The ‘Why’ Behind Data with Rebecca Friedberg

This is Making Better Decisions. I’m your host, Ryan Sullivan. Decisions are where rubber meets the road for organizations. Each week, we’ll be learning from people who are on the front lines of turning raw data into better outcomes for their organizations. This show is sponsored by Canopy Analytic, helping companies make better decisions using data.

Ryan: everybody. And welcome to another exciting episode of the making better decisions podcast. Today’s guest has over a decade of experience in marketing analytics, research and insights. She has a proven track record of driving strategic growth and innovation for global brands. She specializes in. In improving return on investment through advanced research and analysis techniques, helping brands of all sizes achieve their business goals.

Ryan: Please welcome director of marketing analytics and insights at Benefit Cosmetics, Rebecca Friedberg. Hey Rebecca.

Rebecca: Hi, Ryan. Thank [00:01:00] you so much for having me here. I’m so excited.

Ryan: Yeah, I, um, I think that one of the things that’s most exciting since getting to start all of this is getting to meet. So many people from so many different, you know, parts of the business world and even, you know, uh, things outside of business. So I can’t thank you enough for donating your time to come be here, to talk to me and share some of your experience with the listeners.

Ryan: So I want to jump right in and I’ll, I’ll ask you our, our first question that we ask everybody. What is the one thing you wish more people knew about using data to make better decisions?

Rebecca: It’s such a great question. I think that data is at its most valuable when it is accompanied by two additional questions. Why and so what? So,

Ryan: that.

Rebecca: When we think about a data point, and this really speaks [00:02:00] to the name of your podcast, understanding what caused it and then understanding, great, who cares? What is the value of that?

Rebecca: How can we make a better decision based off of this data point? That really separates one data point from a really robust way of thinking about data. Um, When we think about the why, it can, that is a tricky, it’s a tricky question to answer. Identifying differences, not so hard, um, but identifying why that difference occurred, um, requires, um, collaboration with other teams.

Rebecca: potentially to understand what occurred, um, understanding greater context, um, it can include exploratory data analysis to understand, uh, what other factors [00:03:00] may have involved, uh, this change. And so also keeping in mind, um, the so what, what am I going to do with this information, um, after I have it, how does this.

Rebecca: How does this matter? Uh, why does this matter? Um, is it important to also balance the work? Um, because also as data analysts, you can get really focused in on a question and dive really deep and then get it and be like, Oh, okay, this has no value. Like this, like, um, for instance, There was, we’re looking at trends over time and we saw a big peak in terms of spike in impressions.

Rebecca: Um, and we’re trying to understand what happened and then we found, oh, it was due to, um, you know, um, one of our markets spent a lot more money, um, on this, you know, they spent more money on, uh, on something and so that’s peaked it like [00:04:00] okay great like that’s finding why something happened always has to be balanced by what are we going to do with this information um and i think identifying the most important metrics as well is part of that as well In terms of thinking through, um, the data point, why and implications.

Rebecca: So if we say, okay, great. Um, Columbia had more sales than Peru. Um, what does that actually mean? Like what, not only the why is pretty obvious here. We want more sales. Sales are very important for a business. Um, but understanding the why, uh, is really tied up with, uh, what, what is the implications of sales?

Rebecca: Does sales tell us how many people bought it? Did it, does it tell us how many people, um, uh, how many transactions occurred? Uh, [00:05:00] does it tell us about our distribution? So understanding the metrics that we use, um, and the implications of those metrics. is also incredibly important to answering questions of why did this happen?

Rebecca: Um, and as well as also answering those, so what? And that speak, speaks to the one other point about, um, working with, with people that ask for

data points. So even in a data driven culture, I’ve noticed that, um, there are times when People will ask for like, I, I just want to understand this one thing. And I think in a, in the best case scenario, um, again, if we want to use the, if we want to make the best use case out of data and analysts, It needs to be collaborative in terms of understanding what are we going to be [00:06:00] doing with this information?

Rebecca: Um, why is this data important to you? Um, and what is the greater context around this question is really necessary, um, to help, um, analysts understand how we can best answer this question, um, and answer it in a way that’s really meaningful to what the ultimate goal is. There have been a couple of times where people on different teams will say, I would love, I would love to know, um, the number of users that used this specific, um, tool on our website versus the number of users that, um, Use another tool on our website and as analysts, we can do that.

Rebecca: Sure. Great. Boom. Here, here are two data points, but it’s much more valuable if we understand what, what question are we trying to answer? You know, why is this important? Um, and, uh, also understanding like, what are you going to [00:07:00] do about it after we, you know, what After talking with a stakeholder, um, understanding that, Oh, what we truly want to understand is how do our, was the, was the cost of putting this specific tool on our website worth, um, the value that we got out of it.

Rebecca: That, that helped me partner with them to create, um, the, a different measurement framework, um, to help answer those, that so what, and answer those why questions a little bit, um, more meaningfully for them. And then similarly, I think that even in great data driven cultures, I’ve also noticed that sometimes leaders can ask for, have a hypothesis and then say, Um, I, yes, I think that this platform or, you know, this approach is the right one.

Rebecca: Can you find us some data that supports that? And, you know, again, it is, [00:08:00] it is using data, but I think the, again, the most valuable way we can use data is through a more exploratory, um, more exploratory way of, um, coming in with a more scientific and discovery based mindset where we’re saying, Hey, we found this thing.

Rebecca: We saw this difference and, you know, here’s why, and here’s what the value is, instead of kind of working back from saying, like, we, we think this is right. Let, let’s kind of. back into, let’s back into supporting it, which may seem like a, uh, pretty obvious one, but it’s worth stating because it’s, I’ve seen it, uh, happen.

Rebecca: It really, it’s great organizations.

Ryan: Yeah. Same. I mean, when you were, uh, you know, first starting talking there, he talked about the, the why and the so what I, You also kind of alluded to the fact that people, uh, like us, if I can be so presumptuous as to [00:09:00] lump us into a class, you know, like data people, um, I was always the kid, you know, I’d ask my parents a question and they’d give me an answer and I’d be like, why?

Ryan: And they’d give me another answer and I’d be like, why? Right. And it was just like, let me tell you when I found Wikipedia, right. You know, but it was, uh, I think that there, you Is this innate curiosity that comes in a person that picks data for a lifestyle?

Rebecca: Yeah.

Ryan: You’re right. It is super easy to get in there and just like, you know, burrow into the answer to some question.

Ryan: And I, I really liked how you, you kind of noticed that like over time, as we like realize what our impact is to the business and how can we improve that? Well, it’s like, okay, well I said it right there. It’s impact to the business. So, you know, the. My personal experience is the same as yours. There’s this kind of diminishing return [00:10:00] to, you know, how granular of a why I provide somebody is not always, you know, the best use of, of my, or my team’s time.

Ryan: I think, you know, you talked a lot about the, so what, you know, and I, In my head, I kind of like tacked on a little, you know, so what are we going to do about it? Right? Like what actions, like, where are we going from here? Like, if I, you know, build this really cool report and it’s like, it’s got all the right numbers and it’s super polished and pretty, right?

Ryan: Like it’s got all this stuff and somebody looks at it and they’re like, Hmm, we should do this. And then they don’t do it. Like how good was the dash? Like how much was it really valuable? How much was it worth? And. You know, so like kind of connecting that and I [00:11:00] think that, you know, to bring it full circle right at the end, you talked about the balance between that reality, which is, okay, you know, so long as we’re working for a for profit business, the business is for profit.

Ryan: We’re trying to make money. We’re trying to sell something or provide a service or something like that. How do we balance that with getting maximum

value? Out of these analysts, right. And you had talked about, you know, being exploratory, like that’s absolutely been my experience, right? Like when we’re just like, you’re here to solve one problem, solve the problem, punch in, punch out, you know, here’s your money, right?

Ryan: It’s like, okay, well we can do that. Right. And so long as that was a wisely picked project, we’ll be in great shape. However. You know, if you set one of us free to go figure some stuff out, we find cool things generally, [00:12:00] you know, uh, for example, I was, I was, uh, working at a job that I had out of college and I found a lot of money, you know, just like little tweaks, operational stuff, following the paper trail, writing little programs, having database access, stuff like that.

Ryan: I found a lot of money. I will say this, I comfortably paid for myself the entire time I worked. So. You know, what is your experience, Ben, with figuring out how to balance that? Like, how do we stay focused on the business realities and making sure that we’re delivering on, you know, a value proposition and that there’s predictability so that people know how to invest in us, but also getting to do some of that exploratory stuff that can absolutely blow the top off the amount of value that we generate for the business.

Rebecca: I, I think that that’s also a really great question. It’s such a tough one. You know, this ROI, the value [00:13:00] of exploratory data analysis, like, especially from a bottom up approach, which is, we don’t have a clear question in mind. We just want to, we’re, we’re just exploring. Um, you know, we just have some data and we just want to look at it a little bit and see what we can find.

Rebecca: Um, finding the value of that can be really difficult. Um, I’ve found that it’s a lot easier to go from a top down approach, meaning we have a question. We have a structured question that guides us towards, um, more, like, for instance, we’ve, we found that there’s a difference here. We found that people are more likely to convert on a website versus desktop,

Ryan: Hmm.

Rebecca: um, and then dive into it.

Rebecca: I think. Answering, finding the, identifying the question [00:14:00] first, um, is easier to tie it to the so what, tie it to that value proposition of, um, if we, you know, if we understand it. Why customers are more likely to convert on mobile. Um, that’ll help us change our, you know, CRO strategy. It’ll help us,

which will then translate into impact on sales, which then drives the bottom line, blah, blah, blah.

Rebecca: So I think, Finding the value there is a little bit more straightforward. Um, but the, there is so much, there’s a drawback to that, which is, uh, you, in, in that structure, you potentially lose sight of, um, Like other, other opportunities for improvement. Um, and that is where exploratory can be so [00:15:00] valuable. It can, because it’s so much, like there can be such a wide range of data points to explore.

Rebecca: Um, so I think that. I will say the, the best way that I’ve noticed is more of a top down to, um, having a structure and then, but also keeping it, keeping it open enough, you know, allowing analysts to dive in deeper and, um, kind of have a larger question spur other questions. Um, so for instance, um, we’re seeing.

Rebecca: We’re seeing people convert more on mobile versus desktop. Um, are there other questions? Are there other differences on the website? Or, using website as an example, that we can, That we can [00:16:00] see that could be valuable. So it’s almost like a top down approach where we’re like, um, the overall thesis is how do we improve the website?

Rebecca: Um, but it’s still open enough to create that, that openness to explore. But the value is, um, is in, um, in saying the overall, like the overall thesis is improving the website. Um, you’re automatically, um, creating a value proposition ahead of time. So, you know, in the, in this case, it would be, um, Are there other differences, um, in terms of the website that we could use to help improve CRO that we could use in terms of like improving the customer experience on the website and to help prove the value of that?

Rebecca: There is, um, [00:17:00] You know, you can do different calculations to identify like, what is the conversion rate? Like, um, you can use past experience to say like, Oh, when we’ve made this change, it’s resulted in an X increase in conversion rate. And you can back into like a return on analytics to prove like a monetary value of it.

Rebecca: Um, but I think if you want to get really quantitative about it, but I think in general, um, Proving how this general question translates to the overall business strategies, the rural business purpose can, can in and of itself be really valuable, um, in showing how this, this question, this analysis really ladders up to improving the business overall.

Ryan: Yeah. I, I really like that point. There’s this kind of concept of balance between [00:18:00] those two ideas, right? There’s like, you know, okay, well like how much do we set the nerds free? And like, possibly they go off and like do a bunch of stuff that they find interesting, but has no business value. And then, you know, if we, Put them all in pens and make them only do it.

Ryan: You know what I mean? Like then the nerds become unhappy and they’re not, they’re unproductive nerd. You know what I mean? You gotta, you know, so like there’s this balance and I think there are obviously like a lot of different business models. Like I’ve seen business models out there where they’re like, we are not going to like, we’re a value based brand. Like we compete based on price. Like that structure is a very different business model. However, if I’m talking about like just analytics and like, just like, here’s what the ideal is. would say you definitely want to go with kind of like this kind of more premium model for your analytics people. Like one really brilliant. Analytics person is worth more than like a team of like five or 10, like maybe not as great analytics [00:19:00] people, especially in regard to this question. If you just have like, Hey, we have this set of X reports that need to get built by Y date.

Ryan: You can probably plug and play that. But when you start getting into the exploratory stuff, you need somebody that has some measure of business acumen, somebody that can connect with and understand people from the business. And they can understand the technical things that they’re saying, kind of that blend, and, you know, You know, I think so long as you have those people communicating, here are our business values.

Ryan: Here’s our business plan. Here are the goals that we’re trying to achieve on this timeline. We have these couple of analyses that we want to do, but otherwise we want you to use your skills to contribute to the company, hitting the goal. It’s like. Wow. Okay. You know, I’m going to get treated like an adult.

Ryan: That’s great. I’d love to do that. You know, you gave me some direction. I know some of the tangible things that you want, but you’ve also told me, here’s the direction we’re swimming. And I, I, I know best how to swim in that direction [00:20:00] in my lane. So that’s one of the things. I’ve talked to a handful of people on the podcast.

Ryan: One of the things that has come out again and again about like, how do you really build a data culture? How do you really, you know, build a team that does this stuff really effectively? It’s all about identifying the type of people that you have and then using them. To their max capability, which, you know, when

you say it like that, you’re like, Oh, of course it’s, you know, the same as anything else, you know?

Ryan: But, you know, I don’t know, I don’t know about anybody else. I make things more complicated than, than they need to be sometimes. But I think like you really, you really touched on that balance of like, here are kind of the company outcomes that we’re looking for, here’s the strategy, here are the goals, and then we’re going to have smart people and we’re going to like, trust them to do an awesome job of helping us achieve that, right?

Ryan: Like, I, you know. I think there are ways that you can be a little bit more prescriptive. And there are certainly times and places where you have to be like, I know this is interesting. We’re [00:21:00] working on this right now. This has to be the priority. I’m sorry. I know that we don’t like it. We got to do it, you know, but that, that balancing act is, um, you know, I think it comes down to the, the people you have, right. It’s like, you know, if I have an amazing baseball team, Right. Maybe we’re not going to try and go out and play football. You know, like I just got to, like, I gotta, I gotta play the hand that I’m dealt. One of the other things that I think is, you know, very interesting when I get to talk to all these people, as I mentioned, from all different kinds of like, you know, sectors of the business world is, you know, there’s definitely this kind of data mentality, data gene type thing, where there are like people that just like, Think in terms of data.

Ryan: And what’s really interesting to me, both to provide to the listeners that come also from lots of different segments, but also to give insight into things that are different than what we’ve had experience with is how somebody [00:22:00] with that mentality approaches all of the different business problems. That people experience out there.

Ryan: So you have this experience of bringing a data person’s brain into the marketing space. And so what I wanted to ask you is, you know, what are some of the challenges in the marketing space that you’ve found, you know, particularly difficult or rewarding to solve? Yeah,

Rebecca: before I answer it, I actually want to speak a bit to, things that you said earlier about, um, how, I like building a data team and kind of the freedom to letting them go. I think that the thing that I, I love that idea.

Rebecca: Um, I think that was a great summary. Um, what I would tack onto that is the value of partnerships too. Um, I think that, um, and I, I know this is, I’ve, I’ve heard this mentioned in other episodes of your podcast, [00:23:00] but

even if you have the most intelligent person, like someone who has a lot of business savvy and a lot of marketing, marketing and analytics experience, it is meaningless unless you have the most intelligent person in your podcast.

Rebecca: They work with others on the team to help provide greater context. Um, and so I, I think that is, um, that is, that’s part of having a really, a really valuable, um, data driven culture is that analysts, um, and the analytics team. cannot work in a silo. Um, it has to be driven by, uh, relationships. And that’s also part of proving the value is if you have a relationship with a different stakeholders, um, it is, it also allows you greater leeway.

Rebecca: And, um, in the best case scenario, they can serve as advocates for your team. [00:24:00] Um, in, And to other, to other teams of showing how we’ve added value to specific actions.

Ryan: I love that point. In fact, let’s keep the redirect going for a second. So it’s also been my experience that the more we can craft, you know, I hate to say it this way, but like the business, right? Like, The more we can craft relationships with the business and we can show them like, we understand the questions and the problems that they have.

Ryan: And like, we’re here to use data to help and then vice versa. Right. That they realize that like, we want to help. And the more we build that, like collaborative, let’s solve, And win together mentality, the better. However, that’s sometimes easier said than done, right? Like not every company has amazing culture throughout the entire company.

Ryan: Sometimes it’s just straight up bad all across. Sometimes it’s good all across. Sometimes different teams have better or worse, [00:25:00] you know? So. What are some recommendations that you might have for say, like, okay, we’ve got a data team and we’re trying to go out. We’re trying to forge better relationships and partnerships with other people throughout the business.

Ryan: What are some things you’ve been able to be successful with there?

Rebecca: this is going to be the most obvious one, but I’m going to say it anyway. It’s, um, meeting in person, setting like a small amount of time to just have coffee and meeting in person. And I always lead with. How can we help you? That, that like our team is Not only do we have the same the same ultimate goal of improving our business But I view the analytics team as ultimately Serving the needs of of each team individually, and so we are the In some cases

like the airplane engineer You [00:26:00] know, like we’re, we’re the team that is quiet in the background.

Rebecca: That’s going to help you do your job better. And I found that when you, when you, uh, phrase it as, um, our, our job is to, is to really help you, you know, it’s to, you know, we have no ego. We have no stake in it. Um, it is, it is only to make you guys look better. I found that it’s been, there’ve been, um, it’s been a much more warm reception.

Rebecca: Um, and then, you know, and then the, the hardest part is getting that first project, like, it’s like, okay, let’s just start small, you know, we can let, You know, go through a list of kinds of questions and examples, um, that we’ve, you know, done in the past so that, um, we can say like, oh, we’ve helped this team with this, you know, these are the kinds of, you know, are a high, uh, elevator pitch [00:27:00] of the kinds of things we can help with.

Ryan: I love that.

Rebecca: Um, but you know, are there, are there, um, You know, also making sure that you ask question, of course, ask questions to them about, um, where are some of your pain points? Um, you know, are there, you know, how, like, what’s your typical process? Um, and then it can be, if it’s still hard to identify areas where your team can partner with them.

Rebecca: Um, it can just be like broad questions of like, I just want to understand how you do things. And a lot of times when I’ve asked that question, I’ve been like, Oh, Wait, um, can we measure the value of that? Uh, like, is there a way that you’ve been able to measure the value of that? And they’ve been like, oh no.

Rebecca: And I’m like, oh, well, we, we could help with that. Um, so that you can then prove the value of what your department is doing, [00:28:00] um, to the business, um, which has been. And I’m like, listen, this is just so you can potentially get greater budget so that you can like potentially fund projects that you didn’t, weren’t able to do.

Rebecca: Who doesn’t like money? Like who doesn’t like additional budget? Um, so like that, that, those are some approaches that I’ve used to help forge, um, strong relationships, but it’s, it’s a journey. Like it, it’s taken time to really make those relationships, forge them over time, and you know, those, like all relationships, have evolved over time as well.

One of the things, actually, I think I liked every single morsel of what you said there, but what you started with when you said it was obvious is like, I don’t know, not that it’s obvious, but like, it doesn’t happen that often. Right. Like I, I don’t hear lots of stories of, Hey, you know, I’m going to like, go out and like craft, like deep personal relationships with [00:29:00] people, you know, You know, prior to getting started on, you know, an analytics project together, but like it, it really helps, right?

Ryan: Like, you know, Oh, and surprise, surprise, right? Like you craft a real relationship with someone and the result of that is you have a good relationship. You know, you put effort into it, you show interest in them and you find out, you know, about their needs and their desires. Like, Holy cow, they like you, you know?

Ryan: So like it’s, it’s rock solid advice, you know? And I, I don’t know. It’s one of those common sense things that no one knows. That, you know, like that type of uncommon common sense. Um, you know, one of the other things that, that I really liked towards the end of what you were talking about is, you know, you said it kind of under the umbrella of you’re, you’re having a conversation with someone and they’re like, yeah, I don’t know.

Ryan: I guess I don’t need a lot of help right now. And you’re like, trying to find ways that you can demonstrate value and be helpful to them. And you, you talked about the value of kind of like putting yourself in their shoes and asking questions. And one of the things that. I [00:30:00] personally have found, and I’ve heard from a handful of other people on the, on the podcast is that this is kind of the paradigm for every interaction that we have right now.

Ryan: Of course, right? Like, Hey, data, people should gather data. Right? Like, of course I’m going to prioritize that very highly. That’s how my brain works. But you know, somebody comes to me and says kind of like, Hey, I need, uh, you know, this report of X over Y. And you’re like, okay, cool. You know, tell me a little bit about that.

Ryan: Like, you know, what are you noticing? What are you expecting to see? Like, you know, is X the right metric, right? Like, is it sales or do you want, you know, sales per business day or, you know, like what, whatever the followup questions are and, you know, ultimately. Like you started us off with, right? You’re kind of digging into that question of like, well, why, why?

Ryan: Like, what do you, what, what decisions are you trying to achieve? And like, you’re obviously making these decisions to try and achieve some sort of

business goal. So like, like, tell [00:31:00] me a little bit about like that chain that’s driving you to, you know, my desk to ask me for this report X over Y and frequently what.

Ryan: Don’t get me wrong. Every once in a while, someone’s just like, I said, I want X over Y. Give me X over Y. And you’re like, okie dokie. You got it. X over Y coming right up. You know, but lots of times when you get. To kind of craft that relationship and, and have conversations with folks, you find out that it’s really like, Hmm, well, you need that, but you actually need these other things to support and dig in after the fact, or it turns out maybe X wasn’t what you were looking for.

Ryan: You want like X per day or like, you know, you, you kind of get in and you get to ask these questions and it kind of creates this cycle where if you just have this, like, like you, you mentioned this kind of like hellscape of just like, Person inputs ticket without talking to someone and then someone else makes, you know, some kind of poor projection of what they think that ticket means into like a thing, you know, and it’s just like, Ooh, you know, like if we [00:32:00] just have a real conversation, we dig in, we ask some questions, we find out what’s driving it.

Ryan: Not only do we get it right the first time, but we can lots of times figure out if it was even what we should have been building. You know, and make sure that that person has exactly what they need. I’m super glad that you redirected us. I am curious though, to, to know a little bit more about like that marketing specific flavor, right?

Ryan: So like you, you very clearly, you know, are a data person through and through. And as you’ve, you’ve brought that skillset into marketing, can you tell me a little bit about that? Like, what are some of the challenges? What are some of the ways that data has made, you know, marketing, you know, A little bit easier.

Ryan: What are some of the, the questions that are really hard to solve? Just kind of tell me a little bit more about the intersection of, of marketing and data, if you can. Mm-Hmm?

Rebecca: there are obvious questions or obvious challenges around data privacy, which from a consumer perspective is great. Um, but from a, Uh, [00:33:00] business perspective that is trying to identify as many data points about a consumer as possible can be more challenging. Um, problems around

walled gardens, um, different, uh, uh, platforms not connecting with one another.

Rebecca: Attribution can absolutely, is like one of the biggest questions of marketing. Like there’s the, the famous quote of, I know I’m spending, um, I know I’m spending this much, um, and I know half of it is working, but I don’t know which half. Um, so that’s a, just a classic, um, marketing challenge. Um, which is really, in my opinion, the most important challenge.

Rebecca: Um, because ultimately we have to understand, um, what is our, What is the impact of our marketing? And within marketing, what is working versus not? But that can be really challenging, um, depending on the industry that you’re in and depending on the kinds of marketing that you’re [00:34:00] using. If you’re, if you’re primarily, um, focused on, uh, page search, digital, um, you know, very conversion based, um, analytics, um, and marketing, pretty easy.

Rebecca: Pretty easy to figure out. You can figure out things like attribution, um, how different channels interact with one another in order, and then drive the consumer to purchase, ultimately, if it’s only digital. But, um, in general, you don’t really want a wholly, um, conversion based strategy, um, you really want something that’s more balanced on bringing in new customers.

Rebecca: And so, um, that’s where part of the challenge can be is like, if you want to expand to other areas, um, where the consumer experiences media like podcast or, you know, streaming services, or, um, you know, like, um, You know, [00:35:00] like old school tactics like billboards, um, which, you know, um, is like, I still see them.

Rebecca: Um, those become increasingly more challenging to measure. Um, and there are, and even what’s so interesting is, um, even in identifying, um, a consumer who’s, uh, they comment on our, on our channel. Um, or comment on a post on TikTok and tracking that same consumer to purchase on our website, even that level of attribution, um, and, uh, of, uh, like attribution to our marketing efforts.

Rebecca: The technology is not quite there yet. Um, and that’s what I mean by like walled gardens and silos is, um, and the impact of data privacy, which again is great, you know, from a consumer perspective, it’s really important for us to make sure that [00:36:00] people, um, are, uh, feel. that they are giving data in a way that is, um, intentional, um, and that they’re getting value for the, for the data that they, that they give.

Rebecca: Um, but again, from a marketing perspective, I want to be able to see like, what is the full journey? Does someone who, uh, talked about our competitor and then like, and then, uh, saw a influencer that we’re working with talk about us and then go to them? Our site, like, oh, that’s like, that is really interesting for

Ryan: Yeah. That’s gold.

Rebecca: but it, exactly.

Rebecca: But it’s, it is challenging. It’s a really challenging ecosystem, despite the amount of technology that we, that we do have.

Ryan: Yeah. I, I, I mean, I, I personally think that you kind of, I have done, you know, a fair bit of, of marketing analytics work myself. And that’s, that’s always kind of separate and together the two biggest things, right? Like how do we figure out like what’s [00:37:00] working and how on the attribution side of things and then how do we still do that while being, you know, ethical, responsible, compliant on a privacy side.

Ryan: You know, like of course, from like a pure technical standpoint, it’s like, okay, well I’ll just get like root access and put spyware on your cell phone and I’ll know everything that you’re doing. Right. Did you talk to your family about whether you like my product? You know, but like, obviously like, you know, what, getting all the information that’s possible out there, we as a society don’t actually really want that, you know, like we’re, we’re pretty, pretty all set on that.

Ryan: So it’s kind of figuring out like, what’s the balance of what we can do there. And also like that. Feels to me like it changes really frequently, right? Like there’s, you know, constantly like a new update, whether it’s, you know, Apple with the iPhone or, you know, Google changes, you know, their search algorithm or their email stuff, or like the, you know, all sorts of new technologies that are, that are out there that are constantly changing, which, you [00:38:00] know, I think it comes back to exactly what you said.

Ryan: And it’s just kind of trying to figure out what do you know? And like, what is a good next investment? I don’t necessarily think that we will ever completely get away from the idea of, of what you mentioned, which is like, we know marketing works. Uh, we just don’t always know exactly which pieces are working.

Ryan: Um, you know, but I think that there’s, you know, there’s a lot of cool, um, you know, technology and analytics that are out there and hopefully, you know, that, that keeps people like us employed, trying to answer some of these questions and, you know, solve cool stuff.

Rebecca: Yeah, I want to, like, and I want to make sure I’m clear. These questions can be answered, but it’s, it can be, like, with econometric analysis, uh, greater regression analysis, um, you can design experiments, you can also, like, design [00:39:00] surveys, um, to, like, and, like, understand your consumer more, but, um, that is, that can be in terms of, like, a quick turnaround, um, of something you can.

Rebecca: That’s hard. It’s hard.

Ryan: So I’m a huge fan of the Freakonomics podcast. Um, I don’t know if I’m like allowed to say their name. I’m saying only nice things. So hopefully they don’t come after me. I’m a big fan guys, if you’re listening. Anyway, so they did an episode once, uh, I think it was actually a series like a kind of like a larger one where they talk about exactly this and about, you know, It was funny. I love how they kind of like tell these stories and then like, you know, some business problems, some social problem, like whatever it is.

Ryan: And then they like wheel and an economist and in the situation, the economist just says something like totally economist tastic and everyone’s like appalled by the, you know, the craziness of this. And like, everyone’s like, we can’t do that. That makes too much sense. And it’s great, you know? So like in this particular instance, [00:40:00] they, they had a company.

Ryan: That was like, Hey, so we’re doing all of this advertising, you know, in newspapers, and we don’t know whether it’s working and you know, which markets are working and you know, all this stuff. And so like the economist was like, Oh, no problem. Like come in, set up a randomized control trial, you know, divide all of the markets that you’re advertising in into two populations.

Ryan: And you know, they’re, they’re randomly sorted into the two of those, and then just stop advertising in one half. And then you look at the sales in those same areas and you, you’ll be able to figure out, you know, at least a, you know, a correlation, hopefully, you know, randomized control trials are one of the few ways we can actually get at causation.

Ryan: So like, you know, maybe we figure it out. And the people were like, Stop advertising in half of the places. And on top of that, like you mentioned,

there’s also the time component, right? So like we run that experiment, we got to run that for, you know, one, three, six, 12 months before we, you know, have like a really, really good picture of like 100 percent we [00:41:00] know, so it’s like, you mentioned, I think that there’s, uh, There are a lot of really cool solutions, tools, whether it’s, you know, I’m talking about like some sort of SaaS tool or like a, you know, a regression, like something like that. I think my experience with marketing has been the landscape and the goals change so fast that that speed is, you know, almost like a feature.

Ryan: of the landscape. And so it’s, it’s, it’s a lot. Like I imagine like, you know, playing chess, right? If I have a day to decide every move, I’m presumably going to do better than if I’m playing like bullet chess and I have like five seconds to make every decision. So it’s kind of the same thing. And I, like my experience with marketing has been like, things change, people want results, like all of this stuff is happening so fast that we’re playing a different game.

Rebecca: Yeah. [00:42:00] I love that you mentioned uh, that episode because it’s true. Um, match market tests, which is what you described, are fantastic ways of um, identifying like true causality um, in terms of um, marketing impact. Thank you. But yeah, I’ve literally had marketing leaders talking to me saying like, no, we’re not turning this off.

Rebecca: Like we’re not stopping our advertising for this amount of time. Um, so that’s so real. Um, and it actually speaks to a question that you posed earlier about like the value, the value of analytics. Um, and so I think that that can be a way that you combat, um, the, Uh, need to get results quickly and results fast.

Rebecca: Um, because we’ll spend, you know, hundreds of thousands, if not millions of dollars on marketing, but not the same amount on, [00:43:00] uh, on the analytics to understand if what the impact was.

Ryan: yeah,

Rebecca: And so I think, um, it’s part of the analyst’s job or the analytics team or the champion to identify like what, what is the true, what’s the value if we find out, um, the incremental impact of it.

Rebecca: Um, That can be really challenging though, because, um,

Ryan: sell.

Rebecca: it can be so challenging to sell, um, and if you want to do more and because there are a lot of questions, where, like, it’s really valuable to know incrementality, um, and like if you’re If it’s a primary channel, then sure, you can do a match market, but like, if you want to test, um, multiple channels, um, then it’s a series of, of match market tests, um, otherwise you could do like an econometric model, but then, um, then, then it could be [00:44:00] really difficult to identify, um, whether or not, uh, if, if everything is run at the same level for the same period of time, and there’s no, uh, like variance, it can be really difficult to parse out the incremental impact of specific channels.

Rebecca: And so, um, I, all, all this to say is that, Identifying attribution, um, perfectly is a losing battle. Like, I, I think it’s really difficult, especially if you are, have a robust marketing, marketing mix. Um, if it’s like one or two channels and it’s only digital and it’s all conversion. But if it’s a robust mix, it is incredibly, it’s incredibly difficult to, to run in, within the context of a business, like you were saying, within, um, the, while also adapting to the ever changing, uh, Um, strategies, um, [00:45:00] and also keeping in mind that we want to, um, you know, we, we can’t stop and do a robust controlled experiment for my, we’re not an academic environment.

Rebecca: We can’t stop and do this controlled experiment. Um, we have to keep. You know, increasing our profit, our profit margin. Um, and that’s not going to stop if, um, you know, while this experiment is going on. So it’s something that we, um, as. in analytics have to really balance, um, the need to have information that is good enough and will drive us and gives us a, a like general sense of, yep, we think this is working, um, and planning so that we can potentially, you know, try and work with our partners so that we can try and get close to designing experiments that could, that could get us, um, additional information in a scientific way.

Rebecca: So it’s a bit of an art and a [00:46:00] science in terms of blending the business needs with, uh, the needs for like a robust, um, a robust analysis.

Ryan: I love it. So Rebecca, if I can change up gears, actually pretty considerably, I think, you know, you’ve established over the time period that. You are obviously super knowledgeable about both marketing and BI. I don’t think there’s any question about that, but I think that lots of people also like the chance to get to know you a little bit better, you know, a lot of what we talked about has been about kind of, you know, the importance of the person and seeing how that fits into the picture.

Ryan: So I always like to provide all of the listeners, like a better picture of, you know, like who, who’s this person that I’m learning from. So if you wouldn’t mind, tell us a little bit. About, you know, who’s Rebecca? What do you, you know, what’s your background? What are some of the things you like to do outside of work?

Ryan: You know, give us a better picture. Yeah.

Rebecca: Um, I started my professional career, um, in, [00:47:00] uh, public speaking. So I taught public speaking, um, at San Jose State, um, teaching public speaking and critical thinking for a couple of years while I was getting my master’s, and then I transitioned to the private sector. Um, and I started in, uh, large advertising agencies, um, and collaborating with different creative teams.

Rebecca: Um, and then more recently is when I’ve been transitioning to the brand side of things, working on marketing analytics. Um, and that’s been a fantastic transition. Um, personally, I’ve, uh, I, I really enjoy, um, I really enjoy I really enjoy, uh, blending creativity with, um, my analytical nature. Um, and so I really like, um, crafting.

Rebecca: coloring and painting. I’m terrible. I’m absolutely terrible at it, [00:48:00] but it’s really fun. Um,

Ryan: Most of my hobbies, I’m not very good at either. Like I just take golf balls in the woods all day, but I love it. I have a great time.

Rebecca: yeah, yeah. I was just like, this, this is why this is my hobby and not my career. Um, and so like doing, you know, I love, um, for me, because, uh, I, I think analytics has to be, to be creative to an extent. I love executing more creativity, um, in other aspects of my life as well, because I think it helps infuse, um, into my professional career.

Rebecca: So I do improv. I was part of an improv troupe for a while. Um, it was, it’s really, it’s really fun. Um, and, uh, I also. I have a cat, um, and, uh, which takes up far too much of my time than I should probably admit. and much, and like a huge [00:49:00] percentage of my income goes towards my cat’s well being, toys.

Ryan: my wife and I want to put out there how much we spend on the dog. Yeah, I

Rebecca: Yeah, so, um, big, like, big catwoman, um,

Ryan: I

Rebecca: and, uh, yeah. Yeah, that’s, uh, that, that’s pretty much me. Catwoman, improv, creativity.

Ryan: I

Rebecca: uh, that’s me. In a nutshell,

Ryan: know, one of the things that, that I love there was how you were able to kind of connect all of those different things that could seem as different into like one, like really clear picture, right? Like the integration of something like very technical and quantitative with like, okay, well, here’s how you have to like creatively think about it and all that stuff.

Ryan: You know, I also, one of my favorite kind of. Lessons, if I can be so [00:50:00] presumptuous as to use that word that I could provide a lesson to another human. Um, but like when we have another, you know, a consultant that’s coming on and learning, one of the biggest things that I always talk to people about when you’re talking to a client is that, you know, the one rule of improv.

Ryan: Right. We always say, yes. And you know, that has just served me so well in all of my interpersonal relationships. I think that, uh, improv classes are actually a great way to learn how to just be a better human. Um, so that’s, that’s very cool. I can see how that all connects. Uh, speaking of connecting, if anybody wants to, how can they connect with you best?

Rebecca: oh, um, LinkedIn is the best way to connect with me, uh, Rebecca J. Freedberg, um, on LinkedIn, reach out, um, that’s the, uh, that’s the platform that I primarily use for professional, um, professional development. Um, the, uh, Not on threads yet. Um, but maybe, maybe,

Ryan: We’ll see. We’ll see how that [00:51:00] pans out. Yeah. I’m with you there. I love it. Well, Rebecca, I cannot thank you enough for sharing all of your time and experience with us to come on and do this and to share it with the audience. It has been such a pleasure getting to learn from you and getting to talk with you.

Ryan: Thank you so much for coming on.

Rebecca: Likewise, Ryan. Thank you so much. This was such a great experience. Thank you for having me.

Ryan: Awesome. We were glad to have you. I also want to make sure to thank the audience. Thank you all for listening. Uh, if you liked this, you laughed at something, um, please tell a friend, go on, give us a rating, hopefully it’s a good rating, like, and subscribe. Do all of the wonderful things that everybody tells you to do when they make content.

Ryan: And Rebecca, I want to thank you one more time. We really appreciate you coming on. And this has been another exciting episode of the Making Better Decisions Podcast. Thanks.

That’s a wrap for today’s episode of making better decisions for show notes and more visit, making better decisions dot live a special thank you to our [00:52:00] sponsor canopy analytic canopy. Analytic is a boutique consultancy focused on business intelligence and data engineering. They help companies make better decisions using data for more information, visit canopy analytic.

com. There’s a better way. Let’s find it together and make better decisions. Thank you so much for listening. We’ll catch you next week.