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Dec. 14, 2021

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

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

There are thousands of marketers across a variety of industries that are leveraging big data to promote products and services, almost in real-time.

But in higher ed marketing circles, many marketers are still wary of it. In many cases, a knowledge gap exists—they simply don’t know how it works. They have questions ranging from how big data is collected to the legalities and ethics surrounding it.

Today, we’ve got answers.

In this episode, Roosevelt Smith, Principal CEO / Executive Chairman at Ewebdata.com, teaches a master class on leveraging big data in the higher ed market.

We discuss:

- Why big data is relevant to higher ed markets

- How smart pixel technology factors into capturing big data

- Why how you use the data matters

Reach out to Roosevelt: rsmith@ewebdata.com

To hear more interviews like this one, subscribe to Higher Ed Marketer on Apple Podcasts, Spotify, or your preferred podcast platform.

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The Higher Ed Marketer podcast is sponsored by Caylor Solutions, an Education Marketing and Branding Agency, and by Think Patented, a Marketing Execution, Printing and Mailing provider of Higher Ed solutions.

    

 

Transcript
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,