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The CMO Confidential Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast, I Hear Everything delivers podcast production, growth and monetization solutions that transform your words into profit. Ready to give your brand a voice then visit iheareverything.com welcome to CMO Confidential,
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the podcast that takes you inside the drama, decisions and choices that go with being the Head of marketing. Hosted by five time CMO Mike Linton.
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Welcome marketers, advertisers and those who love them to Chief Marketing Officer Confidential. CMO Confidential is a program that takes you inside the drama, the decisions and the politics that go with being the head of marketing at any company in what is one of the most scrutinized jobs in the executive suite. I'm Mike Linton, the former Chief Marketing Officer of Best Buy, ebay, Farmers Insurance and Ancestry.com here today with my guest, Dr. Ed Dobbles. Today's topic, Research and Analytics in an AI World. Now, Ed has been in research and analytics his entire career, including Vice President roles of Advanced analytics at Diageo analytics and Pricing at H and R Block and Research and Analytics at Supervalue. I can't believe I got through those analytics all the time without messing any of them up. He also received his doctorate in Marketing analytics, where his dissertation was hold on. Driving adoption of advanced analytical tools within sales organizations. Wait for the movie. It's going to be great. So Ed has been in the catbird seat watching research and analytics work to impact companies for over three decades. Full disclosure. Ed and I work together at Best Buy, so we've known each other for a while. Welcome Ed.
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Honored to be here.
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Mike.
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Thank you so much.
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Great to have you. Great to see you again. First things first, let's talk about the difference between research, analytics and then what we're going to talk about a little bit today. Generative Business Intelligence. Give us a lay of the land.
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Outstanding. So the way I always think about it is this research is what you do when you don't know something. You don't know how people are going to react to something. You've never had any information on it. Let's go out and do some research on it. Analytics, you've got the information sitting around somewhere and so you turn your your nerds loose on it to find out what's going on, you know, and that's the, the general split that has always been generative. BI is just a variation of analytics. So it's a combination of AI and traditional reporting in one combination. So think about it. As the simple way to think about it is you get to talk to your data as opposed to talking to your nerds,
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but you can't eliminate the nerds entirely is my guess so. And I, I love the nurse and
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probably sincerely, I hope you don't, because
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I still need a job. So, you know, I guess one thing I would say is, you know, since business, you know, my perspective on business has always been saying we're going to be more data driven, more research driven, more analytics driven. How is that really going from your perspective? Because my, my, my sense would be there's probably five or six decades of businesses saying this and where are we really in that kind of timeline of where businesses really are?
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B
Yeah, I think it's a great question. So if you think back to our Best buy days, one of the best buy values was unleash the power of people. And so the goal in analytics was to free the data, if we free the data. Because for the longest time back in the day, the way you got information was, yeah, you called me, you caught me as I'm walking past your office, you said, hey, get me this thing. And then, you know, a day or two later I'd get you that thing. Because that's how long it took to typically get those things. So the first version of freeing the data was, and you can laugh at this, was an almanac. We had a three ring binder of data that we handed out to key people, say, here's the information.
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Oh yeah, look at png. When you started, you got this thing called the factbook, which is you have all your data in this 3 inch factbook, leather bound with your name on it. And factbooks were sanqua.
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B
San. Yeah, and they're great. And think about what that means. Like it, you had the data in your hands, you didn't have to talk to the research team. And it was great until you had something that wasn't in the factbook. You know, there's always something more like, well, how does that apply in New Jersey versus the entire country? I don't know. It's not in the factbook. You got to do something. So the next version of it. So the default position then was I went from having the factbook to let's just go ask the analytics team or the research team the question. The next version of that was the wave of BI, you know, MicroStrategy, Domo, Tableau, Power BI. And I must have done like five to 10 of these things over the course of my career. And they were a version of the the factbook, the almanac, just with. On your computer and with filters. And, you know, those were okay too, but they didn't.
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And that. That democratized it a little bit too. Right. I didn't have to wait for someone to give it to me. I could go get it. I just had to be trained on how to use tableau or whatever.
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And that's the problem. Because turns out your CMO or your marketing manager or your district manager has a day job. And that day job isn't learning how to do tableau or learning all the quirks of power bi. They had to figure out how to do it. And so it kind of worked, but it didn't fully work because people went back to their natural ways of working of. I'm just going to ask the analytics team and they're going to give me the answer. Like I said, I've been through waves of this, and it consistently pissed me off. We had great tools that just didn't get adopted, like, what's going on?
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And so because nobody wanted to sit around to do pivot tables by themselves.
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Well, hey, hey.
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All right, you're right. I'm sorry. I didn't mean to hurt your feelings.
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But yeah, I had a great Friday night working with basket data when I had super value data and groceries. Turns out, 30 years ago, I knew Red Bull and vodka went together really well. It was always in the basket. Never milk or fruit in those baskets, though. And so the challenge is, people like me have been trying to roll out these tools consistently and consistently not getting the answer. The biggest users of the BI tools was always the analytics and IT teams, the people who put it together as a general. And this pissed me off so much. That's why I went back, got my doctorate. I wanted to actually know why people don't do it. And it turned out, thank God, it wasn't just me. Most of these things fail. 50 to 65% of these things fail because they don't either deliver the value they promised, but more importantly, they don't fit into the ways of working. People want to just talk to the research team, let those guys who know know what's going on, answer the questions for me. I don't want to have to do this crap myself. And so, you know, when you look at this arc, there's been this consistent stream of I want to put data into the hands of the business users and the business users pushing back of, hey, I want to do my job, not necessarily become a data analyst.
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And can I shorthand that by saying the Business units want the answers. They don't want the work. Because one of the things their work is to pick the right answer and evaluate all this stuff. It's not to figure out the data behind the answer, especially if you have a lot of decisions. But that was the whole thing then, which was, if I can just do this faster, better, quicker than anybody, I will win just because I will be applying these insights quicker. And what you're telling me is that didn't happen either? Probably.
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Yeah. I mean, here's. Here's the challenge. So first off, this is why you're good at your job. You just said, you don't want a hole in the wall, you want a picture hung. I need a nail, and I need a picture hung on that wall. And so the fast side of things actually go back to the example of the. The almanac, the PNG factbook.
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Yeah.
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Not much faster than that. The data is in your hands. And did that make you a better marketer? Yeah. Did it revolutionize how you went to business? No, because there's only so many things were in that. So speed doesn't necessarily win. What wins is when you build tools that change a decision that actually make people do something differently.
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So one of the things you hear in business all the time is, we want to be data driven. The data says. And yeah, when we were talking earlier, you said, you know, analytics is hard, but people are harder. What do you mean by that? Because the other thing, I've never been in a company where they said, we don't want to be data driven or data doesn't matter. Tell us what you mean by this. And when you say analytics are hard, but people are harder. Yeah.
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So you actually had a great example, this on your podcast a while back. Joel Shapiro from.
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Yeah, from Northwestern. Yeah, I love that Euro grocer case.
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Yeah, yeah. And so think about his case study that he. He talked about. There was a European grocer that had a way of hired a consulting group to get data to. To the store, say, this is what we should stop. And it paid off. It was six and a half times better than what they were doing. In the end, the board didn't accept it. The analytics that those people must have gone through to figure that out, extremely tough. The math was incredibly tough to figure that out. But the getting people to adopt it, to accept in that case, what was a black box, and say, yes, I'm just going to accept. The black box tells me to stock this many asparagus here and this many asparagus there without Having any knowledge, that's where the people fail on it. So analytics are always challenging, but people are tougher. And that's what I've consistently seen. And that's again, what the research says when you look at this, when it fails, when the analytics fail, it's typically not just because the analytics is spitting out nonsense. It's typically because you haven't connected it into the way people work and the way people want to use technology.
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And so is that because the people don't believe the analytics, they don't like the analytics, or they don't like giving up power or something else.
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So I think about it as influence. You know, you're not going to believe somebody out of the blue to tells you to do something completely different. You have to have a relationship with that person. You have to have a level of trust with that person. You got to have some connection with it. Like, you know, you and I worked together a long time, but we had plenty of fights about analytics, what was right and what was wrong. You know, I've had, I've almost gotten fired because I believe some. I had facts that my CEO didn't believe. And so to your point, the challenge that you run into is, you know, you gotta make sure that you get their data to the right person at the right time, and it's right, but you gotta build the confidence around the question. So transparency, consistency, you know, something that shows a link to what you've done in the past. Those are the things that, that make a difference. You know, it's like, hey, Mike, it's like that one time that we did the project in Albuquerque. This is what this is like. Those build the connections and trust in the data that this says, though, there's
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a giant humor in human interface in between the most powerful insights and the acceptance of those insights. Can you give me any examples in your career of times where, you know, you talked about where there was fighting about what the data said or the insight off the data. And I remember a couple times at Best Buy going to some of our vendors with data, and I'm saying, yeah, we, we get it, but we're not going to change a single thing because that's how we get paid or that's how this works. And we go, okay, so give me any examples you can of this. And then where the human factor, you know, either doesn't do a good job or does a great job.
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Yep. Well, I'll give you one that, that works on both levels. So I worked for H and R block, H and R Blocks assisted business was where they made all their money. And it's, it's still, I looked up the numbers still on a long term decline. You know, business is going down consistently year after year and the, one of the reasons that it is is that they're the high priced solution in the marketplace consistently. You know, all the research comes back, the price is too high and you are priced, you're probably 20 or 30 higher than what an independent tax person would do. And so every new CMO that came in, every new marketing person that came in said, you know what, we should drop prices. But we had the analytics that said we have 80% of our business is with retained customers and they have the lowest price elasticity that I've ever seen in the wild. So if you drop price to take care of this problem, one, you're not going to make it up on volume and two, you're just going to start a price war with independents who can drop it further than you could drop it. And you've just taken margin out of the industry. I held firm on that belief. The data screamed, that was the situation. And eventually my CEO screamed back at me and told me at the end of the season, I almost fired you yesterday and if you, if you don't do something about our pricing, I will fire you. And so we actually had to find a different way to do it. We actually worked on pricing transparency as opposed to just a price drop because that was the way to win in the marketplace. So the combination to your point, what works? The facts were right, the data was right. I had to listen to the consumer and I listened to my CEO to stretch myself into a different direction to find a solution that works. So I was both the human who was dead right and almost literally dead right and, and also wrong. So I, you had to learn like. And that's the thing you have to figure out. You know, again, the numbers are easy, relatively speaking. Figuring out how to manage people and manage your CEO is a different challenge.
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And, and as we get to this new way of doing insights that generative bi. Can I actually start. Does this mean I can start modeling these things in advance of my data or do I still need the human in between that?
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Here's the.
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I'll give you one example. This is the same thing that happened with DNA and when Ancestry and 23andMe and everyone was fighting, the data would always say more people would come on board if you drop the price, which they would, but then they wouldn't convert to family history. And then everyone just kept Dropping the price until suddenly you took all the margin out of the business and you couldn't get any more new users because everyone had tried. So you had, over time, probably eaten up a ton of your profit margin. And that was just an ongoing argument all the time. And one of the things about this. And when you look at Generative bi, can it do the modeling for you? Can it have any of these arguments for you? Or is this still a human frontier?
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So the answer is both. So think about this as AI solves a whole host of problems. And take it from a guy who spent his entire day talking to Claude. I'm on Claude. I think I burned a billion tokens last month.
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So you're costing them margin.
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All right, I'm costing them margin and I'm destroying the environment at the same time. Good for me. What I think about this is a couple things. So Generative BI can take the human out of the loop of, can you get me this answer? And I use, I use a artificial analyst to gain data for me. Tell me what's going on, Tell me the prices for my dispensary client, or tell me what's going on with, you know, menus, alcohol menus in different places. I have an AI that tells me that that's a version of Generative bi. I also use the tool to help me brainstorm. So I need to think like this type of a person. How do I put myself in that frame of mind? You see some of that coming out in different ways with the AI analyst. That's what Generative BI is. And some of this, think like this person. That's where you get some of those synthetic panels. Synthetic consumers work. And so there's, there's analytics AI that's creeping into all parts of analytics and making us different. You just have to be smart about how you use them and understand the risks associated with them.
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Let's talk about deterministic systems, probabilistic systems, and also synthetic panels. All three of those things. And then, you know, we'll talk what, how our users should be thinking about this stuff.
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So deterministic is like a calculator. You should be able to punch in 2 times 2 and get 4 every time. Probabilistic is like asking your smart co worker, you know, well, what do you think is going on in the marketplace? Yeah, this. You ask them tomorrow. Yeah, it's not going to get the same answer. You're going to get something else. The best analytics people that you've ever met, that I've ever met are a Combination of both those, you can consistently get the core answers out of people, but they also can take context and continue to change your answer. The AI system of the future is going to need to do that as well. And you have to. But you have to build that logic into the system and that's something that you're going to have to develop. So here's an example. My AI, because I don't trust my AI to consistently get the right answer, I put a sentinel question in every time. So you may not have ever heard of Malort. Malort is Chicago based spirit. It tastes nastier than Jagermeister. It's only, only on menus in Chicago. It's on about 6%, 7% menus in Chicago and it's on 0% anywhere else. So whenever I have my AI analysts pull data in the alcohol category, I say check this data point, make sure you can do it. Because if you pull that data correctly, I know you're probably on the right tab. Or if I'm really nervous, I'll have two AIs both pull the data separately and I'll compare answers. That's using deterministic and making sure you build deterministic into your system. The probabilistic side of things.
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In deterministic, if Malori, I guess I'm saying it right, is sold. Yep. And I'm sure Malort owes us some royalties for putting them on the show. But. But that's deterministic because it's either sold or not sold. And you find it or you don't.
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On the menu or not.
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Probabilistic.
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No, probabilistic is like your smart friend. It's what's going to drive the business. Which, which menu should Malort be on? Chicago, but probably not. The fancy place in Chicago, probably the closest to the university would be a better place for you to. And so you have to build that into the system as well. And that's where you could get a different answer each time. And that still is where humans are better today. Your mind is better than the best AI currently at putting these data points together and saying what you should do with it. But the AIs are catching up. They're getting better all the time.
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And what about synthetic panels?
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Synthetic panels, I got to tell you, I, I believe in them. But what I always say about them is all models. This is a quote from somebody, it's not mine. All models are wrong. Some are useful. Synthetic panels are definitely wrong. But they're also models and sometimes they're useful. So back to the Example, if you're building a panel that is pre fed, your custom LLM, your custom GPT with all of your information, this is the Choosy MOM artificial panel or the, the tech and entertainment enthusiast panel. You can use that to brainstorm. You can say, okay, I know what you, you know about this consumer. You can put yourself in that mind of consumer. Let's brainstorm back and forth. I do that all the time before a pitch. I did it before this podcast. How to think about it. That's useful when you, when you make the leap into saying, well, instead of just using it as a way to help refine my thinking, I'm going to make it in place of deterministic data. I can just ask the panel. That's a big no for me. That's where it fails.
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How do you know if you are training your panel or whatever? If you have bad data going in or you don't have complete data going in? You're building. Are you by definition building bias or omissions or errors in that panel? And how do you know? Because what you have is you don't have any sound input. You think you do, but you don't. And then you get these answers. Give our, give, give us some help for how do you know you're building this right?
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Okay, the answer is this is why I used to have hair and I don't have hair anymore. Data always kills you. You always, every place you go, the data is always, you know, a mess. It's like somebody's garage that they've never cleaned out.
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Yes. Never have I gone into company. Everyone says, you know, we have pristine data and we love it.
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Yeah, this doesn't happen. And so there's a couple things you have to do. You have to find just like the Malord example, you have to find some things that are consistently correct and built those into the, to the system. Either your analyst needs to know. These are the things at HR Block, for example, if I ever saw numbers that were pointing up, I was always questioning those because we're in a long
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term business was going down and, and a bunch of states were giving you free tax stuff under a certain income level.
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So yeah, you know, for Diageo, you
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know, that would be the marketplace. Yeah.
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If you don't see Great Crown Royal on the menu in Texas, something's wrong with your data. And so you have to build that type of stuff into the system and your great analysts will know that and you can build that into an AI as well. You know, you have to work that into the system to.
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But you are making a case that if I don't have a human in here somewhere between the decision maker and the data and I have anything wrong, I could make a whole bunch of mistakes. Mistakes.
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You could, here's, here's what I'm saying. So I know most, I know that I've tried to do this for 30 years. I know most of these fail. I am still, and here's the shock. I am still an evangelist for this. I believe this next wave is going to do it. Now maybe I'm like Lucy and Charlie Brown. I'm, I'm going to get the ball pulled away from me again. But, but I believe this one is going to do it because it fits where people are. I want to talk to my data, I want to talk to an analyst. And I think you could build a system that starts out with I'm going to give you the basics and at some point I'm going to say I'm going to have the system tell you you should go talk to a human being. At this point, this is more than I can answer for you. And you can build those guardrails into the system.
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So we got a bunch of people out there that are probably under enormous pressure from their boards or their executive teams or themselves just to make huge progress here. How should they think about building their research and analytics teams for the future? Give us best practices and biggest mistakes people could be making right now.
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Yep. So the pressure everyone's going to feel is the pressure that I have. I've lived over the last year and a half. I went from having a 20 person team to having me and I'm still getting a ton of stuff done because I'm using AI. People are going to want that and they're going to want that in their system. The people who are going to do this best are going to be a couple different people. First off, there are the people who focus on the foundation. How do you clean up the data to the point where you can trust it to live on its own?
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And when you say foundation, you say, I am looking at my database and thinking it is as cleaned up as
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I can make it or as reasonably cleaned up as you can make it. It's never going to be perfect. But how can you make it reasonably clean on the metrics that matter the most? And the reason that's important is because the biggest skill that I've had as an analyst is not statistics or storytelling, it's triage. We had a friend who we worked with, he used to say, 10 times nothing was nothing. And every time a small brand or a small geography asked me a question, I was like, yeah, that might be a big deal for Fargo, but 10 times nothing is still nothing. I'm not going to spend any time on it. But if you could string together enough of those wins because you knew the data was good enough and you can unleash the power of Fargo or your minor brands, that's where people are going to win. So in the short term, that's what you see in the job market right now. You see people spending a ton of time saying, how are you going to build those generative BI solutions that do that? The next thing that's going to happen about that is there's going to be a transition for analytics teams and marketers. CMOs need to know this. If your analytics team is built on a foundation of I give you access to data, those guys are about to be out of the job because the data, the AI system is going to eventually get to a level where it's just as good as you. It's just like coding because there is a right answer and you can say, did I get the right answer or not? Eventually you're going to trust that more than you trust having 10 or 20 or 30 people on your team. So you got to be ready for that, that transition. Which means while you are building the generative BI solution that allows that your analytics team, the leader that you have to be as a cmo, has to say, what are you going to be next? Are we going to give that money back to the CFO? That we're going to go from a 20 person analytics team to a 10 or 5 person analytics team that just supports this function, this tool? Or are we going to invest in higher value things so things that B or AI can't do very well today? The, the next level analytics, the next level thinking that's on there, the more of those probabilistic things versus deterministic things.
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A
And this probabilistic thing, the next level thing would be, I'm creating agents that can talk to me and brainstorm with me. Is that the next thing?
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Ah, that one's. That one can do it. Although we can do that right now. You know, there's all sorts of things you can do to get with the next tool. I think about it as if you can dream it today, you can build it and you know, think about all the times we've spent, all the time you spent trying to educate organizations on the business. What if you built an Automated podcast that just showed up in people's feed.
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That's what we're doing right now.
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We're doing right now. But I can take my AI, can take this job too, and replicate it. You can do some things like that, that make things better. But I think where the future beyond generative BI is for analytics is that deeper research, the predictive analytics. I had a guy on my team at Diageo who was literally a rocket scientist, had a PhD in astrophysics, and he was answering questions like what was going on in Florida? That's not a good use of his time. You know what a good use of his time was? When we built a predictive analytics, a custom forecast for every liquor store, bar, restaurant in America, for every one of Diageo's products. So we knew what to sell into. The Mortons in Fidi versus the Mike's Grill and, you know, rural Ohio. Those are where you're going to spend your money. So if you can take the, the, the lower value stuff and give it to AI and spend your higher value time on that, then your analytics team is going to get bigger or different or at least hold ground as opposed to being eaten alive by your cfo.
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A
And when I look at my data, who should look at my data? And how do I know I've cleaned up my data to an adequate level? I mean, other than outcomes, where there'll be judgment on the outcomes. But like, how do I know if I'm not a data person or, you know, I'm a CMO or director or cfo? I look at the data and I say, this is cleaned up or not.
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B
Yeah. So the way I think about it is twofold. First off, you know, you start with a narrow set of metrics you can trust, like, you don't try to boil the ocean. I'm going to only look at Nielsen data for the first wave of this. And I know Nielsen data is inherently clean. To begin with. We've used Nielsen data consistently. The next level up there are sales data, and we know our sales people. So if you're using data sets that are consistent, all you're doing is slicing it. Those are the ones that you can trust. And then you pilot them with some people who, who flag it as that doesn't look right, something looks wrong about that hoop. Who spot something that fails the Malort test or something.
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A
Well, this is a journey versus a, A.
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It's not day one.
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A single effort. And then before we get to our traditional last question, hall of fame, worst practices you see in the Market today, when you look at, you know, research and analytics.
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B
Oh, yeah. So the thing that drives me nuts is, you know, companies that talk a great game about, I want to. I want to build analytics for the future. I want predictive analytics. And then all of their work is, you know, data polls. Like you, there's a. There's a dissonance between, you know, I want to dream that I can do what Uber does in surge pricing and reality, what I. I'm actually going to use the business for, like, you know, as a cmo, you need to know this is what we're going to do, and this is what we're not going to do. You need to set those boundaries. And that's where. That's. That's where the biggest mistake is. And it drives me nuts. Like, don't hire somebody under false pretenses or don't have them build a function that says you're going to split the atom if all you're going to do is, you know, move things.
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A
As a hiring manager, you have to have. Or a CMO or whatever position we're talking about. You have to have some decent working knowledge of how this all works, or you will ask for stuff that you can't actually produce. Is that right?
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Yeah. Or you have to have a good leader to work with to say, okay, we're going to put some boundaries on this. This is where we're going to go and where we're not going to go, because you can't do it all.
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A
Well, that's a good one. That brings us to our traditional last question. Practical advice we have not yet discussed and. Or the funniest story you can share in the air. You can pick one or both, but you must pick at least one.
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Okay, I'm gonna give you two funny stories.
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A
Okay, Double header.
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All right, double header. I should give you a funny story to tip. Funny story is this. I don't know if you remember this, but Best Buy used to have a racquetball court, and you and I. Oh,
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yeah, I got hit in the nose.
- [33:01]
B
That's exactly it. So you used to routinely kill me. Like, not hit me. You would beat me 15 2, 15 4. I would trash talk like I was winning, but I was consistently losing. And the only game that I ever came close to beating you. I still didn't. Is you took a racquetball to the nose and it took you off your game.
- [33:22]
A
My glasses. I had to go into the next meeting with taped glasses and start the moral story. All right, get it out of your System cheat.
- [33:32]
B
Cheat to win. Injure your opponent, you win. I don't know that's a bad moral, but it's my favorite Mike story. The practical advice for people. So here's what I know about analytics people, and here's what I know about CMOs. The meeting that they hate is the governance meeting. Nobody says, oh great, the governance meeting is next on my calendar. I can't wait to talk about that. I've been working with AI non stop for a year and a half. I'm pretty good at this. And one Friday night when I was working on stuff and I was tired, I'd been working on long, I just wanted to in the day and I wanted it to run overnight. I gave a sloppy command to Claude and I woke up the next morning with a message in my email, your cloud account was funded and I had like 10 of them. And I ended up spending 1500 dollars on. On something that I gave bad instructions with no boundaries to Claude. And it did exactly what I told it to do and spent a decent chunk of money on it. So, you know, it's a good thing
- [34:40]
A
it didn't have access to all your investments.
- [34:42]
B
It's a good thing you didn't actually have empties my 401k to do whatever it was going to do. And so this is why you actually need to think about this because imagine if that happened on a corporate scale versus the dobbles AI scale. Some real cash could be spent. And so I hate the governance meeting as much as everyone else, but there's a real practical reason of why you got to pay attention to it. So the tip is actually set the boundaries like build the things like your spending limits. So now all my AIs can spend no more than $500 on it. So you know, build that into your system or you will get burned.
- [35:21]
A
All right, I think a great way to end the show. Thanks for joining us, Ed. And thanks to everyone for listening to CMO Confidential. If you're enjoying the show, please like share and subscribe. New episodes drop every Tuesday on Spotify, Apple and YouTube. And our catalog gives you access to nearly 170 shows, including Colonel Mustard in the study with the job spec. How poor design shortens CMO lifespans. The AI marketing battle. A view from the front lines. Dissecting compensation. A primer on understanding and negotiating pay. And what does social first mean? And is it right for you? Hey, all you marketers, stay safe out there. This is Mike Linton signing off for CMO Confidential.