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.