Susan Cunningham | Nonlinear Thinking in Analytics

Episode 7 April 12, 2023 00:24:02
Susan Cunningham | Nonlinear Thinking in Analytics
RXA Presents: Real Intelligence
Susan Cunningham | Nonlinear Thinking in Analytics

Apr 12 2023 | 00:24:02

/

Hosted By

Anna Schultz Jason Harper

Show Notes

Our guest for April's episode is Susan Cunningham, Executive Director, Managing Partner of Marketing Intelligence and Data Science at Wavemaker. Susan has over 20 years of agency experience in global account management, analytics, data and audience strategy, primary research, and media measurement. She has developed these skills across many industries, including tech, gaming, fintech, travel, and CPG, at creative and data focused firms. Susan is based in San Francisco and holds a Masters Degree in Counseling Psychology.

In our discussion, we cover many topics including: Susan's career journey, the ever-changing landscape of marketing analytics, career advice, and how to use non-linear thinking to your advantage in applied analytics.

View Full Transcript

Episode Transcript

1 00:00:00,936 --> 00:00:03,388 [Anna]: You're listening to the Real Intelligence podcast, 2 00:00:03,844 --> 00:00:08,862 [Anna]: presented by RXA, a leader in Business Intelligence and Data Science consulting services. 3 00:00:09,439 --> 00:00:13,051 [Anna]: We're here to bring attention to the unique stories, perspectives, 5 00:00:13,548 --> 00:00:16,000 [Anna]: challenges and success that individuals in the 5 00:00:16,100 --> 00:00:19,715 [Anna]: data industry face at every career stage. Welcome to the show! 6 00:00:22,112 --> 00:00:24,590 [Anna]: Thank you for tuning in to the Real Intelligence podcast. 7 00:00:25,282 --> 00:00:28,600 [Anna]: You're on today with Katia Sausys, SVP of Business Intelligence 7 00:00:28,700 --> 00:00:32,717 [Anna]: at RXA and Anna Schultz, Marketing Coordinator at RXA. 8 00:00:33,428 --> 00:00:35,084 [Anna]: Our guest today is Susan Cunningham, 9 00:00:35,620 --> 00:00:38,000 [Anna]: Executive Director, Managing Partner of Marketing Intelligence 9 00:00:38,100 --> 00:00:40,584 [Anna]: and Data Science at Wavemaker. 10 00:00:41,694 --> 00:00:43,200 [Anna]: Susan has over twenty years of agency 10 00:00:43,300 --> 00:00:45,601 [Anna]: experience in global account management, 12 00:00:46,279 --> 00:00:48,614 [Anna]: analytics, data and audience strategy, 13 00:00:49,031 --> 00:00:50,865 [Anna]: primary research, and media measurement. 14 00:00:51,679 --> 00:00:55,774 [Anna]: She has developed these skills across many industries, including tech, gaming, 15 00:00:56,272 --> 00:00:58,011 [Anna]: fintech, travel, and CPG, 16 00:00:58,604 --> 00:01:00,500 [Anna]: at creative and data-focused firms. 17 00:01:01,077 --> 00:01:03,000 [Anna]: Susan is based in San Francisco and holds 17 00:01:03,100 --> 00:01:05,248 [Anna]: a Master's Degree in Counseling Psychology. 18 00:01:06,359 --> 00:01:10,531 [Anna]: Susan has experience with quantitative and qualitative research methodologies, 19 00:01:11,108 --> 00:01:12,167 [Anna]: consumer segmentation, 20 00:01:12,585 --> 00:01:14,801 [Anna]: creative testing and complicated measurement 21 00:01:15,154 --> 00:01:16,200 [Anna]: and effectiveness evaluation 22 00:01:16,300 --> 00:01:18,722 [Anna]: for actionable marketing optimization. 23 00:01:19,418 --> 00:01:21,200 [Anna]: She brings these skills to her current role 23 00:01:21,300 --> 00:01:24,000 [Anna]: where she leads the marketing intelligence and data science team 23 00:01:24,100 --> 00:01:26,500 [Anna]: for Wavemaker across all West Coast accounts. 23 00:01:26,600 --> 00:01:27,900 [Anna]: Welcome to the show Susan! 23 00:01:28,000 --> 00:01:29,142 [Susan]: Thank you. 24 00:01:29,879 --> 00:01:30,379 [Katia]: Welcome! 25 00:01:31,952 --> 00:01:32,652 [Anna]: Absolutely. 26 00:01:34,400 --> 00:01:37,292 [Anna]: We're really grateful that you took the time to talk with us today. 27 00:01:38,307 --> 00:01:41,200 [Anna]: So, we like to start off the podcast by getting to know the real you. 27 00:01:41,307 --> 00:01:45,369 [Anna]: So, I have a few questions that we might not find the answers to in your professional bio. 28 00:01:46,150 --> 00:01:50,630 [Anna]: The first one being, do you have any fun hobbies or talents that people might not know about? 29 00:01:52,563 --> 00:01:55,100 [Susan]: I mean, I paint quite a bit. 30 00:01:56,118 --> 00:01:59,195 [Susan]: You can see a couple, one of, actually two of mine right there. 31 00:01:59,969 --> 00:02:02,467 [Susan]: Yeah. I paint quite a bit. I, um, 32 00:02:03,925 --> 00:02:06,702 [Susan]: I think that's probably my biggest one 33 00:02:07,121 --> 00:02:08,640 [Susan]: that I do on the side. 34 00:02:10,333 --> 00:02:14,329 [Susan]: Yeah. Read a lot of books, but mostly painting. It's my fun hobby that I love. 35 00:02:16,127 --> 00:02:16,766 [Anna]: That's awesome. 36 00:02:18,539 --> 00:02:21,200 [Anna]: Do you feel like you have a favorite artist or artists 36 00:02:21,400 --> 00:02:26,501 [Anna]: that you draw inspiration from? Or do you just kinda... 37 00:02:24,100 --> 00:02:36,500 [Susan]: Yeah. I love Richard Diebenkorn. He's a, 37 00:02:26,895 --> 00:02:30,435 [Susan]: he's an artist who spent quite a bit of time in the Bay Area, a lot of time in California. 38 00:02:31,815 --> 00:02:35,015 [Susan]: He does, kind of, like, big color field paintings, quite a lot. 39 00:02:35,829 --> 00:02:38,886 [Susan]: And he also has some beautiful abstract expressionist 40 00:02:39,225 --> 00:02:43,615 [Susan]: kind of work that I really, really love. So I love... I love his style. I love 41 00:02:44,113 --> 00:02:48,264 [Susan]: his whole genre. Elmer Bischoff as well. Those guys are couple of my favorites. 42 00:02:49,501 --> 00:02:51,438 [Anna]: Awesome. I'll have to look those up. 43 00:02:53,149 --> 00:03:57,500 [Anna]: And do you feel like having art, and that creative outlet helps you 43 00:02:57,700 --> 00:03:01,900 [Anna]: with your role at all? Or do you feel any of those skills translate into your role? 43 00:03:02,100 --> 00:03:05,000 [Anna]: Or is it just something you do to kinda get away from it all? 44 00:03:05,200 --> 00:03:10,400 [Susan]: Yeah. I think so. I think that I, I would definitely not say I'm a fully 44 00:03:10,500 --> 00:03:14,800 [Susan]: left-brained person by any means. I feel like I'm much more, you know, 44 00:03:15,000 --> 00:03:18,950 [Susan]: sort of, center-of-the-brain is the way I like to think of myself. 45 00:03:19,125 --> 00:03:26,000 [Susan]: I feel like in the, you know, with so much data and so many numbers 46 00:03:26,100 --> 00:03:30,200 [Susan]: and so, you know, always being inundated with kind of information and 47 00:03:30,400 --> 00:03:32,163 [Susan]: and chunks of, chunks of data. 48 00:03:32,940 --> 00:03:35,300 [Susan]: What I find useful is being able to try to pull all that together, 48 00:03:35,400 --> 00:03:40,400 [Susan]: find the patterns, and tell the story. And that to me is a much more, kind of, right brain 48 00:03:40,500 --> 00:03:44,760 [Susan]: sort of, you're looking for those connections. And I think art and creativity 49 00:03:45,460 --> 00:03:46,680 [Susan]: is a way to 50 00:03:47,674 --> 00:03:51,100 [Susan]: make those connections and to then tell the story. 50 00:03:51,200 --> 00:03:55,307 [Susan]: And also sometimes to work not linearly. Because a lot of times, I find myself thinking, 51 00:03:55,859 --> 00:03:58,400 [Susan]: and hopefully a lot of my team is doing this too. Thinking about 51 00:03:58,500 --> 00:04:01,385 [Susan]: what is the end goal? Who is the audience? 52 00:04:01,723 --> 00:04:06,311 [Susan]: Where am I trying to go? So that I then can bump along the way on the journey, 53 00:04:06,770 --> 00:04:11,100 [Susan]: or, you know, pull disparate pieces together to ultimately get to where I'm going. 53 00:04:11,200 --> 00:04:15,800 [Susan]: But I think it's a very non-linear process. I think sometimes in numbers and data, 53 00:04:16,000 --> 00:04:20,130 [Susan]: I think it attracts, the field sort of attracts a lot of people who like a linear process. 55 00:04:20,745 --> 00:04:23,500 [Susan]: But I don't find that to be the best. 55 00:04:23,600 --> 00:04:27,145 [Susan]: And certainly in the agency world in which I worked for so long, 56 00:04:28,077 --> 00:04:29,415 [Susan]: you need to be kind of 57 00:04:30,271 --> 00:04:33,243 [Susan]: able to live in the unknowns and the 58 00:04:34,099 --> 00:04:37,100 [Susan]: unclear, lack of clarity, and also just to work sort of more 58 00:04:37,200 --> 00:04:40,200 [Susan]: in that nonlinear fashion, is what I found to be 59 00:04:40,585 --> 00:04:41,245 [Susan]: more helpful. 60 00:04:41,625 --> 00:04:43,805 [Susan]: So yes. I do think it helps a lot. 61 00:04:45,440 --> 00:04:49,400 [Anna]: That's great. Yeah. Our CEO always says that the secret of data science is 61 00:04:49,500 --> 00:04:54,000 [Anna]: it's more of an art than a science. So I feel like that very much plays into that. 61 00:04:54,200 --> 00:04:57,400 [Susan]: Yeah. I think that's true... I think people are very uncomfortable with that idea 61 00:04:57,500 --> 00:05:00,000 [Susan]: because I think everyone really likes, well I shouldn't say everyone, 61 00:05:00,100 --> 00:05:03,200 [Susan]: but it feels as though. There's a lot more comfort in, 61 00:05:03,400 --> 00:05:08,000 [Susan]: you know, the known and the sort of very black and white, you know, 62 00:05:09,083 --> 00:05:09,862 [Susan]: sort of like... 63 00:05:11,474 --> 00:05:13,100 [Susan]: When am I try to say, like, the zeros and ones, you know, 63 00:05:13,200 --> 00:05:17,300 [Susan]: it's either this or that. But in reality, there are so many ways, 63 00:05:17,400 --> 00:05:20,100 [Susan]: and those of us who've worked with data and numbers before. I mean there's 63 00:05:20,200 --> 00:05:25,500 [Susan]: so many ways to craft the story, to pull the pieces you want and ignore the ones you don't. 63 00:05:25,600 --> 00:05:27,800 [Susan]: You know, it's not, it's not always. It is... 63 00:05:25,600 --> 00:05:30,500 [Susan]: There's a lot of art in it. And there's a lot of heart, there can be a lot of heart in it, 63 00:05:30,600 --> 00:05:34,485 [Susan]: I think too because it's also about... What is that story you're trying to tell? 64 00:05:35,299 --> 00:05:37,357 [Susan]: What is it that you're trying to understand? 65 00:05:38,056 --> 00:05:40,333 [Susan]: Rather than just, you know, here's the number. So... 66 00:05:41,451 --> 00:05:45,558 [Katia]: Exactly. Put reality to it. When we were wondering how to name our 67 00:05:46,094 --> 00:05:46,594 [Katia]: podcast, 68 00:05:47,171 --> 00:05:52,230 [Katia]: we played with the idea of artificial versus real intelligence. That's how it came about. 69 00:05:53,127 --> 00:05:55,382 [Susan]: That's fantastic. I love that. 70 00:05:56,517 --> 00:05:58,700 [Susan]: And there's so much AI now. I mean, everything is AI, 70 00:05:58,800 --> 00:06:01,861 [Susan]: all the models are AI-based. So I mean, it's all just, you know, 71 00:06:02,675 --> 00:06:03,175 [Katia]: it's the buzzword 72 00:06:03,515 --> 00:06:07,175 [Susan]: Isn't it interesting? Yeah. It's the latest buzz word. One of the latest. 73 00:06:09,690 --> 00:06:12,270 [Anna]: Absolutely. I completely agree with all of that. 74 00:06:12,890 --> 00:06:15,500 [Anna]: And I think it's something that's not talked about as much, which is really, 74 00:06:15,600 --> 00:06:17,600 [Anna]: you know, interesting to hear that perspective from someone who's 74 00:06:17,650 --> 00:06:20,200 [Anna]: been in that industry for so long. 74 00:06:20,400 --> 00:06:21,400 [Susan]: Yeah. Yeah. 75 00:06:21,614 --> 00:06:23,900 [Anna]: And now before I turn over to Katia to get into kind of the 75 00:06:24,000 --> 00:06:26,257 [Anna]: more meat-and-potatoes of the interview. 76 00:06:26,930 --> 00:06:30,110 [Anna]: Can you, in your own words, kind of walk us through your career journey 77 00:06:30,450 --> 00:06:32,110 [Anna]: and how you got to where you are today? 78 00:06:32,850 --> 00:06:35,000 [Susan]: Sure. I was actually just telling somebody this story the other day 78 00:06:35,100 --> 00:06:38,300 [Susan]: because I feel like kids now have this... It feels like... 78 00:06:38,400 --> 00:06:40,200 [Susan]: It seems like there's a lot of pressure on them to really 78 00:06:40,300 --> 00:06:44,900 [Susan]: know what they wanna do in a way that I don't think I experienced in my 78 00:06:45,000 --> 00:06:47,800 [Susan]: years. I mean, there was some pressure. you know, what are you gonna do? 78 00:06:47,900 --> 00:06:51,800 [Susan]: But it it didn't feel like I had to actually know. It felt like I could kinda figure it out. 78 00:06:52,000 --> 00:06:56,300 [Susan]: And it seems to me that there's a lot more pressure on kids now. So I feel like 78 00:06:56,400 --> 00:06:58,977 [Susan]: my... You know, I went to college. I got a 79 00:06:59,769 --> 00:07:00,965 [Susan]: bachelor's degree in counseling... 80 00:07:01,443 --> 00:07:02,740 [Susan]: Sorry, communication studies. 81 00:07:03,675 --> 00:07:06,800 [Susan]: And really because I like watching TV, that is God's honest truth. 81 00:07:07,000 --> 00:07:10,700 [Susan]: And then I was like, I don't know what I wanna do. Maybe I'll be on the news. 81 00:07:10,800 --> 00:07:15,500 [Susan]: I'll be a reporter because I like watching TV. Like, that was it. That was my big decision. 82 00:07:15,600 --> 00:07:18,560 [Susan]: So I ended up as an intern at CNN 83 00:07:18,937 --> 00:07:23,662 [Susan]: in Washington DC at the time, and this will age, date me, but I... It was during the 84 00:07:24,415 --> 00:07:26,675 [Susan]: Iran-Contra hearings. So Oli North and 85 00:07:27,535 --> 00:07:31,050 [Susan]: Fawn... whatever her name was, the admin who shredded the documents. 86 00:07:31,590 --> 00:07:34,250 [Susan]: Anyway, it was an exciting time to be there. But what I learned is that, 86 00:07:34,590 --> 00:07:37,250 [Susan]: this a sidebar story, but what I learned is that 87 00:07:37,710 --> 00:07:40,010 [Susan]: TV production is much like film production, 88 00:07:40,350 --> 00:07:45,500 [Susan]: which is slow and like, you drive everywhere, you get a two-minute shot 89 00:07:45,600 --> 00:07:49,800 [Susan]: and it takes forever to, like, get that shot. And so anyway I kind of then 90 00:07:50,000 --> 00:07:52,800 [Susan]: was thinking to myself. I'm not sure this is right. Ended up 90 00:07:52,900 --> 00:07:57,550 [Susan]: back in the Bay Area, worked for a local TV station. Again, thinking well I like TV, 91 00:07:58,045 --> 00:08:01,400 [Susan]: And I ended up literally falling into a job where I was like a research analyst. 91 00:08:01,500 --> 00:08:04,800 [Susan]: And at the time, it was about creating sales docs for... 91 00:08:05,000 --> 00:08:09,000 [Susan]: for the sales team to go out and sell our programs, ads within our programs. 91 00:08:09,100 --> 00:08:13,900 [Susan]: And I liked it. What I found is that, like, I could see the pattern in the numbers, 91 00:08:14,000 --> 00:08:17,600 [Susan]: and I could tell a story with the numbers, more importantly. And I think 91 00:08:17,700 --> 00:08:23,400 [Susan]: that was amazing training. And I then stayed in radio and then cable because 92 00:08:23,500 --> 00:08:26,600 [Susan]: it was all about taking the numbers and crafting the story 92 00:08:26,700 --> 00:08:29,500 [Susan]: that would help the sales people sell that air time. 92 00:08:29,600 --> 00:08:32,746 [Susan]: And that, it was an incredible foundation. 93 00:08:33,362 --> 00:08:37,547 [Susan]: And then, you know, the... I go to grad school, I get my counseling psych degree, 94 00:08:38,520 --> 00:08:41,600 [Susan]: I also find that people are really tricky when you sit with them one on one 94 00:08:41,700 --> 00:08:47,340 [Susan]: in counseling because talk about not black and white. It is all gray, all the time. 95 00:08:47,934 --> 00:08:50,400 [Susan]: And that was very interesting and very, very helpful and 95 00:08:50,500 --> 00:08:52,800 [Susan]: I learned a lot of stuff in Grad school about listening to people, 95 00:08:52,900 --> 00:08:55,700 [Susan]: and allowing space for what's next, and things like that. 96 00:08:56,558 --> 00:08:59,500 [Susan]: And yet, the Internet was starting to take off and I was thinking 96 00:08:59,600 --> 00:09:02,123 [Susan]: gosh, I feel like I'm missing something and I really kinda of missed 97 00:09:02,779 --> 00:09:06,000 [Susan]: numbers and research and telling a story with data. And so 97 00:09:06,100 --> 00:09:10,885 [Susan]: I fell back into the agency world in one of the very earliest .com 98 00:09:11,345 --> 00:09:13,385 [Susan]: agencies, called iXL. 101 00:09:14,437 --> 00:09:17,784 [Susan]: And I was there for a little while, kind of bumped around the .com boom. 102 00:09:18,382 --> 00:09:21,392 [Susan]: Katia and I got to work together at an agency also. 103 00:09:22,304 --> 00:09:24,100 [Susan]: And then I just... I've been in the agency world for 103 00:09:24,200 --> 00:09:27,400 [Susan]: so many years now, which is ironic because 103 00:09:27,500 --> 00:09:31,400 [Susan]: seriously in my very first, very first agency at iXL, I thought 103 00:09:31,500 --> 00:09:35,300 [Susan]: I am never doing an agency job again because you basically have two jobs 103 00:09:35,400 --> 00:09:37,970 [Susan]: where you're doing the work, and then you're also 104 00:09:38,470 --> 00:09:40,700 [Susan]: managing the clients, which is like a separate job. 104 00:09:40,800 --> 00:09:43,500 [Susan]: So I just thought this is it. This is my last agency, 104 00:09:43,600 --> 00:09:46,900 [Susan]: and then meanwhile, you know, twenty years later, I'm still in the agency space 104 00:09:47,000 --> 00:09:51,369 [Susan]: because there's so much I do love about it. So that's the arc, the trajectory. 105 00:09:53,981 --> 00:09:57,913 [Katia]: Awesome. Thank you. Yes. I do remember fondly 106 00:09:59,024 --> 00:10:00,480 [Katia]: our time at Modern Media, 107 00:10:01,176 --> 00:10:02,912 [Katia]: eventually Digitas, 108 00:10:03,966 --> 00:10:05,263 [Katia]: where we would brainstorm 109 00:10:05,919 --> 00:10:07,110 [Katia]: solutions to analytical 110 00:10:07,609 --> 00:10:09,386 [Katia]: problems, and measurement challenges 111 00:10:09,924 --> 00:10:11,023 [Katia]: with Andrew Hoebrichts, 112 00:10:11,960 --> 00:10:15,374 [Katia]: our lead at the time who was so passionate about it, remember? 113 00:10:16,590 --> 00:10:18,900 [Susan]: He was, oh my gosh, yes. 114 00:10:19,285 --> 00:10:23,800 [Katia]: And I think that one of the most important questions that we women, 114 00:10:23,900 --> 00:10:28,100 [Katia]: humans in data get asked, is how can I prove that my marketing budget 115 00:10:28,200 --> 00:10:33,520 [Katia]: was not wasted? That my marketing efforts work? 116 00:10:34,755 --> 00:10:36,055 [Katia]: What in your experience 117 00:10:36,595 --> 00:10:37,975 [Katia]: are the most challenging aspects 118 00:10:38,675 --> 00:10:39,900 [Katia]: of answering that question? 118 00:10:40,000 --> 00:10:45,670 [Katia]: Is it the data that we need to collect, is it the way we collect it? Is it even 119 00:10:46,330 --> 00:10:47,910 [Katia]: how we frame our questions? 120 00:10:49,422 --> 00:10:52,400 [Susan]: I think it is... I think it's the data we collect. 120 00:10:52,500 --> 00:10:56,900 [Susan]: I think it's also the fragmented nature of where the data is coming from. 120 00:10:57,000 --> 00:11:00,388 [Susan]: I think, you know, when you and I were in our agency life, 121 00:11:00,846 --> 00:11:02,800 [Susan]: it was pretty relatively straightforward. 121 00:11:03,000 --> 00:11:06,500 [Susan]: I feel like there were a lot fewer digital channels. The data was a lot more sort of 121 00:11:06,600 --> 00:11:10,739 [Susan]: fluid. It was very basic. It seems very basic to me at the time. 122 00:11:11,158 --> 00:11:12,556 [Susan]: I mean, I remember looking at, like, 123 00:11:14,730 --> 00:11:15,800 [Katia]: Click-through-rate. 123 00:11:16,000 --> 00:11:18,000 [Susan]: Yeah! It was Click-through-rate, and, like, 124 00:11:18,250 --> 00:11:20,900 [Susan]: the view-through-rate. And, like, it was just quite simple, you know? 124 00:11:21,000 --> 00:11:24,319 [Susan]: And now I think both the measurement solutions have gotten 125 00:11:24,856 --> 00:11:26,952 [Susan]: more abundant and maybe more complicated. 126 00:11:27,450 --> 00:11:31,000 [Susan]: And then you have data coming from, you know, in some cases the walled gardens; 126 00:11:31,100 --> 00:11:31,800 [Susan]: but then you can only get this part of the data. Or you can 126 00:11:32,000 --> 00:11:36,500 [Susan]: see this part of the view, but not the entire thing. So I think the complication 126 00:11:36,600 --> 00:11:40,649 [Susan]: comes from trying to piece together this sort of Jigsaw puzzle of 128 00:11:42,063 --> 00:11:47,520 [Susan]: data and measurement solutions. And then the other thing I have found multiple times lately, is that 129 00:11:48,019 --> 00:11:52,500 [Susan]: clients sort of have these, you know, in the old days of kinda like brand media, right? 129 00:11:52,600 --> 00:11:56,700 [Susan]: It was like, we want awareness, and we want consideration, and we'll measure it a certain way. 129 00:11:52,600 --> 00:12:00,200 [Susan]: Or it's like all about sales and then you're measuring it there. 129 00:12:00,000 --> 00:12:04,900 [Susan]: Now what I'm finding is happening is that most of my clients 130 00:12:05,188 --> 00:12:10,748 [Susan]: who, even though they're running brand campaigns 130 00:12:05,188 --> 00:12:06,600 [Susan]: and they may have their performance media running in-house. They 131 00:12:06,700 --> 00:12:16,104 [Susan]: kind of want it all. So they want us to be running the brand campaign, tracking awareness, consideration, 132 00:12:16,601 --> 00:12:17,279 [Susan]: tracking... You know... 133 00:12:18,037 --> 00:12:22,899 [Susan]: Then all the sudden it becomes a traffic-driving campaign. Well, if it's traffic driving, we might do different tactics 134 00:12:23,555 --> 00:12:24,574 [Susan]: with the different platforms. 135 00:12:24,911 --> 00:12:28,400 [Susan]: And also measure it slightly differently. And why do we care then, about awareness? 135 00:12:28,500 --> 00:12:33,084 [Susan]: So what I find getting more and more complicated is also the sort of shifting 136 00:12:33,503 --> 00:12:38,197 [Susan]: objectives, or multiple objectives of the client, also. So the story gets complicated and confusing, 137 00:12:38,576 --> 00:12:39,700 [Speaker_4]: I think is what's going on. 139 00:12:39,900 --> 00:12:43,400 [Katia]: So you need to pin the clients and have them 138 00:12:43,950 --> 00:12:46,250 [Speaker_4]: in blood and sweat and tears 139 00:12:46,950 --> 00:12:49,000 [Katia]: to sign what the business objectives are. 140 00:12:49,100 --> 00:12:50,100 [Susan]: Exactly. 140 00:12:50,600 --> 00:12:53,300 [Katia]: Because you'll be account.. you know, be held accountable. 140 00:12:53,500 --> 00:12:59,200 [Susan]: Yep. I mean, I literally was just looking at a rap report with some team members and it was... 140 00:13:00,146 --> 00:13:02,123 [Susan]: You know, it's tricky, because you're... 141 00:13:02,461 --> 00:13:07,471 [Susan]: You have a campaign where the goal really... You know, the stated objective 142 00:13:08,105 --> 00:13:09,205 [Susan]: is awareness 143 00:13:10,265 --> 00:13:13,000 [Susan]: and, you know, some shift in a couple other metrics. But then ultimately, 143 00:13:13,200 --> 00:13:17,140 [Susan]: what they still ask about is how much traffic got driven. So... 144 00:13:17,800 --> 00:13:22,000 [Susan]: But it's two sort of different things. And I think our investment teams, 144 00:13:22,200 --> 00:13:24,500 [Susan]: it's hard for them too because they're trying to sort of 144 00:13:24,600 --> 00:13:29,226 [Susan]: deal with multiple objectives within one channel or one partner, and it's... 145 00:13:29,764 --> 00:13:35,990 [Susan]: It just... It makes the story convoluted, and it makes it hard for my team to ultimately sometimes tell the story in a clear way. 146 00:13:36,650 --> 00:13:37,790 [Katia]: Yeah. Yeah. Yeah. 147 00:13:38,930 --> 00:13:42,925 [Katia]: Is, are there, is there any topic that people come to you, most about? 148 00:13:43,904 --> 00:13:47,600 [Susan]: Yeah. I think the thing I hear more and more and more because 149 00:13:48,139 --> 00:13:49,118 [Susan]: more clients 150 00:13:49,498 --> 00:13:53,607 [Susan]: have their performance media in-house, or their acquisition media in-house, 151 00:13:54,024 --> 00:13:56,159 [Susan]: and they will... And I have this 152 00:13:56,816 --> 00:14:02,080 [Susan]: for a number of clients, a number of clients will have their brand or awareness media through an agency like ours. 153 00:14:02,733 --> 00:14:10,200 [Susan]: And more and more, there's a call for - help me understand what this media that you're running out in the agency world is doing to my bottom line. 153 00:14:02,733 --> 00:14:16,940 [Susan]: Help me, like, get really have, almost like a direct connection, and this kinda of goes back to what we were talking about earlier, that 154 00:14:17,854 --> 00:14:22,100 [Susan]: sometimes it's not a perfect linkage. It's a little bit loose. Right? 154 00:14:22,200 --> 00:14:28,666 [Susan]: And it either has to be modeled, or you have to use some other you know, means of measuring or making that connection that's not 155 00:14:29,124 --> 00:14:31,000 [Susan]: a hundred percent clear. 155 00:14:31,100 --> 00:14:32,400 [Katia]: Some other signals. Yeah. 155 00:14:32,500 --> 00:14:38,100 [Susan]: Yes. And everyone wants the very clear, like, if this then that, and it's just, it's much more broken up than that. 155 00:14:38,200 --> 00:14:39,900 [Susan]: That's the biggest thing. 155 00:14:40,100 --> 00:14:43,500 [Katia]: And that's where you get your paints out and you try to put some art to it. 155 00:14:43,600 --> 00:14:48,000 [Susan]: That's right. Then I start painting. I'm sorry. I'm not able to help you at the moment. 156 00:14:48,505 --> 00:14:52,300 [Susan]: But it does it... What it means then is because it's so... That is a complex question. 156 00:14:52,400 --> 00:14:58,190 [Susan]: I mean, on one hand, it can be simple if you say, let's just do MMM or one of the more modern versions of 157 00:14:58,948 --> 00:14:59,448 [Susan]: MMM, like 158 00:14:59,865 --> 00:15:07,141 [Susan]: Robin, which is a meta solution or we have some agile MMM solutions in-house. You know, if they're willing to do that. 159 00:15:08,700 --> 00:15:13,500 [Susan]: But because the performance media is handled by the client most often, 159 00:15:13,600 --> 00:15:16,700 [Susan]: if you're also then suddenly bringing together teams of people and get... 159 00:15:16,800 --> 00:15:21,400 [Susan]: It almost becomes an organizational challenge then, on top of even just the data challenge. 159 00:15:21,500 --> 00:15:25,800 [Susan]: Because you're wrangling or working with someone to help get the data from the client, 159 00:15:26,000 --> 00:15:30,000 [Susan]: this performance media data. Then you're trying to get the, you know, the external data 159 00:15:30,100 --> 00:15:33,671 [Susan]: from all the various platforms and all of that, and then 160 00:15:34,287 --> 00:15:39,612 [Susan]: create the models to bridge the gap. And it's just, it's not... I will say I've not found it 161 00:15:40,084 --> 00:15:41,700 [Susan]: a simple question. 163 00:15:42,036 --> 00:15:43,651 [Katia]: Absolutely. Becomes a, 164 00:15:44,067 --> 00:15:48,890 [Katia]: a different project. It's not a data project anymore. It's, yeah. 165 00:15:49,270 --> 00:15:53,530 [Katia]: It's organizational and lots of other skills are needed. 166 00:15:55,282 --> 00:15:57,914 [Katia]: Thank you. Yeah. Totally resonate with that. 167 00:15:58,990 --> 00:16:00,408 [Katia]: So tell us... 168 00:16:00,905 --> 00:16:02,681 [Katia]: Because I'm sure there are a lot of 169 00:16:03,098 --> 00:16:04,290 [Katia]: people, women 170 00:16:04,868 --> 00:16:07,724 [Katia]: who are early in their careers, are probably 172 00:16:08,262 --> 00:16:10,957 [Katia]: going to listen or get to this podcast eventually. 173 00:16:11,655 --> 00:16:13,632 [Katia]: Do you have any advice for them? 174 00:16:14,544 --> 00:16:17,200 [Susan]: I mean, what I always say... I mean, I say this to my team 174 00:16:17,300 --> 00:16:19,500 [Susan]: because they tend to be a lot of very young people. 174 00:16:19,600 --> 00:16:22,640 [Susan]: It's sort of interesting working in the agency world. I mean, 175 00:16:23,179 --> 00:16:24,438 [Susan]: you don't have to be 176 00:16:24,777 --> 00:16:28,713 [Susan]: very old to be the oldest person in the room sometimes, it seems like. 178 00:16:30,704 --> 00:16:33,356 [Susan]: Yeah. I think what I always tell them is, 179 00:16:34,730 --> 00:16:34,929 [Susan]: you know, 180 00:16:35,965 --> 00:16:38,738 [Susan]: it's great to have a goal and a trajectory. 181 00:16:39,289 --> 00:16:40,667 [Susan]: And also don't be 182 00:16:41,084 --> 00:16:45,672 [Susan]: afraid if something comes along that intrigues you and seems interesting, you know, 183 00:16:46,963 --> 00:16:47,463 [Susan]: a project, 184 00:16:47,881 --> 00:16:49,000 [Susan]: a career path, 185 00:16:49,050 --> 00:16:50,537 [Susan]: an interview. 186 00:16:51,075 --> 00:16:55,106 [Susan]: Why not pursue it? You have, you have nothing to lose. And I always say, like, 187 00:16:55,999 --> 00:17:01,740 [Susan]: the other thing is, it's okay not to know the answer to questions. In fact, it's very good to ask those questions. 188 00:17:02,378 --> 00:17:06,961 [Susan]: I mean, we all hear that all the time. We've all heard that all time from, you know, our 189 00:17:07,339 --> 00:17:09,500 [Susan]: past bosses and, you know, leaders say that all the time. 189 00:17:09,600 --> 00:17:12,900 [Susan]: But I really do try to encourage the team to feel like they can ask me anything 189 00:17:13,000 --> 00:17:17,300 [Susan]: or ask each other too, you know. Ask your peers, if you're not comfortable asking your boss. 189 00:17:17,339 --> 00:17:21,100 [Susan]: Just make sure you're asking the question and don't assume that, you know, everyone knows. 190 00:17:21,675 --> 00:17:24,400 [Susan]: And the other thing is, like, take every interview that comes your way. 190 00:17:24,500 --> 00:17:27,815 [Susan]: Take everybody who reaches out to you for an informational. 191 00:17:28,195 --> 00:17:30,900 [Susan]: Do it. You never know what you're gonna come up with. 191 00:17:31,000 --> 00:17:34,702 [Susan]: You know, you never know. And I will say that I think the last, 192 00:17:35,720 --> 00:17:35,960 [Susan]: gosh... 193 00:17:37,771 --> 00:17:45,256 [Susan]: It's been a long time since I've actually, like, applied for a job. I feel like all of my, most of my recent 194 00:17:45,929 --> 00:17:48,900 [Susan]: interviews, maybe Katia since the way back days when you and I worked together, 194 00:17:49,000 --> 00:17:54,500 [Susan]: were kinda like somebody who knew somebody who knew about me. They called me and then I was on the phone with them. 194 00:17:54,600 --> 00:17:58,595 [Susan]: And even though many times I was not necessarily looking or ready to leave my job, 195 00:17:59,015 --> 00:18:03,272 [Susan]: whatever the conversation was, it was intriguing enough that it felt like I was... 195 00:18:03,400 --> 00:18:08,500 [Susan]: I was good. Sure. Let's do it. Let's see what this is. So it's been an amazing and interesting ride, 195 00:18:08,600 --> 00:18:13,272 [Susan]: and I encourage people to just be open to that. And not feel like they have to do something for a certain 196 00:18:13,650 --> 00:18:15,588 [Susan]: length of time, necessarily. 197 00:18:16,007 --> 00:18:17,365 [Katia]: Mhm, great advice. Thank you. 198 00:18:17,939 --> 00:18:20,277 [Katia]: I mean, I've been lucky to have 199 00:18:20,656 --> 00:18:25,591 [Katia]: great bosses so far. I certainly shared one with you. 200 00:18:26,703 --> 00:18:28,199 [Katia]: Did you have any 201 00:18:28,695 --> 00:18:29,195 [Katia]: mentors 202 00:18:29,531 --> 00:18:31,545 [Katia]: in your career, that you can call mentors? 203 00:18:33,689 --> 00:18:35,605 [Susan]: You know, It... That's a really good question. 204 00:18:36,163 --> 00:18:36,663 [Susan]: Sadly, 205 00:18:37,800 --> 00:18:38,300 [Susan]: well 206 00:18:39,356 --> 00:18:42,171 [Susan]: I've had some bosses who have been 207 00:18:42,564 --> 00:18:46,173 [Susan]: really good mentors. I worked with a woman named Michelle, 208 00:18:47,188 --> 00:18:51,000 [Susan]: and she was my boss when I was at Cara. I was at an agency called Cara for many years 208 00:18:51,200 --> 00:18:54,770 [Susan]: and she was an amazing boss, an amazing mentor. 209 00:18:55,670 --> 00:18:57,930 [Susan]: Really a wonderful advocate for 210 00:18:58,390 --> 00:18:59,810 [Susan]: me, for our department. 211 00:19:01,044 --> 00:19:04,215 [Susan]: But I do... I remember distinctly, I had a moment, 212 00:19:04,592 --> 00:19:09,496 [Susan]: early, early in my career, actually my very first job, and there was a woman and she had this amazing name. 213 00:19:10,029 --> 00:19:14,500 [Susan]: And she said to me, sort of in passing, but it so stuck with me, 214 00:19:14,600 --> 00:19:21,800 [Susan]: but she was like, you'll go far in your career. Just, you know, remember who you are and what you know. 214 00:19:20,900 --> 00:19:26,100 [Susan]: And I was... And she literally said it to me in passing, but it had such a huge impact. 214 00:19:26,200 --> 00:19:30,100 [Susan]: So, I mean, I guess another piece of advice, and probably I should take this to heart as well. 214 00:19:30,200 --> 00:19:34,600 [Susan]: That we all can have such impact on each other and on young people who are coming up in their careers. 214 00:19:34,800 --> 00:19:41,499 [Susan]: And it doesn't have to take a lot of energy or effort or even forethought. I mean, I think she probably said that just 215 00:19:42,478 --> 00:19:48,183 [Susan]: willy nilly, and it was like... I mean, here I am however many years later and I still remember it and I'm still so touched by it. So... 216 00:19:49,597 --> 00:19:51,453 [Katia]: Awesome. Thank you. 217 00:19:52,748 --> 00:19:54,324 [Katia]: And Susan, what is 218 00:19:55,835 --> 00:19:58,535 [Katia]: a quality that people admire most 219 00:19:59,355 --> 00:20:00,275 [Katia]: about you? 220 00:20:06,524 --> 00:20:09,777 [Katia]: I can start you off - you're a great communicator and a great listener. 221 00:20:10,833 --> 00:20:11,591 [Katia]: Thank you, Katia. 222 00:20:13,521 --> 00:20:17,093 [Susan]: I think I've gotten a lot of feedback that just people... It's a very, 223 00:20:17,471 --> 00:20:21,400 [Susan]: it's like a soft skill, soft quality, but people, they enjoy being around me. 223 00:20:21,500 --> 00:20:26,380 [Susan]: They feel sort of upbeat, and there's sort of a lightness to things. And which I think is important. 224 00:20:27,560 --> 00:20:28,280 [Susan]: Gosh, sometimes, 225 00:20:29,014 --> 00:20:33,300 [Susan]: I think we take ourselves so seriously. And in the agency world, and I just think, you guys. 225 00:20:33,400 --> 00:20:39,100 [Susan]: I mean, it is important. And on behalf of our clients, of course, we wanna be as responsible and as, 225 00:20:39,200 --> 00:20:42,212 [Susan]: you know, buttoned up as possible. And also, 226 00:20:43,664 --> 00:20:50,615 [Susan]: let's keep in mind that we are all in advertising and media, and it's all okay. You know? So I think, 227 00:20:52,169 --> 00:20:55,000 [Susan]: I think sometimes the lightheartedness that I might bring to a lot of situations. 227 00:20:55,100 --> 00:20:59,262 [Susan]: I mean, I have been told that I needed to be more serious, especially in new business pitches. 229 00:21:01,334 --> 00:21:03,500 [Susan]: But I think that that's probably, that's probably one. 229 00:21:03,600 --> 00:21:08,835 [Susan]: I mean, you know, our friend Peter Lenn, Katia. I remember him at one time saying to me, Susan 230 00:21:09,689 --> 00:21:15,298 [Susan]: everybody likes you. And I was kinda like, they do? You know, but I think I have some likability factor that I 231 00:21:15,916 --> 00:21:16,800 [Susan]: I don't... 231 00:21:17,000 --> 00:21:18,510 [Katia]: You do. Yeah. You do, admit it. 232 00:21:20,003 --> 00:21:21,102 [Katia]: Alright. Last question. 233 00:21:22,080 --> 00:21:25,715 [Katia]: Is there one thing you would love to be an expert at? 234 00:21:27,285 --> 00:21:28,941 [Susan]: Oh, god, that's a great question. 235 00:21:29,517 --> 00:21:31,212 [Susan]: Around what I'm doing work-wise? 236 00:21:32,027 --> 00:21:33,581 [Katia]: You can take it anyway you want. 237 00:21:35,270 --> 00:21:36,687 [Susan]: I would say, 238 00:21:37,223 --> 00:21:38,122 [Susan]: oh, gosh. 239 00:21:38,658 --> 00:21:38,698 [Susan]: Well, 240 00:21:39,615 --> 00:21:41,568 [Susan]: An expert I know. 241 00:21:45,091 --> 00:21:47,004 [Katia]: You cannot be an expert in painting, right? 242 00:21:47,921 --> 00:21:49,099 [Susan]: No. 243 00:21:49,755 --> 00:21:51,151 [Susan]: I mean, I would say, like, 244 00:21:52,003 --> 00:21:56,169 [Susan]: I think if I could crack... If I could have a simple, concise 245 00:21:56,106 --> 00:21:59,500 [Susan]: answer to the, how do we understand the impact of this on this? 245 00:21:59,600 --> 00:22:04,000 [Susan]: I would love that. To have a more pithy, boxed up answer 246 00:22:04,350 --> 00:22:10,585 [Susan]: that isn't about how complicated it is. I would love that. Secretly, I would love to be like, a dance choreographer. 247 00:22:11,805 --> 00:22:17,525 [Susan]: That would be like... I would love that. Like, I would love that to have that, like, special skill to be able to craft a dance 248 00:22:18,260 --> 00:22:18,760 [Susan]: that was 249 00:22:19,100 --> 00:22:22,820 [Susan]: movement and action, but it communicated a feeling. I think that's just beautiful. 250 00:22:24,420 --> 00:22:29,600 [Katia]: It is, it is. You are a storyteller. Thank you Susan. 245 00:22:29,800 --> 00:22:31,000 [Susan]: Yes! 251 00:22:29,232 --> 00:22:35,020 [Anna]: Yeah, thank you, Susan so much for, you know, taking the time to 252 00:22:35,519 --> 00:22:37,857 [Anna]: share your career journey, your advice, 253 00:22:38,516 --> 00:22:40,800 [Anna]: some things that people might admire about you. 253 00:22:41,000 --> 00:22:45,782 [Anna]: I think you're really likable, this is my first time meeting you, but I can already say I definitely agree with that. 254 00:22:46,920 --> 00:22:50,400 [Anna]: Before we log off today, is there anything else that you would wanna say 254 00:22:50,500 --> 00:22:55,266 [Anna]: or any advice you wanna leave our listeners with before we close down the recording? 255 00:22:57,043 --> 00:23:00,100 [Susan]: I would say that in the world of data and analytics 255 00:22:00,300 --> 00:23:04,506 [Susan]: there's a lot of intimidation that happens or it can feel very intimidating 256 00:23:05,003 --> 00:23:07,777 [Susan]: because it's complicated and because it's nonlinear, 257 00:23:08,410 --> 00:23:11,500 [Susan]: even though you would think it was. And because it's fragmented, 257 00:23:11,600 --> 00:23:13,764 [Susan]: and there's not really a lot of easy direct answers, 258 00:23:14,283 --> 00:23:19,500 [Susan]: And that's okay. Just... I think I would say, like, be okay to have that intimidated feeling, 258 00:23:19,600 --> 00:23:24,040 [Susan]: but just know that that's natural and normal and not to let it 259 00:23:24,420 --> 00:23:27,500 [Susan]: stop you if you enjoy the career. Because you'll just keep learning, 259 00:23:27,600 --> 00:23:31,200 [Susan]: and it should be about learning for you, in your career the whole time. 260 00:23:31,500 --> 00:23:44,777 [Anna]: The Real Intelligence podcast is presented by RXA, a leading data science consulting company. 260 00:23:37,955 --> 00:23:44,777 [Anna]: RXA provides project based consulting, staff augmentation, and direct hire staffing services for data science, 261 00:23:45,115 --> 00:23:47,111 [Anna]: data engineering, and business intelligence, 262 00:23:47,684 --> 00:23:50,477 [Anna]: to help our clients unlock the value in their data faster. 263 00:23:50,600 --> 00:23:57,100 [Anna]: Learn more by visiting our website at www.rxa.io 264 00:23:57,100 --> 00:24:02,500 [Anna]: or contact our team at [email protected] today.

Other Episodes

Episode 3

December 14, 2022 00:29:26
Episode Cover

Kristie Rowley | Careers in Data Science

The Real Intelligence team sat down with Kristie Rowley, Principal Data Scientist and Director of Data Science at Domo, to discuss careers in data...

Listen

Episode 5

February 08, 2023 00:22:28
Episode Cover

Claude Silver | The Heart of Tech

The Real Intelligence team interviewed Claude Silver, Chief Heart Officer at VaynerMedia, where she fuses empathy with agency to unlock employee potential and foster...

Listen

Episode 4

January 11, 2023 00:30:06
Episode Cover

Heather Fitzgerald | Building a Data Story

The Real Intelligence team interviewed Heather Fitzgerald, Senior Vice President of Distribution Intelligence, Data, and Salesforce at Jackson. Heather discusses her career journey and...

Listen