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Understanding Customer Segmentation

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Male Speaker: This is “Think Retail,” a podcast where top designers, strategists, thought leaders, and business people discuss what’s coming next.

Melinda: Hi, I’m Melinda, and you are listening to “Think Retail.” And today we’re gonna be talking about some different approaches you can take to customer segmentation. Understanding your target consumer is key to success, doesn’t matter what industry you’re in. With the many tools available today to gather data about consumers, the ability to create a clearer picture of your customers and how they might fall into different categories has never been stronger. However, it’s difficult for most companies to make use of every tool available. And that means we have to pick and choose which tactics we want to develop strong portraits of our consumer segments. So today we’re gonna talk about the pros and cons of different approaches. JP Lacroix is back with me today. How are you doing?

JP: Fantastic. So it’s kind of scary personas.

Melinda: I know, I know. It is a scary thing to approach and sometimes we get a little intimidated, but there’s lots of great tools that we’re gonna talk about today. So have no fear.

JP: Okay. Are we gonna… At the end of this, will we be able to analyze what kind of persona you have?

Melinda: Maybe, maybe. The frightening thing is that you probably could.

JP: Okay.

Melinda: So let’s talk about the traditional approach first, which is, you know, some focus groups and a survey that determines some demographic and maybe some psychographic information. So what are the pros to taking this approach?

JP: Well, first of all, you know, why do people segment? I think we can take a step back and look at, you know, why is segmentation really important? You know, there’s been and continues to be a big movement towards personalization and, you know, taking all of that wealth of data and customizing it to be able to understand what is driving that individual consumer need. And segmentation is kind of the beginning of that journey. It’s not the end of the journey because the way segmentation, we’ll talk about that being done today, is not gonna lead to that kind of high-level personalization, but it helps understand different buying groups, what motivates them to purchase because a lot of it’s emotionally-driven, and how do we harness those insights to be able to deliver products and messages that are relevant to them?

That’s really the foundation of segmentation, been around for a long time. It typically has been, you know, psycho demographics, looking at their social profile, their income, how many children in the family, you know, what is their household spend? All of that. Unfortunately, that type of segmentation, which has been around for the last 30 years, really doesn’t help us understand the customer. Because it really takes kind of a unified look based on very basic behaviors. And we know that consumers, what they say they’re gonna do and what they’re actually gonna do are two different things. And a lot is driven by how they feel about things, and segmentation doesn’t talk about how they feel.

Melinda: Right. I mean, I think the traditional approach that benefit to it is that it’s easy. It’s easy to throw a survey out there, it’s easy to conduct some focus groups, but like you said, the problem is that you may be gathering information about, you know, what their brand preferences are and how old they are and where they live and how big their house is. But that doesn’t capture those innate things that may also not necessarily be able to be categorized by demographics. People may feel differently from people who are in the same demographic group as them. So the cons to that would be, like you said, the intention action gap. So let’s talk a little bit about that because I think a lot of the time when we receive, at SLD, we receive market research that our clients have done, and it generally tends to be one of two things, which is either focus groups or a survey. And it does tend to come back looking a lot like the kind of information that you talked about, this kind of demographic information. So let’s talk about the intention action gap and why we need to have more than just this information. So tell us about the challenge there.

JP: Well, the challenge is that, you know, we did a fizzy digital study on digital transformation last year. And what we found out is most organizations approach transformation in silos. And why is that relevant to segmentation and research? The reality, research today is used as a tactical tool. It’s a validation tool. It’s a referee. I like this design, you like that design, let’s put it to research and see what the consumer says, the people who vote with their pocketbook. The issue is that type of research only scratches the surface. On the flip side, the challenges, how do you get a total view of the customer, a 360 view of the customer? You know, what’s their lifestyle? What activities do they participate in? What’s their social profile?

You know, what are the things that they’re passionate about? You know, what clubs do they belong to? You know, what hobbies do they have? Because those elements create a clear picture of the individual and what they value in life. And that’s very difficult because most organizations are siloed, as I mentioned, and that information is captured in different areas. The call center has information but it doesn’t link to the marketing department segmentation study, and the finance department has a profile on that customer, but again, they don’t have the full picture. So very few companies have a 360 view of the customer. And that lack of data allows for what I call information bias. You know, the marketers are making decisions based on precedences and biases on their part and not based on facts, but the facts are hard.

Melinda: Right. Yeah. And from my perspective, when I look at, you know, most of the market research that we get handed to us that has been done by other companies, what they’ve managed to capture is attitudes and preferences. So you can ask a customer, “What do you prefer?” And the customer can tell you what they think they prefer. But when they get into the store, they might not buy that product that they said they preferred because they might get to the store and it might be more expensive or the one might be in the cooler and one might not be, or the package looks a little different. So, I think that the challenge with that type of research is that it can’t really predict how consumers are going to act because we don’t have that 360-degree view.

JP: Well, it’s also, and more importantly, this is like the critical thing, is people act irrationally. And so they may say they’re, you know… So the fact that you invite them to a focus group, they’re already biased. The fact that you invite them to an online study, they’re already biased because you’re paying them for their opinion. And so what you really need to do is gain insights on behaviors at the moment of purchase, not when they’re in their home, on a computer, or in a office tower doing a focus group. You know, they’re not linked to the environment which they’re really making the decision. And so there’s been new technologies, some around for the last five, six years, you know, there’s eye-scanning, eye-tracking that allows you to do heat maps of where the consumer looks when they shop.

We did that for Shoppers Drug Mart where we did the private label program. We set up an artificial store and we asked the consumer to shop the category. We wanted to look at where were they looking at the shelf level, what information were they looking when they picked up the package? And they gave us a fairly good understanding of the shopability of the category and what was relevant to the consumer. There’s an organization called Explorer Research that takes it on a much bigger scale where they will reenact an entire store and track where the consumer looks and goes as part of their shopping journey, which is really important because as consumers enter a store, they shop differently as they get to the category and browse the shelves. But that’s one example of getting closer to the moment of purchase, giving you clearer pictures. And how’s that linked to segmentation? It helps you start building a profile on how people shop. You know, some people are cherry pickers. Some people are, you know, driven by, you know, by purposeful purchase. You know, they’re in there to buy a specific list of products and they’re not interested in browsing.

And then you have the browsers, they’re in there…they haven’t created a shopping list and they’re there to discover. Each one of these journeys has a very different customer profile. And also how you’d want to communicate to those customers. There’s another piece of technology that we just got exposed to. So ultimately, the holy grail of segmentation is when you look at the map of that consumer, 80% of the information you gathered was about emotions, and only 20% is about demographic, psychographics, all need states, all that fun stuff. Because it’s 80% of all buying decisions are done emotionally. It’s understanding, what is the emotional equity of that consumer? What is driving them emotionally? And there’s technologies now. Immersion is a research thing that tracks your body energy on your smartwatch. And so they can get a sense to how you feel about your shopping experience when you’re looking at that department and looking at that category in context of your daily life. So it’s not like it’s planned. You subscribe to the platform, you do your shopping, and then they analyze, while you’re doing your shopping, the brands that you purchased and how you felt when you’re buying those brands.

Melinda: Right. There’s a lot of tools that are helping us capture more emotional information, even, you know, monitoring on social media, you can capture emotion.

JP: Sentiment.

Melinda: Generally, it’s on the extreme ends because people are mostly posting about really positive or really negative experiences, but it allows us to cast a wider net to capture some of that emotional equity. There’s some challenges in linking that though to useful demographic information that the marketers, of course, are gonna want because they want to know, who am I targeting in these ads? And I have to use some demographic information to decide how I’m targeting. So as these technologies become more sophisticated and are able to provide us with at least a demographic outline so marketers know who to send these ads to, they’ll improve. And I do think there is a potential for some privacy concerns around some of these technologies that I think we’re gonna start to see governments start to legislate them. So, you know, before you make a big investment, maybe just make sure that you do a double check that what you’re doing is legal and isn’t gonna end you up in hot water in a year or two.

JP: Well, look at facial recognition. I mean, brands are not allowed to use facial recognition in any form or, you know, specifically if it ties to an individual. That becomes, you know… You can use facial recognition to identify ethnicity in shopping patterns, ages. But when it comes to actually identifying consumers, which technology can easily do and obviously all security…governments with security agencies have done that for the last 10 years. I know when I go to China, I’m sure they recognize me. Wherever I go, I’m popular, like it or not. But yeah, there’s… But it’s about connecting on the emotional side. And the challenge is, to your point is lot of data held by different departments, and how do you create a total view? You know, and the banking industry have gone through that in a…they’ve done a huge migration to Salesforce to give a one view of the customer. The customer may have had…in one database, they had wealth, but they didn’t have their banking or they had their banking, but they were in another database. And so the bank didn’t have a clear picture of the total view of that customer and the products they’re buying within the bank. They had very fragmented by channel and by product, which wasn’t conducive to cross-selling. And so you’re seeing a huge push by organizations, and that leads to, you know, segmentation and the development of personas. What’s important is that the data they use to do that is clean data, that it’s not biased data or data that was acquired improperly because then you’re making a lot of business decisions on segments and personas based on false information. And that’s a big issue.

Melinda: Yeah. And I think a lot of the time when you’re seeing mergers and acquisitions as well, how do you merge that data? You may have completely different systems. So making sure that you’re transferring it in a clean way. Because companies actually have enormous amounts of information, but the problem is they’re just not able to use it effectively because like you said, it’s segmented within the organization or it’s just they don’t know how to leverage it.

JP: Well, the other big, big concern is AI, right? We’ve seen most recently AI biasing based on ethnicity, based on age because the algorithms that were set up in AI to make those decisions were set up by humans and with human biases. And so AI will help aggregate all of this data and properly manage and provide deeper insights on who those segments or personas are and actually help them adjust those personas as more and more data comes in.

Melinda: Right. I wanna talk a little bit more about behavioral segmentation because we get asked this a lot and I think people have a hard time wrapping their brain around, what does that look like? If I’m not segmenting people based on, you know, it’s a, you know, 34-year-old mom who lives in the suburbs, if I’m not using that kind of information, what does it actually look like? How would I then segment people? So tell us a little bit about what that looks like, how is it different?

JP: Well, behavioral segmentation or, you know, behavioral personas are to a certain degree predictive of what they’re gonna do. And basically, those behaviors are not only how they shop, but how they live their lives. So behavioral, an example would be a consumer who doesn’t own a car, and who is an urban consumer, and who has a dog, single person. Their behavior is gonna be very different than a couple living in urban center with children. What’s gonna drive them to make decisions is gonna be very different, their value proposition, which is the foundational behavioral economics and predicting behavior is gonna be very different. And so understanding, what are those values and what are those factors of behavior, play an important role. And that will tell us, for example, let’s call them X, Y, Z persona, they’re gonna shop these categories. They’re not gonna do full shops when they go to supermarket, they’re gonna do basket fills. So their average shopping transactional will be, let’s say, $25 versus a couple with two children, or a couple, a married couple with a pet will have a very different dynamic. They will do full shops and they’ll shop once a week. And you can see that if you’re a retailer, you would talk to these two different segments differently because of their behaviors. So there’s a simple example, but they go even deeper than the behaviors.

Melinda: Right. I think the thing that, you know, I would say is that sometimes you’ll find through behavioral segmentation, that certain demographic information is relevant. So say, for example, you might find that it skews more towards women, but you may also find that it’s not relevant. And so I think part of the challenge that I have with using demographic personas is that you make a lot of assumptions and you start to limit yourself before you’ve really even investigated who the customer is, like the customer who likes to eat a lot of cheese. You know, that could be anybody. So what is similar about people who like cheese is a more interesting and probably more specific way of attacking a problem of creating a persona than thinking, okay, let’s talk about women who like cheese, or, you know, millennials who like cheese, these really broad categories that really don’t tell us much about the people who are eating cheese.

JP: Well, we did a huge persona study for one of the largest C-store chains in the U.S. And we had different persona. We had a road warrior, these are people who live their whole life… They’re delivery companies, they’re on-the-road salespeople, and their motivations and their behaviors in a C-store, and what they buy and how often they buy is very different than soccer mom coming in doing a…you know, buying treats for the soccer team as they’re on their way home, versus workers, gardeners, their behavior is also very different. For example, gardeners, their behavior is predominantly breakfast versus the road warrior, predominantly throughout the day, breakfast, lunch, and dinner, because they’re on the road, you know, 12 hours a day. And so understanding these behaviors through creating personas around behaviors really helps you understand how to develop those combos that are relevant to them. And what price point is gonna trigger buy and value for them? It’s really important. But they’re driven by behaviors, and their behaviors are not what happens in the store. Their behavior is how they live their life or how they navigate life, right? So road warrior is about navigating life. It’s the fact that they’re on the road a lot.

Melinda: Right. So when we’re taking the approach too, let’s say a company comes to you and says, “We, you know, need to create some new customer segments,” would you say, is there an ideal approach?

JP: Well, there’s no ideal approach because a lot of it’s dictated by the information that’s available, right? And so very often, we were doing a large U.S. bank, and they had no segmentation, believe it or not, or personas. And so what we did is we went through the industry studies, syndicated studies, and we pulled the data from there, and then we synchronized that with their brand. We were able to kind of look at that data through the lens of their brand to develop these personas that are unique to them. But really, the approach is, you know, doing evaluation of the data that’s available, look at where are those…you know, look at the behavior. Hopefully, the data helps understand, where are those behaviors? If not, as we’ve done in many situations, we will do segmentation based on behaviors and value attitudes. And that will guide us in the development of these personas. And then we use those to test these personas in real-world environments, right? Where we’re testing and getting a sense, what did they buy? What did they shop? What’s their pattern? And you talked at the beginning of the presentation, reward programs. It’s a great platform to mine these behaviors and understand who these customers are. It’s one of those data points that’s very useful.

Melinda: Absolutely. Well, thank you for chatting with us today. And again, listeners, if you’re interested in getting in touch and finding out more, you know where to reach us, info@sld.com.

JP: Thank you.

Male Speaker: For more information about “Think Retail,” you can reach us at info@sld.com. For more episodes, visit us online at sld.com/podcast.