Just a Spoonful of Honey (Guest: Terry Young)
Just a Spoonful of Honey (Guest: Terry Young)
Our guest on this episode started a company with one of the best names ever! Terry Young, the founder and CEO of Sparks and Honey, has created a very different way of approaching cultural trends. Sparks and Honey’s active learning system, Q, brings top-grade data science and machine learning to the art of trend spotting and understanding how our culture is changing. I sat down with Terry at his Madison Ave headquarters in New York City.
Ben: Welcome to the Masters of Data podcast, the podcast where we talk about how data affects our businesses and our lives. We talk to the people on the front lines of the data revolution. I'm your host, Ben Newton. I love creative business names. Our guest today started a company with one of the best names I have ever seen. Terry Young, the founder and CEO sparks & honey, has created a very different way of approaching cultural trends. sparks & honey's active learning system, Q, brings top grade data science and machine learning to the art of trend spotting, and understanding how our culture is changing. I sat down with Terry at his Madison Avenue headquarters in New York City. Without any further ado, let's get started.
Ben: Welcome everybody to the Masters of Data podcast. I am excited to be with Terry Young, who's the CEO of sparks & honey. Appreciate you taking the time, Terry.
Terry: Yeah, happy to do it. Thanks for having me.
Ben: We're sitting in your office here in New York City.
Terry: We are.
Ben: [inaudible 00:01:06] just walk in, I totally start getting the sense of who you guys are as a company. I'm definitely looking forward to talking about that. I think I brought a little bit of the heatwave with me. It was hot in there.
Terry: It has been hot here the last week or so.
Ben: Yes. I forgot what humidity's like, living on the West Coast. I'd love, Terry, to start off and just get to know people and about your background. Tell us a little bit about who you are. How'd you get to where you were, and what led you to the point, you know, being here, running sparks & honey?
Terry: Yeah, sure. I mean, I'll give you the short version. I grew up in Kentucky, so southern boy. Made my way to Texas, went to UT Austin, studied advertising there. I got involved in what was happening with the shifts around internet and the latest internet technologies when it was just first starting to take shape. Then I took that and went into the advertising business, working for a couple of advertising holding companies, mostly always in the strategy, data, [inaudible 00:02:08] world with a digital overlay. Did that for a while, and took a little stint at McKinsey management consulting in Greater China. Came back to the U.S., and after a bit of time, continued in the holding companies.
Terry: I just decided to take a break. I left ... At that time, I was with Omnicom, which is one of the ad holding companies. I took a summer and wrote the business plan for sparks & honey. That was about six and a half years ago, and so you just walked in. So here we are now, six and a half years later. When we first wrote that business plan, it was a pretty pioneering idea, you know?
Ben: Where'd the name come from?
Terry: sparks & honey-
Ben: I love the name, by the way.
Terry: Everyone asks that.
Ben: I was sold when I saw the name. I was psyched about talking to you guys.
Terry: [crosstalk 00:02:59]. The idea of the name is the spark is the little spark that you see in culture, something that's just starting to ignite. So back to our conversation that we'll have around signals. And honey, that stickiness between organization and that spark. We wanted to try to look at how you identify something that is first emerging, and then how you turn that into an opportunity for an organization.
Ben: Oh, that's pretty cool. I'm from the south too, so is it ...
Terry: Where are you from?
Ben: I'm from North Carolina.
Terry: Oh, North Carolina. Okay.
Ben: Yeah. When I saw honey, I immediately went to honey and biscuits, but that's [crosstalk 00:03:32].
Terry: That too, you know?
Ben: Where are you from in Kentucky?
Terry: A small town in Kentucky called Leitchfield. It's western Kentucky. Grew up there, and made my way out and ...
Ben: My grandad's from Kentucky.
Terry: Oh, really?
Ben: So got a little bit of history there.
Terry: I've been reading the book, have you read Hillbilly Elegy?
Ben: Yes, I read part of it. Yeah.
Terry: Yeah. It's all about the migration out of Kentucky. Really interesting.
Ben: Yeah, yeah, no, I find that really interesting. When you started sparks & honey, what was the problem you were trying to solve? As part of that business plan, what gap did you see?
Terry: Yeah, so a couple things. Number one, I saw flaws in the advertising model in general. The way that advertising had been constructed, the business model, was really a media-driven model where you would build out these big campaigns. It was still fairly TV focused, though digital was still becoming a much, much larger part of what people were doing from campaigns. But it really didn't use a lot of the advantages that you see in other industries. When you think about where the world was going at that time, you were starting to have access to data that we hadn't had access to before.
Terry: We were starting to have technologies start to emerge that allowed us to be much quicker in our decision-making. I looked at a lot of the advertising models and saw them kind of slow, kind of dinosaurs, in a certain way. I said, "What happens when we start getting more data on consumers?" We have technology to make sense of that data even quicker. We can work less like maybe a traditional advertising company and more like the newsroom at CNN, where it's fast-moving, based on what you see with the signals. The very initial premise was trying to break that mold of kind of a slow-moving agency, and bring that fast way of understanding culture into the agency, as opposed to being campaign-driven, be real-time in nature.
Ben: So kind of an older model, because a big part of what you guys do is try to observe culture, right, and understand what's going on?
Ben: So how would an older agency model have done that? How would they observe culture?
Terry: Well, in older model, there's still a lot of agencies that work exactly this way. I mean, you're going to do some traditional research to try to understand consumers. You're going to get a bunch of creatives and planners together, and you come up with sometimes data-driven ideas, sometimes gut ideas. Those ideas become campaigns that are out in the marketplace, and juxtapose that to a system and a set of tools that allow you to understand consumer behavior in real time, observing those shifts that are happening in culture, and based on those shifts, quantifying them, and based on that quantification, determining what those next campaigns can be. That's where we started. You ask about where we ... We started in that model. I would say probably two-thirds of our business today is not focused on advertising and marketing. It's focused on innovation, kind of where the world is going, and what does that mean to big, large Fortune 100 companies. What's the next business model? What's the next ingredient I should think about for my product? How do I plan out my roadmap that's five, seven, 10 years out?
Ben: Yeah. Well, one interesting thing when I was planning to talk to you, you and I talked about the book, Sensemaking, before. I was planning to talk to you and Christian a little later, is one thing he says is that a lot of these companies doing things with data without cultural context leads to bias. Do you feel like that's part of what you guys can help some of these companies with, is to understand how their products and their brands perform outside of their traditional areas, when they're going international or they're going to groups that maybe they're maybe not as familiar with? How do you think about that?
Terry: I mean, it's a fantastic question. I think bias has become a very important topic, in particular after the election. Right? I mean, every big brand in the U.S., in particular, and probably even on a global basis. When you think about Brexit, you think about Trump, people started to ask the question of, "[inaudible 00:07:36] groups or micro tribes or these cohorts that we don't understand," and, "Are we making all of our decisions without understanding those groups?" I think there's two things to answer your question. One is, when you think about culture, culture is representative of all people, and it's good, it's bad, it's underbelly, and it's most important things. You can't judge culture. Culture is what culture is. Right? Our job at sparks & honey is to build systems to understand culture in all of those different iterations.
Terry: When you build a people-driven business, and even sometimes when you build technology-driven businesses, you can build biases into those systems. One of the things that we've tried very hard to do, is we built a structure that looks at cultural tensions. Tension spaces are very important because if I say ... I just use the example of, people want to be constantly connected to their technology. You have your phone in front of you, and you're talking about how it can do all these different things. It's 24/7.
Terry: The other side of that tension, and one simple way of thinking about cultural tensions, is digital detox, people needing to break away from technology, being overwhelmed by technology. Both of those things exist in parallel. There are people that lean a little bit more on the left, a little bit more on the right. There are people that oscillate between those two spaces. When you were thinking about the world and how to operate in the world, it's very important to understand the binaries, and understand what those tensions are. Number one.
Terry: Number two, if you are just doing traditional research and you're saying, "I'm going to conduct a focus group," and you go out and you use only that focus group and you haven't understood the tension, you may be only conducting research on one side of the tension. So there are things that we knew early on in constructing sparks & honey, that we have a ton of bias because of the way we're built. We have a geographic bias because we're only in a few big East and West Coast cities. We have an educational bias because we don't represent everyone that culture represents.
Terry: To fill in some of those gaps, we've used technology. One example is building persona bots. These bots look more like the people that we would never have at sparks & honey. We want to know what someone from ... We were just talking about Kentucky ... someone that's on the assembly line working in Appalachia thinks, or someone who's a coal miner, or someone who has very different views than the employee base we may have at sparks & honey. Without having that, you can run the risk of having bias in the way you think about the world.
Ben: Just the word persona bots [inaudible 00:10:17]. I mean being in marketing, I talk personas all the time. I guess [inaudible 00:10:23] transitioning technology, you guys have got this system that you use called Q, right?
Ben: Is the persona bots part of that whole system?
Terry: It is. The system basically consists of four layers. One layer is what we call our elements of culture. It's a taxonomy that allows us to make sense of the chaos of culture. Right? So you see something happen in culture, well, you need to connect that signal to something, some language, some way of behaving. That's what the elements of culture do. We break culture down into microtrends, into macrotrends, into megatrends. Microtrends are those things that are happening around us every second. Macrotrends are things that are going to impact organizations, one to three years. Megatrends are much further out. That's one piece of it.
Terry: The second piece is, we use a lot of different data inputs in a lot of different platforms in order to analyze culture. We have our own platform, which is signal-oriented, but we overlay data from Bloomberg, from CBN sites, from Quid, and 20 plus other tools. Both of those are signal-based and platform-based. There's also the human side of it. This is where the social sciences part of understanding what's happening in the world becomes very important. We're basically triangulating data science and social science together. We have what we call our human network which is one side are 40 plus CEOs and well-known individuals in their particular industry that allow us to validate and understand what may be happening in beauty, artificial intelligence, memes, so forth and so on. Then we have scouts on a global basis that are basically curious individuals that are monitoring what's happening on a geographic level or an industry level. All of that is getting consolidated back into one centralized platform. That platform for us is Q.
Terry: Then what you are going to see in about 40 minutes is something we do every day, which is our daily culture briefing. In the daily culture briefing, we bring everyone in the entire company together, and we host about 200 guests a week. We come together, and it's a crowdsourced hour of pattern identification. We always are at the horizontal, meaning that we may jump from something coming off of Reddit, to a pattern, to a meme, to some new academic research, so forth and so on. What our teams are trained in, they're trained in automatically understanding where those small signals cluster against our elements of culture. Then they are looking for patterns that you may typically not see. When you do that every single day, you basically begin to build muscle memory, and that muscle memory allows you to see things that you may miss otherwise. It's almost like you can train machines to look for patterns. You can also train people to look for patterns. The beauty of sparks & honey is, we bring those two things together.
Ben: That actually is an interesting point because being in a data field myself, I think one of the big struggles with data analytics, in general, is where the human and the machine meet.
Ben: It sounds like part of you guys have been doing is really about trying to solve that. So just having all that data that you guys put in, if you didn't have humans involved with that, it wouldn't be nearly as useful, right?
Terry: That's the idea at sparks & honey, is that we have all this data, we have all kinds of amazing tools, but it's the social sciences that we lay over that which makes it so compelling. Because we're using those tools and those data inputs to narrow the world of culture into what we think of as like this cone of possibilities. These are territories or areas that we could invest in, or that could be the next big product, or that could help us understand the future of autonomous driving, whatever that is. But you still have to place your bets somewhere. There's still a human element to it, and that human nuance, I think, is critical. When you don't have that, I think sometimes you have the blinders on. That's where bias comes in, and that's where you may not really completely understand both sides of a tension.
Ben: Well, actually, an interesting question came to mind. As the CEO of the company, a lot of what we talk about is these new kind of skill sets emerging, right? Now we have very much a data-driven economy, right? I mean, one of the things I loved about meeting you guys is how you're kind of on a different part of the spectrum than where [inaudible 00:14:50]. I'm thinking about the cultural data. How do you train your team and look for people that are actually ... I mean, what kind of skills are you looking for in those group of people that are doing that, you know, looking for those trends and spotting those patterns? What kind of skills does that involve?
Terry: Yeah. Great question. There's a couple of things that I think are fundamental for someone working at sparks & honey. One, we look for individuals that showcase a high level of curiosity, and curiosity in all aspects of their life. Right? Those are individuals who when you talk to them, they are doing really interesting things. They have side gigs. They're just on their own. They're exploring what's next, what's in the future, so forth and so on. That's one piece.
Terry: The second is, people tend to come from two or three different areas. Almost everyone here comes from some kind of strategic background, meaning that they are either coming from consulting firms, they're coming from startups, or they're coming from some type of planning or futurism type of discipline. They come from those areas.
Terry: Then the other piece is having a deep understanding of the social sciences, so anthropological research, ethnographic research, understanding psychology and sociology. Those disciplines really round out the idea of purely just being data science based. We have plenty of people who are also coming from the data science, machine learning disciplines, but we always want to mesh together the social science part of it.
Ben: Do you have a hard time finding people that have both of those? I guess, that's part of the argument going on in the industry right now, is that ... I remember, I was a physics and math undergrad, and there wasn't a lot of time for humanities classes. I regret that now. Looking back at it, I wish I had spent more time on that. Is it hard to find people that have that mesh?
Terry: Yeah, definitely. We don't find people that have it all day one. I guess what I would say, is that we hire people that come from those purist data science backgrounds. We get people who are coming from strategy, futurism, entrepreneurial backgrounds, and then we have a pretty robust training program. Most people that come through here, I would say, it takes probably six or eight months to really master ... I don't know if master's the right word, maybe it takes longer than that to master ... the tools, the methodologies, the frameworks, because we have created all new frameworks, all new methodologies, new research practices. We have our own tools that we've created. So someone coming in, just because you've used a tool in the past, doesn't mean that you would ... It takes a while to ramp up on that.
Terry: So I think part of what we do, is bring those two things together in the way we train people who come into sparks & honey. I think you'll see in the briefing today ... And this is kind of an amazing thing about the organization ... is the briefing you will see, we do it every single day for one hour. You'll see every single person in the entire agency is in that room. There'll be like eight to 12 people that'll be the cast members and two briefers up front. But anyone in sparks & honey can participate in that briefing at equal levels. It's really, we democratize the way you think about interactions between levels. We could have a CMO of a company, a CEO, someone from the UN, someone the Department of Defense, and our intern, and they all sit beside each other and they engage in the conversation, where they unpack the trends.
Ben: Yeah, that's really cool. Yeah. I mean, after watching a few of those, I think I haven't really seen something exactly what like what you guys are doing here. I think it's really interesting. Saying that, now you've been running Q for what, like four years now [inaudible 00:18:21]? Or ...
Terry: Well, sparks & honey has been around for about six and half years. I would say for the first two and a half years, we did a lot of the analysis manual and kind of low-tech using a lot of outside technologies. Three or four years ago, we started building out our own tools. We launched Q officially probably two years ago.
Ben: Okay, two years ago.
Terry: We were using it unofficially before that. Now, we're building out derivative products coming off of Q. Q is used by our consultants on behalf of mostly Fortune 100 companies. So you'll have a large company will use Q, and then we'll help guide them on their path for the future. We now have begun to launch other SaaS products. We have one called [Living Segmentation 00:19:13], which maps culture back to your segments. And then we're building out a version of Q that will be a SaaS product as well.
Ben: That's cool. Now that having had a few years of running Q, I mean, what are some of the things that stand out to you that you've learned over the last couple years? Because I've looked at a couple of your reports. They're fascinating. I mean, you guys have really done some interesting work. I mean, what stands out to you over these last few years?
Terry: Well, there's probably a couple of things. One, that over the four or five years that we have been running the briefings and running sparks & honey, basically what we did, is every single day monitored culture. We tagged culture. We tried to make sense of something that is extremely complex and variable and chaotic. That became some incredible training input into Q. I mean, because now of a sudden, I can see the sharing economy or eSports or any of these things that have emerged over the last five or six years, and see how they shifted over time, the language that people talked about them when they very first started to emerge, and how that changed. So that was one of the best things, was understanding how the methods by which we create it could help fuel a tool like Q.
Terry: Second thing is, is to just see some of what Q is able to extract. I tell the story, which is probably four and a half years ago. We were observing people on YouTube, watching other people play video games. Right? When we see anything like that, you'll see in the briefing today, we'll say, "Oh, that's an interesting behavior. Let's go do some research on that behavior. Let's get an anthropologist to go monitor that," and we watch it over time. Then you're watching it every single day. It was probably like four or five months later, you know, Twitch launched and you started to see everything happening with eSports. Then fast forward a year and a half, we had some executives in the telecommunications industry in, and we're telling them about Twitch and eSports. People still didn't know what it was. And you fast forward to today, and it's a whole industry.
Terry: That's kind of the magic of what Q is. It identifies things when they're very first emerging. They're the kinds of things that without quantification, they would be very easy to dismiss because they probably reside in a small micro tribe. They're probably incredibly fringe. But what you see is the building momentum, gaining velocity, and then you can track that over time and understand how language changes, how consumer behavior changes. And then you can determine how you translate that into an opportunity.
Ben: What do you feel when you go to businesses with this, that they ... I mean, your example there is a great example, because being able to kind of anticipate something like Twitch coming along and now kids are spending an enormous amount of time watching video games online. How do you feel like businesses respond to that? What's the challenges you have in convincing businesses that this data actually ... that the trends you're spotting are real?
Terry: It's a great question. I mean, I think it's the same challenge that businesses have in understanding that things are changing and could disrupt their organization. I mean, from Kodak to Instagram to Blockbuster to Netflix, it's a very similar process in that what we're doing is we're quantifying culture, identifying things that may be small today, but that can be a tidal wave to an industry tomorrow. Right? Some clients are incredibly receptive, some are very incredibly dismissive, and you have everything in-between.
Terry: The thing that I would say, is that we live in a world that is ... the rate of change is accelerating. I think most CEOs and CMOs feel under pressure because the rate of change is happening around them. They've seen whole industries change. You look at what's happened with CPG industry. I saw a great slide over the weekend looking at the Bank of Amazon, and looking at how Amazon is completely disrupting the financial industry. These things are real, right? They're out there. They're creating [crosstalk 00:23:16] ... Yeah, exactly. So I think a lot of Fortune 500 CEOs are looking for guidance that allows them to have a different methodology for understanding the way change happens, and to get out in front of it. People are much more receptive today than they were when I started the company six and a half years ago.
Ben: [inaudible 00:23:39]. What do you think has changed, if there's a recognition that the technology can actually help, or that they're feeling more overwhelmed with the changes going on? Or ...
Terry: I think it's both of those. I think there's probably a whole series of things. I think that the number of examples of industries being disrupted has probably increased. I think almost all those are driven by technological change. I think that people now, you know, C-Suite executives are asking themselves the question of, "I don't want to be on the wrong side of disruption. I want to be on the right side of disruption. Therefore, how can I leverage technology? How can I leverage new data sources in order to rethink about my industry?" I think they're more seeking it out, as opposed to putting up the barriers.
Ben: That makes a lot of sense. It reminds me of a conversation I was actually having with somebody just last week, is that when you think about trends, if you hop on a trend when the trend's already big, then you basically just ... You don't get it. You're basically coming in and you're pretending to be part of a trend that you really don't ... Whereas if you can get on a trend early and have a voice in that early, then you actually have the ability to perhaps even guide that trend, but definitely be a part, and actually have a real perspective people respect, maybe is the word I'm looking for, is that the people that are part of that will respect you.
Ben: Whereas if you come at the end ... Like in our industry, it's things like technologies that come around like Kubernetes or these technologies that [inaudible 00:25:12] up. Everybody's that's jumping on that train now, they seem like ... I don't know ... posers. You know what I'm saying? It seems like that's part of what you guys can do, is if you can identify those trends early and then these business leaders can actually make bets on a few of those and they actually have the opportunity to be part of those trends and nurture them instead of being surprised with them and then kind of, you know.
Terry: Yes, but it varies a little bit based on the company and the industry. There's some companies that are very mass market oriented that are trying to drive huge volume at scale, you know, a QSR company like Mcdonald's, right? So catching a trend at the top of the wave might be really interesting for someone who needs to drive volume at scale. Then you take a startup who's trying to disrupt. They want to find something very early. But sometimes when you find something very early, it doesn't have scale, so you have to balance those two things. We were working with a beauty client, and we were looking at the time frame to get product to market. There is a beauty startup called Milk. They launched a cannabis mascara. They basically infused cannabis into the mascara, sold it at Sephora, and they took the cycle to launch down to like four months. The typical cycle of identifying an opportunity in the marketplace and getting it on shelf is 18, 24 months for the really big houses that are producing new products.
Terry: That's a good example of, it may not scale to the level of a traditional flagship product, but this idea of being able to identify something very early, but then not only being able to identify it, being able to have the infrastructure operationally to do something with it, not every company can. That's why startups tend to be able to be much more nimble on turning those trends into something really fast, because you take a very large corporation, they may sometimes struggle in reducing the manufacturing cycles.
Ben: Oh, okay. I see what you're saying. Because if you're moving too slowly, then the trend may already have [crosstalk 00:27:17].
Terry: Yep. That's what happens. You see some of these big companies, they grabbed the trend, by the time they can get it on shelf, it's old, not interesting. Sometimes, what they do, is pick up something that is really going to be around for a long time that has more scale and that works better for them. Right?
Ben: Yeah. Well, I mean, I guess that's part of one question I wanted to dig in a little bit, is that if you're identifying all these trends, are you recommending that your clients find a couple to focus on? Or do they tend to say, "Okay, here's a [inaudible 00:27:48] I'm going to experiment and kind of get a feel for all of them, and then ..." Because [inaudible 00:27:53] with a system like this, you could actually get overwhelmed with the trends too.
Terry: Yeah. Well, the system is designed to narrow to like a set of trends that are quantified and manageable and relevant to a particular organization. We take the world of culture, narrow that down to the impact on an industry, and then to an individual brand within the industry. Right?
Ben: Oh, got it. Okay. You filter by the brand. [crosstalk 00:28:16].
Terry: Here is what it means to you, insurance company or automotive company or telecommunications company, and here's what it would mean to your audiences, and here's how you put a pro forma around that and begin to understand that. The other question that you ask, it would vary by client. Some clients want to experiment. They're like, "Let's try different things and put things out into market and see what sticks." Other clients want to take something that already looks like it has an upward trajectory and has already been somewhat proven. It really depends on the client to the way they execute in market.
Ben: Yeah. That absolutely makes sense. I guess then kind of to wrap up here, I mean, what you guys are doing is super cool, because like I said, you sold me on the name. I'm like, "sparks & honey, I got to talk to these guys."
Ben: [crosstalk 00:29:01] But then also when I looked at your system, you know, what you guys have built with Q, I think it's pretty amazing. You usually don't hear about ... At least where I sit, you usually don't hear about that kind of analytics and data work outside of pure technology companies. I think what you guys are doing is really cool. What's next? Where do you guys go from here? What's your next big hill to climb?
Terry: Yeah, great question. I mean, there's a couple of things that we're doing. One is, we were born in advertising, right? sparks & honey rose up to a holding company called Omnicom. It's one of the big, big holding companies. But we really are less focused on impacting marketing communications, and more focused on impacting the enterprise. We would see ourselves as a different methodology to tackle exactly the same problems of some of the big consulting firms. McKinsey, Bain, and BCG, Accenture, the kind of things they're tackling with their clients, we want to use the system to get a glimpse and make change visible and understand where the world is going. So that's where we're pushing from a consulting standpoint.
Terry: At the same time, we built some pretty incredible technology. The technology has primarily been used by us on behalf of Fortune 100 companies. We now want to take that technology and democratize it and turn it into a product that we can get out to more than just the Fortune 100 at a price point that makes sense, so that other people can use that in order to make sense of what's happening in culture. Those are kind of the two areas. Both of those are designed to help us scale, but also impact organizations outside of just marketing communications.
Ben: That makes a lot of sense, because I think what you guys are doing here could have some really broad implications beyond just that typical kind of brand marketing. I think that what you guys are doing here is really cool.
Terry: Yeah, I appreciate it. Well, thank you for taking the time and coming in to sparks & honey and visiting.
Ben: Absolutely. I'm excited about the cultural briefing. I've been looking forward to that for couple weeks now, so thank you for your time, Terry.
Terry: No, thank you.
Ben: I appreciate it.
Terry: Yeah, we'll talk soon.
Ben: Thanks everybody for listening in. We'll see you on the next episode.
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