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00:00:31.995Hello, welcome to another episode of OpsCast brought to you by MarketingOpscom, powered by all the MoPros out there.
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00:00:34.522I'm your host, michael Hartman, flying solo today.
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00:00:43.837We'll get Naomi and or Mike back soon, I'm sure, and I know Mike is getting ready for Spring Fling 2025 coming up in a little for a month.
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00:00:47.472So if you haven't already done that, go check it out and sign up, if you can go.
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00:00:50.138But joining me today is Jeremy Katz.
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00:00:55.094Jeremy is currently SVP of Product Solutions, identity and Data at Merkle.
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00:01:02.962Jeremy is a data analytics and marketing technology leader with 15 plus years of experience delivering value and building high performing teams.
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00:01:08.703He is skilled at solving complex challenges at the intersection of technology, strategy and storytelling.
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00:01:15.484His background spans digital marketing leadership and ad tech slash market tech across multiple organizations prior to joining Merkle.
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00:01:17.075So, jeremy, thanks for joining me today.
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00:01:19.615Thanks for having me Excited to be here.
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00:01:28.644Yeah, this is going to be fun and you know, our topic is kind of in the realm of first-party data and things like that that we're going to focus on.
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00:01:49.786But before we, before we get into that, I'd love for you to, because you have an interesting uh as I would say is the case for most of our guests, right a interesting career journey, and you recently joined merkle, which is uh, for those who don't know a large consultancy agency, but prior to that, you spent most of your working career in in-house at brands.
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00:02:15.381So once you maybe walk through a little more of your career and how you know what led to you joining merkle, and while you're doing that this is something we didn't plan for, but I typically throw this in there like I would love to know if there were any sort of key, pivotal moments, you know, decisions you had to make that led you one way or another, or key people, right, who played an outsized role in your career trajectory.
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00:02:20.290Yeah, great, and that could be one of two ways right.
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00:02:25.276It could be somebody who I learned from and learn what to emulate, or it could be things I learned to mistakes.
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00:02:32.080I learned not to repeat as I got out of my career, things I don't want to do, things I will not do when I'm in that position, kind of thing.
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00:02:33.890Exactly so.
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00:02:39.824I started my career sort of where many people in data and analytics start as an English and journalism major.
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00:02:47.742That's a little bit tongue in cheek, but when I came into the corporate world I was really trying to find where do I add value?
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00:02:52.698And I had a background in journalism and English and I ended up starting in communications.
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00:03:03.640So communications sort of quickly turned into marketing and I worked in a lot of marketing roles and digital marketing roles early on in my career and a lot of it was getting thrown in the fire.
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00:03:07.596So for example, hey, do you want to learn how to run an email program?
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00:03:09.502I'm like sure, I can't code HTML.
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00:03:11.056I don't know how to do any of that, but I'll learn.
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00:03:14.158So I got thrown in the mix a lot early on.
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00:03:20.717And yeah, I mean you talk about pivotal moments my first job my boss quit two weeks in.
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00:03:33.508So all of a sudden I was leading, you know, marketing for North America at UTC United Technologies in Connecticut, for North America at UTC United Technologies in Connecticut, and doing PR, marketing, all kinds of stuff that I was not really ready for.
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00:03:36.157But I don't know if you're ready for a lot of things early in your career.
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00:03:36.919You got to learn to go.
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00:03:44.723So learning this marketing as a practitioner and marketing automation.
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00:03:48.310I'm not sure we even called it back then, but a lot of it was hey, you got to figure out.
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00:03:49.816It was e-marketing when I was doing it.
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00:03:54.861Yeah, E-marketing website marketing Right A lot of terms that are no longer being used.
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00:04:14.979I was able to kind of, like I say, hop from channel to channel and really kind of start putting a picture together of how these things worked in harmony and you know, eventually I ended up moving into the exciting world of insurance, which I actually say is very interesting when you get into it, but from the outside it can be a little dry.
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00:04:22.723And when I did that, I eventually moved into sort of more of a support role, I would call it at the center of the organization.
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00:04:27.332So now you know, all my customers were marketers, like I, had been up to that point.
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00:04:33.795So now you know, all my customers were marketers, like I, had been up to that point, and we had basically built out sort of a centralized MarTech function at that time.
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00:04:35.658And but I kind of stumbled into that.
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00:04:39.679I would say I'm always been interested in the technology aspects and kind of how things work.
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00:04:45.923And it was back in the day when the MarTech conference there was one in Boston used to be in person.
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00:04:48.384They're all digital now, but back then it was in person.
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00:04:49.605Well, not all of them.
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00:04:51.486I mean Muff's Blues is not.
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00:04:51.947That's right.
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00:04:52.407There you go.
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00:04:59.264But I had gone to that conference and just pulled together some slides and I came back to my my CMO.
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00:05:02.596She wanted to know what what I learned and I said, well, there's a thing called a CDP.
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00:05:11.495I'm not sure if you've heard about it, but it's kind of a big deal.
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00:05:13.783It's growing and eventually that led me into the role to build a MarTech team.
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00:05:15.509You know, on board a CDP in the organization and you know.
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00:05:24.360So a lot of my career has been luck and timing, but always being ready, always staying hungry and learning and trying new things and ultimately I think that's kind of what led me to Merkle.
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00:05:44.994You know I've gone to date on the analytics, you know, sort of by accident, not by accident, but you know, as my path has taken me and you know I decided to kind of challenge, take on a new challenge, sort of reinvent myself and sit in a different perspective yet again, which is now I get to work with, you know, companies and clients across many different verticals and I'm learning a ton again.
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00:05:50.605I feel like I'm almost back at the beginning of my career in some ways with the amount of learning that's happening.
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00:06:00.823But yeah, I'm excited at what I'm doing and I feel like if you're not learning and if you're sort of not progressing forward, you're falling behind, especially in this climate today.
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00:06:03.239So yeah, that's kind of a quick snapshot.
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00:06:08.809So you mentioned you did communications and then into marketing, and so I'm always curious.
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00:06:14.023So I have my idea of, like, when I think about communications versus marketing, what it is.
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00:06:16.177But how do you differentiate those?
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00:06:23.398Well, I have a very direct take of how I got into, I would say, communications, which was, you know.
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00:06:29.966I used to write articles for newspapers back when they were printed on paper and delivered on paper to write articles for newspapers.
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00:06:42.918Back when they were printed on paper and delivered on paper, and I remember creating articles 600 word articles, investigative sort of journalism in nature and I remember I got like $100 an article or something like that and they asked me would you want to do like work for a business profile?
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00:06:52.923I think I got paid like 3x the amount for the business profile and I was like, oh okay, this is a way to take my journalism skills and maybe apply it for companies.
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00:07:05.515And so my first job, I actually brought a portfolio of my articles in that I had written at the time and they're like oh well, you can write for our internal company blog and actually created an internal social media site at the time.
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00:07:09.173So I was kind of like taking the same skillset a social media site at the time.
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00:07:15.153So I was kind of like taking the same skill set and I would call that more like internal comms, which led into pr and then supporting our, our leadership team too.
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00:07:17.799So that's kind of how I think about the communications aspect.
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00:07:19.812But okay, that's kind of how I do too.
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00:07:39.582I just I was like it's like internal communications, pr, ir, executive, executive presentations and things like that, right, okay, yeah, yeah, yeah, yeah, that's as opposed to marketing, which is more customer like, directly customer focused in general, right, whether it's demand gen or you're doing sales enablement or product marketing, yeah, okay, interesting.
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00:07:52.635So my guess is you're the guy who, when you're an English major, that you like diagramming sentences because you know you like the structure, oh, I got a nerd out on diagramming sentences.
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00:07:53.298I still would.
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00:07:54.883I don't do it anymore, but I yeah.
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00:07:55.969I love diagramming sentences.
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00:08:00.701I like breaking down things to their essence and putting them together and seeing how they work.
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00:08:23.499So it's so funny because I'm the engineer here in this call and I'm a data guy and I nearly failed out of seventh grade english because I just like this diagramic sense thing is the stupidest thing I've ever heard of and I, like I, I did the bare minimum, like I, literally I think it's the worst grade I've had all my entire life and I really was very close to failing that class.
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00:08:31.457So, um, so irony there, you know, the data nerd, the guy who likes structure, is the one who didn't want to diagram sentences.
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00:08:52.333All right, so you mentioned that you've been doing a lot with data and analytics and recently in your career you've been spending time with first party data, identity resolution and you mentioned CDPs, identity resolution and you mentioned CDPs.
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00:08:54.865So, yeah, we've had guests on and have talked about CDPs and we've had guests on and talked about first party data.
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00:08:57.115I don't think we've talked to anyone about identity resolution.
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00:09:10.718But maybe what we start with, what are your working definitions of those and if there's any other terms that are relevant for our conversation, like what's the working definition of those and how are they related and different?
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00:09:17.435Sure, yeah, and I think you know first party data is probably one of the easier places to start.
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00:09:23.098You know it's data that you're collecting, you own, and it could be current customers.
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00:09:27.193It could be you know prospects who fill out a form on your site but don't convert.
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00:09:29.942But it's data that you've collected as an organization.
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00:09:46.081You have the rights to that data, you own it and you can leverage that for acquisition, for cross-sell, up-sell, et cetera, versus like a second party where it's a partner sharing their data with you, or a third party where maybe you're buying or renting data that you can use to decorate.
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00:09:49.360We call it that first party data Decorate okay.
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00:09:50.731Which you know.
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00:09:53.701It's an old-school term, maybe a little bit, but I still like the term decorate.
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00:09:55.096It makes it sound fancy, I guess.
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00:10:03.062But ID resolution to me is there's all these pieces of information around who you are.
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00:10:04.514I'll use myself as an example.
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00:10:05.619I live in Chicago.
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00:10:10.393I live in an apartment building, so I have an address associated with that building.
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00:10:15.310I've got my name and my first name and last name on my postal box downstairs.
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00:10:23.636I've got an email address that I use, a Gmail address, and I've got some Yahoo stuff I use for fancy football, old email addresses.
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00:10:26.262I have all these pieces of information.
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00:10:30.019Call it terrestrially me as a person that lives in an address.
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00:10:35.040And then I go on the Internet at my house and I have an IP address.
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00:10:42.863You know the user agent pings against my device and knows my device ID, all these digital fragments and pieces of information out there.
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00:11:05.543And to me, the resolution or the resolving piece is understanding that all that belongs to Jeremy Katz and bring that down to a single identity Right, and usually that's through an identity graph which is looking at the relationship between all those things and I would say, like the distance between those points, the stronger they are, it's a shorter distance.
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00:11:12.156So it's funny because, like, my email address is the same for the last 20 plus years, the one I use, you know, every day.
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00:11:21.689Sure, my address has changed three times in the last five years, so you know it depends on which piece of information is updated and how frequently it's updated.
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00:11:31.976But understanding who people are is really the foundation of identity resolution and usually you're engaging, you know, an outside organization to do that, and that's sort of what we do in my current role.
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00:11:34.497There's a number of companies that do parts of that.
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00:11:39.377So now is that is that so?
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00:11:41.565yeah, I think we talked about this like my.
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00:11:46.773Where I cut my teeth in the marketing domain was database marketing and I was building.
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00:11:53.841I built a 50 million household database and the whole concept of householding was new to me at the time.
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00:11:57.589It was mostly a consumer-oriented kind of idea.
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00:12:03.995But is that related to this, or is it just a separate thing altogether, or how does that fit in?
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00:12:05.639That's a good question.
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00:12:12.922I think entity resolution is the broader, you know domain identity resolutions contained within the entity.
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00:12:21.635So we think about grains and I'm trying to get too nerdy too quickly here but grains of data.
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00:12:23.986Right, like me, as an individual, I roll up to a household and it's even more complex than that.
00:12:23.986 -->
00:12:34.584Right, I mentioned living in an apartment building, so we have, like, the family household, the TV household that I have inside my walls here, and then I live in a unit, a physical unit.
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00:12:42.077Right, all of that is a grain that goes up from me as an individual.
00:12:42.077 -->
00:12:43.020And it's the same on the B2B side.
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00:12:47.913Right, like you work at a company, that's an entity, you have a role within that company and you probably have a business profile.
00:12:47.913 -->
00:12:53.985You know that's the stuff that your business email, you're going on LinkedIn, your LinkedIn account, you know, has business content on it.
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00:12:56.799So it's like those different grains.
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00:13:01.962I think about them as, like the whole the old school, you know, grandparent, parent, child relationship.
00:13:01.962 -->
00:13:05.016You know when you think about it and know it could be one to many.
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00:13:19.004It could be, you know one to one, but those grains are kind of what we talk about in in that entity resolution space interesting, and my guess is, some of this gets out into the uh for lack of better term dark web.
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00:13:37.279Because it's funny, because, like literally may have been today, maybe today or yesterday, I got an email that was sent to four like one email, but was sent to four different email addresses that are all linked to me, two of which I don't really use.
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00:13:47.660I think one was from my you know, my, my right I graduated college from, and one was like an old hotmail account that I don't really use anymore and it was weird.
00:13:47.660 -->
00:13:48.673I was like what did like?
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00:13:49.507So I was like an old hotmail account that I don't really use anymore and it was weird.
00:13:49.507 -->
00:13:49.658I was like what did?
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00:13:49.780So?
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00:14:02.139I was like, oh, somebody somehow found all this stuff about me and I probably deleted it because I was like this is probably not somebody I want to be dealing with, that they're going out there and scraping stuff that's out there, like that.
00:14:02.139 -->
00:14:08.192So okay, so that all makes sense.
00:14:08.192 -->
00:14:13.593I see the connection to my database marketing days when I was building households.
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00:14:14.554I get the.
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00:14:30.303I actually think the complexities for B2B are more significant because of the complexity one bit, which is, you know, not only are you part of an organization but maybe you're part of a specific business unit or a location, right.
00:14:30.303 -->
00:14:43.553And if they have multiple locations or multiple business units I even worked for one company where one person could be we had sort of separate this goes down to CRM, right.
00:14:43.553 -->
00:14:50.399We had separate accounts for different almost like not quite team level, but kind of like team levels for specific things.
00:14:50.399 -->
00:14:58.670We sell this product to this group and this other product to this other group, and sometimes somebody could be in more than one place so we'd have like intentional duplicates too.
00:14:58.670 -->
00:15:02.279So you're dealing with all that kind of noise.
00:15:03.682 -->
00:15:11.822So maybe before we get into what you're doing now, but like when you were in-house, do you have any project?
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00:15:20.021Like, give me an example of how this concept of these different things and how did that get happen?
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00:15:21.124What were some of the big challenges?
00:15:21.124 -->
00:15:22.164And then, how was that?
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00:15:26.794I guess this is a technology solution at the end of the day, right, just bringing this stuff together.
00:15:26.794 -->
00:15:30.159How did that get leveraged to help the business?
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00:15:36.858Yeah, and I'll try to tie it back to the CDP example too, because I think that's where I first really got deep into this.
00:15:36.858 -->
00:15:46.035You know we actually created you know for all intents and purposes a customer database, you know to start here, and it was called Customer Hub.
00:15:46.035 -->
00:15:54.482That Customer Hub, if you will, had sort of multiple lines of business, all the information we had on our customers brought into one physical.
00:15:54.482 -->
00:15:58.778At the time it was an on-premise database, one place.
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00:16:05.280Now, when you do that, even inside an organization, you may have a customer that sits in two business lines.
00:16:05.280 -->
00:16:07.533To your point, that may have bought two different products.
00:16:08.235 -->
00:16:15.318So we didn't even try to resolve it at first, we just brought it all together and we said, okay, you know, Michael Hartman has three email addresses.
00:16:15.318 -->
00:16:16.261What's the best one to use?
00:16:16.261 -->
00:16:20.221If he's given us three email addresses, what's the one that he usually transacts with us on?
00:16:20.221 -->
00:16:30.871And so I think that was the first foray was kind of building an internal asset that we could use for customer information across our organization.
00:16:30.871 -->
00:16:36.793The uses of that are relatively straightforward, right, Like I want to send you mail or I want to send you an email.
00:16:37.576 -->
00:16:44.290You shouldn't have the experience you had where you get, you know, three emails sent to your different email addresses, as if you're a different person in each one.
00:16:44.290 -->
00:16:47.852So I think there were some really basic use cases to start.
00:16:47.852 -->
00:16:52.956And then when you bring a CDP on top of that, it's like how do you ingest all of that?
00:16:52.956 -->
00:16:58.839And some light resolution happens in a CDP it's all your first party data, but then that goes out to multiple channels.
00:16:58.839 -->
00:17:04.103So that might go out to email site personalization etc.
00:17:04.103 -->
00:17:11.307And you're able to know it's one person you're orchestrating from a central place and then distributing that out across multiple channels.
00:17:11.307 -->
00:17:15.339So I know CDPs have evolved quite a bit since the early days.
00:17:15.339 -->
00:17:18.789Almost everything is called a CDP now and it's very different.
00:17:19.952 -->
00:17:28.567I was trying not to laugh when you said CDPs, you know old school, because they haven't really been around that long, right yeah, in the grand scheme of things.
00:17:28.567 -->
00:17:30.506Five, maybe 10 years.
00:17:31.019 -->
00:17:34.048Yeah, it feels like in the digital marketing data tech space.
00:17:34.048 -->
00:17:35.652That's like somehow way longer.
00:17:39.622 -->
00:17:43.612But being able to tie those things together really helped me understand all the components.
00:17:43.612 -->
00:17:50.006And then the question was all right, you've got all your customer data together, you've got a CDP, you're able to market to people.
00:17:50.006 -->
00:17:51.843What about your website?
00:17:51.843 -->
00:17:55.712What about all the different experiences happening in your website?
00:17:55.712 -->
00:18:06.941And we at the time I think we had like four or five different portals for different parts of the business with different logins, and I mean just making sense of that is a relatively complex challenge.
00:18:06.941 -->
00:18:12.108But then, at the end of the day, you've got data coming in and you've got to figure out how to make it all connect.
00:18:12.108 -->
00:18:21.741And I think that's ultimately where it started going deeper into identity resolution and digital signals and like bringing that all back to the same profile right right, um, and it's a.
00:18:22.222 -->
00:18:23.886It's a never-ending battle.
00:18:23.886 -->
00:18:26.571Right to get that right sure, okay.
00:18:26.612 -->
00:18:30.887So I have a couple questions.
00:18:30.887 -->
00:18:53.233So, on the like you brought all this stuff back together in CDP and you mentioned pushing it out, I think you said pushing it I'm paraphrasing pushing it back out to some of the maybe frontline systems, right, whether it's CRM, Was it being used to maybe inform transactional stuff?
00:18:53.233 -->
00:19:02.807So, hey, I'm a salesperson, I can see this activity of key people in my account and you have that as something that you inform, like if I have to make a call or send an email.
00:19:02.807 -->
00:19:04.325Was it being used that way?
00:19:04.325 -->
00:19:17.625Or was it being used for, say, website personalization or targeting with social based on whether it's lookalikes or whatever like how was it?
00:19:17.625 -->
00:19:21.347How was that used once that you did that kind of brought everything together?
00:19:22.878 -->
00:19:28.032Yeah, and that was the fun part for me being a marketer and knowing what everyone wanted to do across the Oregon.
00:19:28.032 -->
00:19:31.329I think I had 180 use cases to start.
00:19:31.329 -->
00:19:35.204They had to prioritize and you know, knock some off that list.
00:19:35.444 -->
00:19:44.649So one of the cool things we did was we used a different service for identifying people in the phone centers.
00:19:44.649 -->
00:19:49.488So when somebody calls in, it would pop, you know the phone number and get their profile started.
00:19:49.488 -->
00:19:51.261So when you greet somebody you know who they are.
00:19:51.261 -->
00:20:05.450So we were really able to take that to a different level, improve the match rates and improve the ability for our sales reps to be able to know who was calling in, in in on the service side too, and then also kind of pop a little profile of that customer.
00:20:05.450 -->
00:20:09.530What's their most recent four or five interactions, you know.
00:20:09.530 -->
00:20:11.281But there was a lot of things we did too.
00:20:11.281 -->
00:20:14.645We were able to help with our survey programs for NPS programs.
00:20:15.126 -->
00:20:15.366Okay.
00:20:15.386 -->
00:20:18.391Just by kind of like bringing all that data together for the deployment.
00:20:18.490 -->
00:20:27.956So for our listeners and watchers who NPS, if you're not familiar, net promoter score right so.
00:20:29.000 -->
00:20:29.805Yeah, A lot of acronyms in this space.
00:20:29.805 -->
00:20:30.670Thanks for spelling that out for me.
00:20:30.670 -->
00:20:40.795And then you know, COVID is actually a really good example of, you know, having to send an email out to your entire customer population a couple of times, especially in the insurance space.
00:20:40.795 -->
00:20:43.605You know, we actually people weren't driving All of a sudden.
00:20:43.605 -->
00:20:46.051One day people are driving and the next day no one's driving.
00:20:46.051 -->
00:20:50.827So a lot of companies, like ours at the time, gave back premium at that time.
00:20:51.368 -->
00:20:57.750So we had to send a lot of communications to customers and it was timely because we had built this capability.
00:20:57.750 -->
00:21:09.519We were able to be very precise in our mailings, understand who was getting emails, who was, you know, kind of more traditional paper based and be precise in how we mailed and communicated with our entire customer population.
00:21:09.519 -->
00:21:19.372You're talking, you know, millions and millions of customers and relatively large company and it was wasn't seamless, wasn't perfect, but it was pretty smooth, based on what we had built.
00:21:19.372 -->
00:21:20.723So just basic communication.
00:21:21.205 -->
00:21:21.928Yeah, and it was also.
00:21:21.928 -->
00:21:39.059So you're kind of hitting on the next part of follow up I had, which is I've been at different places, only one that I did tried to do the exercise of defining the characteristics of this is a customer or this is a person.
00:21:39.059 -->
00:21:40.103What is the minimum?
00:21:40.103 -->
00:21:41.686How do we define that?
00:21:41.686 -->
00:21:47.903Did you go through that kind of exercise of having clear definitions about that?
00:21:47.903 -->
00:21:55.423I'm just curious A how long did that take and how did you?
00:21:55.423 -->
00:22:02.104Because I'm going to guess, if you get enough people in the room, you get 10 people and you're going to get 10 different opinions about what is a customer.
00:22:02.987 -->
00:22:03.187Yes.
00:22:04.383 -->
00:22:05.046I joke all the time.
00:22:05.046 -->
00:22:09.807The hardest thing as a marketing ops person is when somebody comes to you and says we need to send an email to all our customers.
00:22:09.807 -->
00:22:13.506Yeah, what do you mean?
00:22:14.420 -->
00:22:16.969It seems like that should be an easier answer than it is.
00:22:18.443 -->
00:22:21.112It absolutely sounds like it should be a no-brainer, but it is not.
00:22:21.819 -->
00:22:21.980Yeah.
00:22:21.980 -->
00:22:24.709So I guess I'll stick with the because it's fresh in my mind.
00:22:24.709 -->
00:22:28.766I'll stick with the insurance example Independent insurance agents.
00:22:28.766 -->
00:22:30.127Are those our customers.
00:22:30.127 -->
00:22:38.726The actual insureds that we support are those customers Non-customers who are engaged in an accident with our customers claimants.
00:22:39.980 -->
00:22:56.147There's roles for each of these things and so I think, like I tried to expand the definition of a customer to at least include anybody who is a current we call them enforced customer, has a current policy with us, as well as anybody who is a current we call them enforced customer, has a current policy with us, as well as anybody who we've interacted with.
00:22:56.588 -->
00:23:11.187You know, in the last we actually pulled 10 years of data in to start that we have information on they're either a former customer they quoted with us, you know they had some interaction with us and we had them as a core customer, and then we kind of had the agents as a separate group.
00:23:11.187 -->
00:23:28.963But think about all the attributes and interactions and roles you're now attaching to all those profiles, and that's where I think like it's less, it's less just science, there's some art to it and getting all the business people in a room and making sure they they can, you know, have their input into how you define those things.
00:23:28.963 -->
00:23:32.011But ultimately, ultimately you got to keep it simple, right, it's got to still be usable.
00:23:32.011 -->
00:23:37.231So you can't have, you know, 55 versions of, maybe a data element.
00:23:37.231 -->
00:23:40.990You've got to try to keep it somewhat grouped logically, so it's usable.
00:23:41.560 -->
00:23:44.529So in my experience, I agree with you that we should keep it simple.
00:23:44.529 -->
00:23:51.647But in my experience, what tends to happen is people keep thinking of I don't even know that I would call them outlier cases.
00:23:51.647 -->
00:24:04.221But take a small example right, one of your customers over the lifetime of them being there, like at any point in time they may be, you know someone who's in the process of engaging with you.
00:24:04.221 -->
00:24:06.994They're not currently a paying customer, what you call enforced, right?
00:24:06.994 -->
00:24:13.605Uh, then they become that, uh, they end up being a claimant in another case, like with someone else who's an enforced guest.
00:24:13.605 -->
00:24:20.625So it could easily go like oh well, this person, at any point in time or in a given situation, has a different status.
00:24:20.625 -->
00:24:24.379Like, how do you, did you deal with that, did you?
00:24:24.379 -->
00:24:36.256Oh, yeah, yeah, okay, how did you go about getting to resolution on those kinds of seemingly simple but challenging questions?
00:24:37.860 -->
00:24:42.091Yeah, we actually had some really smart people on the data science side to who helped me.
00:24:42.091 -->
00:24:52.153In fact, I think we actually got a patent on one of the data models that we built out and it was really it kind of brought together time time as a constant, like place and time.
00:24:52.153 -->
00:24:55.328The individual, who they are, doesn't change as much.
00:24:55.328 -->
00:24:58.769Right, the attributes around them change, but who that person is doesn't change.
00:24:58.769 -->
00:25:04.859And then some of the roles and the kind of like you know I won't get into all the detail, but the aspects around the data model.
00:25:04.859 -->
00:25:22.812So you may be a customer at one point, a prospect, at another point, a claimant, you could be an agent, you could be an independent agent selling our product and the customer, and so the ability to sort of build a flexible data model that reflected those changes over time is also important.
00:25:22.812 -->
00:25:28.060And now there's a whole practice around customer journey analytics and understanding the customer journey.
00:25:28.521 -->
00:25:31.047This is in the early stages of when that was kind of picking up.
00:25:31.047 -->
00:25:39.649So that's kind of what led me into analytics is they're like all right now we want you to help build out that journey analytics perspective for our claims and operations area.
00:25:39.649 -->
00:25:44.288So yeah, we dealt with that quite a bit and I think it's not linear, right, it could change.
00:25:44.288 -->
00:25:59.066It goes back and forth in between those situations and, to be honest, to this day I still remember the most valuable customer segment in our prospecting was always former customers, because they've seen you, they've engaged with you, they know you, so the response rates are always higher.
00:25:59.066 -->
00:26:08.701So, very interesting in how you think about you know the loop, as opposed to like a linear you know flow of how people interact with you.
00:26:09.321 -->
00:26:21.748Yeah, and I wanted to drill down into those because I suspect you know, given what I know about the profile of our listeners and our audience, is there's going to be enough people who have probably never gone through this.
00:26:21.748 -->
00:26:31.106And would you know if they were said, hey, let's go, we want to bring in a CDP or we want to build a data warehouse or data lake that brings all our customer data together?
00:26:31.106 -->
00:26:43.064It will be more complicated than you think it will be because of all these things that will come up.
00:26:43.064 -->
00:27:04.807Customer is a great example of the kind of word that I talk about a lot with people, where, if you say that word and you're in a room of people like everyone's hearing it from their own context and they're going to have an idea of what that means and they may overlap with the person next to them or the other people in the room, but there's probably not a hundred percent overlap.
00:27:06.711 -->
00:27:30.468And, yeah, you get that into a broader context of you know, just within marketing, you probably don't have it If you go marketing and customer support or we go marketing and sales, and it starts to get really challenging to, to to get to a decision on that, and so I'm with you.
00:27:30.468 -->
00:27:51.070You want to keep it simple, which probably also affects the ability of whatever technology you're using to handle volume and things like that, and then also affects usability, but also you want it to be as complete as you need for the use cases, like it's not a small task.
00:27:51.070 -->
00:27:53.427So appreciate you sharing that.
00:27:54.620 -->
00:27:56.807Yeah, if I could opine for one second on CDP.
00:27:56.827 -->
00:27:57.730Please do, I just did.
00:27:58.421 -->
00:27:59.064A lot of them.
00:27:59.064 -->
00:28:07.250It's like just load your data in and magically it'll all be cleaned up and you'll have perfect profiles, and that's something that I've learned through the pain of going through this.
00:28:07.250 -->
00:28:09.307It's not that easy.
00:28:09.307 -->
00:28:11.500You have to do some prep and get your data in order.
00:28:11.500 -->
00:28:16.564When you bring a tool like that and that goes for any tool right, that can go for a marketing automation platform too.
00:28:16.564 -->
00:28:25.863There's upfront work that'll make your life a lot easier, and it's not the fun work sometimes, but it's the work that is going to help you be successful at the next stage.
00:28:26.965 -->
00:28:27.607Well, I think this is.
00:28:27.607 -->
00:28:29.873It falls into the category to some degree.
00:28:29.873 -->
00:29:02.475It falls into the category of why I tell people, like, don't go down the path of going from no reporting to say we're going to build a dashboard because you're going to and also are issues, and then you can go back and address them right, whether it's a process issue or a system issue or a people issue, and usually some combination.
00:29:02.475 -->
00:29:10.144So like, don't, like, I want to make sure what I didn't hear from you is don't do the cdp thing until you get your data in order, because if you do that, you'll never do a cdp thing, which is fine.
00:29:10.686 -->
00:29:11.829But take a step.
00:29:12.221 -->
00:29:23.591That's how you learn yeah, yeah, yeah, no, I'm a big believer like, expose this stuff and it brings light on it, and then you can make improvements and it becomes a bit of a uh, a fly.
00:29:23.591 -->
00:29:25.204We can get a flywheel effect right.
00:29:25.204 -->
00:29:27.836You find a problem, you solve the problem, it gets better.
00:29:27.836 -->
00:29:29.807You find the next problem, you solve it, you get, you know, and so I'm a big believer in that.
00:29:29.807 -->
00:29:30.631And you know the problem, it gets better.
00:29:30.631 -->
00:29:32.519You find the next problem, you solve it, you get, you know, and so I'm a big believer in that.
00:29:32.519 -->
00:29:35.990And you know, reporting CDP has probably fallen into a similar category.
00:29:36.920 -->
00:30:02.233Okay, so I want to get back into this identity resolution stuff, because something that you and I talked about as we were kind of getting ready for this is that you've now started work I don't know, I can't remember if it was at Merkle or where it was before, where you were, um, you were actually connecting professional profiles and personal profiles together about a person and somehow leveraging that.
00:30:02.233 -->
00:30:05.345So a number of questions like hey, did I understand that right?
00:30:05.345 -->
00:30:05.866And B?
00:30:05.866 -->
00:30:09.367Um, how does that generally work?
00:30:09.367 -->
00:30:19.389And then C, are there any implications in terms of privacy or you know that kind of concerns there that you have to deal with when you're doing that.
00:30:21.520 -->
00:30:24.529Yeah, I'll try to take the first two and then the privacy one.
00:30:24.529 -->
00:30:31.006We could go, we could talk a lot about that, because there's obviously that's a that's a challenge in a lot of components of this, even on the consumer side as well.
00:30:31.006 -->
00:30:46.461You know, and again I I mentioned earlier I think about how I I got exposed to a lot of things very early on in my career and, like the example I've already given, you know, we had small business, medium businesses, consumers, sort of all in the in the insurance space.
00:30:46.461 -->
00:30:55.589We had all those different categories, um, so it was something that we had to, I had to understand relatively early, and it's could be different partners you work with too.
00:30:55.589 -->
00:31:03.634So it's not always the case that you're going to work with somebody who's really good at the identity resolution for a consumer and data for a consumer.
00:31:03.634 -->
00:31:11.516You may have to work with a different company who's really good at the business side, right, and oftentimes they approach it from the other direction.
00:31:11.516 -->
00:31:15.096So when I think of a consumer-based identity resolution, you're starting with people.
00:31:15.096 -->
00:31:20.397You're starting with their personal profile, like I mentioned earlier, all the information about me as an individual.
00:31:20.397 -->
00:31:26.417On the business side, you get a lot of companies that are really good at understanding the company dimension first.
00:31:26.417 -->
00:31:27.730So they kind of go in inverse.
00:31:27.730 -->
00:31:42.973They go from what's the industry SIC code and what is the types of things they sell and they kind of start with the company and then they ladder down to divisions and organizations at the top, decision makers, and they kind of go that way down to the individual.
00:31:44.356 -->
00:31:52.209I think the other thing I would just mention is it's not that hard if you think about a business email like you don't have to be that smart to think about.
00:31:52.209 -->
00:31:54.677If I know the format of a business email like you don't have to be that smart to think about.
00:31:54.677 -->
00:31:56.704If I know the format of a business email, is it first name, dot, last name at company dot com.
00:31:56.704 -->
00:32:05.058It's relatively easy, probably for a lot of people that are in the more in the sales space or B2B space, to understand how to reach individuals in that company.
00:32:05.058 -->
00:32:10.738The challenge is it's so ethereal like your role could change your company changes.
00:32:10.738 -->
00:32:16.126Role could change your company changes.
00:32:16.126 -->
00:32:22.206So like the durability of that business email is not as powerful as like your physical address or your personal email, but it's the linkages that happen across both.
00:32:22.247 -->
00:32:28.307I think LinkedIn is a great place you know as an example right, most people are on LinkedIn.
00:32:28.307 -->
00:32:32.305They have their personal email address is how they sign up for the platform.
00:32:32.305 -->
00:32:35.471But they have their company and they have information about their.
00:32:35.471 -->
00:32:39.759You know their job, you know, probably, history of their jobs, you know in that profile.
00:32:39.759 -->
00:32:50.681So I'm just going to use that example and, you know, think about how, how LinkedIn is also marketing right to individuals and enabling advertisers to reach those individuals on platform.
00:32:50.681 -->
00:32:54.075You know it's it's really based on those business characteristics.
00:32:54.075 -->
00:32:57.451Once they go off platform, you're still marketing to people.
00:32:58.153 -->
00:33:04.916So that linkage of like, say, email, your login information that they're able to aggregate up and that gets to the privacy piece.
00:33:04.916 -->
00:33:10.388Right, usually it's done in the aggregated way, it's not in an individual way to build audience pools for targeting.
00:33:10.388 -->
00:33:15.447And that connectivity is something that my current company does really well.
00:33:15.447 -->
00:33:20.328It's an interesting kind of approach, which is we have all this depth of knowledge on people.
00:33:20.328 -->
00:33:27.613We've also built this secondary, you know, kind of view of businesses and business information and now we're trying to bridge that gap.
00:33:27.613 -->
00:33:34.248So we've connected on those you know elements, right, those identification elements like a personal email and business email.
00:33:34.268 -->
00:33:44.866But at the end of the day, it's about reaching those people with advertising or with messaging or with experiences, so it can get relatively complex based on the information you have.
00:33:44.866 -->
00:33:46.591You could also have phone numbers.
00:33:46.591 -->
00:33:50.690You could have business addresses, right, but it's a similar concept.
00:33:50.690 -->
00:33:53.718It's just you're going to get a smaller pool to start with.
00:33:53.718 -->
00:34:07.952You're going to actually get a drop from the personal information into the business side, so your quality is going to be hopefully better, but your audience size is going to shrink and part of that is because of the nature of the identifiers.
00:34:07.952 -->
00:34:10.409They're not as durable as the personal side.
00:34:10.409 -->
00:34:13.356But I started touching on it a little bit.
00:34:13.445 -->
00:34:36.528I think you know privacy and security there's, there's concerns that you know are whether you're in the U S, outside the U S, that that very state by state in the U S and certainly you know, in the EU is probably the only place I can think of that has a relatively large number of companies under a single framework with GDPR, but a lot of that is focused on digital.
00:34:37.209 -->
00:34:43.100You know, if you think about like I'm old enough to remember the phone book, you would get a phone book dropped in your driveway.
00:34:43.100 -->
00:34:45.168It's got everyone's address and everyone's phone number.
00:34:45.168 -->
00:34:56.192Like the nature of how terrestrial data has has evolved over the last 25 years, 30 years, has evolved over the last 25 years, 30 years.
00:34:56.192 -->
00:34:56.774It's pretty easy to find.
00:34:56.774 -->
00:34:58.139You know people's like phone numbers and addresses and things.
00:34:58.139 -->
00:34:59.123Even now you know out there on the web.
00:34:59.123 -->
00:35:07.372So I think there's like almost like we've been conditioned to expect that in some ways, and there's services out there where you can like send people mail right to their house.
00:35:07.372 -->
00:35:09.666Digital is kind of a new frontier though.
00:35:09.666 -->
00:35:17.817Digital it's like when you sign on to a site now and the pop up comes up, you know, do you accept cookies and privacy policies and all that?
00:35:18.205 -->
00:35:23.295Yeah, these are terms and conditions which we all agree to, but nobody ever reads Just click and move past it.
00:35:23.316 -->
00:35:34.740Yeah, I mean, usually, if you stopped and look at what they're putting in there, it's going to say things like you're allowing us to use your information for aggregated advertising or different solutions.
00:35:34.740 -->
00:35:37.851And I'm not saying don't click on that, that's your own decision to make.
00:35:37.851 -->
00:35:40.614But you saw the whole Google thing over the last couple of years.
00:35:40.614 -->
00:35:42.050Right, cookies are going to go away.
00:35:42.050 -->
00:35:45.975Now they're going to kind of put that choice back in the hands of consumers.
00:35:45.975 -->
00:35:49.235Do they want to click yes or no and accept cookies or not?
00:35:49.235 -->
00:35:51.352So it's a rapidly evolving space.
00:35:51.352 -->
00:35:51.954And accept cookies or not?
00:35:51.954 -->
00:35:54.405So it's a rapidly evolving space.
00:35:54.405 -->
00:36:01.885What I would say is expect that you're going to get aggregated pools of people to target, but at some point they're based on real identity.
00:36:01.885 -->
00:36:02.749It could be a seed.
00:36:02.749 -->
00:36:07.729You know that it's based on and lookalike models and other things kind of boost those audiences up and that's what.
00:36:07.769 -->
00:36:08.452Google's doing.
00:36:08.452 -->
00:36:13.014All these walled gardens are doing is building kind of inside their, their walls.
00:36:13.014 -->
00:36:15.970You know they're building targeting pools based on interest.
00:36:15.970 -->
00:36:19.768You know, like I love sneakers, I get targeted with sneaker ads all the time.
00:36:19.768 -->
00:36:34.736I'm sure I'm part of some pools all over the place that say you know sneaker lover, or you know jordan fan, that, um, that enables me to get targeted advertising as I'm crossing throughout the web, even if they don't know who I am at an individual level.
00:36:35.965 -->
00:36:57.293Interesting, yeah, okay, but that's I think it's fascinating because I think that's and maybe this is all related to tied to the COVID stuff too I think there was a more of a push to connect personal and professional profiles because people at least a large chunk of, say, office workers were now working from home.
00:36:57.293 -->
00:37:02.059Right, they were no longer in an office identifying where they were and who they are.
00:37:02.059 -->
00:37:09.475You know it's, it was a new, new challenge, but it sounds like there's progress on that for better or worse.
00:37:09.475 -->
00:37:13.411Right, it's, it's like I'm.
00:37:13.411 -->
00:37:14.781I'm like you, like I remember a time.
00:37:14.922 -->
00:37:21.767I remember when I was doing database marketing and, um, there is one question I have for you because you touched on it.
00:37:21.787 -->
00:37:32.355But, like, I hired third parties to provide data and then process data because the volumes were so big that we didn't have the internal technology to be able to process it at the volume it was.
00:37:32.355 -->
00:37:48.653But I remember, even at that time, right being just sort of dumbfounded about how much data was either directly about people or could be inferred about people based on things like their five-digit zip code, right.
00:37:48.653 -->
00:37:55.711And when you got a nine-digit zip code, it's like in the US, right is even more granular in just how accurate.
00:37:55.711 -->
00:38:02.672It was in a general sense, and now you've just got gobs more data because technical term, yeah.
00:38:02.672 -->
00:38:04.576So, uh, do you still?
00:38:04.576 -->
00:38:11.685Do you still find that you have to when you were doing cdp stuff and all that, like, um, I guess now all this is cloud-based.
00:38:11.685 -->
00:38:25.091This that wasn't the case back when I was doing this, so is there still a lot of data processors that are doing this for some of the companies you've worked with, or what's that market like?
00:38:25.994 -->
00:38:26.615Yeah, there are.
00:38:26.615 -->
00:38:31.777I mean thinking back again to things I wish I knew at the time.
00:38:32.298 -->
00:38:32.498Right.
00:38:32.538 -->
00:38:34.724Thinking back again to things I wish I knew at the time.
00:38:34.724 -->
00:38:43.553We ended up building the customer hub asset in a legacy on-prem database and then immediately moved it to the cloud like a year later, and that took like a year and a half, so these things are not overnight.
00:38:43.553 -->
00:38:53.357Luckily, if you start in the cloud today like Snowflake is a good example there's a lot of composable capabilities now where you can separate storage and compute.
00:38:53.357 -->
00:39:00.296So now it's like I only pay for the data that I store and then when I want to call it or query it, I'm only paying for that compute.
00:39:00.296 -->
00:39:02.353I don't have to buy a bunch of space ahead of time.
00:39:02.985 -->
00:39:08.478I can kind of like match it to my supply and demand, which has been a pretty big game changer.
00:39:08.478 -->
00:39:35.759One of the things that I think is fascinating you hear the word composability everywhere now Like we're actually taking a lot of our identity services and instead of saying, send us your data in a very secure SFTP secure way, we'll do the processing and append data and send it back to you, we're saying we're going to package up our apps and then we're going to containerize them and let you put them in your database so you can kind of run those services as a native app inside your environment.
00:39:35.759 -->
00:39:40.355It's almost like bring your own right, you can bring your own identity, bring your own data into your ecosystem.
00:39:40.985 -->
00:39:50.057I will say that's leveled the playing field a little bit, where you don't have to now go out and buy a huge set of on-prem hardware and build those databases.
00:39:50.057 -->
00:39:53.975But you still have the challenge of it's expensive when you do massive scale.
00:39:53.975 -->
00:39:57.916So it kind of depends on like what industry you're in, what's your target market.
00:39:57.916 -->
00:40:03.310Are you talking about 5 million people or are you talking about, like you know, a couple hundred million?
00:40:03.310 -->
00:40:11.028It can scale quickly, but I think that's less of a challenge now than it used to be, largely because of the rise of some of these cloud providers.
00:40:11.468 -->
00:40:45.105Yeah, so you touched on the volume, which is, I think you and I both have worked at B2C, b2b you more than me, probably in both worlds, but I mean my view is generally I think B2C the big challenge is volume and B2B the big challenge is volume and B2B the big challenge is the complexity of the data and the structures and the lack of controls about the data, particularly when you start getting into sales realm, right, sales and customer success, because you've got people who are trying to get you know.
00:40:45.105 -->
00:40:50.873They've got incentives not necessarily to focus on quality, data quality and it's not their fault, right, it's just the reality.
00:40:50.873 -->
00:40:59.673So, like in your experience, what do you see as things that are similar common challenges in the B2C space?
00:40:59.673 -->
00:41:05.939Based on the B2B space, do you see other major differences or am I off my rocker about the ones I saw?
00:41:07.564 -->
00:41:08.025No, you're right.
00:41:08.025 -->
00:41:17.165I think the volume challenge usually means you have to start with, let's say, your first-party data, to go back to that and build a lookalike model and scale it Right.
00:41:17.165 -->
00:41:19.551Yeah, a couple hundred million people.
00:41:19.551 -->
00:41:22.938We have, I think, like 270 million or so people in our graph.
00:41:22.938 -->
00:41:25.331It's like the 18 plus population of the US.
00:41:25.331 -->
00:41:27.737Like how much of that market do you want to go after?
00:41:27.737 -->
00:41:29.189And you can kind of scale up from there.
00:41:29.189 -->
00:41:31.676The business side is a lot more complex, though.
00:41:31.676 -->
00:41:33.918You get down into the all those relationships right.
00:41:33.918 -->
00:41:36.420Like you want to reach a decision maker.
00:41:36.420 -->
00:41:41.106You want to reach somebody who's responsible for buying this type of software, if you're selling to them in that regard.
00:41:42.109 -->
00:41:44.796So quality becomes an important aspect.
00:41:44.796 -->
00:41:46.420But also it's more expensive.
00:41:46.420 -->
00:41:50.390I mean the scale on the consumer side means you can get cheaper inventory.
00:41:50.390 -->
00:41:52.734You know at scale you're paying lower CPMs.
00:41:52.734 -->
00:42:04.329Anyone who's advertised on LinkedIn versus advertised like in Google right, you'll know like it's scale on one side and lower CPMs and a lot tighter scale and it's like more expensive per.
00:42:04.329 -->
00:42:23.596So I think it actually makes it even more important that you know what your target is in the B2B space and you're able to kind of use the insights you can gather around somebody to make sure you're, you know, putting putting your uh, your eggs in the right basket and making sure you can drive the results you want, um, especially when you get to like frequency and other aspects.
00:42:23.636 -->
00:42:31.288So um yeah, that's the only thing I would say to you is the, the uh, the spend side of it is different.
00:42:32.750 -->
00:42:35.194The spend side of it is different.
00:42:35.194 -->
00:42:36.336Spend side is different.
00:42:36.336 -->
00:42:39.260It's higher on the B2B side because of the complexity.
00:42:39.260 -->
00:42:50.738Okay yeah, more costly Kind of makes sense to me, right, Because you're trying to solve a little more complex network problem, right?
00:42:50.738 -->
00:42:54.242Connections, all these different things, Okay, Okay.
00:42:54.242 -->
00:43:02.430Okay, we've covered a ton of ground and we're going to probably need to wrap up here soon, but is there anything that we haven't covered here that you like?
00:43:02.430 -->
00:43:02.831Hey, what is?
00:43:02.831 -->
00:43:08.737These are the major trends that are happening in CDPs or identity resolution.
00:43:08.737 -->
00:43:12.793You know that people should be aware of as they're working with their organizations.
00:43:14.606 -->
00:43:16.295Yeah, so this will be our little future.
00:43:16.295 -->
00:43:18.445We have to use the word AI in a conversation.
00:43:18.565 -->
00:43:25.079We can't talk about data and not mention AI Just ai at the end of everything.
00:43:25.079 -->
00:43:31.347So I guess the one thing I'll mention is AI-driven audiences, and synthetic data is a huge thing.
00:43:31.347 -->
00:43:35.932So you're getting a lot of like think about the globe, the entire world.
00:43:35.932 -->
00:43:41.533It's hard to get addressable IDs on the entire globe, especially with this patchwork of privacy.
00:43:41.533 -->
00:43:48.814So one of the things that's happening now is AI driven audiences or synthetic audiences, where I can like model out a whole country's population.
00:43:48.814 -->
00:43:55.490I can test things in that, instead of doing like a survey panel, I can test things out before it happens, right.
00:43:55.490 -->
00:44:01.611So that's a really interesting and fascinating space for anybody who's interested in AI and synthetic data.
00:44:01.611 -->
00:44:05.579I would say retail media networks are fascinating.
00:44:05.579 -->
00:44:09.327I'm getting more into that now that I'm on the agency side Retail media networks.
00:44:10.412 -->
00:44:10.974what does that mean?
00:44:11.585 -->
00:44:15.857So that means I am a retailer or a company and I have my first party data.
00:44:15.857 -->
00:44:17.672I'm actually going to become an advertiser now.
00:44:17.672 -->
00:44:25.031I'm going to use that information that I have for my customers and I'm going to kind of there's kind of two paths right, and usually you do a little bit of both.
00:44:25.031 -->
00:44:33.119I can create audiences of my first party customers and can serve those to other second parties to use for targeting.
00:44:34.706 -->
00:44:37.393Think about like Walmart and amazon and all those big companies.
00:44:37.393 -->
00:44:39.906The other side of it is like my own space.
00:44:39.906 -->
00:44:45.538If I'm like a grocer, maybe I have like advertisement screens in my aisles and I can.
00:44:45.538 -->
00:44:48.489Cpg brands can use that space to advertise.
00:44:48.649 -->
00:44:56.806So it's kind of what I thought, yeah, it's kind of what I thought, yeah, and it's kind of a space that really only large publishers could do before Right.
00:44:57.006 -->
00:44:57.949Yeah, it's blowing up now.
00:44:57.949 -->
00:44:58.330Yeah.
00:44:58.450 -->
00:45:01.850Yeah, okay, interesting, and then anything else.
00:45:02.886 -->
00:45:04.394Last thing I'll say is data collaboration.
00:45:04.394 -->
00:45:15.881It used to be like you know how do I use my data to drive my outcomes, but now it's like how do I partner with another company and overlap our data and see where there's opportunities and that's where you get into, like clean rooms and other aspects?
00:45:15.921 -->
00:45:28.316so just to drop one more buzzword at the end there I'll clean rooms, yeah, meaning, meaning where we combine our data, but we don't let like we don't uh no one's share it.
00:45:28.316 -->
00:45:34.233Yeah, yeah okay, okay, that was always used for measurement before now it's used for.
00:45:34.666 -->
00:45:45.306Let's actually overlap and see you know what, what shared audiences we have or what unique audiences we have, and um, a lot of partnerships are happening now around the ability to do that at scale now.
00:45:45.306 -->
00:45:47.812So interesting yeah, I'll.
00:45:47.812 -->
00:45:52.531I won't say any more buzzwords that'll, otherwise I'll be here yeah, no, it's okay.
00:45:52.751 -->
00:46:01.596Um, this has been fascinating and it's a little bit of me remembering where I started my marketing journey.
00:46:01.596 -->
00:46:03.780So very interesting stuff.
00:46:03.780 -->
00:46:04.728Jeremy, appreciate it.
00:46:04.728 -->
00:46:08.809If folks want to connect with you, learn more about what you're doing or just follow up with you.
00:46:08.809 -->
00:46:10.214What's the best way for them to do that?
00:46:11.365 -->
00:46:12.790I'd say LinkedIn is probably the best way.
00:46:12.790 -->
00:46:13.854Look me up on LinkedIn.
00:46:13.854 -->
00:46:17.693I think it's JeremyCCatz at LinkedIncom, or something very basic.
00:46:18.354 -->
00:46:19.998Got it, got it Okay, perfect.
00:46:19.998 -->
00:46:21.790Well, thank you, jeremy.
00:46:21.790 -->
00:46:40.013Again, thanks to our audience for continuing to support us and, as always, if you have suggestions for topics or guests or you want to be a guest, feel free to reach out to Naomi, mike or me through LinkedIn or through the marketingopscom community and we'd be happy to talk to you about that.
00:46:40.013 -->
00:46:41.315Until next time, bye, everybody.
