certainly do some research online and and educate 441 00:29:40.349 --> 00:29:42.349 yourself, but I think it's one of those things. It's going to be 442 00:29:42.430 --> 00:29:47.390 a marketing tool that is going to continue to grow and it's something that, 443 00:29:47.670 --> 00:29:48.990 whether you use it right now, you at least need to be aware of 444 00:29:49.029 --> 00:29:53.019 it. Thank you, Bart. That brings us to the end of another 445 00:29:53.220 --> 00:29:57.579 episode of the High Ed Marketer Podcast, which is sponsored by Taylor solutions in 446 00:29:57.700 --> 00:30:03.140 education marketing and branding agency and by think patent did, a marketing execution company 447 00:30:03.220 --> 00:30:10.769 specializing in printing, mailing and other services for higher ed institutions. On behalf 448 00:30:10.769 --> 00:30:14.210 of my cohost Bart Kaylor. My name is troy singer. Thank you for 449 00:30:14.289 --> 00:30:21.799 joining us. You've been listening to the Higher Ed Marketer. To ensure that 450 00:30:21.920 --> 00:30:25.920 you never miss an episode, subscribe to the show in your favorite podcast player. 451 00:30:26.839 --> 00:30:30.079 If you're listening with apple PODCASTS, we'd love for you to leave a 452 00:30:30.119 --> 00:30:33.599 quick rating of the show. Simply tap the number of stars you think the 453 00:30:33.640 --> 00:30:36.150 podcast deserves. Until next time,