Ep 119 | The Economics of Identity with Max Kirby of Publicis Groupe

This week Max Kirby, Director of the Cloud Solutions Practice at Publicis Groupe joins Allison Hartsoe in the Accelerator to talk identity. In previous shows we’ve covered privacy from both the US and European angles so this time we’re going to cover the economics behind identity. What happens when customer data is widely available for analysis (and when it is not?). Max and I discuss the exchange of value and how customers may win or lose in the battle for their most private information.  

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Article – 5 Factors to Make the Right CDP Buy or Build Decision

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Allison Hartsoe: 00:00 This is the Customer Equity Accelerator. If you are a marketing executive who wants to deliver bottom-line impact by identifying and connecting with revenue-generating customers, then this is the show for you. I’m your host, Allison Hartsoe, CEO of Ambition Data. Every other week I bring you the leaders behind the customer-centric revolution who share their expert advice. Are you ready to accelerate? Then let’s go! Welcome everyone. Today’s show is about the economics of identity. And to help me discuss this topic is Max Kirby. Max is the digital identity and privacy expert that leads cloud solutions at Publicis Groupe. Max, welcome to the show.

Max Kirby: 00:44 Hi Allison. How are you?

Allison Hartsoe: 00:46 Really good. Thanks. Now tell us a little bit more about what your team does and what you specifically do, and maybe also how you got into this particular.

Max Kirby: 00:54 Sure. Yeah, it’s an interesting kind of hybrid of a couple different things that you see in the industry. But my team was really birthed out of the advent of cloud adoption and specifically cloud adoption by the CMO and the marketing organizations in the enterprise space. Because as I think we’ve all been learning, right? There’s this divide between marketeers and technologists that are becoming smaller and smaller as we all mature. And so cloud, which originally was the backend infrastructure virtualized into a scalable framework and available at all times, and really changed how we were looking at technology was a little late to affect the marketing organization, but the same basic concept held where the virtualization of things like where data is, where it’s processed, what’s happening to it, and who has access and availability. They’re all affected in the same way as any data. It just happens to be customer data. And so there’s this movement that we’re seeing towards customer data platforming these in parallel with the movement to the cloud. And so what I do and what my team does is we look after all of the solutions that a publicist group is doing with customer data on a cloud environment. And that’s, you know, 95% CDPs. Wow.

Allison Hartsoe: 02:09 Sounds like a big job. And I also just want to call out that you have a background with the London school of economics, which is an interesting place to start and then end up in technology.

Max Kirby: 02:19 Yeah, well, so in the beginning, I was really studying concerned behavior, which I think a lot of people that go into marketing where students of human behavior, economics is another kind of proxy for the sum of all of our decisions. The role that I kind of carved out was more so around the fact that technology was going to change how we were looking at things. And I went to attack that from a business valuation lens. And so the professors that I had at LSC were great, and they were doing things that were really experimental at the times, like, you know, valuating the impact that a board member has based on their relationships as quantified by a social map, right? And taking the theory that executives who have more relationships, you know, generally speaking, will make companies successful or more successful than someone who is maybe isolated. That was really revolutionary at the time. And I was studying under professors who were trying to pull at the discounted cash flows model, all of the legacy ways that we were thinking about what value is in a data and tech-driven world.

Allison Hartsoe: 03:15 Well, that is a perfect fit for exactly what we talk about with customer equity on this show. So thank you for being here, but let’s start with why I should care about the exchange of value. When I ask my customers for say an email or a birthday in exchange for a discount or some other value now for just the small chunk of information, we both win, right?

Max Kirby: 03:39 Generally speaking, you both win. There’s a line at which you could both lose, but most of the time, you both win. Another way of framing this is the companies that you are expecting to meet your expectations, have a much better time meeting them if they know what your expectations are and were up against the two-sided coin of, I want to protect my data. I want to know what you have, of course, the privacy movement in general, but the real value of sharing information out into the market, in general, is that companies can take action on that information to bring you what you want. And that’s how we want the market to work. That’s why it’s a market.

Allison Hartsoe: 04:12 So on the one hand, I want to protect all my information. And on the other hand, if you don’t give me your information, I can’t serve you. It’s almost like know me to serve me. I think, as a slogan, I sometimes hear.

Max Kirby: 04:24 Yeah, that’s right. So, there’s this term micro-moment right. And that’s become popular because the best way to understand what someone wants is when they themselves know what they want. And the truth is we don’t often know necessarily what we want at all times. And so these moments of context where the customer or the individual knows what they want, or perhaps what they know of, what they want is really precise in that moment. That’s when you want to have that exchange, but it all comes back to this idea that if I’m going to satisfy what you want, I need to know what it is. I’ll give you just a quick example. If you were at a restaurant and the waiter were to come and ask, do you have any allergies, or are there any dietary preferences at the table tonight? You would probably answer that question.

Max Kirby: 05:10 And the reason why you would answer that question is because you understand that he needs to know that information in order to bring you the food that you want. If he asks for your social security number, probably not relevant to your dinner that night. And so that’s one way of thinking about it is the companies that are serving you when they come and ask for your data or they try to collect it, or you know, that they want it for some reason. Um, how close is that collection to your expectation of the value that you’re getting? And we’re doing some research on this that is giving us reason to believe that those fit certain patterns for certain industries.

Allison Hartsoe: 05:42 Well, let’s talk a little bit more about that research because that sounds really fascinating as far as how close the expectation is to the fit. That seems like a spectrum when most people think about it as an on-off switch.

Max Kirby: 05:53 Sure. Well, in the simplest terms, what we’re doing is we’re trying to ascertain the extent to which individuals understand what kinds of data they’re actually sharing when they take certain actions, you know, download an app, log into a site, even walk down the street, right? If you want to talk about location tracking and things of that nature, and the initial results are indicating that the average person actually has no idea, because when we ask them, are you comfortable with these types of companies knowing the information that flows from those actions? There’s a major divergence in those two answers, they’re uncomfortable with the companies having those pieces of information, but over at the other side of the equation, when they actually give the information, they don’t know necessarily that that information is going. And so the study is still ongoing, and we’re doing this every month and trying to get a sense if there are any differences between different regions or people who speak different languages, different cultures, but at the high level, what we can draw out is that not many understand what data is going to, which companies, and if they did, they might have an issue with it.

Allison Hartsoe: 06:56 Well, this reminds me of a little bit of some of the research that Google has done, where they would oftentimes go into companies, and they would talk about micro-moments for marketing, and particularly they were trying to build up mobile marketing. And what I find interesting here is that you said before, people don’t always know what they want at the particular moment. So maybe I don’t know. And then I start to understand a little bit more about what I want, and there’s this inflection point, or there’s this tipping point where the micro-moment happens. And all of a sudden, I’m comfortable with a company having a certain piece of information to give me what I want. The problem is that in the Google scenario, Google had that information, and the company had that information, but the individual didn’t necessarily know which is, I think what your research is stepping on. Is there some way consumers should know, is there a way they should be thinking about this?

Max Kirby: 07:52 Well, this is what we’re debating right now in the legal world. The question could be phrased as who’s responsible for making sure that an individual understands what data they are sharing, and do they have an opportunity to let them intend to be known about the rules? And then you have the new laws, you know, we’re just one week away from CCPA going into enforcement after a very long grace period. Um, it’s technically in effect, but the California attorney general has given everyone a couple of months to sort of get their act together. That is a different opt-in opt-out standard, then GDPR, which has been enforced for a while. And so, there are different philosophies on how to attack this as a matter of law. And I’m not sure that we have enough to be able to answer the question outright, but I can tell you that generally speaking, what you don’t know that someone else knows about, you can be very powerful.

Max Kirby: 08:42 What I mean is when I know that you know something about me, I might have a conversation with you differently. When I don’t know that you actually know a lot about me, it’s a reason for hesitation, right? It’s that feeling that you get when someone comes up and says, Oh, I’ve heard so much about you, and your mind goes racing to what possibly could they have heard. You have to define that for me. So, I understand how to have this conversation. And if you think about what the big platforms have on sensibly, everyone, that’s a very large power dynamic to cross when you realize that individuals don’t know what any of these platforms actually know about them. I mean, that’s why they can just be so persuasive to us because it is a form of power.

Allison Hartsoe: 09:19 Well, it sounds like we’re saying two things here. One is when the individuals don’t know how much the companies might have on them. They are subject to a lot of persuasive tactics that may not be fair. Maybe the company is using information that like, I didn’t know, I had a credit score, for example, that was a certain amount. And that’s why you’re marketing to me. There can be these other underlying factors, but at the same time, I’m also hearing that. And I certainly know firsthand, there are a lot of companies that just don’t have their data together, and yet the individuals are making the assumption that they do. So we’ve got these two dynamics happening where individuals are like, Oh my God, don’t use my data. And the company is like, I’m not using your data. It is the real power inflection here, more with the data aggregators than with the individual companies.

Max Kirby: 10:10 I’m glad you asked that question, Allison, because I think about this on a regular basis, who is the party that’s most responsible for the state of information exchange. And to some extent, it’s hard to put that on the individual on a kind of caveat emptor status. It’s very hard to expect people to understand these things. At the same time, customer expectations are always generally increasing. And so as your competitor maybe starts building a customer data platform and uniting their view of the customer and using it to infuse their marketing and not just their marketing, but the journey and the personalization aspect of things that might create a categorical expectation, your entire industry in order to use data in that way, or at least to be able to anticipate. And you’re seeing this in the kind of slower to move industries, getting their fair share of disruption with the kind of fast movers that can come in and have a better experience.

Max Kirby: 11:05 And basically, they’ll never need a customer data platform because they are never going to build a data silo. Right? And so, it really is a difference in advantage that people have to take a look at. I think that the aggregators, the data brokers, I think that there are two roles for them. And one of them is I think in trouble. And the other is I think very important. And the exchange of information in its raw form, I think, is starting to go by the wayside. I think a lot of folks are starting to realize that they don’t actually need to share the data itself to be able to share the intelligence behind the data. And I think that’s a very good thing for the industry because we don’t talk much about the post CDP world. Now, maybe we should start having a conversation about what that looks like, but every major enterprise is working on this topic. And so that world is coming. The world where everyone could monetize their data to everyone else. And it’s only natural that at that point in time, we’re going to be knocking on the doors of different industries and saying, Hey, you have information that I could use. Let’s come to some type of terms about how we would exchange that information. I just think that the standard is going to be less. So, here’s the raw data and more so what you’re seeing with regards to clean roaming, which is becoming the new industry standard around differential privacy,

Allison Hartsoe: 12:17 I love that term, I haven’t heard that term clean grooming, but if I could take a guess at summarizing it, it sounds like when I understand my customer base really well, I might have certain attributes that describe my customers related to my products or related to how they use my products. So, let’s say I have automobiles and it has to do with how far people drive. And maybe I categorize them by long-distance drivers and short drivers or short distance drivers, local drivers, the clean rooming data that I might trade with another company is the fact that you’re a long-distance driver or a short distance driver, but I wouldn’t necessarily share your name, address, and phone number.

Max Kirby: 12:57 That’s mostly right. I mean, somebody that we already have that with certain tactics like encryption methods, hashing, and selective sharing, but you’re right on Allison. I mean, the clean room is an upgraded version of that. That also takes into account methods to foil the identification of specific individuals with synthesized data. And so the way to understand the clean rooms and why they’ve kind of come to market is because of the risk of re-identification. And this was coming up a lot in the wake of Cambridge Analytica and sort of the data breaches that we’ve been watching. And that’s the ability for a good data scientist to take some anonymized data and actually re-identify all of the individuals within the anonymized data if they added other data to it. And so a clean room foils, even those really much more advanced methods by basically fighting back and saying, you can only ping this customer record X so many times before we omit it from any of the exports going to you. It makes it statistically unlikely that you’re going to find them doing that. And Facebook is doing this. Google is doing this. Amazon’s doing that. I think everyone is moving towards clean rooming as a standard. And that’s going to give us some more flexibility because we won’t need to share the data to be able to share the information.

Allison Hartsoe: 14:09 Oh, that’s interesting. Okay. So, if I’m calculating something on an individual basis, this might prevent me from crafting my own calculations. I’d have to rely on what Facebook or Google said or Amazon. And in a sense, this is kind of how retargeting works.

Max Kirby: 14:25 In a way. Yes. If we lived through the multichannel to omnichannel movement in marketing over the last ten years, what we’re about to see what we’re perhaps in the onset of is the multi-cloud marketing movement. And I think what we’re going to get to is actually less. So when Omni had more of a poly Omni, meaning all, I think it’s more poly cloud, which has many, because there are so many different people in the market that are interested in cloud for marketing and every major platform or SAS or pass provider is trying to get in on this movement towards the cloud or being cloud-based and et cetera. And so I think you’re going to see an interesting challenge emerge where marketers trying to build the perfect tech stack are going to realize that there are certain areas of technology take Google as an example, where if you’re on Google technology, you will have a higher level of fidelity of information. The second that you leave Google technology, that fidelity of information may need to go down in order to protect Google from data privacy laws.

Allison Hartsoe: 15:24 What you’re saying about protecting Google in privacy laws reminds me of the main ideas that you get from mobile marketing. Is that an example of where the fidelity might change?

Max Kirby: 15:35 Yep. That’s right. It’s that same sort of as close as you can to the time before and the time after, without necessarily giving me the information that I can use as a bad actor.

Allison Hartsoe: 15:45 Wow. Okay. So that is a really thin line between Cambridge Analytica and made marketing.

Max Kirby: 15:51 Yeah. Well, it’s an interesting thing that’s developed because it’s largely a bottom-up solution. I mean, everyone innovating in this space has been kind of coming up with sandboxes and ideas and testing them. It’s been very, very much emergent. I think that there are some good projects out there in the market that are thinking about how to solve this entirely things like sovereign identity projects. I think we’re ways out from those really getting any play in the enterprise space. Although there are some folks that are experimenting with them. One example is out of MIT, Tim Berners Lee has been working on a project called the solid project. And it’s basically a term which is becoming more popular around data pods. And so you have kind of privacy policies and sidecar loaded next to the data. So if you were to process the data, the sidecar would actually say, here’s what you can use this data for. And that one’s really interesting, but they’re very early stage, and they’re open source as well. So they’re trying to kind of tackle this at a broader method. I think for the next eight to 10 years, the call is going to be towards getting all of the data in one place, building a customer data platform, and then being ready for this sort of mercantilistic economy of data that we’re going to see where you want to get more information at all times than you’re giving out.

Allison Hartsoe: 17:01 That almost sounds like a data velocity or an information velocity. And maybe that gets back to the economics a little bit of data. I’m surprised you said it’s eight to 10 years out, but could you talk a little more about the economic side or the drivers behind this?

Max Kirby: 17:15 Sure. The way to explain it is actually kind of strange. You have to reverse what we’ve been thinking about for the last ten years. Let’s say that you could know everything. And this is actually something that we start many of our workshops where we’re building customer data platforms with this question, which is, let’s say that you could know everything about a customer that you had sort of the mind of God or the all seeing eye or whatever you would say. Would you actually change anything that you do today? And if you can’t answer that question, the pursuit of data can seem almost like good in and of itself, but not really engaging the drivers of value. And often it can backfire. I mean, there’s the more data, more problems factor, which is you may go off and collect noise and then have to figure out where the signal is and add some of your more traditional segmentation methods or use some research, all of which takes time.

Max Kirby: 18:00 And so the connection between controlling data or at least collecting it in order to then control, it is really around what you would do with it in the first place. I’ll give you an example. When we were talking about this at LSE and kind of from then on, we’ve been expanding these models. The way that a wall street analyst covers a tech platform is very different from the way that a wall street analyst covers any other kind of company and metrics like monthly active users. Right? Think about how many retailers or insurance companies or CPG companies could report on monthly active users as a meaningful metric, maybe a few, probably the ones who have invested in digital devices themselves, right? The connected home brewer that connects to the wifi or the auto manufacturer who invested very early in connected cars. They might be able to achieve that level to which a metric like that would actually mean something. For the rest of the market, monthly active users isn’t a proxy for the amount of value that you’re able to drive from those monthly active users. Like it would be for a large tech platform.

Max Kirby: 19:04 And yet everyone wants to start getting into big data and many in tech forward industries, I’m thinking technology, media, telcos, they are in a position to actually build data monetization businesses. And many of them, of course, have right? Zander oath, Amex advance. These data monetization plays can be very valuable, but you have to have enough data to get there. So maybe you can valuate them by the size of their databases. I think the bottom line is if you can’t answer that question, what would you do if you knew everything? Then you can answer the question. Why should I go and collect data in the first place in real terms? Something that the CEO can actually sink their teeth into.

Allison Hartsoe: 19:43 That’s a very nice approach. It’s very tidy question in the beginning because I think a lot of us, a lot of executives assume if I just Lam a bunch of data together, I’ll get all of these insights pouring from my cloud computers. And that just doesn’t happen. And I think you highlighted how many more factors and segmentation and work that is when you start loading more data in, in the search for value. So, you have to define the value in the first place.

Max Kirby: 20:12 Yeah, I’ll give you a quick example, cause it can sound very theoretical. But when you bear down on this thing, it can actually give you some simplicity, there was a client that we were working on. I think sometime last year, a large retail bank and the requests from their marketing side of the business were we need to start trying to figure out who would be a good target or a good acquisition for our checking business. And of course, there are regulations that you have to be careful of in this arena. It’s financial services. So the data you have to treat kind of differently, but this is supposed to be what the credit score does. To some extent, the interesting thing about the credit score, though, is that it’s misleading in many ways, or at least after this project, we were able to show that it wasn’t as predictive as other factors.

Max Kirby: 20:54 You know, the credit score was based off of originally bankers, deciding that maybe they shouldn’t give loans to people who had judgments against them that had been sued and needed to pay up. And so, the banker would go, and they would check the judgment book at the courthouse to see if your name was in there before they gave you a loan. And if your name was in there, maybe they don’t give you a loan. That was the origin of the credit score. What we found during this project that the single most important determiner of a good someone who was financially responsible, someone who we wanted into our checking business was actually a transaction for going to a grocery store on a weekly or biweekly or some kind of regular basis. And so we normalized that to say whether it was every two weeks or every one week, the people who were going to the grocery store to go shopping, that was much better of a predictor of financial responsibility than the credit score.

Max Kirby: 21:44 And it worked very well. And I think it kind of intuitively indicates this thing that we might understand at the human level, which is if you’re going to the grocery store, you’re not ordering delivery, you’re not having takeout. You’re probably planning your week and thinking about what you want to buy. And so, there are all of these behavioral proxies for financial responsibility that are latent. And if you can get to those, that’s very, very valuable data. We tell our clients, don’t start monetizing your data until you yourself are getting value from it, right? Eat your own dog food and prove it works for you. And then maybe you can start moving into a business that is more based on information and therefore, could be measured by monthly active users would look more like a tech platform, but that’s a concrete example of what we mean when we say customer identity capital.

Allison Hartsoe: 22:27 Does that customer identity capital, is it inherently more powerful when the consumer doesn’t know that there’s a relationship between the grocery purchase and the checking account?

Max Kirby: 22:37 So this goes back to the first discussion we were having, right? It’s a question that is hard to answer. So I don’t know if we ever looked at that during the duration of the project, but like we were speaking about the things that a company knows about you that you know of seemed to have less of an overall persuasive effect, mostly because you’re kind of expecting those ads. It’s like when you have that ad that follows you around the internet for the thing you already purchased. It actually hurts your brand. We’ve got some inklings of research that shows that that is not a good thing. Especially the younger you go in consumer. They really don’t like that because you’re hitting the creepy vibe, and you’re also losing points for using data the wrong way. But the inverse, I suppose, would be true as well, which is if you’re able to use data to personalize or to target me, it shouldn’t necessarily be in such a blatant way that I can say, Oh, you clearly know exactly who I am because it’ll have less of an overall effect. There’s a subtlety that emerges with our behavior on how we act when we know someone else knows something about us. And that applies in the commercial world as well.

Allison Hartsoe: 23:40 Oh, I remember this with target, the infamous target pregnancy example. And one of the things they found was that when they sent people flyers for all maternity clothes, it was not as good of a return as when they mixed it in as a more subtle, like here’s a couple of maternity items in the midst of everything else—so same point.

Max Kirby: 24:01 Yep. It makes you more comfortable. Right? And the way to start on this is just to start looking for ways to kill costs in a weird way. It’s not so much about changing your targeting strategy and then figuring out how to do what we’re calling anti personalization, which is what target had to do to kind of bring the temperature back down. The best thing that you can start on is just identifying the ads that you’re spending for when I click on them, the item is actually out of stock, and there are billions of dollars spent on these ads. And it seems so simple and intuitive to say, don’t buy advertising for things that if I were to actually click on it, land on your product detail page, go through the whole UX experience and it’s out of stock, or it’s not in my size or et cetera.

Allison Hartsoe: 24:41 Yeah. And I think that gets back to the economics, right? I mean, that’s talking about velocity.

Max Kirby: 24:45 That’s right. That is an example of the customer identity capital conversation. It’s just the if-then statement, for if you knew everything, what would you do? It’s if I knew the customer shoe size, I would only show them ads for things that are in stock in their shoe size. And that requires about four different integrations across the stack and some data that isn’t even customer data right there. It’s the order management system in that example that has to come in. So not a lot of folks are thinking about customer data platforming as something that will eventually just kind of dissolve as a term. I think that this term is going to be something that we start, but we never really finish. And so, for that reason, I think it’s more of a movement than it is a thing or a buzzword. I mean, they do manifest an actual platform, but to some extent, it’s just the way to be customer-centric. And that’s what we tell our clients. That publicist group is the way forward to the transformation you actually want to live through. It has to be customer-centric. It can’t be about the modernization of the backend first. You should be doing that from the outside in.

Allison Hartsoe: 25:43 Oh, I like that phrase from the outside in. So, let’s say that I want to get started. Can you break it down into more of a step by step? What’s the first place I should start? And then, how should I move forward if I’m really thinking about the economics of my data and trying to get value from it.

Max Kirby: 26:00 Well, in marketing, we’re storytellers, right? We’re able to understand how to tell a story. And I think the thing that to start with is what story are you telling your organization about the value of data in general? And the lens that I usually would recommend is, you know, the internet is changing itself, right? I mean, these laws that are coming out for privacy are ultimately switching our view of individuals from objects, things that you acquire, or you retain to subjects. And that’s what they’re called is data subjects in the law. And a subject is very different from an object. And so telling that story I think is important to getting started because you need to go together on these things. The hybrids are what you’re looking for to run a customer data platform and program, or to start this up. The second thing is once you’ve found some of the allies or some of the folks that are going to be involved in this program, you need to be able to come up with some type of assumption-based modeling to prove that there is intrinsic value in understanding the customer more.

Max Kirby: 26:56 And this is where you want to be choosing. You want to look at the information that you would actually make a decision about the, if the ends because if you’re doing great segmentation or you’re doing great personalization, or you’re getting down to identity-based marketing, there has to be some kind of decision-based on the information that you see, if you’re gonna put them in this segment or that segment, it doesn’t really matter if you’re not going to do anything with it. So, start with that, determine what those are, and then the tech and the data should kind of flow from there. There’s only one sort of caveat that I would add to that Allison to your listeners is space is starting to move pretty quickly. There are alpha programs and beta programs that you want to be a part of that you need to sort of ask into.

Max Kirby: 27:34 And there’s an advantage to being earlier to those than not. So you have to keep both mindsets of, go in the way that we’ll be able to scale and tell the story internally and make sure that people understand that this is going to affect them, even if they’re not in marketing someday, but at the same time, try and start moving towards adopting the cloud. Getting API management frameworks is another big one, make sure that you have a consent management system. These are things that are really on the edge of progress that you need to be thinking about today alongside your incrementally evolving customer data platform strategy.

Allison Hartsoe: 28:04 I guess it’s always very complex. I imagine. And more than I imagined, I certainly see that seeing the complexity across customer groups and across companies, when they’re trying to execute for change, I think it does come back down to quick wins and helping that. What is essentially your second step when you found the allies, and you’ve got your assumption model, that’s the critical tipping point I see for most companies where they can either get it off the dime or they can’t. And sometimes it has to do with the fact that the company is so big that this small projection doesn’t mean that much, but in the larger picture, it really can mean a lot, especially when you’re talking about competitive advantage. Does that sound, right?

Max Kirby: 28:47 That sounds right. I mean, when you think about the disruption that you’re seeing in the market, I was actually just looking at the graph of how blockbuster kind of added new stores for ten years. And then it was just a precipitous fall off and kind of close them all within the space of two. That’s what it feels like, though. I mean, people may be less concerned with where they are today, where they’re going to be tomorrow, even if it’s going to generate great returns in the short term, transformation is the acceptance of the fact that change means that you’re going to have to balance that, right? You might have to sacrifice immediate returns today to invest in the future even if that’s just a little 1% or 2% only on your digital, only on the things that you can track online. That little seed is the beginning of the tech company that you’re trying to become in the future.

Allison Hartsoe: 29:30 Yes, that’s perfect. That’s a great way to sum it up. So max, if people want to reach you, what’s the best way for them to get in touch?

Max Kirby: 29:36 Well, you can always reach out on there’s a website. I have it up called max abstracts. There are videos that I do to kind of explain very complex marketing topics or technology topics as if you were five years old. I think we speak in a lot of code as an industry. And so there’s some contact information there, and you can always reach out on Publicis Sapient. That’s the side of Publicis group, which is one of the big four communications firms. So that’s where we’re working to try to make sure everyone knows how important CDPs are and how to go about them the right way.

Allison Hartsoe: 30:04 Perfect. As always, links to everything we discussed, including Max abstracts, will be at ambition, data.com/podcast. Next, thanks so much for joining us today. It’s been a very enlightening conversation.

Max Kirby: 30:16 Thanks for having me on Allison. I enjoyed it too.

Allison Hartsoe: 30:19 Remember when you use your data effectively. You can build customer equity. It’s not magic. It’s just a very specific journey that you can follow to get results. See you next time on the customer equity accelerator.