Speaker 1:	Welcome to Ignite, an original podcast from Design Sensory Intelligence. This is a podcast for business pros like you. From sports and entertainment to travel and tourism, financial services to economic development, and more, we uncover relevant timely information that will help keep you at the four of consumer behavior understanding. Our host, Chris Wise, the brains behind Ignite, has been deeply committed to research insights and innovation for over 30 years. He knows the right questions to ask, and more importantly, what to do with the answers. Get ready for the engaging in-depth conversations with industry leaders that will inspire you to take action and connect with your audience on real human terms. This is Ignite the spark to light your fire.
Chris Wise:	Welcome to Ignite where we have the opportunity to talk with subject matter experts about important and compelling markcom issues. Specifically, we delve into incredible tools for audience identification, behaviors, and ways to communicate with them, allowing for total engagement. Today, we are pleased to be joined by Dave Nugent, vice president data solutions with Aspire North. Dave, welcome. Glad you're here.
Dave Nugent:	Thanks, Chris.
Chris Wise:	Through Dave, we've been working with Aspire North in the development and execution of wildly successful direct marketing campaigns. And when we started working together, we knew it was going to be a long and mutually beneficial relationship. The first project alone produced an increase of 800% in qualified leads for one of our clients, and that is with people having at least $1 million in investible assets. We learned as much as we could about the nuances of the audience, which led to the development of a killer creative strategy, topped off with pinpoint precision in the audience database. Everything worked together as it was intended.
Dave Nugent:	Yes.
Chris Wise:	So Dave first, tell us about yourself, your background, your position and responsibilities, how you found your way to Minneapolis, and what your day to day looks like now compared to two years ago.
Dave Nugent:	Yeah. Thanks, Chris. So I got into the data business almost by luck. I really didn't know what I wanted to do coming out of school years and years ago, and I was recruited by a gentleman who was working for one of the big data compilers at the time, a company called Info USA. They've since been rebranded as Data Axle, but really had no idea how data was used in marketing, but I felt like it was an interesting company. And so I decided to give it a shot and loved it. I got in there and started learning about data and how it's used for creating targeted lists for mail. At the time, that was the primary channel for targeted lists, how it's used for analytics, how it's used for customizing messaging, all that fun stuff. And I was there for a couple years before I decided I wanted to become a little bit more impartial in the types of data that we provide.
	So I started my own company at the ripe age of 24 and made a career of providing lists and analytics and developing some proprietary and patented technologies that we can probably talk about a little bit later, but found my way to Minneapolis, because about 10 years ago, I sold my two data companies to a top 100 printer in the country named American Spirit Corporation, headquartered in Minneapolis. They're the parent company to American Spirit Graphics and Carlson Print Group, and several other organizations, but it was an interesting movement at the time. 10 years ago, all these new marketing channels were emerging and marketing budgets were being fractionalized. And so people in the print industry wanted to figure out what was their next move. And American Spirit Corporation took a liking to data. They thought, how do we make print more purposeful? How do you target more effectively? How do you customize the messaging to be really relevant? How do you measure performance on the back end and make little tweaks here and there to find incremental gains?
	And so they loved data. They liked how we operated as an organization, so they brought us into the fold. It was about five years in where I was asked to come up north to Minneapolis to be with most of the sales people in the organization to help them position data more effectively. It was a fast moving industry. There were a lot of changes going on. And so I was brought up in a leadership role to help educate the crew on data, how to position it strategically and help guide a lot of sales presentations. So, yeah, that was about five, six years ago. I love it up here in Minneapolis. It's a great organization. It's a great state, great people, and haven't looked back since.
Chris Wise:	So data is your middle name, right? And that's just what we'll call you, although that's kind of science fiction. [inaudible 00:05:00]. Anyway, how have things, just in the last five years, the evolution to where we are today in database management?
Dave Nugent:	Well, a ton has changed. In one way, the application of data has grown tremendously. When I got into the business, this idea of buying a mailing list... Actually, you never buy it. You rent it. You rent the data from one of the big compilers, like an Info USA, and you use that rented data to power a targeted marketing campaign. And back then, that usually meant direct mail. Then came email, and then it's just blown up. We live in this world called addressable media. All media is pretty much addressable. If you have a physical address, you can send media to them. It doesn't matter if it's through social channel. It could be programmatic digital display. It could be connected television, obviously direct mail and email. So all of these channels, now you're able to send messaging to individuals in households. And so that was a huge change, and that really opened things up for us data folks.
	We were able to say to our clients, "Look, you have the ability now to create these hyper targeted audiences using all of this rich data that's available, thousands and thousands of data sets that allow you to pinpoint the types of consumers or businesses you want to reach. And now you're not limited to just direct mail. You can use every channel under the sun, pretty much. Full omnichannel is the world we live in now." And so that was a big change. It's just the proliferation of channels by which marketers can reach their targeted prospects and customers. But then, I think the biggest change has come in the form of data security and data privacy, and it was definitely needed. You hear of GDPR and CCPA. These are all laws and regulations that have popped up that I think were needed. I think there's a balancing act between being invasive, intrusive and being responsible and making sure that your marketing messages are relevant. The last thing we want is marketers to have a lot of waste out there.
	And I'm actually talking physical waste. Yes, direct mail that's mistargeted is wasteful when it comes to environmental issues and monetary issues, but we just don't want to have mismatched messages going to people, because it's irritating. So you have to strike a balance between having good, relevant content being targeted... Because actually, consumers want that. They want to feel like businesses know them, but you can't cross that line. You can't imply that you know too much about them, because that becomes creepy marketing. So that's one of the biggest changes that I've seen over the last couple of years, is how do you maintain this ability to hyper target and have really relevant messaging without crossing that line and being creepy and getting people thinking that their privacy has been totally invaded.
Chris Wise:	That's an interesting point. And it's not an issue that's going to go away. In fact, it's heightened as more and more privacy controls and legislation we put into place. With that in mind, how do you stay at the fore of that understanding to make it all work and ethically sound?
Dave Nugent:	Absolutely. Yeah. You need to make sure that your good stewards of the data. And one thing that I hope we get to talk about a little bit later is how do you put these controls in place, especially with companies that have first party data? First party data... I use that term pretty frequently, so I forget maybe not everybody knows what first party, second party, third party data is. First party data is basically in any organization's customer data, their in-house data that is so valuable, not only to the organization themselves, but making those insights available to other organizations in a totally compliant sort of way. Because if you can glean insights from other people's data to make messaging and services and offers more impactful for the population, you're doing everybody a service. You do it in the wrong way though, and you've crossed that line. So it's an interesting environment we're living right now, but believe me, most people in the data business understand the need for these controls. We don't want there to be some of those bad apples out there that's going to spoil the bunch for everybody.
	It takes one over the top creepy marketing campaign to get people thinking, "We got to shut this down." And I actually think that's not what's in the best interest of everybody. And I know sometimes I sound like a spin doctor, but when I talk to people about all this data and how you use it, I oftentimes say, "Look, you're not going to stop the marketing machine. People have to pay for the internet. All that content that you're consuming, it's not free. It costs a lot of money to produce that content. You have to be able to accept advertising. It's going to be in your life. Now, you have to ask yourself, do you want advertising that is relevant to you and your lifestyle, your interests, your behaviors, your attitudes, or do you want these advertisers just to cast these unbelievably wide nets?" And maybe they get you, maybe they don't, but most people will say, "You know what?"
	I'll use myself as example. I have teenage kids. I like to play golf. I enjoy certain types of TV programs. If you send me some golf related advertisements, that's way better than, for example, skiing. I'm not a skier. I live in the state where skiing's really popular, but I'm just not a skier. But if somebody targeted me as a Minnesotan thinking I'm a skier or I like to ice fish, no, you're going to miss the mark. But if you know something about me, you know I'm a golfer, go ahead and pepper me with golf advertisements, because that's what I would prefer to see. So I do believe that making good use of data in an ethical sort of way with good guardrails in place is mutually beneficial, not only to the marketers, but also to the consumers.
Chris Wise:	Super. Shifting a little bit, have you ever hit a brick wall when it comes to client understanding of legitimate application of direct marketing? And if so, how did you change their heart and mind, or did you?
Dave Nugent:	Yes, absolutely. Sticking with the theme of having relevant messaging, that's the big one. I can't tell you how many times I've come across somebody that says, "You guys have just too much data. You shouldn't be able to have access to that much data." And you instinctively jump right into this explanation about how this information is public record, right? I could go to the courthouse, local courthouse and find out when you bought your house, how much did you pay for it? Do you own pets? All this stuff is public record, but people don't want to hear that. They would just want to know, "Well, why do you think it's okay to have this much information?" And that forces me to get into a similar discussion that we just had, and that's, we have to be responsible marketers.
	If you look at the research, consumers want advertisers to know about them. And so that brick wall is, you guys are... I actually had a guy one time say to me... I told my wife, she kind of laughed. But in the moment, I was like, that was really mean. This guy came up to me and said, "There's a special place in hell for people like you." And I was like, whoa, that's really aggressive.
Chris Wise:	It's a little strong.
Dave Nugent:	It was in the context of a data solution where, you mentioned earlier, Chris, identity. There's a lot of utilization of data in what's called identity services these days. It's basically trying to remove the anonymity of consumers that are showing certain types of behaviors, whether it's places they're going, what they're searching, and that is... You got to be careful with that stuff, but we choose to deal primarily in voluntary type identity solutions. And I remember we were working on a campaign in some major league arenas where we were running some text promotions, and it was trivia, or who's going to win this race. And if you were texting, we were trying to figure out, "Okay, well, where's this text coming from? What's the phone? It's a caller ID." It's like, "Oh, okay. Well, that's so and so that's participating. They're in stadium."
	And maybe while people are in stadium will create an aggregate view of the people that are participating in these texting contests, and we'll find out that, "Wow, there's a lot of boat owners in the stadium today." So instead of having a billboard that just reads some insurance company, you have an insurance ad that talks about boat insurance and how much can be saved when you combine your boat with auto and home and whatever. So you're using all this identity data to customize the experience for these consumers. And I'm thinking, this is really cool. The data nerd in me is like, look at all this rich data that we're mining in real time, and we're customizing the in stadium experience to be really relevant to the audience of that particular game. And this guy says, "There's a special place in hell for people like you," looking at us. I was... Holy smokes.
	So you have to get with those people and say, "Look, if it's not done well, if it's not done tactfully, yeah, you cross the line. But if you do it tactfully and you do it in an environment that is governed, then it really does serve a benefit to the consumer. And all the research shows that the consumers do in fact want it." So yeah, that's the brick wall. It's those people that can't wrap their head around the idea that good utilization of data is actually a benefit to them.
Chris Wise:	Yeah. And we also know that consumers want and respond positively to what feels like, and legitimately can be, a very personal one-on-one experience, no matter how large your brand is, that you do identify with them and they with you. So you can't do that by just shouting into the wind, and you really have to understand them and appreciate them. In research, we use a term called universal positive regard. That means you treat everyone in a positive way and try to understand them, in a positive way. So that's how you communicate with them. That's how you build a trusted relationship. So you can't just look at a picture. You got to have to know more about the person.
Dave Nugent:	Absolutely
Chris Wise:	Shifting again now to... Moving to a specific topic that is near and dear to our hearts, and both yours and ours, please share, in as much detail as possible, how to best leverage audience mining and profiling to enhance respectful, appropriate ways to communicate with and motivate the myriad of people representing the 26% of the US population who are disabled adults. In other words, how can inclusivity, in the truest sense of the word, be properly embraced in the marketing funnel?
Dave Nugent:	Yeah, that's a great question. I would say that more utilization of first party data is needed to create better marketing experiences and creating more opportunities for disabled individuals or people dealing with some sort of a disability. For example, we're living in a world right now where, because of GDPR and CCPA, there's an emergence of what people commonly call data clean rooms, and it's a safe house for people to share their first party data so that it can be combined with other people's data for analytics and activation of marketing campaigns. And this was really important because most companies don't want to share their customer information, specially the PII, the personally identifiable information. So we needed to figure out, as an industry, how do we overcome this? Because the best data that's out there is first party data. I'm a huge fan of third party marketing data. Companies like Experian, Epsilon, Axiom, those are some of the big names in third party marketing data, and they have great data. You wouldn't believe how many resources go into compiling and maintaining these third party marketing databases of essentially every US adult.
	And so whether it's them buying research companies because they want to project attitudes and opinions and interests against the US population, or they're building these sophisticated models to predict if they're going to be in the market for something in the future, these are great, great databases. There's no doubt about it. However, first party data is king. And we needed to figure out, as an industry, how do we make people feel comfortable sharing their first party data? And that's where these data clean rooms have come in really, really handy. You can load your data in an anonymized sort of way so that you can combine your data with other data sets to create insights, to create really targeted marketing campaigns. And I think if more companies share their first party data, especially when you're talking about certain types of disease states or disabilities, you're going to be able to influence the entire marketing approach of a lot of brands so that they're making sure that they're catering to a much more segmented population.
	So for example, if you happen to... If you're mining some first party data, and you're finding out that there are certain people with some sort of mobility disability, and you're trying to promote an event at a ballpark, let's say, you probably don't want to say to somebody that has mobility issues, "Hey, run the bases during the seventh inning stretch," or something like that. That doesn't resonate. And that may even be depressing. Who knows? But if you were able to carve out a segment of your target audience and know that they may have some sort of disability, some sort of mobility issue, you can position the brand very strategically so that they feel included, that this is something that they can align themselves with, that they can generate this affinity towards that brand.
	And if we use more and more data... I have a client that is in... It's a nonprofit organization that helps people that are transitioning into a world of blindness. Whether you have some sort of a macular degeneration or glaucoma, or something that is going to eventually get you to a point where you're partially blind or completely blind, when that happens, your world changes, right? And so how you move around your home, you need to be trained on that. You need to make modifications to your home. You need to learn how to navigate the world around you. And so that data is so necessary to have and use. Because if you're a brand and you are trying to create, let's call it onsite marketing materials, signage, for example, if you know that there's a big percentage of people that are attending a particular venue where there's vision challenges, maybe more braille is going to need to be available inside these locations.
	So there's probably lots and lots of examples of how we can leverage this data more effectively for a better experience and more inclusion and more equity, but those are probably decent, just off the cuff examples. But again, I have to emphasize, it does come back to creating an environment where organizations are comfortable sharing their first party data. And that is the future of these data clean rooms. It's completely protected. It's safe. They're built in a, as I called it anonymized non PII sort of way, and it's compliant with all of these emerging regulations, like GDPR and CCPA. So that's the future, Chris.
Chris Wise:	Have you seen or had any interaction with clients that have demanded inclusivity, even if you've helped them through the messaging process, demand inclusivity, or say, "We don't want to do that"? Have you had any extremes of absolutely need it or absolutely don't want it?
Dave Nugent:	Yeah, we see that quite a bit, and it's oftentimes, they're not doing it on purpose, right? They're kind of doing reverse discrimination almost, where they're saying, "I want to target certain groups, very, very specific groups, but unintentionally, they left out certain folks that should be included." And that's another slippery slope, right? A great example is in financial. And I know this doesn't have anything really to do with people's suffering from these different disabilities, but in the financial industry, we had a long time where people would come to us in the data industry and say, "Hey, I'm a financial institution and I want to create some loan products, and I want to target certain types of people." And they're typically focusing on levels of affluence, and they're focusing on home values and stuff like that. And not intentionally, they were leaving out big swaths of the population.
Chris Wise:	Sure.
Dave Nugent:	And we said, "Well, you can't really do that because you're kind of inadvertently discriminating against groups that could use your services." And so what the industry did, which I applaud, is they said to financial institutions, "Look, if you want to build targeting models, if you want to build these predictive models that help you identify households or individuals that are going to be really geared towards your particular products and services, we're going to limit the types of data you can use in your models." So that they call them FLA friendly data points. So fair lending act, FLA, fair lending act. So FLA friendly data points. For example, you can't use age, gender, ethnicity. You can't use those data points in any kind of model if you're a financial institution trying to sell loan products, which is great. They've gone even a step further. And this is how the industry somewhat regulates itself. It says, "You can't even use data points that were built off of age, gender, ethnicity, et cetera."
Chris Wise:	Wow.
Dave Nugent:	So for example, one of my favorite data sets in the marketplace is Experian. Experian's non-regulated marketing database is called consumer view. And they have a data point, and they're called mosaic. And mosaic is a clustering system that basically, there's 71 of these really, really ultra defined groupings, and every household is slotted into one of these 71 clusters. And these clusters are defined by age, gender, marital status, ethnicity, home ownership, length of residence, inferred credits, whatever, interest categories, behaviors. They're really, really descriptive. Well, a part of mosaic has some of those data points that are dangerous. So we, as an interest, is not only can you not build models, targeting models, bank or mortgage company or whatever, loan company, any financial institution, you cannot use those data elements that we described earlier, age, gender, ethnicity, et cetera, but you can't even use mosaic, because those were built on age, gender and thousands of other data points.
	But because a couple of those data points are sensitive, you can't build your targeting around that either. So the industry said, "All right, financial institutions, if you want to build targeting models, you have to use what's called FLA friendly data points." And there's still hundreds of them, but it's a way for us to make sure that we're not somehow excluding people, because we want to promote inclusion. And I think the same thing could happen in the world of marketing to people that are dealing with some form of disability.
Chris Wise:	Oh, yeah. I believe that's to be true. And the 26% disabled, also within that category, there are all those other data points as well. So racial, ethnic minorities are in that data point, as well as you age, gender. All those things fall in there. So we talk about disabled, but from our work, it is really truly the diversity inclusion, equity and accessibility for all that it's really important. Recent study we did showed that not only do the 26% care about being portrayed in advertising or marketing communications in a positive way and being there at all, but basically up to 60% of the total US population would prefer to do business with a company that is sincere and intentional in the inclusivity work, and this is becoming larger and larger as societal empathy continues to grow. And it's not buried somewhere. It's front and center and will continue to be that way.
Dave Nugent:	Absolutely. It shows the humanity of a business, right?
Chris Wise:	Yes.
Dave Nugent:	It's not just about the bottom line. It's about... Of course, every business is looking to have a healthy bottom line, but in the process, if you can show that you're showing sympathy and empathy towards others, because most of us do have it, but you somehow get into the business world and it almost has this perception of everyone's really jaded and they're singularly focused on profit. But most of us want to have an impact on the world beyond just making money for others. We want to be able to lift people up and support people and create that inclusion. So I think you're right. It doesn't surprise me that your studies have shown that people want to know that these organizations are trying to foster an environment of inclusion and equity.
Chris Wise:	Yeah.
Dave Nugent:	[inaudible 00:28:26]
Chris Wise:	And it must be sincere. Yeah. We're going to continue to share that with all of our clients, and certainly in the work that we do with you. You touched a little bit, but beyond the privacy issues, what, looking to next year and beyond, be the oh soothsayer that you are and share what challenges, fears and joys that you see lurking ahead.
Dave Nugent:	My biggest fear, it almost sounds like it's a [inaudible 00:28:56] thing, is more and more restrictions on this data. I think that there is this belief that most data people are using data for evil. When in reality, they're trying to use it to create a more positive experience. I just saw a commercial just yesterday, a new Apple commercial about the new iOS, and it was this auction venue. And this group of what looks like shady people in the auction room are bidding on this gal's private data. And so they're painting this picture that everybody is trying to grab your data in an nefarious sort of way and they're going to use it for evil. When in reality, if you're upfront with clients about what you're collecting, why you're collecting it, and giving them the option to not have it collected or to give them the option to know what has been collected... If you give the control back to the consumer a little bit, not a little bit, if you give the control back to the consumer period, then I think it's going to be a win-win.
	But that's my fear. My fear is that you're going to wake up one day and marketers are going to be so severely hamstrung that they can't do targeted messaging, they can't do targeted marketing. And then all this free content goes away, right? The next thing you know, you have to pay for every single website you visit. You have those... Oh gosh, I'm drawing a blank on the name. The pay walls, I think they're called. You get to these sites and they tease you with little content. And then, oops, do you want to subscribe? And I mean, there is subscription fatigue out there, even in entertainment.
Chris Wise:	Yep.
Dave Nugent:	How many frigging streaming services do I need? I'm just getting tired of subscription. Can you imagine a world where these content providers are forcing everybody to pay for the content because advertisers don't want to advertise anymore because they don't want to cast such a wide net, they don't want to have that much marketing waste. Everybody's looking for marketing efficiency. That's a huge word right now, buzz. Marketing efficiency, marketing efficiency. If we can't use this kind of data, we won't have marketing efficiency. So that's my big fear. My joy is that every research paper I read shows that consumers want brands to know about them. Well, you can't have it both ways, right? You can't say, "I don't want you having any of this information," but at the same time you say, "But I want you to know more about me." How do we bridge that gap?
	And I think we're doing it right now. And I'm excited for what that means. I love the development of these data clean rooms. This is creating a safe house, a Switzerland of sorts, for these brands to share their data in a neutral area, where you control it. You can still control your data, but it's completely anonymized. And inside these clean rooms, the data is being merged, and it's being mined and targeted audiences are being developed. And then straight from those clean rooms, you can push those targeted audiences to these advertising platforms, whether it's a Facebook or a platform for you to deploy programmatic display. I'm excited. I'm excited for that because I think that's the next evolution in data-driven marketing.
Chris Wise:	Perfect. Dave, you have been great. Thank you so much. Appreciate it.
Dave Nugent:	This has been fun. Thanks, Chris.
Chris Wise:	And thank all of you for listening to Ignite, a podcast from Design Sensory Intelligence. If you want to know more about the various ways we gain audience intelligence, then turn that intelligence into solid marketing solutions. Just send a note to me, Chris Wise. Until next time, stay wise.
Speaker 1:	Thank you for listening to Ignite, a podcast from Design Sensory Intelligence. If you want to learn more, head to designsensoryintel.com. Until next time, continue your pursuit of quenching your unending thirst for intelligent understanding of human consuming behavior.