Subscribe on

Enjoy this episode? Leave us a review.

All Episodes

Episode 141

Debunking "Attention" with Marc Guldimann

Elderly intoxicated people pay 33% more attention to ads than sober viewers but remember half as much. That's just one reason why optimizing solely for attention can backfire spectacularly.

This week, Elena, Angela, and Rob are joined by Marc Guldimann, CEO of Adelaide. Marc explains why Byron Sharp is right about attention being wasteful when misused, but wrong about dismissing it entirely. The team explores how attention should measure media quality, not creative sensationalism or audience manipulation.

Topics Covered

• [01:00] Why optimizing for maximum attention creates unintended consequences

• [06:00] Where Byron Sharp gets attention metrics right (and wrong)

• [13:00] The problem with legacy verification companies' attention metrics

• [18:00] How Adelaide rates media quality like a credit rating agency

• [23:00] Why cost-plus agency models create perverse incentives

• [28:00] YouTube podcasts and premium CTV as today's best media bargains

Resources:

2022 The Media Leader Article

Marc Guldimann’s LinkedIn

Adelaide Metrics Website

Today's Hosts

Elena Jasper

Chief Marketing Officer

Rob DeMars

Chief Product Architect

Angela Voss

Chief Executive Officer

Marc Guldimann

CEO of Adelaide

Transcript

Marc: The market is totally irrational. There are lots of valuable placements that are underpriced, but I do think there's a lot of opportunity in CTV and on the web and in all mediums to use attention data to more efficiently buy attentive reach.

Elena: Hello and welcome to the Marketing Architects, a research first podcast dedicated to answering your toughest marketing questions. I'm Elena Jasper on the marketing team here at Marketing Architects, and I'm joined by my co-host Angela Voss, the CEO of Marketing Architects and Rob DeMars, the Chief Product architect of Misfits and Machines.

Angela: Hi guys.

Elena: And we're joined by a special guest, Marc Guldimann. Marc is the co-founder and CEO of Adelaide, a company redefining how we measure media quality through attention metrics. Adelaide's Proprietary Metric AU is the first omnichannel media quality score proven to predict business outcomes, helping brands understand, not just if their ads were seen, but if they truly captured attention. Before founding Adelaide, Marc launched and led Parsec Media, the world's first cost per second platform for mobile advertising, which was later acquired by Cargo. A graduate of Carnegie Mellon with a background in decision sciences, Marc has spent his career at the intersection of technology, data, and human behavior, challenging the industry to move beyond exposure based metrics towards more meaningful measures of effectiveness. So Mark, thank you for joining the pod.

Marc: Thanks for having me. I'm excited to be here.

Rob: This is gonna be super fun. But before we get into all this smart marketing talk, do I have this right? Because I feel like I would've had a lot of fun hanging out with you in college because you were, for pure sport, hacking wireless networks. Is that correct? And that you ultimately then developed the world's first wireless virus filter? Are all these rumors true?

Marc: You dug deep in my old bio. I worked at a company in high school called Panics, which was Public Access Unix. It was like the WELL of the east coast. And so I got really interested in computer networks and stuff, and this was in the early nineties. And when I was looking at schools to go to, Carnegie Mellon had the biggest wireless network at that time. So I ended up going there and messing around with their wireless network. My first job out of school was a sales engineer for a wireless security company that was basically running VPNs because WEP was so insecure. And so I would go around hacking wifi networks to prove that they needed to use our product. And then there was, I think it was probably 2002, and there was a virus called Welchia, and Nimda, I think this is a long time ago, and I'm really digging around in the roots of my brain here. But it was Welchia and Nimda, and we were able to figure out, based on the header of a TCP IP packet, if a computer was infected, and then put that computer in a separate VLAN. It was a virus that would infect other computers over the network. And so as kids were going back to school, and I think we did this with a UC school with a big campus, as kids were coming back to school, they were really worried that these laptops were gonna infect all the other laptops in the network. And so, yeah, that's the story. We built a wireless virus filter using our technology.

Rob: Well, there's a lot of great reasons to choose Carnegie Mellon for your post-secondary education, but you're probably the only person I've ever heard say I chose them because they have the largest wireless network. That's quite a bragging right.

Marc: I wasn't a decision scientist at that point. It was only after I went to CMU that I became better at making decisions and I don't think that was a good basis in that one.

Rob: Awesome. Well welcome. You are among nerds, so we're excited to have you.

Marc: Great. I feel at home.

Elena: Perfect. Well, we are back with our thoughts on some recent marketing news, always trying to root our opinions in data research and what drives business results. And I'll kick us off as I always do with some research. I chose an article today from our guest, and it's titled "Byron Sharp is Right: Chasing Fleeting Attention is a Waste of Money." In this article, Marc responds to a growing debate sparked by Professor Sharp's comments at the Mi3 LinkedIn B2B Next summit. Sharp said that paying more for more than fleeting attention is a waste of money. Marc agrees that buying more attention for its own sake is wasteful, but argues that's not what attention metrics are for. He says attention should be treated as an input to gauge media quality and price fairly, not as the end goal. Optimizing to attention seconds can lead to sensational creative and over-targeting familiar audiences without improving outcomes. And that is just a tiny preview of what we're gonna cover today when it comes to attention. But Marc, thanks again for joining us. To go back in time a little bit, you've had a really entrepreneurial journey. Clearly I just read that intro. Could you share the common thread that's connected your different ventures and what led you to eventually focus on attention?

Marc: I've always been interested in measurement. Even before I knew it was called measurement, I was the kid who counted the steps on the way to school every morning to try to find a more efficient path to school. And I got into advertising after studying decision sciences, which is basically an undergrad MBA with a little bit of economics, and got into advertising and I was like, oh wow, there is really bad measurement here and people don't really understand the underlying asset that's being traded. And the companies that I've been working on for the past 18 years or so have always been around trying to figure out better metrics for advertising or better ways of understanding data. So yeah, I think advertising, a lot of the weird things that we do in this industry and a lot of the perverted incentives that people face can be traced back to bad measurement or a lack of shared understanding of quality.

Elena: So going back to that article that we opened with, Byron Sharp likes to comment a lot on attention metrics. I know that you're familiar with that, but in your rebuttal in this article, you said that there's a lot that he gets right about the critique. So what does he get right about attention? What are the main challenges of a brand investing in attention metrics?

Marc: Well, first of all, we named the company Adelaide after where Byron Sharp is from, after where Ehrenberg-Bass is based, and we read How Brands Grow and we were very inspired by his approach to advertising. So when he started talking about attention, it was a kick in the teeth. He was like, oh, all this attention stuff, you don't want more attention, you don't want more attention. And the reality is we 100% agree with him. I think he was speaking very broadly. There wasn't a lot of nuance in what he was saying. There was some specificity around you don't want more attention. But the amount of people that sent that article to me with a laughing emoji, like, haha, the guy who you named your company after is talking trash about the whole category you're trying to create, was pretty funny. So to his specific point, we have been in the attention space, me and my co-founders, for about a decade, and our first business was selling media on a cost per second. It worked really well. And it was a pretty lucrative business. We were charging about a penny per second. We were generating about three and a half seconds of attention per impression, and it was these big full screen mobile ad formats. So we were using Moat data to count how long the ad was on screen. So three and a half seconds, a penny per second. It was a $35 CPM. It's a really good business, and the crazy thing is, even with that high of a CPM, we were delivering more efficient incremental outcomes than the competition, than an ad network that an advertiser might be deciding between Parsec and some other ad network. And when they were looking at the cost of incrementality, we were more efficient. We looked really expensive, and the viewability rates weren't that great though. So the old metrics of cost and viewability that people were holding us accountable to, we didn't look that great. So that was the first challenge. The second challenge was I got a little greedy and I went to the engineers. I'm like, we do three and a half seconds. Let's do four seconds. Let's do four and a half seconds. This is straight to the bottom line profit if we can go past three and a half seconds. And a group of really strong crack engineers, they did it. And the numbers start going up. We're doing four seconds, four and a quarter, four and a half. And along the way we started making some really weird creative decisions. We were choosing creative that was more whizzbang and more interesting and less heavily branded because there is essentially this tension between how much attention people are gonna pay to something and how heavily branded it is. There was a campaign for a company that was launching a hummus and we made two ads and one of them said, "This brand now makes hummus," and the other ad was a spinning hummus container. The spinning hummus container captured a lot more attention, but did a lot less work in terms of recall than the one that said, "This brand now makes hummus." So that's a very specific example of you don't want the most attentive creative, and you can take that all the way down to, if you think about what the most attentive creative is, it would probably be puppies and kittens or naked people, neither of which actually—Carl's Jr. ran an ad campaign with very scantily clad people eating hamburgers and they had to cancel it because it didn't work. So you don't want the creative that captures the most attention. I think there's some common sense that people can use when they're designing creative and that they can stay away from just this full throated optimization towards the most attention, but that's not the same as you find with audiences because a lot of times the audiences—programmatic tools will just optimize towards the audience that's generating the best result. And if that result is attention, you're going to be optimized into old drunk people. Because old drunk people pay more attention to things than everybody else. In fact, it takes people 33% longer to read things when they're intoxicated and they remember half as much as sober people. And there's a bunch of other likes.

Elena: I knew Rob was gonna love that fact.

Rob: I mean, that's just outstanding. Wow.

Marc: But if you let the algorithm go and you're like, just get me the most attentive audience, you'll skew. And we found this out by buying the Nielsen DAR data. And a lot of our delivery was skewing later. It was skewing towards older men later in the day, hypothetically slightly intoxicated, having a few cocktails at night. There's some less comedic things that happen as well. Because people who are aware of brands will actually pay more attention to an ad from a brand than someone who's not aware of it. And that's the exact opposite audience that you wanna reach. People who've been over-frequencied will actually pay more attention than people who've been under-frequencied. So the net is, Byron is 100% right. You do not wanna optimize towards the maximum amount of attention. You run—this is something called Goodhart's Law, right? It's a leading indicator, but when you start optimizing to it, things start to go really weird. The example I always give to people that have run sales organizations is imagine you find that the number one and two salespeople in your organization are sending the most emails. You said, oh, great, that's a great leading indicator. Now, the new KPI for the whole sales team is who can send the most emails. You know what would happen immediately, right? The people who care the least about their leads would just hammer their leads with as many emails as possible. The quality of the emails would go down, and all of a sudden the number of emails sent would no longer be a leading indicator of performance. So that's called Goodhart's Law. And I think the same thing happens with attention. Attention is fundamentally necessary for advertising to work. But if you optimize impressions towards the amount of attention, you create all of these unintended consequences. So it's our view that you shouldn't measure a media campaign based on the duration of attention, but media's responsibility is to create an opportunity that creative capitalizes on and holds your attention, and ideally changes the way that you think or behave. So if you wanna judge the quality of an opportunity, use probability. That's the basis of our score, which is the probability of attention by any person to any creative in a placement.

Elena: So you've done a really nice job of pointing out where you and Byron Sharp agree, and it actually seems like you agree a lot more than you disagree. He's not the only one who's challenged attention. When we were talking before this interview, you pointed me to an Ad Age article that has some critiques over attention. They have this study, they said, you know, attention doesn't move brand or sales outcomes. So we talked about what people are getting right when they debate attention. What are people getting wrong in your view in this current debate?

Marc: Well, so that article was by Jack Neff and it talked about one of our competitor's products that was being used by an RMN. Now, RMNs have a lot of incentive to not pay too close of attention to media quality because audiences are small, like the audiences that they're selling against are smaller, so they wanna make sure they spend through the entire budget. So they have a sort of built-in proclivity to steer away from media quality. So I think you have to take their opinion with a little bit of a grain of salt. The other problem is that it was an attention product from a legacy verification company. These legacy verification companies have an innovator's dilemma. They've been telling the market for 20 years that viewability is all you need to understand. Oh, and also brand safety. Because it's a good idea to advertise on recipes instead of news, which everybody knows is not true. So they've been out there with a narrative which is much more of a narrative than it's actually evidence-based. And so when they went to make an attention metric, they just sort of bundled up a bunch of their viewability stuff. They haven't done a lot of the model training that we have. And it didn't work. And we win the vast majority of head-to-head competitions we have because of the way that we've constructed our metric. And I do think that at this point it's been so consistently proven that fourth generation attention metrics like ours are predictive of outcomes, and they do work way better than viewability, that the biggest risk to the widespread adoption of attention metrics is bad attention. Because an advertiser will use them and they'll use one of the verification companies' attention metrics, or they'll use a third generation duration-based attention metric that's trying to count the number of seconds. They'll get bad results and they'll say, ah, you know, I've done that learning. I can check off the attention metric test and I'm gonna move on to the next thing. I'm gonna do an AI test. And that is by far the biggest challenge that we have to overcome now is, oh, there's different kinds of attention metrics. You should try it this way. Try doing a programmatic optimization instead of just a retrospective, or try these different approaches. So there's a lot of people who have taken a shortcut to create an attention metric. Or I will say, I won't say there's a lot of people, I'll say the legacy verification vendors have taken a little bit of a shortcut to release an attention metric.

Elena: So then what kind of attention metrics or what way of utilizing attention metrics is valuable for brands in your opinion?

Marc: And this is, you know, take this with a grain of salt because I've got a bag to sell. And I of course think our approach is the best and I think our metric is the best. I think that attention should be distilled down into, are we talking about creative? And we've talked about you don't want the most attentive creative. Are you talking about audience? You probably don't want the most attentive audience. You can use attention to sequence ads to audiences in interesting ways or to maybe pick up if they're already aware, if they've been over-frequencied. So you don't really want the most attentive creative, you don't want the most attentive audience, but you do really want the most attentive media. So you want to break it out to be about media. Now if we're just addressing media, we don't need to measure impressions. We can rate placements. Since media ostensibly—impressions originate from a placement. So if we can use attention metrics to rate the quality of placements, we can fundamentally change the way that a lot of people think about media buying. Advertising is only one of three industries in the world where people will spend $10 million and say, oh, I wonder what I got. Let me look at the measurement. The other two industries are gambling and venture capital. Every other industry in the world trades on a clear quality and quantity and then a negotiated price. And so I think that if we start to think about using attention metrics to rate the quality of placements, we can move the industry from post-campaign measurement and "oh, I wonder what I got" to here is a clear concrete understanding of what we're about to trade. You can use attention metrics to do this by assigning a score to a placement that is the probability of attention on any impression that comes out of that placement. And you can also start to think about how that placement does in terms of driving outcomes. Because advertising isn't about creating attention, it's about moving the needle for businesses, right? It's about actually getting more sales, more awareness, about moving that business forward. So at Adelaide, our approach is very similar to a bank that's trying to come up with a new credit rating for a group of people. And I'll walk you through very quickly what a bank might do. The first thing a bank would do is they would go and work with researchers who would tell them what are the characteristics of people that make them more or less likely to pay back debt, like what school they went to, what kind of car they drive, what their income is. Then they would go gather all that data about the audience. They would build a model that would create a score for the individuals in the audience. And then they would train that algorithm using historical debt repayment data. So we take the same approach at Adelaide. The first thing we do is we work with eye tracking companies and we're probably one of the most prolific licensers of eye tracking data in the world. And that eye tracking data tells us what are the characteristics of placements that make that placement more or less likely to capture attention in a specific channel. For example, on the web, it's coverage, clutter, position, duration, page velocity, audibility, and about a hundred other metrics we use now. On podcasting, it's totally different. On podcasting, it's like, is it host read or is it stitched? What's the genre? What's the player? How deep into the podcast is this ad actually playing? How dense is the pod? Is it one—is it like a Howard Stern type thing where there's 30 minutes of ads? I guess I'm dating myself. But used to drive to school and you would hit, if you hit a Howard Stern ad break, it would be like the entire drive. Or is it like a one minute long break? So that's the first step. We try to figure out what are the characteristics of ads that are more or less likely to capture attention. Then we go gather that data about every placement in the channel. And then we build a model. The model's not trained yet, but it's still able to generate a score for every placement. Then we go get historical outcome data from impressions that are associated with those placements. And we use that to do very simple reinforcement training. And so what we're left with is a metric that is based in attention data and rigorous research, but is coming out of a model that's been trained to predict outcomes. So it's very—we like to think of ourselves as a credit rating agency for media quality.

Angela: Okay, so I wanna drill into that a little bit, Marc. We have a lot of debates, conversations, both good and some not so good, related to media quality, sometimes related to television. That's the space we play in, right? Both CTV and linear. And I think this might be unique to television just due to the variance in cost. One commercial break, or not even the break, one commercial spot in a pod versus the next can be wildly different in terms of what that agency or that brand negotiated for it. So when we think about media quality, obviously we're after an audience. That audience is vast in terms of their viewing behaviors. If you think about how you watch television, how I watch television, I was watching NFL football yesterday, which was probably the most expensive way to get in front of my eyeballs. But I watch a lot of other television too. And so we take that audience first mindset because cost is such a big lever in terms of performance for our brands. So I'm curious, you gave a little bit of backstory there in terms of how you got to that and how you think about it, but point blank, how do you define media quality?

Marc: At Adelaide we think about media quality as the probability of attention and then the probability of an outcome. You raised a bunch of very good points and I think that historically, big tent pole events have been more expensive because of the attentiveness of the content, but also the deduplicated reach that you're able to get through them. That sort of wanes with programmatic and the ability to do addressable audiences on CTV. What we find is that the market is totally irrational. There are lots of valuable placements that are underpriced, that people hopefully can use our data to go out and identify and buy more of. And then over time the market will start to correct. But I do think there's a lot of opportunity in CTV and on the web and in all mediums to use attention data to more efficiently buy reach, by attentive reach. I don't know if you saw our partnership with Nielsen that we announced, but now Nielsen has our data inside of Nielsen One so that they can look at the concept of attentive reach instead of just treating all units of reach as the same.

Angela: Love that. I think just bringing new perspectives. We're in such a unique time, I feel like, where you've got marketers that are still buying based on TRPs, GRPs, impressions, age old ways of buying, and then you've got marketers that are purely buying based on performance. And it's this in-between of what ultimately drives effectiveness. That's what we're talking about here every single week. So just you and your company providing that data set to help brands think more holistically about it. Love that. I wanna shift gears a little bit. You've said that agencies shouldn't have to disclose their margins or their business models, which is a hot take in this industry. How do you reconcile that view with today's demand for transparency?

Marc: So, I think that in a lot of ways, the fixation on transparency in advertising is just the wrong kind of transparency. There should be transparency of quality and not transparency of margins. Transparent margins is closer to Marxism than capitalism. It's just like Marxism with a little bit of money. Maybe that's becoming more popular in the US in certain cities, but to answer your question, a lot of people, like every five years, brands figure out that there's kickbacks and they hire a forensic auditing company and they come in and they look at everything and they're like, yeah, there's kickbacks. And the brands say, well, we just gotta switch agencies. They don't change the fundamental economic relationship between the brand and the agency, which is cost plus or FTE. Now, it is a felony if you work for the US government to sign a cost plus contract because the knock-on incentives are so well understood right out of the gate. It creates the principal-agent problem, and then it inevitably leads to price inflation in order to support kickbacks and declining quality. However, cost plus is a natural human reaction if you don't understand the quality of the thing you're buying. Inevitably people will default to this doctrine of fairness of, oh, you should get five to 10 to 20% margin on what you're acquiring for me. If you went to the gas station tomorrow and the gas station attendant says, sorry, we have no more gallons or octane and all we have are buckets of gas, you would start to behave exactly like an advertiser. You'd say, well, how much did you pay? I'll pay you 10% more than you paid for that bucket of gas. And then you'd wire up your car with an attribution system to try to figure out what did that bucket of gas do towards your business outcome of transportation. You would just track the spark plugs, the tire pressure, how heavy your passenger was, were your windows up, were your windows down, all of these various factors. We do the same thing in advertising. We just wholesale invade the privacy of 400 million people because we don't have gallons and octane. So this behavior of advertisers and the idea that they trade on cost plus can be traced back to a lack of shared understanding of quality. And we hear pretty consistently when people work with us that they become less fixated on transparent costs and less fixated on attribution when they understand the quality of the media they're buying. I think that agencies should trade non-disclosed media. I don't think that agencies should be selling outcomes, or platforms should be selling outcomes. Agencies, maybe, because they're a little bit closer to where the rubber meets the road for a brand. But if you are a brand and you're buying outcomes from a platform, that platform is going to get better at producing outcomes than you do. And they're gonna take all of your data, and then they will 100% sell those outcomes to the highest bidder, which chances are it's your competitor, right? They're gonna sell you the worst version of the outcome. They're gonna get better at finding those outcomes than the marketer. And that's the core purpose of being a marketer is to turn resources into demand for the product. So I think it is a fundamental mistake to trade on a business outcome. But if we're in a media marketplace, we should be trading on the quality of the asset, right? We should be trading on the quality of media and the media agency, and I think maybe the name agency is wrong here. Maybe it should be the media broker or the media supplier. They should be held accountable to the quality and the quantity of media, not the outcomes that it provides because there's so much more stuff that happens between the ad exposure and the sale actually happening.

Angela: Hundred percent. Yeah. I agree with you. I feel like we're in a time where maybe it's due to AI, there's a lot of things going on from a tech and data perspective, but the question's being asked more and more. But appreciate that perspective from you.

Rob: And speaking of perspectives, I think this is the first time we've ever talked about Marxism on this podcast, so I'm loving these spicy takes. What's your spiciest take? I mean, you've already been peppering us with quite a few good ones, but we love a good contrarian perspective. Throw us your biggest one.

Marc: I think that the agency business model is the spiciest one, especially when I started to talk about Marxism, which I think can turn people off from my opinions. Yeah, I think it's literally like all of the weird stuff we do in this industry can be traced back to the fact that we're ostensibly in a lemon market for media. And all of the behavior is really predictable. If you study lemon markets and you study adverse selection and the principal-agent problem, the way that agencies behave and the way that ad tech companies behave is very predictable. So yeah, that was it.

Rob: That's a good one.

Elena: I don't know if I've ever seen someone say that publicly, you know? So we love it. Love that we can broadcast it.

Marc: Oh no.

Elena: No, it's great. It's great. Well, Marc, you made me think of a few more questions. I hope that's okay. I was curious, with all the data Adelaide has now, have you found any trends or patterns in most attentive media quality by channel? Are there any channels that consistently seem to do well in your rankings or how does that work?

Marc: Yeah, so I think I need to be careful because we do need to be independent as Adelaide. We need the market to trust us as an independent auditor of quality. But at the same time, I think we're allowed to have taste. And I think that a lot of what goes into our ratings, and as I've been learning more and more about credit ratings agencies, because I think that's literally what we're starting to become, I've been surprised by the amount of taste and sort of manual input and of opinions that actually go into these ratings. Fitch and Standard and Poor's and Moody's, they all get together. They get all the data they can about a debt issuer or any instrument issuer, and then they try to come up with a rating for that. So I think that we should be allowed to have some taste and we should be allowed to have an opinion. We've done a lot of work with YouTube to help YouTube understand the quality of media placements within YouTube, and then also help compare apples to apples with the rest of CTV. I think YouTube podcasts and pockets of premium CTV are the best bargains in media today. Low quality CTV is probably one of the biggest ripoffs. A lot of these new age CTV outcome platforms have just figured out how to game attribution. Which I guess maybe there's a hot take I forgot to share earlier, which is this thing I call the law of outcomes, or the rule of outcomes, which is any platform who gets to be large enough will figure out that it's easier to predict outcomes than it is to create them. And so what you'll find is that platform figures out when you're about to buy something and then serves you an ad, which has no impact, but then it's able to take credit. So I think that there is a lot of low quality media that has a lot of scale and a lot of data behind it. And those platforms are really just able to predict when you're about to do something. If you go on your phone to 8 Sleep and Blue Nile, three big brands, and then put your phone down and then open up a large social platform who will not be named because we need to be independent, you'll notice all ads from all of their competitors.

Elena: Yep.

Marc: And so what they're doing is they're just basically saying, you're very qualified in-market for this thing, and we're gonna start to try to figure out how we can drop as many attribution signals as possible. So I think to answer, that's a very long-winded answer to your question. We do a lot of podcast advertising because podcasts are a great way to reach a very well-defined audience and get a lot of their attention. I am trying to make an AI ad with our new mascot, the Quality Koala, to run on YouTube because I think YouTube is an amazing bargain in terms of reaching audiences with high attention placements.

Elena: No, that's super helpful. Thank you. I love that you created a mascot too. Sounds super cute.

Marc: He is from Australia, the quality koala.

Elena: Couldn't be better. I love it. Okay, let's wrap up with something kind of fun. If your attention were money, what would you say you're overspending on? And Marc, why don't you get us started?

Marc: I wish that I was more cerebral, but it's doom scrolling. I mean, it's like I open up Twitter or X or whatever we call it, and it's getting too much of my attention when I should be reading a book. So I am 100% wasting my money and my attention on the feed-based apps.

Elena: Probably lots of people doing that.

Angela: Lots of people doing that.

Rob: Yeah, yeah, absolutely. Absolutely.

Marc: That next little dopamine hit, right? You want that next little?

Rob: Yeah, for sure. They've got us wired in, plugged in, like the matrix. All those eyeballs are somehow juicing alien technology somewhere else, right? It's just being siphoned from us.

Elena: Gosh.

Rob: That's the Matrix, Elena.

Elena: Okay. I haven't seen it, so...

Rob: Alright. You should. I'm gonna have to go with a recent obsession, and I go in and out of reality TV and then when I go in, I'm like, oh, I just can't stop. And right now, for some reason, I'm hooked on The Voice and I can't stop watching. It starts off with the auditions and then the battles, and you got Snoop Dogg on it this season, who's just couldn't be more fun to watch. So I'm just, and afterwards I'm like, that was absolutely empty calories. I got nothing from it, but I'm still watching.

Angela: I went the non-content route. So something getting attention from me is college planning for my oldest. And you'd say, well, that's a great use of attention. That feels like quality media, so to speak. But I'm doing college planning for a child that makes decisions based on emotion, how it feels when she gets on campus. And yet I still have spreadsheets built 'cause I'm a type A freak, characterizing every little piece of every college. So I should probably back off.

Elena: There is something to that feeling you get when you go on campus though.

Angela: It is. No, that's a column, but she makes the whole decision based on that. It's one of the attributes. I agree.

Elena: Well, mine's similar to Rob. It's reality TV and there's way too many shows right now. But Rob, mine is Dancing With the Stars. Had never watched it. Yeah. Now I got pulled in and now I'm really into it. So yeah, I've...

Rob: I've gotten in and out of that one too. So that's the one thing about reality TV shows is you get so into them, but they are easy to get off of them too. But then once you start tasting that sweet heroin, you're back on.

Elena: I like to think I'm learning. I'm learning a new respect for dancing for sure.

Rob: Yes, that's right.

Elena: Great. Well, Marc, thank you so much for joining us today. Where can people follow you and learn more about you and what you're doing at Adelaide?

Marc: So our website is adelaidemetrics.com. Or my LinkedIn. My Twitter is GULDI, G-U-L-D-I, but I don't really tweet or post that much. But yeah, on LinkedIn or adelaidemetrics.com for the company.

Elena: Nice. Short handle.

Marc: I've been around. I'm pretty old. I've been around the internet for a while.

Elena: Yeah. That's impressive. Okay, great. Well, thanks so much for joining us today.

Marc: Thanks for having me. This is a lot of fun.

Angela: Great to have you.

Rob: Thanks so much.