My guest today is Kate Ioas, Senior Customer Insights at Abercrombie & Fitch.
The original Abercrombie & Fitch was founded in 1892 in New York City by David T. Abercrombie as an outfitter for the elite outdoorsman. Today Abercrombie & Fitch has over 44,000 employees serving 854 locations.
Prior to joining Abercrombie & Fitch, Kate served in the research function at Disney Parks, Doner, The Martec Group, and Rocket Companies.
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- Abercrombie & Fitch: https://corporate.abercrombie.com/
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Jamin Brazil: Hi, everyone. You’re listening to the Happy Market Research Podcast. I am Jamin Brazil, your host. Our guest today is Kate Ioas, senior customer insights at Abercrombie and Fitch. The original Abercrombie and Fitch was founded in 1892 in New York city by David T. Abercrombie. He was an outfitter for the elite outdoorsman. Today Abercrombie and Fitch has over 44,000 employees serving 854 locations. Prior to joining Abercrombie and Fitch, Kate served in the research function at Disney parks, the MarTech group, and rocket companies. Kate, welcome to the podcast.
Kate Ioas: Thank you so much. Really happy to be here.
Jamin Brazil: The Michigan State University’s Master of Science in marketing research program delivers the number one ranked insights and analytics degree in three formats. Full-time on campus, full-time online and part-time online. New for 2022, if you can’t commit to their full degree program, simply begin with one of their three course certifications, insights design, or insights analysis. In addition to the certification, all the courses you complete will build towards your graduation. If you are looking to achieve your full potential, check out MSU’s program at broad. msu. edu/marketing, again, broad. msu. edu/marketing. HubUX is a research operations platform for private panel management, qualitative automation, including video audition questions and surveys. For a limited time, user seats are free. If you’d like to learn more or create your own account, visit hubux.com. It’s a huge honor to have you here. And as always, I like to provide a little bit of context for our listeners. Let’s start with the first question. What did your parents do, and how did that inform what you do today?
Kate Ioas: For sure. So kind of interesting. So I’m obviously big in the corporate world and neither of my parents really were. My dad was actually a teacher growing up and he volunteered a lot in our school, as did my mom. Taught us art lessons, were involved in all the school carnivals and after prom and all that. And my mom managed a doctor’s office. So totally different than the path that I ended up on, but both really good parents.
Jamin Brazil: And I imagine pretty supportive of the career choices you made. How did you wind up in market research?
Kate Ioas: I think everyone kind of winds up in market research different ways, but mine, originally I wanted to be an architect because I liked math and science and art. And I kind of thought that was the only career path that was artistic and kind of math focused. But the older I got, I got really drawn into psychology and ended up studying psychology in college. And for some reason in there, I was really drawn to stats. Stats was kind of the class that I felt like I did really well in and really enjoyed. And so then, I don’t know, one thing led to another and this is kind of a long story, but I ended up interning at Curiosity Advertising and I basically didn’t know what I was getting into. But the president at the time had told me like, hey, I think with your psychology and stats background, you’d be better on our insights team than doing something creative or on an account or something. And so that was kind of my first exposure to the market research world. And I just kind of fell in love with it right away in college. And then from there, I just knew that that’s kind of what I wanted to do. It was my perfect balance of art and math, I guess.
Jamin Brazil: And it’s interesting how much art is actually involved in the science of statistics.
Kate Ioas: Yeah, totally. I think people don’t always consider statistics as creative, but I think when we think of things like, I think our topic for today segmentation is super creative. And I think, figuring out ways to tell stories with data is also really creative.
Jamin Brazil: Since you’ve already done my segue, thank you. As you know the research industry has evolved over the last 10 years. There’s a ton of new blood, thankfully, that has entered in, and they have not really been many of them exposed to applied statistical techniques. Meanwhile, so the counterpart of that is customer segmentation. It becomes, or has become a cornerstone for fast growing brands. So my question is, what is customer segmentation and how is it used by business stakeholders?
Kate Ioas: Great question. And I think before I even talk about segmentation, I think it’s really important to kind of describe the difference between segmentation and personas. Because I hear them used interchangeably a lot, but I do think that they’re different and have different uses. So I don’t know if you think about them kind of the same or differently, but segmentation is a group of people, it’s Gen Z, it’s dog moms, it’s tech savvy consumers. It’s a way to describe a group of consumers. Whereas a persona is more of like a, it’s a personified profile of a person. So it’s like, yinessa the yogi or something, a person to describe a full group. So today we’re talking about segmentation, not personas.
Jamin Brazil: That’s a really good point. And they are very different. The personas enable brands to connect to consumers with- in a way of like- a way of empathy. What segmentation does, at least how I’ve applied it through my career is it provides us the size of the market opportunities by various segments, market segments. And it can be used, segmentation can be used to, very effectively to help craft the actual personas that the customer or the brand is looking to craft. And a good example that just occurred to me. You think about the world that has maybe, I don’t know, let’s say it’s 8 billion people in it, and let’s pretend that China has 2 billion people in it. So if you just look at the math, So what is that, reduced one in four. So one in four of your children are going to be Chinese, which we know doesn’t actually work that way. Mathematically though, you can see how you can kind of like lean in there. So the broader point that I’m trying to make with a little bit of humor is that segmentations tell a big part of the story of who the consumer is, but it’s really important to kind of roll that into your persona work, which is usually a little bit more qualitative in nature. Is that kind of how you think about it?
Kate Ioas: Yeah, I think so. And I think you touched on this too, of personas can often be based off of segmentations. Where it’s like, you understand this group so well, and now you want to pull someone out and use this one person as something to describe the whole group. And I think you’ve- we’ve talked about this before too, of like, if you’ve got a good segmentation and you want to use a typing tool to recruit for focus groups and then you can talk to people that are in that segment and really personify them that way too. So there’s so many ways to kind of build creatively off of segmentation to further kind of personify and connect with your audience.
Jamin Brazil: So let’s get into the question. What exactly is customer segmentation?
Kate Ioas: I think a lot of people think it’s like black magic on the back end. It’s like, I wonder how it all works. From the root of it, it’s a data reduction technique. So in my mind, I think there’s two main data reduction techniques. The first one is factor analysis. So if you’re picturing a raw data set, you’ve got columns and your columns are your different variables. You’ve got maybe gender, age, satisfaction with different things in the columns. But then in the rows is individual responses or individual people. So factor analysis is combining the columns. So lumping different variables together. So maybe you’re creating meaningful buckets of different demographics or different experience related metrics or different product related metrics. That’s factor analysis, where you’re combining these variables by the columns. But where segmentation comes into play is the other type of data reduction technique, which is cluster analysis, where you’re actually combining the rows. So you’re clustering individuals based off of how similar or different they are in the column. So I think cluster analysis, you’re basically clumping people together based off of how they answer certain things or how they react to certain things or fit in certain categories. And each cluster is going to be similar to each other, everyone’s kind of similar to each other, and they’re significantly different than the next cluster. So it’s, I think at the root of it, it’s just a data reduction technique.
Jamin Brazil: In both ways. And I think you’re- you’ve done Michigan State’s program very proud, with the way that you framed it. And it’s funny because I’ve done this over 20 years and I’ve never heard anybody articulated exactly like you did. And I think it’s totally on point. So factor analysis is functionally just reducing the number of variables into these common themes. And then cluster analysis is then combining the people groups by those factors. So you can see how they relate strongly or negatively to those factors or to those variables. And what’s interesting about that is we can only hold so many things in our head at once. And so when you think about a- the variables that go into a segmentation, it’s usually quite a few that you’re looking at and trying to understand the relationship between the interaction effects and things like that. And so my question is, is as you hold those things, like number of segments regarding cluster, as you cluster the- your population into segments, how do you usually frame that out? Do you try to start with- do you have a in- number in mind? Is it five, is it three, is it eight?
Kate Ioas: You have to think about the end goal of it, as you’re trying to understand your audience, so that marketing likely can target them in a better way. And you can only appeal or understand so many groups. And so what’s interesting is if you’re thinking about a cluster analysis academically, you don’t want to set any limits on it because the kind of modeling in the program will pop out the exact number of clusters that’s the most statistically significant. But I think from a marketing standpoint, I usually think anywhere from three to five is ideal. Anything more than that starts getting kind of overwhelming, or maybe your groups aren’t meaningfully different. And I think that’s the part that you have to think about kind of that art that we talked about of like, maybe they’re statistically different, but from a marketing standpoint, or just a general understanding standpoint, these groups aren’t super different. So you want to think about it in that way of like, they have to be different, but also meaningfully different for marketing to actually take any action with it. So I think three to five is usually what I go for.
Jamin Brazil: So cluster analysis of course, is a statistical technique by which you’re able to reduce an audience or segment an audience. What types of segmentations are there, and when should they be used?
Kate Ioas: That’s a good question. So I think generally, there’s four types of segmentation that you think about. And I have admittedly all of my experience is pretty much in psychographic, but I’ll go over the four that I’ve seen. So demographic is a pretty simple one. It’s could be generational, it could be life stages, it could be targeting women, it could be targeting high income audiences. That’s usually used for targeting ads or messaging. So maybe you’re looking to target families with small children with an ad, or maybe you’re looking to target certain occupations with certain resources that might help them. So really functionally, just targeting certain audiences based off of who they are as people. So that’s the first one. The second one, like I mentioned is psychographic. So this is the one that I- is personally my favorite, maybe just because I have the most experience with it, but I’ve heard it also referred to as attitudes or needs based segmentation. Where you’re really getting into the root of who these people are and what makes them tick. So we’re breaking up people by their motivations or their beliefs, or why they purchase a certain product. Really just understanding them on a slightly deeper level because you could have millennials, but then you’ve got millennials who maybe do certain things for certain reasons. And you have to get kind of that one level deeper than kind of that demographic. So this psychographic segmentation is used, I think usually for marketing, for messaging, for campaign targeting. And I think my personal favorite is just understanding customers, creating empathy with your clients. Or as a company, just really understanding them and thinking about who are we solving problems for, who are we talking to, who are we kind of thinking about when we’re creating these products or these marketing messages.
Jamin Brazil: And that really gets down to sort of the why of their purchase?
Kate Ioas: Yeah, because I think good marketers understand that just by understanding this high level stuff, you’re probably not going to get too far with customers. But by understanding the why, why do they purchase the way that they do and in targeting that is going to be a lot more powerful than certain other things.
Jamin Brazil: Let’s talk about typing tools.
Kate Ioas: Wait, let me tell you, so the other two.
Jamin Brazil: [CROSSTALK] two more, right?
Kate Ioas: Yeah. The other two are pretty similar. Geographic, just like you don’t want to target someone in Florida with a winter coat, doesn’t make sense. And then behavioral or transactional, which is basically how a customer interacts with your company. So it could be their browsing and spending habits online. What type of products they purchase with you. From my experience, that’s usually a little bit less of the market research team doing that and more of a data and analytics or strategy and analytics team kind of segmenting customers that way. But I just wanted to squeeze in those last two as well.
Jamin Brazil: Of course. How much do you see things like demographics and if you think about, at a company level firmographics or psychographics, et cetera, all feeding into a single segmentation?
Kate Ioas: That’s a good question. When you create a segmentation, you have input variables. That’s how you divide up the audience. I don’t usually see them all combined together to divide up an audience, because you can see- this is getting a little nerdy of me, but input variables have different weights. And if you were to use a demographic input variable, a psychographic and a geographic, you’ll find that there are certain variables that just have a lot more weight than others, and then the segment becomes uninteresting. So if we find out that being 18 years old has a much bigger impact than the reason that you buy a product, then that segment is basically just going to be based on age. So you can’t really mix. Maybe every once in a while you can, but for the most part, it’s going to be really tricky to mix in these different types of variables as inputs, because some of them are going to have a lot more strength and weight than others, and it’s going to become less meaningful. But the way that you could kind of add in and kind of layer on those things is, after you create the segment or in the survey that you’re kind of creating the segment based off of, you could kind of layer in these other things as kind of like descriptors of the segment. So you could say, this is a psychographic or needs based segmentation. We got these four groups from that needs based segmentation, and now within each of those groups, we can say, this group skews to be a little bit more female, or this group skews to be a little bit older. So you could kind of use them as descriptors, but I wouldn’t think that they could all be inputs for one segmentation.
Jamin Brazil: When you think about your methodology that you apply to segmentation, you mentioned that you’re most commonly using psychographics. What is the methodology? Do you start with factor analysis and then you move to correlations or cluster analysis? What is the flow for you?
Kate Ioas: So my experience is primarily writing surveys with the intent of we’re going to do a segmentation with this survey data. And so I think the best segmentations come from really well thought out surveys. And usually the way that I found makes the most powerful and kind of meaningfully different segments is agreement statements actually. So just how much do you agree or disagree, scale of one to five or one to seven with these statements. And really understanding how do they feel based off of these agree statements, how well do they describe them, five times out of six, I don’t know what that statistic. How about, probably nine times out of 10, I’m doing a segmentation based off of agreement statements, because they really get at kind of the why. If you have a bunch of agreement statements or something like that, then sure, maybe do a factor analysis to kind of cut down how many agreement statements you have as input variables. Because if you have a bunch of input variables, it starts getting a little mucky, the segmentation isn’t maybe as powerful. So sometimes sure, start with a factor analysis to kind of, like we mentioned, kind of combine the columns and then go into a cluster analysis where you have to do a kind of, a lot of trial and error. Where you’re figuring out which variables hold weight, which variables predict these segments well, and how well they divide up the audience in a meaningful way. So it’s- it really is a lot of trial and error and hoping that the questions that you asked in the survey and the variables that you have are going to meaningfully divide up your audience.
Jamin Brazil: I guess one of the most common mistakes I see with new researchers that are getting into segmentation is their expectation is that there’s a clear math formula that they then plug the numbers into. Run the script, or what have you, and then you’re going to get the output that these are your clusters or your segments. And the reality is that just isn’t- it never works like that.
Kate Ioas: I think- I don’t know if it’s my favorite part, but I think it’s one of the parts that makes it so satisfying at the end is like, when you have a good set of input variables, it works out really well. And you look at the segments on the back end and you’re like, oh my God, I can totally picture this person in this group. I can totally picture this segment. So that trial and error is kind of what makes it a little bit challenging. And you have to have a little bit of experience to figure out, if this variable holds a lot of weight, maybe I take that out and I try this variable or whatever. But it’s all figuring out what’s going to be meaningful for our stakeholders on the back end.
Jamin Brazil: I’m going to take the bait. What is your favorite part?
Kate Ioas: My favorite part. This is going to be cheesy, because I think, I clearly like the creativity aspect of it. And I think the way that I do segmentations is almost like borderline a persona at the end of it, because I love running the cross tabs with the segmentation as the columns. And really understanding how this segment answered every single question in your survey and just feeling like you’re getting to know a friend or something. But then getting to know them, naming them in a fun, creative way, and then actually putting it into a document that stakeholders would see and get pumped about. So I love the aspect of creating this profile page for a segment where it’s like, I am just putting all of this juice out there. I’m putting some quotes from the survey from open ends. I’m showing you exactly what this segment is, and that’s the part that kind of hypes me up, because it’s like, this is the part that people are going to see. They’re not going to see all the trial and error on the front end. They’re not going to see the factor analysis. They’re not going to see all that hard work. They’re going to see this juicy creative segment on the back end, and that’s the stuff that I get really excited to present.
Jamin Brazil: Can we talk about typing tools now?
Kate Ioas: I guess, yeah. Yeah, we can do that.
Jamin Brazil: So one of the best tools for segmentation is a- defining a sample frame based on what’s called a typing tool. And typing tool has been defined very simply as an abbreviated set of questions for classifying new respondents into existing segments. So going back to Kate, your example, you might have something like a 100 questions, agreement questions. And you would reduce those down and then through- into even factors. But you could reduce that further into maybe three questions or five questions that you would ask new participants. And then the typing tool would automatically assign those people to the specific segments. So practically speaking, from your vantage point, what is a typing tool, and how and when should research professionals use it?
Kate Ioas: So I’ll try to do the Michigan State program proud again, by kind of breaking down the black magic or the statistics behind it a little bit, just because I think typing tool is a phrase that gets tossed around a lot. And a lot of people probably don’t know how it actually works. They just know, I ask these questions and it pops you out into a segment, I wonder how that works. We talked about the data reduction techniques, so that’s how you get to the clusters, but it’s kind of like, I guess the reverse of the more predictive and classification techniques. So prediction, I think a lot of us are familiar with multiple regression. You’re predicting a dependent variable like NPS or something like that. There’s also classification techniques, and that’s what a typing tool essentially is. Where under that kind of classification umbrella of analyses, I usually think of logistic and discriminate. And discriminate is the one that we usually use for a typing tool, where it’s really similar to multiple regression, except the output is sorting respondents into one category or another. And so it’s basically predicting based off of the questions that you ask or the input variables that you have, predicting which category this person’s going to fit into. And so a typing tool is basically taking that output from that discriminate analysis, having a formula, very similar to a multiple regression or something like that. And then based off of the answers to a question, it would be plugged into that formula and then it’ll pop out which segment they are. And typing tools can be really accurate or not very accurate depending on how they’re made. So you want to make sure that it’s pretty effective and pretty significant. But in short, it’s a discriminant analysis where you’re just predicting and classifying people based off of input variables.
Jamin Brazil: When should researchers use it?
Kate Ioas: So I’ve seen them used a variety of ways. So one, and I know that we’ve talked about this, is recruiting. So say you have these segments that are really well defined and you want to just get to know them. You’re like, wow, I wish I could do a focus group with people in these segments. You could use them for recruitment. So a screening survey to kind of type people out and say, this person fits in this segment, let’s talk to them. So they could be used for recruitment. One of my favorite use cases that I’ve seen was actually a membership company. They had a bunch of members and we had done a segmentation for them where we segmented out their members. So they knew exactly how to market to these members, they knew exactly what type of materials to send them, to make them feel better about their jobs and more confident or whatever. They had a survey that they sent out to all new members and it would type them into a segment immediately upon joining this company or this membership. And so then from there on out, every new customer was sorted into a segment and then they immediately knew how to appeal to them. They felt like they understood them a little bit better and they knew how to talk to them. So that was one of my favorite. And then another one that I’ve seen a lot of is just kind of layering on additional insight to these segments. So say you’ve got these segments, they’re used widely throughout the organization, and you’re like, wow, I wonder what this segment thinks about this new idea. Or I wonder what this segment thinks about X, Y, and Z. You could do another survey, have this typing tool in that survey and ask those questions. And then you can see, how did this segment react to this, how did that segment react to that. So those are kind of three ways that I’ve seen it used. I’m sure there are other ways, I don’t know if you had any other ones top of mind?
Jamin Brazil: Yeah, I have. I- and I- but I wanted before I mentioned that, I want to go back to something you had started with, which is the ability to use a typing tool for qualitative recruiting. That’s something I see done a ton. And again, the benefit just for the audience’s sake is with a typing tool is that you’re able to actually perform that segmentation project that might have cost you a quarter million dollars in order to create, define the segments and then classify people accordingly. You’re able to actually do that classification on the fly with as little as three to 10 questions. So it’s like, it’s such a shortcut in order to leverage the body of work that you’ve already done, which creates this renewable resource for research, which is one of those things that I just, I love talking about. In terms of things that I’ve- unique ways that I’ve seen it applied actually in a sales context, I- a segmentation tool that I created was used by a top two phone company in their stores and in their retail locations. And they- it’s a telecom. And they would ask the customers when they would walk in a series of five questions, and based on their five questions, they would know what segment the person would fit. And they also then knew what the drivers were for the purchase. So you can imagine the power that information gave a salesperson, and yet it’s able to- it was able to be executed at a very- by quite literally anybody. So very, very powerful application of that in a direct sales context.
Kate Ioas: I love that. And I feel like I’ve-. I love typing tools and segmentation obviously. And so I feel like if I ever get asked a series of questions on a website or something, I’m always like, I wonder if this is sorting me into some sort of bucket. Because I think there are some kind of trendy companies out there, I think like Stitch Fix or places like that that ask you a bunch of questions. And I’m like, I feel like they’re typing me into something right now.
Jamin Brazil: I wonder if they are, I don’t [CROSSTALK].
Kate Ioas: I hope they are.
Jamin Brazil: I hope they’re too. We’ve covered a lot of stuff and we’re really just scratching the surface in truth. I’m hoping that we can come back- you’ll come back on the show and talk more about segmentation next month. But before we kind of move on and I know we’re at time, I do want to say how grateful I am for Michigan State’s MSMR program. As you know, they’re a sponsor of this particular show and a huge supporter of the insights community at large. You are a graduate of that program, and so my question to you is what benefits did you get out of the program and how has it impacted your career?
Kate Ioas: One, I love the program. I think the professors that they have in the program, the coordinators, everyone is just really great, really polished, really knows their stuff. Can’t say enough good things. I would say, as far as how it’s impacted my career and everything like that, I would say I kind of break it down into three buckets. So the first one is just confidence in my market research toolkit. The amount of methodologies that I learned about, like different analytics, as you could probably tell from this call that I love. I just, I’ve gotten so much confidence, and if I have a business question, I could figure out how to answer it with one method or another, because of all of the exposure that I had in the program and kind of the deep level of understanding that I have of all of the topics and all the different methods. So I think that really helped. I think the second one is just telling a really compelling and visual story with data. I think that’s something that a lot of people could probably focus on based off of what I’ve seen out there. But really just figuring out how can I tell a full story with this information. And there was a class in the program, I think it was called marketing communications. And at the time I was kind of like, I don’t know if this is going to help me, whatever. I know how to make a PowerPoint, but the tips that I got from that just were so helpful. And when to use a research report versus an executive summary versus a high level presentation, catering to your audience, figuring out how to even use design principles to visualize your data in a way that’s going to make it powerful. That class was awesome. And I think like you’ve probably picked up on, is I love the creative aspect of market research. So just the visual aspect of that class was really, really cool. And then the last kind of third point about the MSMR program was really just the connections and kind of the community. I don’t know that I would’ve found this market research podcast without the community that I’ve made through that program. So just the support. There’s constantly jobs that are being thrown around in that community of just figuring out how to help other people who’ve been in the program and everything like that. So I think the community is really good as well.
Jamin Brazil: And the fact that they have, I believe it’s 80% of their graduates placed in jobs six months prior to graduating is pretty impressive.
Kate Ioas: I believe that there aren’t too many market research programs out there. And so I feel like it really is a good way to make you stand out and say like, hey, I didn’t just stumble into the market research world. I want to be here and I want to be here for the long run.
Jamin Brazil: My last question, what’s your personal motto?
Kate Ioas: This is a really hard one. So admittedly, I was talking to my husband about this yesterday, because I was like, I think I need to think of a personal motto for this podcast. And we were tossing around really stupid ones, like if you ain’t first, your last.
Jamin Brazil: Ricky Bobby.
Kate Ioas: Which is absolutely not mine, but I do think it sounds really cheesy, but just the work hard and be nice to people, I think is more of my motto. Of just be a good person, be nice to people, and put your head down and do some good work. Because I think it’s just as important to be nice as it is to work hard.
Jamin Brazil: Work hard, be nice. Kate Ioas, senior consumer insights at Abercrombie and Fitch. Thank you for joining us very much on the Happy Market Research Podcast today.
Kate Ioas: Thank you so much. It was a great time.
Jamin Brazil: Everybody else, I hope you found a ton of value here. If you have questions about segmentation, feel free to DM myself or Kate on LinkedIn, either of us would be happy to chat. Have a great rest of your day.