#295: Research and Analytics: the Peanut Butter and Chocolate of Data?

Stephanie Zamit: M. Foreign.

Julie Hoyer: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

Michael Helbling: Hi everybody, welcome. It's the Analytics Power Hour and this is episode 295. You know, a common phrase we hear in our industry is that the data tells us what happened but not necessarily why. And by and large that's true. We keep getting better at inference and some patterns of data are pretty well understood in terms of their meaning. But still there is simply something so compelling about observing how people interact with the things we've built, websites, products, et cetera. Personally, I remember what an eye opening experience it was for me 15 years ago. The uh, first time I was sitting in a usability lab on watching from behind a one way mirror as people use the website that I measured every day with my digital data. So we wanted to talk about it, get into the topic, bridging between research and more traditional analytics. And I want to introduce my co host, Val Kroll.

Val Kroll: Welcome. Hello. Hello.

Michael Helbling: And I know this is a special topic for you because of your background and do customer research.

Val Kroll: Oh my gosh. Doing backflips. Very excited for this.

Michael Helbling: Yeah, I'm excited too. And Julie Hoyer.

Julie Hoyer: Welcome. Hello.

Michael Helbling: I don't know, have you done, have you done much like market research or customer research?

Speaker E: Not myself, but I have gotten a chance to utilize the outputs of some of those studies which has been nice. So I'm really excited to talk about it more. Yeah, excellent.

Michael Helbling: All right. And I'm M. Michael Helbling. And to bring additional expertise to this topic, I'm pleased to introduce our guest, Stephanie Zamit. She is the global Director of Analytics and Insights at Bang Olufsen. Prior to that, she led research and analytics teams at companies like Starbucks and Marks and Spencers. She's worked both as a consultant in the space for many years as well. And today she is our guest. Welcome to the show, Stephanie.

Stephanie Zamit: Hello. Happy to be here.

Michael Helbling: Awesome. We're so glad to have you. And I think this is a topic that while we cover sort of like data and analytics research is one of those things that we really like. And so we were really excited when we met you to sort of dig into this topic more, but to kind of get, catch everybody up to speed, I thought it'd be great to kick off with just you explain a little bit about your background and career and kind of how it bridged these two things and sort of, you know, um, what your journey has been across research and analytics.

Stephanie Zamit: Yeah, absolutely. I'm very much, uh, from a pure hardcore research background that's where I started my uh, career too many years ago. I started as. Actually my first job was at university. I didn't even know what research was. I took a part time job doing the national student survey. Survey, that's like a thing here in the uk. It's how the university rankings are put together. And when I graduated I think a lot of researchers would have a similar story to this that they sort of ended up accidentally in the field. Um, so I graduated during a recession, dark times and I was desperate and I thought back to that part time job I had at university. I'm like what is that? Like is that an industry? Is that a thing I could do forever? So I uh, did some research and I was in the UK at the time where uh, luckily there's an amazing research industry here. So many great consultancies, it's a thriving industry. Took my first job at a company called Quadrangle which was a management consultancy that leaned very heavily on their own in house research function. And it, you know, it was amazing and eye opening and I immediately fell in love with so many aspects of it. I then went to Ipsos, which is you know, one of the big five. Uh, that's where I was like I need some hardcore, you know, research skills. I need to learn about statistics and like hit me with the heavy quant stuff, um, you know, go to a big powerhouse. So was there for a couple of years and then after that I built my own. Well, uh, co founded a research consultancy. At this time I was in the Middle east was a company called Intelligence Qatar which is still very much there today. Although sadly I don't get to play a part in it anymore. And I think that was where previously at research consultancies they really divide the teams up. So you had your market researchers and then you had your analytics department with all the smart people. The statisticians were all there. Then you'd have your fieldwork teams, your data processing teams and it was very siloed. And even market research was pretty siloed. You'd have your qual team and then your quant team and something that was really hard for me as I progressed agency side, you'd have recruiters say to you do you want a qual role or a quant role or. And I struggled so hard to give them the answer because I genuinely loved both. And my favorite projects were the multiphase projects where you get the best of both. But I was also super kind of looking over the shoulder of my analytics colleagues and statistician colleagues. What are you guys up to? What are you doing? So I always naturally was interested in the entire spectrum. And then when I, um, went, I had my own agency again. You know, really investing in the analytics side as well as the research side just brought me closer to those worlds. And then finally went client side. I thought, all right, I had enough of this, uh, consulting game, joined Marks and Spencer's and I was really lucky that they had research analytics together in one department. That's all I've known because it was like that in that first company and we had a great leader at the time that was very adamant that the best deliverables are worked on by both of these teams. So I took that to Starbucks as well, where again, the organization is huge. In Starbucks you're looking around 250 people, but they are all in the same department together. Research and analytics, if sub teams, at least it's still in the same family. So it's something that I've been so passionate about today and I think really set me up for success to do what I do now, which is to lead both the analytics function and the market research function in one team.

Val Kroll: I love that the, the one thing that you, you mentioned there about like not being able to pick what you liked better, the quality, the quant and your favorite were like the multiphase projects that have that start with the qual and then lead into quant. I feel like that's like a little bit novel, like a little bit inside baseball for researchers. Can you describe what that is? Because I have a follow up question after you talk about that a little bit. But just kind of describe like why someone would have like a multi phased, uh, approach to like their research project.

Stephanie Zamit: Absolutely. So every methodology has its benefit. Qualitative research is where you start when you don't know much about a subject and you need to explore. It's very exploratory. You are using it to figure out what your hypotheses even are. You might have one or two hypotheses, but they're fluffy and you need to explore the topic more. So qualitative is where you do that in this kind of limitless way of a very open data generation, um, phase. And then you need to validate because you've spoken to like, I don't know, Max, 40 people. If you're doing a really big qualitative project, uh, so you want to validate that and you need some statistics and some numbers behind it. So you want to do your quant. So I have my hypotheses now I'll run a survey to measure and size those truths and see how statistically significant they are. Um, and methodologically these require two different expertise because qualitatively you're trained in moderation projective techniques. How to read between the lines, how to read people's faces and emotions and hear what they're not saying as well as what they are saying. Um, there's also a much more kind of deep psychology to interpreting those insights because again you're reading between the lines. Quantitative, you need to know about drivers analysis and cluster analysis. In fact, you need to know all of the statistical models that give you the derived insights that are so, so valuable from a survey. So they are usually kept separate. And um, I think that's a shame because the best projects are the ones that do both of these things for a really strong funnel insight.

Val Kroll: Yeah, that was very well explained. And I remember in my market, uh, research days a lot of times like quality, if you think about, if you were to do a survey, you have to a quantitative survey and you're picking the list of options that someone is going to select that is you know, the right answer to the question for them. Sometimes that list isn't always as clear like what should belong in it. Like even if it's like a list of competitors. Like a lot of times like you know, clients will think about in category competitors, about who you, you know, who your competitors are for an alcohol brand. But that same dollar could be spent on, you know, other things that are kind of like out of category competition. And so you can use qual to kind of help explore to figure out what even is the list. Because you could miss so much if you start just with a quant without going broad first. Like to be exploratory like. Exactly. So Stephanie, figure out what your hypothesis is. So, and the reason I wanted you to dig into this a little bit is because this is one of the ways that I love helping like illustrate or describe, especially to people, describe to people in the analytics some of the value of the um, bringing these worlds together. Because it's, it's not just using, you know, one single methodology or tool. It's kind of helps you like illuminate a different part of your question or your process. And so how those two things come together very naturally inside of research is one of the ways you can kind of illustrate the coming together beyond just the research or the direct consumer, um, or you know, B2B context.

Stephanie Zamit: Exactly. And I think from a research only perspective, I was already at a very early stage so, you know, powered up by the idea that if you put these two together, you're getting better insights because you're, you're starting broad and then you're getting specific with the quant. And I took that into. As I gained more seniority in my career and started working especially in house, where you have colleagues in other disciplines, which is all data and insight. I took that into that as well to say, well, how can analytics be part of this? And why are we asking questions and surveys that we already have the answer to? Uh, that seems like a huge waste of time. You know, we actually, how can we be using all these disciplines to get the best insight? And all of this ultimately comes down to a passion to chase the best insight. Right. Methodology should be irrelevant to. It's not about the journey, it's about where you end up. And I think having that crystallized in my mind from the very beginning really helped me see the world in the way that I see it now that who cares what you're trained in? At the end of the day, we use the best method to get the best insight and doesn't matter what, whether that's in this team or that team.

Speaker E: And I feel like you are one of the more rare data leaders out there that recognize it has to be problem focused. Instead of leaders coming to their teams with ordering a solution, they've already thought through, they have a problem, but they're not always communicating that to the teams that are going to help, like service them. Um, they're kind of coming with like, well, I want you to pull these numbers or ask these questions type thing instead of, to your point, just anchoring on. I don't care how you do it, you guys are the experts in that part. But what I'm facing, you know, is problem X and I'm, I'm looking for ways to solve it. Um, and then letting people get creative with how do they maybe partner together together to get them the best solution. I feel like we just run into that at kind of like all levels, um, of the business. We've all had different experiences with, like, trying to overcome that hurdle. So it's really refreshing to hear you, you know, so eloquently like talk about it in the way you frame it.

Stephanie Zamit: And 100%, that's so normal. Everywhere I've experienced it everywhere that you have stakeholders coming to you. We want to do some qual, we want to run a survey. I need a dashboard. And they, they are very prescriptive and I think it's part of our job as insights folks, irrespective of our training, to say, whoa, whoa, hold on there, let me understand what you're trying to do. What decision are you trying to make? The answer might actually not be in a dashboard. It might be in a piece of custom analytics or it might be. So our consulting work is a big part of this, this job to, to find the best methodology for the best answer. We can't expect our stakeholders to know what that is. Um, although they're very welcome to make suggestions, of course. Um, but it's a broad, broad spectrum of tools that we could use.

Speaker E: Yeah, absolutely. And one of the questions I've been dying to ask you is why do you think. And Val, I'd be interested your take too, because you've kind of lived in both of these worlds as well. Like historically, why don't research teams and analytics teams always play together at all? Or if they're playing together, they don't always play nicely together.

Val Kroll: Mhm. Good question. Get into it. Stephanie, you have to go first.

Stephanie Zamit: It's a great question and I think the answer is fear. Um, I think that there is the fear of the unknown. Um, and there is an assumption that the other world is such, so mysterious and so different to our world. We don't understand each other. It's literally a form of othering within teams, within departments. Um, and it's a fear I myself had, you know, oh, I could never keep up with these data scientists and oh, they're just so smart and they understand all these things in a way that I never could. And then you start working together and you, you see that you actually have more in common than you do have differences. And they see it too. You know, they understand that they can learn from the research process and understand. Oh hey, I thought research was just qual. I don't know if you've ever heard that, any of you guys, especially Val, that the assumption that research equals qual, that's the qual work. You know, it's like I just did a survey with 8,000 people and like this hardcore conjoint statistical model, like you can't call that qual. But there, there isn't an understanding, there isn't enough knowledge. And everyone assumes that the, the other side of the coin is so different. It's a whole different world. And I think it comes from the way that agencies are set up and departments are set up to separate these, these skills when actually they're stronger together.

Speaker F: Michael, why does every quick question come with a 20 minute origin story?

Michael Helbling: Well, that's because our metrics have, I don't know, lore. Conversions might mean three different things, depending on who's presenting and how close we are to the next quarterly board meeting.

Speaker F: And I mean, every time you switch tools, you have to re explain the lore like you're reciting ancient prophecy.

Michael Helbling: On the seventh day of Q3, the tracking broke, and lo, that metric was doubled for July.

Speaker F: That's why we're excited about AskWai AI, uh, and Prism, because it has memory that actually, you know, remembers. You don't have to repeat the lore.

Michael Helbling: Yeah, the JAM system keeps context across sessions. What your org means by revenue or conversions, which tables the source of truth and the weird exception you always forget until it's too late.

Speaker F: Plus, you can save your best workflows as skills, portable expertise. You can actually reuse like a human.

Speaker E: Ah. Ah.

Michael Helbling: So I don't have to manually normalize UTMs or fix or tweak that GA4 channel grouping or deduplicate leads without breaking down into tiers? Yeah.

Speaker F: So instead of rebuilding the same process, like every week.

Michael Helbling: Yeah, I guess I just run the skill and, uh, go about the rest of my day kind of happy.

Speaker F: So, want in? Go to Ask Y AI and join the wait list. It's in beta, but you can get in on the ground floor.

Michael Helbling: Yeah. And if you use code aph, you'll get pushed to the top of the wait list. That's Ask Dash the letter Y AI and use code aph. All right, let's get back to the show.

Val Kroll: And where you landed on that, I think, is one of the drivers that I see in my mind is if you think about how these two disciplines grew up in the world. Like, research used to live within marketing. It used to be like marketing research. And so we would sit within marketing teams. All of my clients back in the day were CMOs or reported up to the CMO. My first web analytics job, I was in it, and it was very, like, technology heavy. It was about the tools and the way the data was collected. And like, surveys, like, people think like, oh, like pen and paper. Like mailing surveys or someone chasing you down at a. At a mall with a clipboard. Like, would you like to take a survey? It's like there's so much more to that, like, with, you know, panels and different ways that you can contact people nowadays.

Stephanie Zamit: So I think lines have moved on.

Val Kroll: Yes, yes. There has been an evolution. Yes. And so I think it's just kind of, um, been just how it grew up is kind of thought of differently, budgeted for differently like research, I think has always has this like wrap a little bit. I'm interested in your thoughts on this too, Stephanie. That like a lot of times it can be like costly, like not just dollars, but time. So that it takes a long time to, you know, every, you know, we only do the brand tracking study once a year because it's such a big, you know, piece of research or like it's actually not valuable to keep track of that on a more frequent basis where a lot of the costs from some of the other analytics practices or areas are some more hidden costs because they're in the technology or actually the human solutions. And so especially when we're talking about like in house, I think that it's just budgeted for differently and so people aren't really like connecting the dots. But I think the organizations where you can break down those silos. Because I've actually never worked for a client that's had the, the setup that you're talking about, Stephanie, which would be like, ah, uh, nirvana to have them like coming together. But I always found ourselves making the suggestion about like, did you talk to that team? And they're like, who? They're like, well, I was scrolling through your active directory and I found this person with this title, like you should reach out to them to see if they can help us. But yeah, so that's kind of what I think is like part of the, the rationale that I think that I do hope that this is like a movement though, like this evolution towards thinking more flexibly about the methodologies and what's the right F for the question at hand and what's going to serve the business best.

Stephanie Zamit: Yes. And I should add that the nirvana, I'm maybe making it sound more nirvana like than it actually is. I mean, you know, again, when I, when you look at huge organizations like, like Starbucks, which it's just such a mass, there's thousands of people at head office. Although we were all in a department together, the reality is that the silos were still very strong. Um, at the time when I first joined Starbucks, I was in service to the loyalty team. So the rewards and the app, um, and how our customers use the app, et cetera. And I realized that I was trying to serve these stakeholders with insights about app usage and loyalty program behaviors and all the rest of it. Meanwhile, you had folks in analytics who were also answering questions for the same stakeholders and there was just such a clear overlap in. We're both talking about behavioral data, we're both talking about what the customer wants and Needs. And, and um, so what we did was we formed a little within the department, a little community of people who are in service to the stakeholder group, irrespective of where in the mega data analytics and insights department you are. We come together and we talk about all our projects. We formed a little, um, I don't want to call it a steerco because I hate the word steerco.

Val Kroll: Very anti allergic.

Stephanie Zamit: Oh my God. But just like a little forum of round robin. What's everyone working on? And then we can say, oh, you're doing that. I've got a survey coming up which directly overlaps with the objectives of what you're looking at, but I can tell you why and you're measuring what. So why don't we put them together and hey, our stakeholder will get something that's more complete and less confusing rather than, you know, 10 different reports that all kind of overlap. But the actionability is lost because you know, we're, we're, we're pointing in different directions on similar and yet not quite the same topics.

Michael Helbling: That's crazy because I literally have built something almost identical but coming from the data side to the research side at a company I used to work at which was forming this little team that we kind of met and sort of were saying like okay, let's get together and get our, all of us together so we have a coherent story. And it's always stuck with me that like why is the organization having to be managed sort of bottom up in that regard when the reality is the structure or the layout of the. Org should be thought through to enable that kind of capability from the very top. And it's just one of those things that sort of sticks out as a sore thumb and I don't know if I have prescriptions for that, but I will say it's, it's um, like Val, you mentioned, like we walk into a client, you're kind of looking around like where's your research team and why aren't we talking to them too? Um, which I think is really apt. But also like certain companies like you're going to do analytics work and there is no research team or nothing named as such. And it's sort of like a lost function or a missing function. And it might be kind of Julie, to your earlier point, um, the leaders of the org just sort of think they've got it figured out so they don't need um, someone to sort of like think about what the customer actually thinks because they're thinking for the customer if you will. Not a great plan. Uh, but anyways, I'm just curious Stephanie, like, because you've done some consulting in this space as well. Like how do companies sort of jump the chasm? Um, first from just not even having a concept for doing research like this because everybody's got analytics. Like we've all got snowflake and databricks or something running the back end of the big all of our data. But a lot of companies have zero going on in terms of like either customer or market research.

Stephanie Zamit: And if they do, they outsource it to agencies. So they would do a one off project that an external company will run. Um, and this is where you're right, it adds complexity because it's rare that a company would have a full function in house research team because the manpower that you need on a per project level is huge. Right? We're talking thousands of people, like okay, not thousands, but at least 50 going out interviewing in different countries and then your data processing and then your statistics and then so the per project value for money of that much manpower is just not worth it. So they outsource to agencies who have economies of scale. Those agencies then in turn don't think to ask, hey, do you have a data team? Do you have an analytics team? They sell analytics. Why would they say hey, we're going to build a segmentation for you, we'll do all the research but we'll hand over to your in house analytics and they can do the clusters. Like they're never going to say that. They'll be like yeah, we do end to end, you know, woo hoo. But so I think that's a challenge. And what I would advise organizations to do to mitigate against that is to have even just one person. At uh, Bang and Olufsen we have one person, he's a superhero, he's a uh, one man research department, one person to coordinate all research projects. And you can still use vendors but you have an internal knowledge bank being built and internal consistency even. And then that person knows to work with analytics and to blend the two together while also getting the economies of scale from the agencies.

Val Kroll: Closing the chasm. I want to spend a little bit more time on this and your thought about making sure you have someone to represent that perspective versus does anyone want to add any new questions to this year's tracker? It has to be so much deeper than that for it to be meaningful. Um, but my first boss when um, I was in market research, she grew up Lynn Bartos, if you're listening, um, she grew up at Burke. And so she was hardcore, like you were saying, having those skills. And one of the things that she always talked about was that she spent two weeks touring all the different departments. Like someone who sat within, you know, data processing, someone who sat within coding for all the open ended responses to get an appreciation for the operations and like how the sausage was made. Because that makes you smarter when you request your banners or when you think about like, you know, how do I develop the questions to get me to a driver's analysis or things like that. Um, so, and so then we did that ourselves, um, on her team and I really had an appreciation for that. Do you think that the closing the chasm is cross training people so that they have like a better appreciation for each other's skills? Like when they do both exist in house? Or is it more, you know, pizza lunches like dog and pony show of like results or Because I love what you talked about of having the little non steerco steer co that's aligned to a stakeholder group. Like I would have never have thought of that. Um, but I'm just wondering if there's some other things, unfortunately like you were also saying, Michael, bottoms up that people could do to kind of close this chasm between the team. Like what things would you recommend to listeners for who have an interest on the other side?

Stephanie Zamit: Absolutely, yes. Um, so for anyone listening who's not managing a team but is in one of these roles, whether you're in data science, you know, data engineering, research, whatever it might be that touches on data, I'd really recommend the best that that person can do for their own career is to have a natural curiosity for the methodologies, um, and the training that the others in the department have. Um, and I always say to people in my team, if you find yourself in a conversation where you have no idea what people are talking about or it feels like scary or just very different to what your expertise is, that is where you will learn that you should lean into those conversations. You know, we should go out of our way to understand, not in an annoying way like hey dude, what are you doing every day? But like just to have a natural curiosity of how does your work fit with my work? And that's not even just for analytics and insights or for data. That should be for everyone in a corporate job working for a brand where we serve our customers, right? We should all be understanding how, how do our worlds fit together for the customer, but especially within data, because data is the customer, we represent the customer, have that curiosity. And for anyone who's listening, who's in a leadership role then, then yes, I really recommend fostering that within your teams, that natural curiosity and getting people to take a um, moment to question if anyone else in a data related role can contribute to the project to add further insights. So would the data science team know anything here? Maybe they don't have a deliverable, but maybe from their investigations or their work they would have context that would add valuable insight to my work. Would the research team maybe have had a project about this? Maybe not. But again, the data folks are in, you know, they're hands dirty in the data every day. The amount of information that they process and that they gain exposure to, which is not reported ever is huge. Right. We could never report all the facts. Uh, but it's there, it's in their heads and in their experience. So it might not be reported anywhere. But that's a good person to talk to because they would have context. Same with the researchers. You know, they're out conducting intercepts, ethnography, focus groups. Not everything makes it into the final report, but if you sit down and talk to each other, you realize, oh yeah, I know something about that. Uh, I remember seeing something about that. I have a good quote that brings what you're doing to life, whatever it might be. See, I really encourage curiosity. My first job, I started in Qual actually, which uh, is like the furthest away from data science and analytics. And I was so scared to move toward quantum and I got reassigned to a tracker. So I went from like the Qual team, you know, ultra open to a, to a tracker like 100% only ever working on this one tracker. And at the time I was so grumpy about it, I was just like, this sucks. Like, where's the creativity, you know, where's the art? Um, but actually it was the best thing that could have happened to me because I didn't know anything about tracking and I, you know, I would have been pigeonholed if I'd have just followed my natural heart. It forced me to learn about a different world. And then I realized, actually, this is interesting. Actually quant. Quant is interesting. Look at that. Who would have thought tracking is actually interesting? There's insights here, but unless you're kind of forced into it, or at least when, when you're young, you need to be forced a little bit. It's difficult to just naturally expect that you would find these other worlds interesting. But I guarantee if you really scratch at that or uh, peek into those boxes, you will find a lot of very Interesting things that will help your own role.

Speaker E: You touched on this a little bit earlier too, Stephanie, and I'm curious. So when you're leading your team and you have this great point of view on how these things can work together, and obviously we talked about trying to encourage the curiosity of everyone on your team, but are there some more, like formal processes that you've also put in place for your team, or how you guys pick up projects or execute projects, um, with your stakeholders that really help these come together in the best way possible? Because you mentioned earlier, like the, you know, starting with the, uh, the qual and then you get a hypothesis, then you follow up with quant. So I wanted to like, dive a little deeper in that area and hear what some actual harder boundaries or processes you utilize to help.

Stephanie Zamit: Yeah, 100%. And there's so many examples. I'm going to try to stay focused here. So, first rule of thumb, no survey asks a question that we already have the answer to. And the only way to know that is to go and talk to the data team and know what we have the answers to. So just as a rule of thumb, and even from our customer experiences, right. Customers shouldn't be telling us, like their demographics if they're signed up to us and we should know who they are. Second rule of thumb, every research project, a subsample, even if it's not in your objectives to interview clients, even if you're, say you're doing a new customer acquisition piece and um, you want to go out to a purely external sample, you don't want any internal sample. Even if that's the case, a subsection of the survey respondees should be from internal known customer sample. And especially if you have a segmentation or you have certain key questions or certain key profile, you know, data points that you want, you want to continue building that knowledge internally, use your surveys in that way. So for example, we're doing segmentation work now. We've designed, we're starting, we actually started with data. So what do we know about our client? To what extent can we profile them before it becomes a mystery? Right. That's the point at which now we take it to research. We fill in the blanks with research, but we interview as much of our own customers as we can so that then once the survey is complete, we bring all that data enrichment back in house. It's tagged to known customers, and we can use both worlds to create the segmentation. So you have your attitudinal stuff, you have your profiling that you would never Be able to know just from data. And you have your behavioral data as well. And we have this amazing analytics team. They can do the segmentation. We don't have to use an agency for the entire thing. So we've saved money, you know, we know where we can stop the agency to make use of our internal skills. Now we've got money left over for a different project. Great, let's go do some quality with these segments and get to know them, bring them to life, do ethnography, take video. Now when we go out to our stakeholders, we've got our segmentation. It's attitudinal and behavioral. We've got all this great qualitative bringing them to life. Right, so, and this goes for every project. So you're doing, um, a piece of work with drivers analysis. Okay, great. We might do a survey. You know, you do your usual like drivers analysis. But then let's say, okay, how can we, some of that survey was internal customer sample. How do we bring that back to the analytics team and say, well, we want to grow. So let's identify these people and do an internal business drivers analysis based on the learnings from the survey to see if we can replicate those drivers in our data. Lo and behold, you've got an internal business drivers analysis that you can now track because it's in our data. So if we move the needle, did we actually grow? We can actually say, yes, that insight was successful and we did actually grow. There's many more examples. But wherever we can put both worlds together in a single project, I absolutely recommend we do. The other thing is communities. If you're a business that's lucky enough to have a customer community, which is a qualitative research tool. It's like a panel of customers that you can do quick polls and surveys. It's like a social media for your customers and you get so much qualitative insight. Those communities, the best versions of those, are built on top of internal data lakes. So you can follow the strings down to who these people are and how they are transacting. Every project you run in your community now has a behavioral data trail to look at. Okay, we've got insights. The business made a decision. Now you can measure the impact of that decision because we can track these people. Did they actually spend more? Did they actually convert? So the possibilities are endless. And it honestly all comes from recognizing that we're all after the same goal here. Um, and especially research. Quant research quants have analytics teams. They have very similar backgrounds to in house analytics teams. Like I said, there's more similarities than there are differences. But the in house analytics teams might not naturally be tasked with. We're going to run a conjoint, um, or we're going to do you know, things like MaxDiff, which is research analytics. It's not really as well known methodology in in house analytics but they can learn why, you know, why shouldn't we bring those tools to in house analytics? And then it's interesting for the analytics teams to learn these methodologies as well. Over time you save money because you're spending less on external agencies.

Speaker E: That's awesome. I love those.

Stephanie Zamit: And you have better data.

Speaker E: Yeah, I love the rule of thumb. Um, yeah, I feel like it's an accelerator the way you're talking about. I kind of hate the word flywheel but it makes me think of a flywheel.

Stephanie Zamit: But the researchers also need discipline. Like they need to be really close to in house data analytics report reporting. Like it's amazing how many researchers I've met that have never used like the dashboards, you know, they're not in the BI at all. And like why wouldn't you be? Again, as a rule of thumb, if you want to conduct good research, you need your sample to be representative of your customer base. How do you know what that looks like? Well there's BI reporting that shows you, you know, you should be feeding that to the vendors to build the sample plan and the weighting plan. So it's all connect, it's all connected. Um, it's all one thing.

Val Kroll: And I think when, when you're talking about this connectivity too, you can be smarter about like you know, even breaking it up like uh, an example, especially if you have the panel or if it's a known population instead of asking like amazing, like how likely are you to buy this again over the next six months or how often. You know I, I remember working on advertising awareness research for a cruise line and they would always ask like how likely are you to, to plan a cruise for you and your family over the next year? I'm like over the next year? Like these people, like they don't know what they're doing, they don't know what they're having for lunch. Like why are you asking over the next year? Like let's look, there's gotta be other data for this. But in the same way, like there's so many people who will be building out a fallout report in Adobe analytics, like looking at them like trying to discover the friction points and like coming up with the why. Like on Their own like, oh, they couldn't make it to this next step because. And it's like, well, did you ask them if that was part of the friction? Because usability labs, like, you know, pop up surveys. There's like so many different tools or ways that we can connect with the customer nowadays that, you know, not trying to fill in the blanks. Like there's a lot of different ways that we could just find out directly.

Stephanie Zamit: And that is another of my, my rules of thumbs that I didn't mention earlier is the power of derived research versus stated honestly. You're wasting your money on stated surveys. They're just no one knows. Humans do not know why we behave the way we do. Right. It's all like deep psyche. We're super weird creatures. We have all these quirks that we don't understand. So there. Yeah, there's. You're wasting your dollars on how likely are you to book a cruise. Like whatever. It's. It's not. Sorry. Oh, uh, we can swear, but we're

Val Kroll: supposed to let you read this stuff. Yes, go for it. Let it go.

Michael Helbling: Encouraged.

Stephanie Zamit: Yeah, exactly. Absolutely. Like that needs to be the best quant research has analytics. If you're running quant without analytics in your surveys, I don't know what you're spending your money on. Honestly. Um, it's just not good value. Um, and that again that ties then to internal analytics.

Michael Helbling: This is so good. So I'm sitting here from a data practitioner standpoint and just loving the conversation. At the same time, I'm going to admit to you that like you're throwing out certain terminology that I vaguely familiar with, but don't necessarily know. Like what are there resources that could help someone kind of like level up and get better understanding of just sort of topics, structures, stuff like that. Any like good overall books or resources online, like anything you might recommend?

Stephanie Zamit: Yes, and this is a really important point. I think another reason for the othering that happens in these fields is purely language. Um, so you're right. I'm trained in research terminology.

Michael Helbling: Yeah. Which is totally fine. I just am um, like admitting that I don't know all the words you used. So.

Stephanie Zamit: Yeah, but a lot of the words I'm using there is an analytic or data equivalent of it. It's just that different terminology is used. So data might talk about addressable audience, um, which is like sample plans. The best advice I can give is research is grounded in academia. It came from you know, like scientific studies or social studies. And so for anyone who was at university doing research projects as part of their university degree that is the foundation of modern commercial research. But there absolutely are some great tools. There's a really great book that I recommend. It's one book. It's the only one you need. What's uh, it called? I think it's called. It's the Market Research Society's main book. I think it's called Intro to Market Research. And that's. Yeah, that's your one reference of just looking up these words and you'll be amazed how many of them you look up that you'll recognize as not actually that unusual. So like an attribution model, how different is that from a customer journey? A research team would run a customer journey study. A data science team or analytics team would run an attribution model or call it customer journey. But you know, depending on where your

Michael Helbling: journey is, it's a, it's a customer journey has been used all over the place for all kinds of stuff.

Speaker E: Really.

Val Kroll: Whatever you want.

Speaker E: I mean just like use case.

Val Kroll: Oh God.

Michael Helbling: The journey of customer journey is a little bit tricky.

Val Kroll: That's, that's, that's a, that's a cartoon strip right there. Um, well, so there actually is. So to your point, Michael, there is one because you were just starting to talk about this, about the difference between like stated versus like derived importance, which gets to max diff. Which I, I literally couldn't love anything more than studies where we get to do that because there's so many, there's so many different applications. But when I was, I worked on the telecom vertical is my first job, um, out of college. And um, we would use that to figure out like back in the day, like cable Internet tv, what should be involved in that packaging and at what price points. And people would say things like, oh yeah, I need access to like 600 channels when we ask like what's most important to you. But when we actually did like the force ranking or there's like different techniques that, that actually is one of the things that fell to the bottom that it was about, you know, how long is it going to take for the technician to install the, the cable box? And there was like all these other things that were like, um, not something that someone would say necessarily, but it really, when it comes out in the wash. But anyways, that's just like one example. But could you, especially if you have an example that you can, can pull upon to talk a little bit about Stata versus derived importance or one of your favorite examples of that. It's a really fun one.

Stephanie Zamit: I mean in A nutshell, the difference is asking someone how likely are you to do this? And with. Whereas with derive, you basically give them an exercise and then you observe behavior. So for me, derived research is the same as what would be happening in a data team where you're observing the behaviors. Right. Because in data there is no stated like you're not stating it, you're just watching people behave and you're tracking their data. So it's the research version of that that we give them different exercises or force response between many, many, many choices again and again and again, mixing them up again and again. So they could never remember. Yeah, like you could never remember the pattern. Um, and through the continuous. Like you. It's a choice between these four things or a choice between these and again and again, different scenarios again and again, you can derive what the true behavior is or will be. Um, so it could be used predictively to say when this is true. This is the behavior that we want. Similar to building a predictive model based on behavioral data that you can, you know, take all your data and map it over time and start to say, you know, just like run correlations to sort of say when this is true, this is more likely to happen. So again, very similar outcomes, but just completely different methodologies.

Michael Helbling: All right, I've got another question that's probably going to reveal how much I don't know about this topic, but why are Personas so bad most of the time?

Speaker E: Yeah, segmentation makes my skin itch.

Val Kroll: If people are like, well, let's look at the segments.

Speaker E: I'm like, can we not. I.

Michael Helbling: Because, Because I honestly think, I honestly think this is also a driver of the divide in a lot of orgs. Like, as a data guy, like, I see people come up with these Persona studies and stuff and they're dog. Like, they're really like, terrible. And I'm like, scrap your Personas. It's let's all behavioral based it all. Like you don't. Because what I observe people do is they just make up, um, who they like their customer to be. And then they're like, this is, this is Sally and she's a hip mother of three and she drives a van. And. But she's got this cool thing that we like about our brand. And so that's one of our Personas. And it's like Sally doesn't exist in our database anywhere that's not our customer. And the people who buy the products you're talking about don't look like her at all. Like, it's no correlation anyway. Sorry, I'm now getting into my rants.

Speaker F: But

Michael Helbling: what's happening there? Like, why is it so bad?

Stephanie Zamit: I love it. And I think the word segmentation or Persona in themselves can mean so many things. These are words that are overused and not necessarily always used in the right way. And I don't think there is actually a fixed definition. I think it just depends internally on what definition you choose. But why are they so bad? I have run uncountable number of segmentations, mostly from my market research background where I have more years of experience. And I think that the win or lose of a segmentation is in how it is translated or brought to life for the business. So you cannot have a good segmentation without really solid underlying data. You know, hardcore like, like a good. The factor analysis and the clustering, like, and all of that has to be. And you had the right variables and you had the right ingredients. All of that is really important. But that's not what actually matters or has impact. That's just designing the output. It's like a BI report. You can have like, you know, the BI report with a thousand million, like every possible data point. It's amazing. But unless it's um, you know, user friendly, then it just doesn't have impact. It's the same with the segmentation. So without, if you only had a segment descriptively, this is Sally, you know, and Sally does this and Sally does that. Without knowing why or without finding Sally in the data and like saying like, this is Sally, like specifically, look, we're gonna, we know what Sally wants. So we're gonna, we're gonna send her a, we're gonna do a CRM, um, strategy around Sally. We're gonna sell to her. And now we found Sally in our data, we can actually see, look at her changing her behavior. That's when a segmentation is really powerful. And I think the best segmentations to get to that level, you need both your behavioral data and your insight, your research. Because research gets to. How does Sally think? Like, what matters to Sally? You're never going to get that from just observing her in data. You need to get into her psyche. So you put the two together. Now the marketing team have a strategy on like, who is Sally in terms of her psychology? What's going to get Sally really freaking excited and get her to behave the way we want to do. But it's underpinned by existing data. So you can actually see her, you can ab test with her and over time see the impact of your segmentation. And video, video like I cannot overstate the power of a customer Inside video. Just like Sally walking down the street. Like, you can read this is her life. It makes such a difference, stakeholders in understanding who that person is beyond her being a data point.

Speaker E: You make it sound like so. I mean, it is so ideal, but it makes it sound so like, duh, if you just did this, you know, you'd get everything you want. Because then we go where I go, work with clients, and it's like they're just so far from that point and it feels like such an uphill battle to try to help them fit together. The two worlds that you're talking about, research and analytics, you know, segmentation is all based off of outcomes. And it's like, but you want them to change behavior to drive outcomes, but now you've split them by outcome already, and then you ask them questions about outcome. It just feels like something's been lost in a lot of the situation and it's, it's sad to see because they would have to, I really think, like, start from the ground up to get it to where they're utilizing analytics and research in the right way to get the benefits you're talking about.

Stephanie Zamit: Research hurts because every time you do a study, you need to pay money. Especially because, like I said, no one has in house research teams, right? Not like fully intense. That's not a thing. I think maybe Sky TV have one, but most, uh, companies don't. And so you'd have to make a business case to say, why should I spend money doing something that the analytics team can do internally using this data? Why is that not good enough for stakeholders to understand that without having ever seen what good looks like is really difficult. Um, and it's something I find really challenging in my job, actually. Just explaining the value of something without having it to handle. So it's like hypothetical to a stakeholder. Right? And this is where research teams are lucky. If you have good relationships with agencies that will send you case studies and they feel sort of safe enough to send you examples that you can use to build your business cases. But when you're talking in hypotheticals, it's very difficult to get the budget. And it's not cheap. Market research is expensive, it's slow. It's a big investment for any company to make. But what I do find is once you start investing in it and putting the two together, showing the impact of that, the stakeholders will then understand, like, wow, I get it so much more now because I've got my data, but I've also got My why? And I get how this person thinks. Put those two together and it's suddenly you think, how did I ever make a decision without knowing this full picture? And then that's where you will wet the appetite. And it snowballs from there. But it is very difficult to do the very first one. And hopefully agencies can help with those case studies.

Speaker E: No, I have kind of a random question, so I'll save it to both Michael when he wants to wrap.

Michael Helbling: Okay, I have a random question.

Val Kroll: Love this. So I actually had a very the opposite experience of you, Stephanie. I started on trackers and I wanted to kind of branch out of that. And so I got thrown on the ihuts which are in home usage product testing, which is like, could not be further from the, the tracking world. But I love that like when you said the power of video. Um, Gillette was one of our clients and we, they sent out these new like beard trimmers to like men and they asked them to do videos of them shaving. And it was just so funny that like, that they were watching like, oh, like why in God's name are they putting that clip. Don't hold it like that. You're going to cut their nose off. Like, oh my gosh, we need to change our directions.

Speaker E: But.

Val Kroll: Or um, it was so funny. Including, um, like testimonials like the voice of customer, like in some of those reports that could be like leveraged a hundred different places. We also had Hanes as a client and they were trying to. They was testing all the tag lists back in the day when everyone was switching. So it was like T shirts and underwear. So we were sending out boxes and boxes of like whitey tighties and asking people like, tell us about did the tag scratch your butt? Like that was like the question, I think. But the quote that we got, I'm like, I really wish I could see this used in the internal decks, like where this went. But anyways, to your point about it's an investment in the first one if you only think about it as I'm going to send out a survey and I wonder what they're going to respond on. Likelihood to agree to a certain attitude or statement versus thinking more creatively about the different ways that you can interact with the customer. I think can get people really excited about ways that can be injected. So if you take one thing away, don't think myopically about what research is and the ways it could be applied because it can actually be pretty fun and pretty enlightening. So.

Michael Helbling: All right, Julie Lightning round random Question time, as we do have to start to wrap up here, actually.

Speaker E: Fine. Michael says we have to start to wrap. Um, okay, my question, because you were saying research is not fast and it is not cheap. But nowadays people like fast and people like cheap and people like AI because AI is.

Michael Helbling: Oh, that was literally my doing it, Julie.

Speaker E: So I have a slight spin. Let's see if you went this far too, Michael, because maybe we were totally, totally parallel thinking, which I love. Um, because there were the two things, the faster and the cheaper. So we've had an episode in the past about synthetic data. I was curious, like your thoughts on using synthetic data in this space. Maybe some pros and cons. Um, but then it immediately my brain jumped to, well, AI in general is the fast and cheap option. And both of those things feel like people are very quickly going to grab for them to fill the gaps of, you know, classic research. But we've spent this episode saying that we're weird creatures and like to actually figure out the why you can't just ask them why. You got to take the time to observe. And that's. Those things are just at such a opposite ends of the spectrum.

Stephanie Zamit: So it depends who you are as a company, how relevant or how useful synthetic data would be for you. So for example, at Bang and Olufsen, we work in small data. Our transaction volumes are relatively low. We're lucky if we get a data set of, I don't know, a couple of hundred thousand. Um, um, Rose. So our client is so niche and so under, so misunderstand or so not yet understood. Because we are a luxury brand in the consumer electronics space. We can't learn from other consumer electronics behaviors, but we can't learn from other luxury behaviors. So synthetic data is just never going to be relevant for us as a, as a brand. There isn't enough volume and there isn't enough lookalike profiles and, and we're still exploring the category, being the pioneers of the category. If you're a CPG company and you sell in the typical supermarket or grocery store, then yes, absolutely. Um, that makes sense. I would say though that there is a watch out, that we're living in a changing world. So my rule of thumb when it comes to any kind of behavioral insights work is that they have a shelf life of around three to five years. But that's been my rule of thumb since pre Covid. And I do think that the world is changing more quickly now post Covid than it was pre Covid. So you have to think culturally is the world the same enough for me to rely on synthetic data which might go back. It depends where your cutoff is, right where you start your data set from. Um, so I would warn against just caution to think about that if there's a huge world event, you're probably going to need to go in with fresh questions or fresh exploration. Um, and yeah, just the world we live in right now, it is so turbulent that yeah, it's not the three to five year shelf life thing might not be as applicable anymore.

Michael Helbling: Oh, that's really good insight. And yeah, that was basically Julia, the question I was going to ask about AI and its place in this because I've seen startups going around, you know, being like we can create a hundred personal digital twin research panel for you on the fly with AI and you can do your pre research research with it and stuff like that. And I think there might be a place for it. But like, like you said Stephanie, you have to kind of like think through the applicability. And I like the way you specified like hey, for our brand we understand how unique we are. So um, a group of averages is not going to get us to an insight that we could use which is I think very, very relevant. That's really good. All right, we've got to start to wrap up. This is so fascinating. So thank you so much Stephanie for joining us. And it's very uh, good educational and really fun to talk about. And I know Val, you probably also are loving this episode. So okay, what we've got to do, last calls, something we do every show. We just go on the horn, share something that might be of interest to our listeners. Um, Stephanie, you're our guest. Do you have a last call or a couple you'd like to share?

Stephanie Zamit: I do, I have two last calls. And you know what? Of all the prep I did for this episode, this was the thing that stressed me out the most. Because you guys last calls are so good. I was like, I gotta come in with something good. It can't just be any old thing. I uh, did lose some sleep over these, but I think I got two good ones for you. So the first one is it's actually a game. I'm a gamer. I love any type of game, board game, video game. And I attended a leadership training that was organized by our amazing HR team team at Bang and Olsen. And we played. It's essentially a simulation game. You're given a group of employees and they have to deliver a project together. And you know, it's a bit like Monopoly. You get chance Cards and things go wrong. It's like, oh, the project, you know, somebody went on stress leave. What are you going to do? How are you going to keep to the time and the budget? Oh, your main stakeholder has suddenly decided that they forgot what this was all about. What are you going to do?

Val Kroll: Sounds dramatic.

Speaker E: I was like, this is giving me, like, stress sweat.

Julie Hoyer: It was so funny game every day.

Speaker F: Stephanie.

Michael Helbling: What are you talking about?

Speaker E: Sorry.

Stephanie Zamit: No, but you're sorry. We do play that game every day. But the fact of having the safe space where you could have these, oh, shit, like, everything's going wrong in my project moments, but you're learning how to deal with those in a safe space so that when it comes to your real life game, you're prepared. I thought it was a great idea. It's, uh. The game that we played was called the Playmakers game. It's made by a company called, um, the Works Works with a Z. And I think they have a bunch of other, um, sort of professional world simulation games as well. Super recommended.

Julie Hoyer: Yeah.

Michael Helbling: Next on site.

Stephanie Zamit: Um, and it was a great way to build, um, rapport with your stakeholders as well. Right. Because save space and you play the game with a team of actual colleagues. So it was, uh, good for the bonding.

Val Kroll: Oh, my gosh. You should, like, reverse roles. Like, I get to be the stakeholder this time.

Michael Helbling: Yeah.

Julie Hoyer: Yeah.

Speaker E: That would actually be hilarious.

Val Kroll: What would I do with all this power?

Michael Helbling: My problem would be like, really? You're gonna do that? Like, no.

Stephanie Zamit: Well, there is.

Val Kroll: This is it.

Stephanie Zamit: You have to all agree on the decision. That's the game.

Michael Helbling: Like, how are all that all gonna do it?

Julie Hoyer: Okay.

Stephanie Zamit: It's like, oh, no, we lost the team to stress leave.

Val Kroll: Damn it.

Stephanie Zamit: Like, we. So, yeah, it was.

Michael Helbling: That's awesome. That's very cool. What else?

Stephanie Zamit: Well, I. Have I mentioned my second one just because I, uh, I'm really excited that this is, like, hot off the press. It's literally just gone live, I think, two weeks ago. As you know, I work for Bang and Olufsen, and we've just opened the factories up for anybody who's interested in audio to go experience the, um, manufacturing of our products. And honestly, if you're a sound nerd or an audiophile, it's a incredible experience. Like a really, just sort of once in a lifetime, uh, immersion into the world of audio in a very beautiful part of the world. So, um, yeah, that was my. My second one.

Val Kroll: Fun.

Speaker E: Wait, Stephanie, I did see your note. You have to. You have to say the freaky part.

Stephanie Zamit: Oh, the Freaky part. Yes. So it's a tour all through, um, the, you know, like how the, um, tonmeisters they're called, how they find the perfect sound. And there's a lot of different rooms that are, um, created in a way for you to experience sound in different ways, which is how the products were developed. And one of the rooms is the 100% noise proofed room. And it is the scariest place. Like, honestly, I couldn't, I couldn't stay in there with the door closed. You wouldn't believe how scary 100% noise proof is. Soundproof is. You can hear your blood flowing through your veins. It's terrifying.

Speaker E: Insane.

Michael Helbling: Wow.

Speaker E: Oh, insane. I was like, trying to imagine that and I was like, that's just like breaking my brain.

Stephanie Zamit: Honestly. 10 minutes and you're like, get me out of here. Like, I'm going crazy.

Speaker E: Yeah, I bet.

Michael Helbling: Okay, now I need to go do this. Ah, Stephanie, great job. You've upheld your end of the last calls by far.

Val Kroll: 10 out of 10.

Michael Helbling: All right, absolutely. Yeah. Who wants to follow that? Valley, what's your last call?

Val Kroll: Um, so mine is going to be a little, uh, research related. So I thought that one of the things we might discuss, and I do think we spent a good amount of time on it, is how to be curious and how to, um, get broader in your, um, understanding of these different methodologies or teams that might exist, um, inside your own organization. And so my recommendation is to look outside for some different communities that you could be a part of or join. Um, the one that I'm still a part of today is the Women in Research. The wire group actually started in Chicago a long time ago. Um, but I have benefited so much from my, um, different mentorship conversations. Uh, I still stay in touch with the mentor I was assigned with, I think 13 years ago now, 14 years ago. Sherry Binky, Shout Out. I know she's a listener. Um, but there's like, I'm a part of, like, women in product groups. And it doesn't have to be like, women only groups too, but they have, um, so many different great events where you can get out and talk to people. So get out, touch some grass, talk to people. Like, learn about how people are putting some of these different ideas to use inside of organizations. Because, like, that can totally be an inspiration for the way that you bring it to your own work. So that's my last call.

Michael Helbling: Outstanding. All right, Julie, what about you? What's your last call?

Speaker E: Mine's a little bit random. Um, but honestly, this is something I'VE definitely heard mentioned before quantum computing. And I do think this is like the next leap, probably after AI if like, my naive take on it is anywhere close to true. Um, I was reading a newsletter that I always get and there was this one mention of like, the next big, like Leap, I think it was called. And so I clicked on it and it was all about quantum computing. It was an infographic about. I'm like, well, I've heard it mentioned. I don't know what it is. Like, sure, I'll take a look at a infographic. Um, and it, it was really good. And the way they broke it down within, like, not a very long read. I totally have a new appreciation of what this means and why so many companies are going after it. Pretty much they're saying, like, instead of using electrons for zeros and ones, like, we're going to use a subatomic particle that is like super finicky to keep states table. But pretty much we go from being able to compute things one at a time linearly to doing things, um, what's it called? Simultaneously. So all these computations simultaneously that you could do. And you think about how much computing like AI is doing or different industries like finance or supply chain even, and they, they walk through like a supply chain example. And it was amazing to think, think that you could go from something that would take a normal supercomputer, like they even said like a trillion years in their example, to doing it so much quicker with quantum computing. Um, and they said that some of this quantum computing power could actually happen in the next two to five years. And some of these people working at companies were like, I was told I wouldn't see some of the milestones we have hit recently in my lifetime. And like, they have hit that them. So it was one super interesting. I finally feel like I kind of understand what it is. Um, it was a really quick read too. So it got me kind of freaked out and excited and I just felt like, oh, I learned something.

Stephanie Zamit: Sounds good.

Speaker E: I like it.

Val Kroll: Yeah.

Speaker E: What about you, Helbs?

Michael Helbling: Well, I, uh, as per usual, love everything I read on commoncog.com Cedric, uh, Chin and he wrote an article recently about how to make sense of everything that's happening in AI because I just sort of feel. Feels overwhelming most of the time. And it actually sort of ties back to the episode in a way because one of the points he was making was sort of like, don't listen to what people say about AI. Watch what they're doing with it in the real world. To use that as a guidepost for how you should be responding to AI, which kind of goes back to sort of like user research or so anyways, the article is really good, but it's very practical in terms of just better sense making around. Like, okay, there's still this hype and concern and all these other things, but, like, look at actual detailed examples of how people are actually using it and then ask some questions from there, like, what other outcomes are possible? What actions could I take? What matters most in my context? Those kinds of things. So, anyways, really good article. Just to like, take some of the pressure out of what I think a lot of us are feeling about AI. Like, half the time it's like, is it going to take my job? And the other half of the time, this is so cool. I can't believe I don't. You know, I'm just doing everything with AI now, so there's some balance. We have to find a balance, otherwise we're going to blow up anyway.

Stephanie Zamit: So that's my best call.

Val Kroll: Blow up.

Michael Helbling: Yeah, tune in. What's the old, uh, whatever. I won't try to remember what the hippies used to say. Stephanie, thank you so much for coming on the show. This has been so fun. Thank you.

Stephanie Zamit: Yeah, thank you for having me. It's awesome to get to talk to you guys and talk about nerdy topics that I love. So thank you.

Michael Helbling: Yeah, no, it's been great. And I'm sure as you've been listening to the show, you might have questions or you might have ideas. We'd love to hear from you. The best way to reach out to us is, um, through the measures like chat group or, um, LinkedIn or via email at. Contact analyticshour IO and please feel free to reach out and leave us a review on the platform that you listen to us on. Whether that's, um, Apple or Spotify or whatever, you know, whatever one, we'd love to hear from you. We love getting feedback on the show. Um, so definitely do that. And we're still asking you to give us some questions. Just a couple weeks to go until. We're going to be recording a show live at Marketing analytics summit on April 29th in Santa Barbara, Barbara, sunny California. And we've got a survey which is out on the show notes page. Go fill it out. I mean, perfect example. Hopefully we did a good survey. I'm pretty sure we probably did, right? Although, again, I just want to caveat each time, I had nothing to do with the art. At the end of that survey, you'll have to fill the survey out to see what I'm talking about. But it was. I had no editorial control whatsoever. Um, but if you have a question, we want to gather lots of great questions from listeners, either if you'll be there or not. We're going to answer them live on the show when we record it there at Marketing Analytics Summit. So looking forward to that. It's just a couple weeks away. All right, great show, very fun. Um, and I know I speak for both of my co hosts, Val and Julie, and I say no matter what your market research says, keep analyzing.

Julie Hoyer: Thanks for listening. Let's keep the conversation going with your comments, suggestions and questions on Twitter, ah, nalyticshour on the web at AnalyticsHour IO, our LinkedIn group and the MeasuredChat Slack group. Music for the podcast by Josh Krohbhurst

Val Kroll: those smart guys wanted to fit in, so they made up a term called analytics.

Michael Helbling: Analytics don't work.

Julie Hoyer: Do the analytics say go for it no matter who's going for it. So if you and I were on the field, the analytics say go for it. It's the stupidest, laziest, lamest thing I've ever heard. For reasoning in competition, Julie has this

Val Kroll: mic that like she could be across the room and she'd be like. It's like.

Michael Helbling: Julie probably inadvertently must have bought one of those ASMR mics or something like that.

Speaker E: Literally I have like the gain turned as far down. It's like more than an arm's length away from me. I'm like speaking, you know, I'm trying to speak on the softer side. And I've turned down my volume in Riverside.

Val Kroll: No, I think it sounds good.

Speaker E: And I've made sure my humidifiers are off, so hopefully no background humming. I unplugged my wine for fridge. Like all the things.

Julie Hoyer: Yeah.

Michael Helbling: And the worst part though, Stephanie, is we have this really great ah, engineer Cony who goes through and does like all the audio editing and he gives very specific feedback about whose audio quality was terrible. And so we get these notes back from Tim of like, oh, Michael's awful this episode. And we're like, oh, thanks, that's so great.

Stephanie Zamit: So that was like self conscious decision.

Michael Helbling: I know you sound a little soft, but you sound okay. Yeah. Anyways, no, it's just one of those things where you're like, you think we'd have it nailed down after so many years, but we're still like every episode we're sort of like, uh, fine tuning.

Stephanie Zamit: You know what, y' all ready? Just saying yeah, that's right.

Speaker E: Yeah. You're the person to ask about that. I should go look.

Michael Helbling: Well, I'll wait for Val to stop typing. I know.

Val Kroll: Usually it's Mo, and she's like, keyboard cat. Like, you're like Mo.

Michael Helbling: Yeah. We're a very serious and professional podcast.

Val Kroll: That's right.

Michael Helbling: Bringing it all together. Here we go. All right, I'll give us a five count, and we'll get started. We'll go in five, four, three,

Speaker E: Rock Flag, and two Worlds, one family.