When Data Goes Wrong, with Aaron Wollner

WEBVTT 1 00:00:08.199 --> 00:00:12.880 Conversations from the front lines and marketing. This is B two B growth. 2 00:00:17.320 --> 00:00:22.480 Today I'm excited to have Aaron Wahlner here with me. He's the CMO over 3 00:00:22.519 --> 00:00:25.480 at quantic and Aaron, welcome into B two, be growth. Thanks so 4 00:00:25.559 --> 00:00:28.359 much, glad to be here. Yes. So, I know in our 5 00:00:28.440 --> 00:00:32.200 pre call man we packed a lot of discussion in and when we were getting 6 00:00:32.439 --> 00:00:36.759 acquainted, just talking about man. What's the most helpful topic we could talk 7 00:00:36.799 --> 00:00:40.280 on? And you said something then that I think is just a really good 8 00:00:40.280 --> 00:00:43.920 starting point for this episode and for us today. You said when it comes 9 00:00:43.920 --> 00:00:49.600 to data, it's easy to go wrong and it goes back to inductive verse 10 00:00:49.960 --> 00:00:54.759 deductive. So if you're trying to just basically prove a point with the data 11 00:00:54.799 --> 00:00:56.600 set, you're going to be able to do it. And I was like 12 00:00:56.719 --> 00:01:00.799 Yep, like yeah, you hit the nail on the head right there. 13 00:01:00.840 --> 00:01:03.959 So talk about for you in your career Erin how you've seen that play out 14 00:01:04.000 --> 00:01:07.319 and why this matters a lot to you. Yeah, for sure. And 15 00:01:07.319 --> 00:01:11.920 and this is one of my favorite topics just in general. I'm a bit 16 00:01:11.920 --> 00:01:14.840 of a student as much as I am, you know, a leader to 17 00:01:15.280 --> 00:01:18.439 to my team and hopefully you know more or less within the industry as a 18 00:01:18.439 --> 00:01:21.719 marketing leader and a thought leader, but I truly am a student and one 19 00:01:21.760 --> 00:01:27.280 of the biggest learnings has come from my observation, especially over the past, 20 00:01:27.599 --> 00:01:32.799 I'd say, five years. It's really been pretty, pretty sharp and visible, 21 00:01:33.400 --> 00:01:36.840 which is, you know, everybody comes to the table with their data 22 00:01:37.480 --> 00:01:41.000 and so who's right and WHO's wrong? And it's even less important about right 23 00:01:41.000 --> 00:01:46.480 and wrong, but I think just openly, openly acknowledging that, you know, 24 00:01:46.599 --> 00:01:49.079 while numbers are black and white, right, three is a three, 25 00:01:49.640 --> 00:01:53.840 you know how you use that three and how it's presented. It is often, 26 00:01:53.959 --> 00:01:57.760 you know, misrepresented, and so so I think that's really where things 27 00:01:57.760 --> 00:02:00.719 get get interesting and you know, I think it just comes down to that, 28 00:02:01.000 --> 00:02:05.120 that awareness, and if you have that awareness then you can sort of 29 00:02:05.159 --> 00:02:07.159 engage in discussion. And okay, so when you when you see that, 30 00:02:07.199 --> 00:02:09.599 when you say that, what do you mean right when this goes up and 31 00:02:09.599 --> 00:02:14.439 that goes down and you're seeing those two things be related? Tell me more 32 00:02:14.479 --> 00:02:17.120 about that. Right. So I think it's more about that awareness that, 33 00:02:17.680 --> 00:02:21.400 you know, a data point is in the end of the discussion. It's 34 00:02:21.400 --> 00:02:23.599 the beginning of the discussion. Great way of saying it. I want to 35 00:02:23.639 --> 00:02:28.000 know before we go further down this rabbit hole, because as a CMO, 36 00:02:28.400 --> 00:02:32.199 obviously data matters, but there's also this aspect when someone thinks of a cmo 37 00:02:32.280 --> 00:02:36.240 or someone thinks even, let's just say, of someone in marketing, they're 38 00:02:36.240 --> 00:02:40.000 not going to naturally jump to two numbers. So do you find yourself as 39 00:02:40.000 --> 00:02:44.479 an outlier among CMOS? Do you find yourself as as having a unique voice 40 00:02:44.479 --> 00:02:46.800 and that's that's helped you when it comes to data or like? I don't 41 00:02:46.800 --> 00:02:50.719 know. I just wonder what that's been like in that development of really loving 42 00:02:50.719 --> 00:02:53.360 this as a topic for you and how that's influenced your your CMO role. 43 00:02:53.560 --> 00:02:57.840 Yeah, the two word answer is not anymore, which is great, right. 44 00:02:57.879 --> 00:03:01.400 I think that the well Cmo, the chief marketing officer a isn't that 45 00:03:01.439 --> 00:03:07.159 old, right, and they're you know that the famous Harvard Business School paper 46 00:03:07.240 --> 00:03:12.280 that was written probably six seven years ago at this point, that the average 47 00:03:12.520 --> 00:03:15.879 lifespan of a chief marketing officer is less than three years, was like two 48 00:03:15.879 --> 00:03:20.840 point something right. So that tells you something. That tells you really what 49 00:03:20.879 --> 00:03:24.039 it tells me is is that the role is poorly defined and evolving quickly, 50 00:03:24.520 --> 00:03:29.919 and so I think that we're sort of settling into a spot where it's better 51 00:03:30.000 --> 00:03:34.479 understood what's expected from a chief marketing officer, and that really is growth, 52 00:03:34.759 --> 00:03:38.319 right, and that's more sort of connected to the business. And the tricky 53 00:03:38.400 --> 00:03:42.680 part is that there's different types of chief marketing officers and that that article in 54 00:03:43.639 --> 00:03:46.000 that Harvard put out a number of years ago went into this. But you 55 00:03:46.039 --> 00:03:53.680 know, the CMO at PepsiCo is a fundamentally different than Cmo than you know 56 00:03:53.800 --> 00:03:58.360 CMO at a fintech startup, and the CMO to fintech startup is going to 57 00:03:58.439 --> 00:04:02.520 be way more numbers oriented and probably came up in that environment where it was 58 00:04:02.560 --> 00:04:08.479 all about cost per type metrics and being laser focused on acquisition as opposed to 59 00:04:08.599 --> 00:04:12.360 sort of big brand place. So it's it's an interesting sort of evolving thing. 60 00:04:12.879 --> 00:04:17.759 It is very interesting and that idea of just having your eyes on revenue 61 00:04:18.000 --> 00:04:24.120 and on lead creation, I think, is a big deal and a big 62 00:04:24.160 --> 00:04:27.240 part of your role. So it does make sense. And it's interesting though, 63 00:04:27.279 --> 00:04:30.439 because different people, different CMOS, will come in with like a heavier, 64 00:04:30.959 --> 00:04:33.240 more design background, or some will come in with a heavy data background, 65 00:04:33.279 --> 00:04:36.680 but ultimately it's it seems like those that rise to the top did a 66 00:04:36.680 --> 00:04:42.120 lot of work in to understand the business more holistically, and that's obviously in 67 00:04:42.240 --> 00:04:46.639 any position in business. But the more you understand from a large scale it's 68 00:04:46.680 --> 00:04:48.959 it's going to be beneficial to your career. So that makes sense. Okay. 69 00:04:49.000 --> 00:04:54.079 So jumping back into just the data side, if the problem is data 70 00:04:54.120 --> 00:04:58.560 manipulation, and I would say it's rampant, I want to paint the clear 71 00:04:58.600 --> 00:05:00.800 picture of what the cost of that is. I mean, I guess play 72 00:05:00.800 --> 00:05:04.319 out the horror film that is data manipulation in your opinion, and maybe like 73 00:05:04.639 --> 00:05:09.120 a real world example for us, Aaron, sure, yeah, I give 74 00:05:09.160 --> 00:05:13.560 you one from yesterday. So we've got a pretty healthy a B testing program 75 00:05:13.600 --> 00:05:16.600 on on my team. That's well run in my opinion, and we take 76 00:05:16.600 --> 00:05:20.920 a scientific approach. We have hypotheses and you know, we pay close attention 77 00:05:21.000 --> 00:05:26.000 to statist still significance that we've got general sort of best practices, and so 78 00:05:26.079 --> 00:05:28.839 we have a good we have a good program right where a B T always 79 00:05:28.839 --> 00:05:30.439 be testing. It's one of the one of the acronyms we have on the 80 00:05:30.480 --> 00:05:36.160 group with the group, and yesterday we were looking at the latest results from 81 00:05:36.160 --> 00:05:42.360 the last few months of tests and the biggest increase came from a test where 82 00:05:42.199 --> 00:05:46.360 we removed F D I C right, like, F D I C insured 83 00:05:46.360 --> 00:05:48.839 in terms of hey, we're a bank, you know your your deposits are 84 00:05:48.839 --> 00:05:55.680 insured. We removed that from the top of the page and that told us 85 00:05:55.680 --> 00:06:00.839 that it increased conversion or APP starts. And the more we looked at it, 86 00:06:00.879 --> 00:06:02.600 the more we realized that we didn't really keep to our best practice rules 87 00:06:03.199 --> 00:06:08.920 and we let that test run only seven days. And one of the one 88 00:06:08.920 --> 00:06:11.240 of the rules we have is, you know, no, no, two 89 00:06:11.240 --> 00:06:14.759 weeks are the same. So two weeks minimum for tests, even though the 90 00:06:14.800 --> 00:06:17.480 separation was strong. So this is where things get tricky, right like there's 91 00:06:17.480 --> 00:06:20.920 no one right way to do things. That that's the hard part. So 92 00:06:21.279 --> 00:06:27.759 we did the right thing in terms of we've got statistically significant separation between A 93 00:06:27.879 --> 00:06:30.199 and B, but we didn't really let it run long enough and even those 94 00:06:30.279 --> 00:06:35.360 rules are fuzzy. And so you would conclude that or you could conclude that 95 00:06:35.399 --> 00:06:39.199 we should take F D I C ensured, you know, away from this 96 00:06:39.279 --> 00:06:42.920 site entirely across all, you know, bank product pages, before you go 97 00:06:42.959 --> 00:06:46.399 and do that, think hard about that one test and maybe run one another 98 00:06:46.439 --> 00:06:49.480 test exactly the way you ran it the first time or run a similar test 99 00:06:49.519 --> 00:06:57.439 elsewhere. So I think the broad application of an exciting looking result is a 100 00:06:57.639 --> 00:07:02.240 really easy trap to fall into. So, with that being literally, like 101 00:07:02.279 --> 00:07:08.160 a very fresh example, that was was the conclusion. Basically the insight like 102 00:07:08.240 --> 00:07:11.040 we need to just run a longer test or we need to go back to 103 00:07:11.040 --> 00:07:15.720 our best practices. Exactly we're going to run the test. Yeah, I 104 00:07:15.759 --> 00:07:19.160 think it's interesting because part of the breakdown, and you you said this really 105 00:07:19.199 --> 00:07:23.639 well. It's actually had me thinking ever since our first conversation, but the 106 00:07:23.959 --> 00:07:29.959 breakdown between data and actual value or actual insight. People think like well, 107 00:07:30.000 --> 00:07:33.040 I have all these these numbers, I have all this information, but that 108 00:07:33.079 --> 00:07:38.279 doesn't naturally lead to like the right next step, the right insight, the 109 00:07:38.360 --> 00:07:43.160 way forward, and so this is something that you've been a student of but 110 00:07:43.199 --> 00:07:47.920 also like helped define. Talk to me about how would you move from data 111 00:07:48.000 --> 00:07:53.680 to actual insight? I would love to just break that down for our listeners. 112 00:07:53.959 --> 00:07:57.959 Sure. Yeah, so, my last cent in the agency World I 113 00:07:58.079 --> 00:08:03.680 ran the data analytics practice us and had a pretty talented group and we developed 114 00:08:03.839 --> 00:08:07.879 sort of our own methodology and it was it was a fourth step process. 115 00:08:07.519 --> 00:08:13.879 M that started with data and then stepped into information, then into knowledge and 116 00:08:13.920 --> 00:08:18.120 then then to value. And so that's sort of a four phase or four 117 00:08:18.240 --> 00:08:22.759 step approach where you have sort of you know, you can look at each 118 00:08:22.800 --> 00:08:26.560 step in isolation and understand how you went from a number on the left, 119 00:08:26.680 --> 00:08:31.040 right the data, all the way to value, right, and and so 120 00:08:31.560 --> 00:08:35.480 just to kind of go through each step. Data would obviously be a data 121 00:08:35.480 --> 00:08:37.480 point. Right, think of like a back end data base where you'd have 122 00:08:37.559 --> 00:08:41.919 a set of raw numbers. So you know, you know three percent and 123 00:08:43.399 --> 00:08:46.080 right, so you've got these two numbers that use rat and on. That's 124 00:08:46.120 --> 00:08:50.799 data. To make that information, right, you have you'd have to sort 125 00:08:50.799 --> 00:08:54.600 of carve that out more clearly and define that better in terms of the time 126 00:08:54.639 --> 00:09:00.320 period, in terms of relative movement over time, right, so three weekly 127 00:09:00.399 --> 00:09:05.360 reporting or exactly like like web traffic over time. Right. So now you're 128 00:09:05.360 --> 00:09:09.080 starting to look at information. Right. So you went from data to information. 129 00:09:11.080 --> 00:09:15.480 To go from information to knowledge, it really is about more more context, 130 00:09:15.799 --> 00:09:18.480 right, so that that's where you sort of zero in on the thing 131 00:09:18.519 --> 00:09:22.279 that you're trying to figure out or the interesting thing that you're that you're looking 132 00:09:22.320 --> 00:09:26.399 at. And again that's just more context. You could be slicing it in 133 00:09:26.480 --> 00:09:31.039 terms of a type of traffic, rights organic traffic, and you could be 134 00:09:31.039 --> 00:09:37.399 looking at organic traffic going up over that time period and the amount of PR 135 00:09:37.440 --> 00:09:39.960 that you put out. Right. So you're trying to correlate to things. 136 00:09:39.000 --> 00:09:43.919 For example, what is the relationship between the stuff that we're putting out there 137 00:09:43.919 --> 00:09:48.720 and PR for the brand and organic paid search for your brand term? Right? 138 00:09:48.519 --> 00:09:52.159 So now, now you're in. Now you're in step three, you're 139 00:09:52.200 --> 00:09:56.039 you're in that knowledge space and then getting from there to value. That's where 140 00:09:56.039 --> 00:10:00.759 the magic happens and I think that's where insights live and you know, that's 141 00:10:00.799 --> 00:10:05.759 where you sort of create hypotheses and it's supposed to lead to tests, right, 142 00:10:05.759 --> 00:10:07.799 because I think all all great marketers, you know, tests before they 143 00:10:07.840 --> 00:10:13.080 just roll out for the most part. And Yeah, so we created sort 144 00:10:13.120 --> 00:10:16.399 of this this process where we can go from, you know, a number 145 00:10:16.440 --> 00:10:22.360 to delivering value in a little bit more of a systematic way. It's interesting 146 00:10:22.360 --> 00:10:28.279 because you could look at data and draw like like, you can walk through 147 00:10:28.320 --> 00:10:33.279 that whole process, but to actually create true change, like you'd also have 148 00:10:33.360 --> 00:10:37.279 to be able to identify issues in the data right, like wrong data. 149 00:10:37.919 --> 00:10:41.679 Then you walk it through that whole process. Information, knowledge, value could 150 00:10:41.759 --> 00:10:46.600 lead you one way, but there's you have to be able to actually look 151 00:10:46.639 --> 00:10:50.600 at the data and and see any sort of inconsistencies or or real issues there. 152 00:10:50.639 --> 00:10:54.639 What stands in the way of that? When we're looking at the data? 153 00:10:54.799 --> 00:10:56.679 Are there ways we see it wrong or ways we get it wrong? 154 00:10:58.320 --> 00:11:01.200 No, I think if you follow that a right, it's not going to 155 00:11:01.320 --> 00:11:05.600 lead to this beautiful, you know, nugget of wisdom every time, right, 156 00:11:05.000 --> 00:11:09.240 but it will help you sort of follow a path and understand where you 157 00:11:09.279 --> 00:11:13.360 are in that process. So I think that just being able to sort of 158 00:11:13.519 --> 00:11:16.679 you know, let's say you're in a meeting and you know your your peer 159 00:11:16.720 --> 00:11:20.360 brings some data in terms of the product. Right. Let's see, you 160 00:11:20.360 --> 00:11:22.919 have an application to sign up for a new bank account, like we do. 161 00:11:22.120 --> 00:11:26.200 Right. So if they bring in, you know, data around the 162 00:11:26.480 --> 00:11:31.919 application to submit rate, just sort of making up a metric and how that's 163 00:11:31.960 --> 00:11:37.440 been volatile in to two picking a time period, right, you can help. 164 00:11:37.600 --> 00:11:41.960 It helps you contextualize. Where does that live on sort of the spectrum? 165 00:11:41.200 --> 00:11:43.879 Right, is that data? Is that information, is that knowledge or 166 00:11:43.919 --> 00:11:48.159 is that value? And then you that can help you sort of situate like 167 00:11:48.279 --> 00:11:52.080 where, where do we start this conversation? Because again, when you when 168 00:11:52.120 --> 00:11:56.600 you look at a data point, you know with your peers, it really 169 00:11:56.639 --> 00:12:01.039 should be the beginning of the conversation at the end. What I like about 170 00:12:01.120 --> 00:12:05.039 thinking about data in this way and these four steps is it actively exposes kind 171 00:12:05.039 --> 00:12:09.720 of how hard it is to go from data to valuable insight. And once 172 00:12:09.759 --> 00:12:13.039 you know the steps, obviously there's there's a part of it that's like it 173 00:12:13.080 --> 00:12:16.440 gives some ease, but in reality I think people, a lot of people, 174 00:12:16.440 --> 00:12:20.200 will jump from data thinking they have an insight and they miss those steps 175 00:12:20.200 --> 00:12:22.759 in between. I wonder if you zoom out and you look at how in 176 00:12:22.879 --> 00:12:28.720 like the B two B space or businesses at large think incorrectly. What what 177 00:12:28.720 --> 00:12:33.960 does it look like to maybe pivot towards more effective use or pulling of insights 178 00:12:33.080 --> 00:12:37.080 from data? Yeah, I think from especially from a B Two b standpoint, 179 00:12:37.320 --> 00:12:41.600 you know you're talking about a longer sales cycle. You're typically not talking 180 00:12:41.639 --> 00:12:45.639 about you know, let's just say something like hundreds of thousands of consumers that 181 00:12:45.679 --> 00:12:48.279 are purchasing clothing. Right. That's the typical sort of B two C model. 182 00:12:48.840 --> 00:12:54.480 So it's actually much harder. It's not necessarily good news for the audience, 183 00:12:54.480 --> 00:12:58.039 but you know, I'm not saying anything that that probably the folks in 184 00:12:58.039 --> 00:13:01.519 the B two, b were a don't already intuitively understand. But you know, 185 00:13:01.600 --> 00:13:03.840 if your sales cycle is a year, let's just say, right, 186 00:13:03.879 --> 00:13:09.440 and you're selling software to enterprises, you don't have tens or hundreds of thousands 187 00:13:09.440 --> 00:13:13.679 of data points to to look at and sort of you know, draw from. 188 00:13:13.720 --> 00:13:18.080 And then you've got all these wacky outliers, right, like a company, 189 00:13:18.120 --> 00:13:22.240 for example, that we spoke to in two thousand seventeen pop back up 190 00:13:22.240 --> 00:13:26.519 on our radar because of this really unique situation and the leader that had the 191 00:13:26.559 --> 00:13:33.360 software from another company joined the company we talked to in so there you go. 192 00:13:33.440 --> 00:13:37.120 You've got an example. You technically have a data point, but what 193 00:13:37.159 --> 00:13:39.240 does it mean? What do you do with that? Right? So, 194 00:13:39.559 --> 00:13:43.159 just to sort of finish that off right, that could spark an insight of 195 00:13:43.360 --> 00:13:46.159 okay, let's scrub our database and see if we can identify, you know, 196 00:13:46.480 --> 00:13:52.840 prior customers, are current customers who have moves companies in the past six 197 00:13:52.840 --> 00:13:56.360 months. Right, and now we're talking right, and now, you know, 198 00:13:56.360 --> 00:13:58.840 we're not debating the numbers, we're not talking about how hard it is 199 00:13:58.840 --> 00:14:03.639 to draw conclusions from so few examples, but you're sort of, you know, 200 00:14:03.679 --> 00:14:07.559 putting a good idea to work based on not an insight again, but 201 00:14:09.120 --> 00:14:13.039 something that's further upstream that sort of you know, is able to propel the 202 00:14:13.080 --> 00:14:20.799 discussion forward. B Two B growth will be right back. M M. 203 00:14:22.080 --> 00:14:26.720 It feels like the hardest leap would be from knowledge to value. If you 204 00:14:26.759 --> 00:14:31.200 were thinking about it almost as like a muscle, if you will, and 205 00:14:31.279 --> 00:14:35.559 like needing to strengthen that knowledge to value muscle, like, what would you 206 00:14:35.759 --> 00:14:39.000 you provide as like the way forward for that, Eran? What what can 207 00:14:39.039 --> 00:14:43.120 we do as B two B organizations to to strengthen that knowledge to value muscle? 208 00:14:43.840 --> 00:14:46.919 To me it's business understanding, right, and the B I person, 209 00:14:46.960 --> 00:14:52.000 the business insights person, is one of my favorite people. Wherever I am, 210 00:14:52.039 --> 00:14:54.159 you know, I make sure that that person is by my side, 211 00:14:54.759 --> 00:15:01.559 because having good context of the business, how the business runs, the mechanics 212 00:15:01.639 --> 00:15:05.200 of it, will really help you crystallize and jump from knowledge to value. 213 00:15:05.320 --> 00:15:11.480 Right, because what matters you know how is revenue actually being generated? You 214 00:15:11.480 --> 00:15:15.679 know what's there between a smaller customer versus a bigger customer. So to me 215 00:15:16.200 --> 00:15:22.519 it's really strong under a really strong understanding of the business and, and this 216 00:15:22.600 --> 00:15:26.759 has just worked for me personally, common sense, like I do not want 217 00:15:26.799 --> 00:15:31.720 to under emphasize this, and I actually remind my team pretty regularly. If 218 00:15:31.759 --> 00:15:35.960 you've got a good, strong sense of you know what makes sense, that 219 00:15:35.039 --> 00:15:39.679 goes a long way and it actually helps you create really strong insights, believe 220 00:15:39.799 --> 00:15:41.919 or not. Can you give me an example like what you mean by that 221 00:15:43.039 --> 00:15:45.840 or like how you would emphasize that to your team the common sense side? 222 00:15:46.320 --> 00:15:50.360 Yeah, just to go back to the C D F I example, right, 223 00:15:50.360 --> 00:15:52.200 and I can do another example as well if you like. But you 224 00:15:52.200 --> 00:15:58.320 know, to not jump out of our chairs at lift and conversion based on 225 00:15:58.399 --> 00:16:03.000 removing se D F I, right, and to apply some common sense there 226 00:16:03.120 --> 00:16:08.399 of okay, so that that, you know, icon is meant to represent 227 00:16:08.480 --> 00:16:12.639 security. Right, your money is secure. So does it make sense to 228 00:16:12.720 --> 00:16:18.039 remove that across the board? Does it make sense to lower that down on 229 00:16:18.080 --> 00:16:22.159 the product page? Like so, thinking more practically about put yourself in the 230 00:16:22.159 --> 00:16:26.039 consumer shoes, you know, makes sense of the thing that you're testing and 231 00:16:26.080 --> 00:16:29.279 what you're trying to achieve. And always, you know, one of one 232 00:16:29.279 --> 00:16:32.320 of the things that we one of our core values at quantic is know the 233 00:16:32.360 --> 00:16:34.159 goal, right. So always know the goal and I would put that in 234 00:16:34.200 --> 00:16:37.960 the bucket of common sense. So, you know, I think that again, 235 00:16:38.080 --> 00:16:41.159 it's not you know, as marketers we see we see, you know, 236 00:16:41.320 --> 00:16:45.639 positive result and we just want to sort of, you know, passing 237 00:16:45.679 --> 00:16:48.279 around town. And before you do that, you know, I think you 238 00:16:48.320 --> 00:16:52.200 know, apply, apply a layer of hey, what do I think this 239 00:16:52.320 --> 00:16:55.600 actually means? What are we testing here and what are the implications of that? 240 00:16:56.279 --> 00:17:00.120 Yeah, if you're gonna give like the B two B growth audience and 241 00:17:00.200 --> 00:17:04.400 in our community, a homework assignment of sorts and invitation towards improving ourselves and 242 00:17:04.519 --> 00:17:08.559 the way we think of data, after this conversation, what would you advocate 243 00:17:08.640 --> 00:17:12.400 for? What what should we be thinking about maybe changing or starting Eron? 244 00:17:12.799 --> 00:17:15.960 Yeah, I think it's a little bit of statistics. I think that in 245 00:17:15.960 --> 00:17:19.880 the B two B world, whether you're in bad or marketing or somewhere in 246 00:17:19.880 --> 00:17:25.920 between, I think having at least an appreciation and understanding of the basics of 247 00:17:26.000 --> 00:17:30.119 statistics is so important, right, so that you can sort of be more 248 00:17:30.119 --> 00:17:33.960 confident in the numbers. Right, I think the biggest thing is, you 249 00:17:34.000 --> 00:17:38.559 know what is statistically meaningful, right. So, for example, we just 250 00:17:38.640 --> 00:17:42.640 put out survey to our broker audience for the B Two B side of our 251 00:17:42.680 --> 00:17:48.279 mortgage business and we sent it out to I don't know, let's say fifteen 252 00:17:48.279 --> 00:17:52.720 thousand broker partners again on the B Two B side of the mortgage business, 253 00:17:52.279 --> 00:17:56.680 and we have so far received a little bit under a hundred responses. Right. 254 00:17:57.119 --> 00:18:00.960 So you could rush that to press and of a whole plan around like 255 00:18:00.960 --> 00:18:03.319 how we're going to use that in a Webinar and draw people in. Hey, 256 00:18:03.440 --> 00:18:07.880 you know, listen to what your peers are think about the mortgage market 257 00:18:07.920 --> 00:18:11.039 going into que for it's really it's going to be a great campaign that we're 258 00:18:11.039 --> 00:18:15.519 gonna put out there in a number of different ways. But, you know, 259 00:18:15.680 --> 00:18:19.200 and equals, right, like there there is a statistical requirement there that 260 00:18:19.279 --> 00:18:22.759 you need to, you know, check the box that you're not, you 261 00:18:22.799 --> 00:18:27.640 know, giving out results that are not statistically valid. So I think no 262 00:18:27.799 --> 00:18:33.319 matter who you are, what you do, having some statistical basics what will 263 00:18:33.359 --> 00:18:38.319 go along way in Mi co well, I really appreciate you taking time and 264 00:18:38.440 --> 00:18:41.400 being with us on B two B growth today. I know data is a 265 00:18:41.440 --> 00:18:45.720 recurring topic, obviously that we're thinking lots about and something that people are always 266 00:18:45.720 --> 00:18:49.119 trying to to refine that skill. I find the data, information, knowledge 267 00:18:49.200 --> 00:18:52.359 value thing to be something that's stuck in my mind, both in the context 268 00:18:52.400 --> 00:18:56.759 of B Two B marketing but even outside, just in like life, when 269 00:18:56.839 --> 00:19:00.039 people say a stat now er and I'm thinking about what you said. So 270 00:19:00.559 --> 00:19:06.400 thanks a lot for that. But tell us a little bit more about quantic 271 00:19:06.599 --> 00:19:10.200 and the work you guys are doing before we close out today. Absolutely. 272 00:19:10.359 --> 00:19:14.680 Yeah, so we're at an exciting phase as a company. So quantic is 273 00:19:14.839 --> 00:19:18.680 a digital bank that was a community bank. It's about ten years old, 274 00:19:18.720 --> 00:19:23.079 a little bit older, and we fully went through digital transformation right, which 275 00:19:23.119 --> 00:19:27.119 is just basically a buzzword at this point. But the cool part about it 276 00:19:27.200 --> 00:19:32.519 for us is that we can point to no branches. We can point to, 277 00:19:33.279 --> 00:19:38.000 you know, fully bringing in and servicing customers digitally online. So we 278 00:19:38.039 --> 00:19:41.359 did it. We actually kind of did a sink or swim approach, which 279 00:19:41.400 --> 00:19:45.960 was a bit scary, and closed down our last branch about two years ago. 280 00:19:45.079 --> 00:19:51.039 So an exciting time. We've we've grown our our customer base about four 281 00:19:51.279 --> 00:19:55.759 x in the past eighteen months. So I know the name of the game 282 00:19:55.799 --> 00:20:00.039 here is growth. So so that that's a nice data point to sort of 283 00:20:00.039 --> 00:20:03.160 know, showcase that. But a lot has gone into that. So I 284 00:20:03.200 --> 00:20:06.680 got a talented marketing team and and data analytics, of course, is it's 285 00:20:06.680 --> 00:20:10.680 a big part of that. And sort of the last thing that I'll highlight 286 00:20:10.720 --> 00:20:15.799 here is that we're innovators and we see that as a real differentiator for us 287 00:20:15.119 --> 00:20:21.400 and some of the stuff we've innovated and put out there recently is a wearable 288 00:20:21.440 --> 00:20:25.680 payment device in the form of a ring, so you can pay for stuff 289 00:20:25.720 --> 00:20:29.200 with a ring on your finger. Super Cool and it comes free with your 290 00:20:29.279 --> 00:20:33.559 checking account. We were the first digital bank in the metaverse, which was 291 00:20:33.640 --> 00:20:38.400 just sort of a really interesting exercise, and we explore the metaverse and invited 292 00:20:38.880 --> 00:20:44.240 our customers to explore the metaverse. And I believe it's your cover photo on 293 00:20:44.480 --> 00:20:47.599 Linkedin as well. Right. Yeah, it's so cool. It's so cool 294 00:20:48.160 --> 00:20:51.440 we have like there is no quantic branch, but now you've got this sort 295 00:20:51.480 --> 00:20:53.680 of you know, digital three D quantic branch in the metaverse. It's just 296 00:20:53.720 --> 00:20:56.920 it was super on point for us. It was a ton of fun to 297 00:20:56.960 --> 00:21:00.880 execute and I think our customers really enjoyed us sort of inviting them into the 298 00:21:00.920 --> 00:21:04.039 metaverse of showing them, Hey, what is this thing all about? So 299 00:21:04.119 --> 00:21:07.559 we just we just put a bunch of innovative stuff out there, including a 300 00:21:07.559 --> 00:21:11.720 crypto rewards card as well, so earning one point five percent back in the 301 00:21:11.759 --> 00:21:15.799 form of Bitcoin. And Yeah, the Digital Bank side of the business is 302 00:21:15.839 --> 00:21:19.359 a ton of fun. And then we are we're mortgage lenders as well and 303 00:21:19.400 --> 00:21:25.359 we live in a pretty unique space in terms of lending out to those who 304 00:21:25.400 --> 00:21:30.480 are often overlooked, outside the box, borrowers, and we've got a whole 305 00:21:30.480 --> 00:21:34.240 sort of, you know, set of mortgage products tailor fit to that audience. 306 00:21:34.400 --> 00:21:37.279 So, yeah, it's been it's been a fun couple of years here 307 00:21:37.279 --> 00:21:41.599 at quantum, fascinating. I have never been in the banking World Erin but 308 00:21:41.720 --> 00:21:47.839 I love just the innovation and it's it's really compelling too, from the outside 309 00:21:47.880 --> 00:21:49.720 looking in to see what you guys are up to, and so it's been 310 00:21:49.759 --> 00:21:53.119 an honor to get to chat. People can go to what's the website for 311 00:21:53.160 --> 00:21:57.559 people to check out quantic quantic DOT COM, simple and easy to remember. 312 00:21:57.720 --> 00:22:03.599 Love it. Aaron Walner, thank you so much for being on B two 313 00:22:03.599 --> 00:22:06.119 B growth today. Man, it's been been a pleasure chatting with you. 314 00:22:06.359 --> 00:22:10.720 Absolutely appreciate it. Bendy. For everybody listening, if this is your first 315 00:22:10.720 --> 00:22:15.200 time checking out B two B growth and you have yet to follow the podcasts 316 00:22:15.200 --> 00:22:18.359 on whatever podcast player you're listening to this on, go ahead and do that. 317 00:22:18.440 --> 00:22:22.079 We're having conversations that will help continue to fuel your innovation and your continued 318 00:22:22.160 --> 00:22:25.079 learning, and if you want to reach out to me, you can do 319 00:22:25.160 --> 00:22:27.920 that over on Linkedin. Would love to chat with you about marketing, business 320 00:22:29.279 --> 00:22:32.799 and life. Will be back real soon with another episode. Keep doing work 321 00:22:32.799 --> 00:22:47.960 that matters. B Two B growth is brought to you by the team at 322 00:22:47.960 --> 00:22:51.119 sweet fish media. Here at sweet fish, we produce podcasts for some of 323 00:22:51.160 --> 00:22:55.240 the most innovative brands in the world and we help them turn those podcasts into 324 00:22:55.279 --> 00:22:59.039 micro videos, linked in content, blog posts and more. We're on a 325 00:22:59.079 --> 00:23:03.559 mission to produce every leader's favorite show. Want more information, visit sweet fish 326 00:23:03.599 --> 00:23:11.680 media DOT com. MHM.