80 — Navigating the New World of Research: A Conversation with Publix Super Markets' Lorin Drake

Lenny: 00:00:00

This episode is brought to you by our friends at Dig Insights. Using decision science Dig Insights helps researchers at the world’s most well-loved brands drive growth in crowded categories. Their work is supported by proprietary technology, including Upsiide, the only ResTech platform exclusively built to test and optimize innovation. Learn more at diginsights.com. Hello, everybody. It’s Lenny Murphy, and welcome to another edition of the GreenBook Podcast. Thank you for taking time out of your day to spend it with myself and my guest. Today, my guest is Lorin Drake, consumer insights strategist from Publix Super Markets. Welcome, Lorin.

Lorin: 00:00:55

Thank you, Lenny. It’s great to be here. I appreciate the invitation. Thank you.

Lenny: 00:00:59

It’s great to have you. As we were chatting a little bit beforehand, I shared is—everybody is sick of hearing me talk about my move from Atlanta to Kentucky, but this is one of those times where, in Atlanta, there were lots of Publix Super Markets, and there are none here in Kentucky currently. I definitely miss that, so this is my Publix fix chatting with you today.

Lorin: 00:01:19

Sure. Understandable. I have that myself too.

Lenny: 00:01:23

Are you in Florida?

Lorin: 00:01:24

I am in central Florida, actually. The map behind me here, which, probably, our audience can’t see but is a map of Florida shipwrecks throughout the centuries. I’m here in central Florida in Lakeland, where Publix is actually headquartered. I’m actually across the street from our headquarters and have lived in Lakeland for about 10 years now.

Lenny: 00:01:43

That is a great area. Now, are shipwrecks—is that a—is that an area of interest for you? Do you dive?

Lorin: 00:01:49

Well, I’ve always been interested in maritime history, and then the state of Florida has a lot of fascinating history as well. The idea that somebody went through the trouble of plotting where ships actually were either captured or capsized or taken over by pirates is a fascinating thing. This map here, you can’t see it, but it basically shows where ships went down in the 16th, 17th, 18th, and 19th centuries for a variety of different reasons. The map shows a lot of them. I think it’s a interesting reminder of the history of the state where I live.

Lenny: 00:02:26

Yeah, yeah. Florida’s a great state. Maybe sometimes we can have you come back, and we can talk about the pirates and shipwrecks and maritime history.

Lorin: 00:02:38

Absolutely. I would be happy to.

Lenny: 00:02:39

There’s a different topic. Natalie—our producer—let’s put that on the list. No research at all, just history.

Lorin: 00:02:46

That’s right. Happy to participate.

Lenny: 00:02:48

Okay. Lorin, for folks who don’t know you, why don’t you tell us a little bit about your background and your bio.

Lorin: 00:02:53

Sure. Yes, yeah. A little bit. I’ve got about 25 years of experience in marketing and advertising. I cut my teeth working for—I used to live in the Mid—in the Upper Midwest in Minnesota working for a media company there and a newspaper working in the marketing department then went to work for an ad agency in Minneapolis. Worked there for eight years. My biggest account there was Target. That’s where I cut my teeth in retail. I was a research manager there doing consumer research specifically for Target. Back in the day when Target was starting to get called “Targét” and we were basically putting—our tagline at the time was “Expect more, pay less,” and we were curating a lot of surprises for our customers by putting upscale items—like a toaster designed by Michael Graves, the famous architect who did the Washington Monument—putting them inside a Target store. Things you would not expect from a discounter. I had the Target account. You may have heard of the Sleep Number bed. That was another one of my accounts. We did infomercials with Lindsay Wagner of all people. I worked on Stonyfield Farm yogurt, Johnsonville bratwurst out of Wisconsin, some really, really great brands in CPG and retail. I cut my teeth there, then left there and went—did a couple years as a management consultant working on, basically, customer satisfaction and voice of the customer programs for a number of blue chip companies, financial sector, retail sector as well. Really loved that. The idea of walking into an organization and having to meet with their CEO or C-suite and having to sort of instantly establish credibility with them. You’ve got about 30 seconds for them to decide if they’re going to like you or trust you and do business with you. That was an interesting experience. Did a lot of traveling. Studied under a very well-known or well-respected consultant and then went to work for Harris Interactive, where I was a loyalty researcher doing a lot of research on customer loyalty like, “Why do customers stay? Why do they leave?” Worked in the financial sector. I did insurance, banking, also did some work for Disney. Did a global study with Pfizer on Viagra, of all things, which was really interesting dabbling in healthcare, trying to understand the unmet needs of people throughout the—throughout the world as it comes—as that drug was gaining in prominence. Then, after that, I decided I was tired of winters and moved to Tampa, Florida. Worked for a boutique consulting firm there and, ultimately, decided that I wanted to be on the client side. The job I have at Publix, I’ve been there 10 years now. I’m in consumer insights, and it’s—I like to say that it combines all the best parts of my previous jobs. I get to dabble in marketing, media research, consumer research, primary research, strategy, and advertising. The job I have now, I love it, because it combines all the things I’ve loved about all the jobs I’ve had previously, if that makes sense.

Lenny: 00:05:51

It does. That was a great bio. A lot of company names that made me smile each time, including “Targét” back in the day. Another store that is not close to me anymore, which my wife holds against me. Anyway, we won’t get into all of that. All right. That’s great background. At IIEX North America in Austin this year, you talked about generative AI, the talking of the buzz topic of the day, with a great presentation titled, “Generative AI and the Researchers Toolbox: Friend, Foe, or Frenemy?” Now, we’re a few months out from Austin. Have you determined yet? Is it friend, foe, or frenemy? What’s your take?

Lorin: 00:06:34

Yeah. No hesitation. Definitely a friend. I know there’s anxiety, and I know not everyone is happy about AI. There’s certain sectors that are impacted. I don’t mean to be insensitive to actors and writers. I was just watching on CNN this morning the writer’s strike in Las Angeles, so I definitely want to be sensitive to those people who are being negatively impacted by AI. I don’t want to be cavalier about it, but—speaking from somebody who works in consumer insights, understands consumers, and needs to understand consumer behavior, and does research for a living—I’m going to say friend because the corollary I like to use is that I’m old enough to remember when the internet came along. I still remember my first day on the World Wide Web and my head exploding with the possibility of all that this was going to make happen for us. When I started in this business, we did all of our research over the phone. I used to travel to our call center in Lincoln, Nebraska, and meet with the serving agents, our field agents who would conduct all of our customer satisfaction surveys over the phone, and I would have briefings with the phone room. When the internet came along, it was a very similar sentiment where people were poo-pooing it. They were saying, “Oh, we’ll never do surveys online. The population isn’t there. It’s a bunch of teenage boys or adult men living in their parents’ basement coding online.” I’m not bragging here, but I was one of the first people to embrace the internet as a tool for data collection. I see this time as very similar. I see AI as another tool in our toolkit, and, like anything else, if you use it responsibly, it will work for you, and it will do wonderful things. To quote a line from one of my favorite movies, Spiderman, “With great power comes great responsibility.” We have to use it responsibly, ethically, morally, legally. Overall, to answer your question, Lenny, I would say that AI is going to be a friend, at least in my discipline and my line of work.

Lenny: 00:08:36

That’s great. I think that my head is in the same place now. I wasn’t sure, like many. Now I think we’re in an area or a time of pragmatic application, and we’re discovering all those tools that the benefits are obvious. There was actually a discussion board that I was on this morning, and folks were asking for prompts to utilize ChatGPT for netting open-ends for analyzing and netting open-ends and utilizing just ChatGPT itself versus the many suppliers out there, Yabble, Canva, et cetera, et cetera, that are doing that for coding, which is a no-brainer application. Of course it’s going to be a friend. Now, on that note, you talk a lot about NLP. Of course, that’s a foundational first-generation aspect of what we think about as generative AI now. I think of generative AI as NLP on steroids. Can you talk a little bit about, especially from that client-side perspective, on how you were embracing these tools over time? You talked about the internet, and, yeah, I’ve been in the industry 23 years, so right there with you. I’ve seen that evolution. Talk a little bit about how you’ve been following this progression of solutions over the years until now, right, where we have this latest iteration of innovation through generative AI.

Lorin: 00:10:07

Sure, yeah. I’m happy to do that. I think one of the reasons why I stay in consumer insights and marketing research was—is because of the rapid rate of change. Right? I started in this business, as I said, really pre-internet. I know it makes me sound like a dinosaur, but I’m actually okay with that. If anything, I have the history, so I have the context, I think. Having done a lot of paper surveys, even number 2 pencils, having done a lot of telephone surveys and all those kinds of things, I think it’s been fun to watch the evolution of the industry and having the industry embrace technology. Take unstructured data, for example. When I say “unstructured data,” I’m referring to all of the noise around what people say. Right? All the spoken language, whether it’s through qualitative research or open-ended comments in surveys. Not structured data, which would be, “On a scale of one to five, how would you rate your local Costco?” or whatever the case might be. In this case, it’s unstructured data, which is messy. Right? Unstructured data is messy. It’s people speaking. It’s all of the intonations that they use. It’s the “uhs” and the “ums.” It’s the—them thinking out loud. It’s them not articulating themselves well. You have to cut through all that noise to get at the insight. Back in my day, it was all about manual coding. I had to look at open-ends from surveys, and I had to manually code them in Excel, group them, lump them together thematically, and then decide, “Oh, these 600 quotes are about customer service, these 600 quotes are about product quality, and these 800 quotes are about store hours.” I had to do that manually, and I spent way too many hours doing that. The excitement, for me—to answer your question directly—is what we can do with NLP, with machine learning, and, of course, now AI. To give you an example that brings that to life is I was at a conference, and there were some people there from MIT, Massachusetts Institute of Technology, of course, and they were talking about how they—one of the projects that they were working on was basically a blender that can be bought, purchased on Amazon. It’s basically a blender, but, like anything else on Amazon, you get hundreds and hundreds of reviews. They use machine learning, but really AI, to comb through those reviews and find, really, the top three pain points. This is something that, yes, you could read them, but, because there’re hundreds and hundreds of reviews, the machine was able to find something different than the human eye was able to. They reported this back to the manufacturer. They made these three changes, and, as a result, their reviews went up at least a point on Amazon. They gained at least a point in terms of positive reviews. I think, to me, that’s the holy grail of insight. Right? Trying to extract both emotion, sentiment, but really quickly comb through the reviews. I’ve noticed now that when I’m on Amazon, they’re no longer posting all the reviews. They post a review summary. If you’re looking at a product on Amazon—I recently bought a helmet for my scooter—and I didn’t have to read all the reviews. There was a summary at the top. That summary at the top said, “Customers who purchased this helmet generally find it to be well-fitting. They find it to be comfortable. The ventilation works well, but occasionally the visors fog up.” There it is. That’s all I needed to know. That, to me, is the power of insight and the power of using synthetic intelligence to quickly mine lots and lots and lots of unstructured data for insights. Something that would take me, as an individual researcher, hours if not days if not weeks to do of painstaking laborious research that AI can do. Some people will ask me, “Well, Lorin, is—your job is going to be replaced.” I vehemently do not believe that. In my line of work, I always believe we will need people to—we still need people to present the data. Right? Last time I checked, AI wasn’t able to give presentations. We still have to decide what’s valuable and what’s not to the business. Right? I work for a supermarket. I understand the grocery business. Last time I checked, ChatGPT doesn’t have intimate knowledge of how the grocery business works. We still have to be curators and gatekeepers of all that information. AI is our—the friend, our trustworthy friend—maybe sometimes not trustworthy, but that’s another conversation—to help us mine through all that unstructured data and help us make sense of it. Does that answer your question, Lenny?

Lenny: 00:14:43

It does, and I could not agree more. Nodding emphatically. Actually, I had an experience last week for—it was the first time that I was work—talking to a client, and they were using an AI meeting assistant. Automatically, I got a summary of the conversation after the meeting, and I hadn’t personally experienced that yet. From the standpoint of, “Okay. Here’s what we talked about,” and it wasn’t a transcript. It was a summary of what we discussed including themes and attributions to specific comments. I had to go, “Damn. That’s pretty cool.”

Lorin: 00:15:26

Totally agree. Yeah.

Lenny: 00:15:28

One of those examples of, “Wow.” In qualitative, that makes qualitative research so much easier to conduct. I think that we’re seeing a scaling of what we would think of as qual, the big bucket of qualitative research, at this point anyway: communities, online focus groups, et cetera, et cetera, because technology’s made those things easier. The sticking point or the, really, the—where—the throughput that constricted production was in the analysis of the unstructured data. Now that has suddenly been removed in a significant way. Well, let me ask you a question then instead of putting out my thinking. Within Publix, within your role, do you envision deleveraging the survey as a primary instrument and leaning more into qualitative and unstructured data as a means of insight generation because now we can do it easier than we ever could before? What’s that mix look like for you?

Lorin: 00:16:35

Yeah. Actually, your question gave me déjà vu, and I’ll explain why. Because I had a similar—we had similar conversations when social media came along. It was all about—I remember when Facebook and Twitter—really, those were the two—but then also YouTube and Instagram as well came along, and I went to conferences, and everybody was abuzz about, “We don’t have to do surveys anymore. We can just do social media listening.” Everybody was talking about listening posts and how we can extract all these insights, what people are saying about Publix and Publix subs and our products and our customer service. “We can get all that from social media, and we don’t have to do surveys anymore.” The short answer to your question is, no, I don’t think surveys—I don’t think we’re going to do any fewer surveys, because the beauty of doing—conducting a survey is the precision of it. Right? You get to pick your population, so that’s the other thing. You get to pick whatever population you want, whatever subsets, whatever quotas. You can set quotas by geography. For example, we have five divisions, so we have a—we have three divisions in Florida, and we have an Atlanta division and a Charlotte division. We can set quotas by division, and then you can be very precise. You said you lived in Atlanta, so I can ask you, “On a scale of one to five, with one being poor and five being excellent, how would you rate your Publix when you lived in Atlanta in terms—overall?” That type of directed effort that we can do, and then, of course, once we have that numeric data, we can run all kinds of advanced analytics on it. Right? We can run driver analysis. We can run regression. We can run MaxDiff. We can use all these different techniques. I don’t think that surveys are going to necessarily evolve or change. I do think that we can introduce more open-ends into surveys and more unstructured data, and we’re looking into that as well, our tracking studies in particular. In the past we’ve shied away from open-ends, because what do you do with the mountains of data that you get? We have 1300 stores and, so, you ask people, “Is there anything else that Publix can do for you that you’re currently not—where your needs aren’t being met?” Well, some people, even if you give them—if you give them 250 characters, sure, but some people will write a novel in that text box, which, of course, is good, but the problem has always been, “What do we do with these—this mountains of unstructured data?” In the not so distant future, I believe we’ll be much—not just Publix but, really, any other corporation out there is going to be much better equipped to analyze terabytes of unstructured data in a matter of seconds. I feel like speed and efficiency and agility, right, as we scale up, we’re going to be able to use qualitative and unstructured data in so much more efficient and exciting ways. The idea of scaling up, that is very exciting to me, but I don’t necessarily think it means that we’re going to stop doing Likert scales. At the same time, I think Likert scales—somebody said they’ve been around for more than 80 years. Yes, there are other ways to collect data, but you can use a sliding scale like, “Here’s an unhappy face on the left and an enraged face on the right. Where are you somewhere in between?” I’ve seen those types of scales before, but there’s a reason why the Likert scale has been around for more than 80 years. Because it’s a highly effective way to capture mood and sentiment. Then, when you get that from 500 people, you can run all kinds of analytics on that particular number.

Lenny: 00:20:11

We’re going to take a quick pause to highlight our podcast partner, Dig Insights. Have you listened to Dig In? It’s the podcast brought to you by Dig Insights designed for brand professionals that crave innovation inspiration. Each week, Dig invites a business leader onto the podcast to spill the beans on the story behind some of the coolest innovations on the market. Search “Dig In” wherever you get your podcasts. I really, really appreciate your focus on pragmatic reality. It’s easy with this particular topic to—and it’s fun to think about the future and what’s possible, but, fundamentally, we are in the business of asking and answering questions. What is the most effective way to do that is what is always going to win. I appreciate you, as I attempt to condense what you said.

Lorin: 00:21:08

Yeah. No problem. These are just opinions. They can be challenged at any point, and I’ve had very fiery debates but always very healthy conversations with other practitioners, like yourself, in the industry. They’ve had different perspectives, and I always say, “Well, I learn more. I love it when I get a different perspective than my own.” These are my opinions. Things could go in a different direction. I’m excited by the possibility of what we could not do five years ago that we can do today in 2023. I embrace these new methodologies and these new tools, and I’m not afraid of them. The worst thing that can happen is that something goes wrong, but then you learn from it. Right? I’m not afraid to experiment, and I’m not afraid to try new methodologies. The employers that I’ve had, I’ve always been very fortunate that I’ve had the support of my leadership in terms of their willingness to try new things and see what happens.

Lenny: 00:22:07

Yeah. That actually leads to an interesting piece that you presented on, and that is that there is a new—a new skill set that’s been introduced, and that is prompt engineering. What have you picked up? What have you learned about the wonderful skill of prompt engineering?

Lorin: 00:22:25

Yeah. I would say it’s like the old adage about if you—I’m holding a smart phone here. It’s my Galaxy Samsung S22 Ultra. It’s my trusty sidekick. The joke is that if you know—you need to be smart to be able to use a smart phone. Right? The user needs to be intelligent as well. That, to me, is at the heart and soul of prompt engineering. That’s why there is a—there is a cottage industry and almost a new skillset. I do remember that when social media—again, I keep referring to that—but when social media first came about, all of a sudden the hottest job in America was social media manager and social media listening. Every conference I went to was listening posts, and I think that is—I’m not saying it has fizzled out, but I think that has lessened to some degree as we all accept that social media is part of our life. The idea of prompt engineering, I think, goes back to the fact that, up until now, computing has been a very static one-way street, in a way. I go to Google. I type in something. Sometimes what I get is exactly what I need; sometimes it’s way off. It’s trial and effort, but it’s a—but it’s not a two-way conversation. It’s a one-way conversation. As I said in Austin, when I was there—thank you for brining that up—was, to me, ChatGPT and prompt engineering, it feels more like you have a—you’re having a conversation with a trusted friend. When I presented in Austin, we talked about an exercise that we went through with research where ChatGPT didn’t get it right, and we had to correct them. They came back, and they apologized. The thing I said in Austin was, “What’s the last time Google apologized to you for lousy search results?” I’m not knocking Google. I love the product. I love the company, but it’s a different relationship. It’s a new beginning. Right? Prompt engineering, to me, is—it comes back to the old adage of—we talked about this a lot—garbage in; garbage out. Right? Your research, your inputs are only—your answers are only as good as your questions. We train our CI team at Publix to ask the best possible questions, because otherwise you’re not going to get the answers you want or that you need. It all starts with the right question. To me, prompt engineering is about trial and error. Artificial intelligence, like anything else, has to be trained, and the end user has to be trained. Going back to my smart phone example, you have to be smart to be able to use a smart phone. You have to be smart to be able to use AI. The idea of what—we actually did an experiment where we had two different prompts in designing—doing a survey design, and we changed one word. The results were completely night and day, 100, 300—or how—180 degrees different. It really is about what you put into it. There’s no surprise that the idea that companies like Netflix are paying $200,000 starting salary for a prompt engineer, because that is the job of the future. I don’t know if that answers your question, but I think—I think there’s a lot there to unpack.

Lenny: 00:25:45

I agree. It does answer the question. It brings up an interesting idea, though, that I don’t think I’d really thought about until this conversation, and that is we’ve been trained with the gooey interface. I’m thinking the example of the discussion board topic that I saw this morning of—to utilize ChatGPT specifically to create a table summarizing nets of themes and open-ended responses. What was so interesting, it was—you had to describe, “Here is the output that I want.” It’s not just a question. It’s saying, “Here’s the issue. Here’s the information that addresses the issue. Here is the output that I want to see at the end,” which is a very different way to think about things in interacting with technology than we’ve had for the last 30 years, which is point, click, or write and not describing the entire—the entire process conversationally. It’s really almost a storytelling skill, and I think that’s a really interesting change in mental framework to be able to utilize these tools effectively. What do you think?

Lorin: 00:27:01

Yeah. Actually, as you’re talking, I’m actually pulling up my phone, because I saw something on Twitter this morning that I’m looking for now that I retweeted or reposted now, since Twitter is called X, but—and it was from OpenAI. It’s actually perfect that you bring it up, because they tweeted—they said, “Our new text-to-image model, DALL-E 3, can translate nuanced requests into extremely detailed and accurate images. Coming soon to ChatGPT plus an enterprise which can help you craft analyzing prompts to bring your ideas to life.” Here’s an example of a prompt. “Create an illustration of a human heart made of translucent glass standing on a pedestal amidst a stormy sea. Rays of sunlight pierce the clouds, illuminating the heart, revealing a tiny universe within. The quote, ‘Find the universe within you,’ is etched in bold letters across the horizon.” That’s a very, very specific ChatGPT prompt. Here is the resulting illustration that DALL-E and ChatGPT made together.

Lenny: 00:28:06

Wow.

Lorin: 00:28:06

That’s very abstract, but it actually looks very concrete. Think about how this is applied to your regular life, Lenny. Think about you have an idea or a vision, and you want to visualize it, maybe, in PowerPoint, and you want to tell your boss about it, or, maybe, you want to take it to the CEO of your company. You have this really fantastic idea, but you want to show what it looks like visually. Imagine going into ChatGPT describing your vision and have DALL-E visualize it for you in a diagram. It’s those kind of things that I now see on a daily basis that are so mind-expanding to the point where you see potential after potential after potential and after potential. In the not so distant future—in fact, it’s pretty much here now—we’ll be able to speak into a computer, describe something very abstract, and have the computer return a vision, an image for us that perfectly describes what we’re thinking. That has so many applications. It has business applications. It has graphic design applications. It has advertising implications. The fact that it can draw that within seconds is really remarkable and astounding to me. I don’t think we’ve—I think, at this point, we are scratching the surface of what AI is capable of bringing to every single profession and discipline on the planet.

Lenny: 00:29:27

Yeah. I think what’s interesting about this—and thinking of our earlier conversation on as an enablement component and the role of humans—is what we’re talking about is taking human creativity and human intellect, human ingenuity, human intuition. Right? All of those ephemeral qualities that happen in our PCs in our heads and having the tool that allows us to funnel that in a whole new way that does not require specific training, necessarily. Obviously, yes, the prompt engineering, but the ability to communicate, whether in written or verbal form, effectively to describe something, to have a conversation and communicate what we’re thinking and feeling and wanting, and for then AI—because it doesn’t create. It takes what we create and presents it back to us. Maybe if we talk a little more, not necessarily in this conversation but even this broadly as a—as a species about this new tool to focus on this is a simply a creative enabler. This is not something that replaces a human in any way, shape, or form. It is limited to its inputs. What it’s really good at is, if we give it the right inputs, it will give us an output that matches that. From that perspective, why the hell not would we—would we not be utilizing these tools?

Lorin: 00:31:07

Totally agree with you.

Lenny: 00:31:09

Yeah. Okay. Well, that’s great. I like it, Lorin, when we have guests that we call agree on the same thing. [Laughs] It can be more fun when you can—when there’s a little contention.

Lorin: 00:31:20

We’re likeminded, but I definitely agree with everything you’ve said. Also, taking a pragmatic approach, I—one of the things I put in my deck in Austin was—and I’ll read it to you—I wrote, “Artificial intelligence can likely match human intelligence and maybe even surpass it, but our consciousness cannot be recreated. There’s no replicating the human condition.” That’s where I draw the line. I believe in synthetic intelligence. I believe that we can ultimately, potentially, maybe pass my time on this planet, but we’ll have family members or even synthetic humans who will live alongside of us, kind of like Blade Runner. I believe that that’s reality we’re ultimately headed for. I think there’s huge implications for creating that, but I do think that that’s where AI ultimately will be where we’ll have a friendly companion or think of a butler or somebody who helps you answer questions in your house, just like we would turn to a smart speaker now. Eventually, I think we’ll have somebody who is in our home, probably run by a corporation, and that’s where you start to get into dystopian sci-fi movies and things like that where they start to take over. I do feel like that future is a reality for us, but I also think it’s a good reminder to know that consciousness, the feeling of love, the feeling of pain, all the things that make us human, the idea of self-awareness. Right now, I’m a sentient being. I’m aware that I’m talking to you. I’m aware that I’m a podcast guest. I’m aware of my surroundings. I sense my temperature around me. I may be feeling thirsty or hungry, or I may need to go to the bathroom, and I’m aware of all those things either at a conscious or subconscious level. That, I don’t believe, can be duplicated. One of the things we have to remember is that we as humans are the creators of all of this, or we’re the ultimate decision-maker. I think the sci-fi nerd in me asks the question of what happens when the—those who create become more powerful than the creator? That’s a much larger question that I possibly cannot answer that people who are infinitely smarter than me are going to have to answer at some point. At the end of the day, even though we’re talking about artificial intelligence, what I think is more clear is that my faith in humanity is completely unshakeable. I believe in the human condition. I believe in the human species. I believe in the human race, and I believe we’re capable of anything. I may be naïve, and I’m willing to—I’ll take that on the chin, but I believe that human beings are vastly superior to anything synthetic that we can create ourselves. Someday I might be proven wrong, but, until then, I’m going to maintain that position.

Lenny: 00:34:08

Well, well, well said. We are definitely likeminded. Lorin, we need to chat at some point. I think that we would have—

Lorin: 00:34:16

—Happy to.

Lenny: 00:34:17

Just BSing. I think there’s a lot that we could have fun talking about, because I’m a—I’m a big sci-fi geek. All of those topics are things that interest me intensely. On that note, actually, I can’t think of a better way to end a podcast than on that incredibly uplifting note. Let’s wrap up. Is there anything that you wanted to get into that we didn’t?

Lorin: 00:34:40

No. I think we covered a lot of ground, I think. I wanted to mention that I think—I think, if you are in consumer insights or if you’re in marketing, people who are starting out earlier in their career—I’m 25 years in. I’m closer to retirement than I am to starting my career, but I would say anyone who’s starting out who wants a career in consumer insights or marketing, I would say that you’re entering at a really exciting time. There’s so much to learn. The reason I stay in this business and don’t try to find another job in another area is because I learn something new every day. I know that sounds like a cliché, but it really, really is true. Every single day, I learn something new. I expand my skill set. If you are a young person who is considering a career in either marketing, consumer insights, or advertising, I would say this is a fantastic time to embark on a career. The industry has been great to me, and, so, I definitely want to return the favor and give back to those who gave me a leg up and believed in me when I was younger. That would be the one thing I’d like to convey to you and to the listeners today.

Lenny: 00:35:47

Yeah. Could not agree more. Again, we’re simpatico here today, my friend.

Lorin: 00:35:53

Very good. Very good.

Lenny: 00:35:54

Where can people find you?

Lorin: 00:35:56

You can find me on LinkedIn. I’m very active on social media. I’m also on Twitter and Instagram. Probably the best place is to look me up on LinkedIn. I have never turned down a connection request. I accept all connection requests. Now, if they start spamming me afterwards or it feels like it’s a bot, I’ll probably disconnect, but any human being, if you’re a human and you’re somehow in this industry or even loosely connected to the industry, I will accept a connection request from you. Lorin Drake. It’s Lorin with an I. Drake is my last name. I work for Publix Super Markets in Lakeland, so I’m pretty easy to find.

Lenny: 00:36:32

There we go. Well, hopefully we won’t get many AI avatars trying to connect, but—

Lorin: 00:36:38

—Believe it or not, I’ve gotten a few. I’m sure you have too.

Lenny: 00:36:41

I have as well. I’ve also seen them in survey responses, but that is a—that is a whole other conversation. Yeah. Lorin, thank you so much. It was a delight.

Lorin: 00:36:51

It was my pleasure.

Lenny: 00:36:52

We will chat again, I’m sure. Enjoy Florida. We really appreciate your time. Thank you to our producer, Natalie, our editor, James, our sponsor, Dig Insights, and, of course, thank you our listeners. We appreciate you taking time to spend it with us. That’s it for today. We’ll be back soon with another edition of the GreenBook Podcast. Bye-bye.