Podcast: The Tools to Manage Your Data
Contents
Show Notes:
This week we've got our first sort of technical dive into data driven marketing!
A few weeks ago we talked about what we mean when we say data in this context. Now we're breaking it down even further. We get into where all of this data lives in terms of tech stack. We're talking CRMs, CDPs, DMPs, DSPs and beyond. To start, we want to give everyone a sense of what these platforms actually are, what they do, and where they fit not just into the eco-system of data, but into data flow for data-driven marketers.
Then we dive in on what we're most excited about, the "marketing intelligence" side of things and the power of "lookalike audiences." Particularly, we get into the power of lookalike audience that aren't just a product of black box algorithms, and instead are transparent and understandable so that data-driven marketers can use the audiences, and well as segments used to create them.
We wrap up by talking about the power of BYOD (bring your own data) inside of marketing intelligence systems (including NetWise of course.)
Graphics:
Venn's Marketing Data Updated:
Links:
Podcast: What is the data in data-driven marketing?
Podcast: The Democratization of Data
NetWise Blog: The Power of BYOD
Zengineering Podcast: On Search Engines
Transcript:
Adam Kerpelman:
So the Olympic trials are starting up. So they have all those sports on TV. How come there's a senior tour for golf and there's no like senior versions of any of those sports. Why can't we watch Mike Phelps swim for another 20 years on the senior tour? He's still way better than any mere mortal.
Brian Jones:
I think it's because we don't have a whole lot old... We don't have a bunch of senior citizens still competing.
Adam Kerpelman:
And this is what gets to the data-driven conversation. People have a sense for their score for 18 holes and so they can still watch the senior tour and be like, okay, these guys aren't old enough to compete, but they're still impressive. So I'll watch.
Brian Jones:
Right.
Adam Kerpelman:
No one has any idea how long it takes for them to swim a hundred meters. So the fact that there's data in that sport just doesn't mean anything because there's no frame of reference for your average-
Brian Jones:
Fantastic point.
Adam Kerpelman:
Hey, It's The Data Driven Marketer brought to you by NetWise. I'm Adam.
Brian Jones:
I'm Brian.
Adam Kerpelman:
Welcome back another for another hang in the data-basement.
Adam Kerpelman:
I'm still conflicted on if I need to say that funnier. Or if I should just say data-basement.
Brian Jones:
You just own it data-basement. I would continue to say it a little different each time, just so it remains unique to you.
Adam Kerpelman:
We'll measure how people respond, that will be the database and approach.
Adam Kerpelman:
How you doing, man? No guests. No guests this week.
Brian Jones:
I'm doing pretty good. It's been a rather productive day, which means I come to this with more energy.
Adam Kerpelman:
That's good, I think you'd be more tired. Is that a product of the type of work that we do? Because if I had a really productive day at woodworking, I would be physically exhausted.
Brian Jones:
I would imagine if my salary relied on the woodworking, that I would drag ass most days.
Adam Kerpelman:
You'd do the minimum possible amount of woodworking in a day.
Brian Jones:
Whatever that is in our psychology that prevents us from doing the thing we're supposed to be doing.
Adam Kerpelman:
So I think one of the reasons I'm excited, there's energy for me this time is this feels like this might be our first kind of like technical dive episode. We've talked about what we think data-driven marketing is and what type of data we're talking about, but it's all been kind of like big, broad, deep dives. This time, we're talking about how to manage this data, which means we're going to get into all kinds of acronyms that I barely understand because I'm new here, but literally, how this stuff is done, how it works, how you should do it, maybe, I don't know how pejorative we want to be about it and stuff like that, which is cool because I think we're going to have doing that a lot on this podcast, but this is the first one where it's really like, I'm going to get to link out to some technical articles about what the hell this stuff means because we've got 25-ish minutes here and there's going to be a bunch of defined terms.
Brian Jones:
Yeah. This will be a good one. There's a lot to talk about. My head's spinning with things to talk about actually.
Adam Kerpelman:
Right.
Brian Jones:
It's how we define the concept.
Adam Kerpelman:
Yeah. So I think maybe the place to slow this spinning a little bit is in this, is starting from the, what do we even mean by data episode essentially? What type of data are we talking about managing for this part of the conversation?
Brian Jones:
Yeah, that's a great question. And that is part of what the spinning is, you know there's tons to manage, right? I think there is sort of the classic thing to manage, which is contact information like your CRM, which I've grown... I've always existed in a professional setting where are CRMs, but they're pretty new. So even the concept of a CRM I recognize is like a digital store of contact information. And project management is new to some people-
Adam Kerpelman:
Customer relationship management software.
Brian Jones:
That's right. That's right.
Adam Kerpelman:
Got it. Oh, oh, I know what that one means.
Brian Jones:
You guess the acronyms and I'll throw out as many as I can.
Adam Kerpelman:
Oh no. I know the one, I mean, CRM, I too have lived in a world of CRMs forever, but I am frequently the one that comes in and goes, you know what you guys need? A CRM. And they're like like but we just have a spreadsheet?
Brian Jones:
No, no, no, no.
Adam Kerpelman:
We just have a Rolodex.
Brian Jones:
We just have-
Adam Kerpelman:
We keep this on paper and binders. Do you want to photo copy it? You think I'm joking.
Brian Jones:
I don't.
Adam Kerpelman:
I have worked at high profile Hollywood places where they were like, oh, you need to look up that number. It's in a binder. We all have a copy.
Brian Jones:
No, no, no, no. People like tangible, tangible? Yeah. They like things they can touch. Touchable? Touchable is not the word I'm looking for-
Adam Kerpelman:
You know it might actually be legitimate security, sort of consideration because sometimes like I've worked at a town agency where it's like that binder contains Chris Hemsworth's personal phone number.
Brian Jones:
That's a standard excuse for not wanting to innovate.
Adam Kerpelman:
Exactly.
Brian Jones:
It's safer in this binder. Yeah. So as marketers, it all kind of starts with information about prospects, right? If you're a consumer marketer, right, just individuals, if you're a business marketer, which is what we tend to be talking about, it's companies and people. And then from there, it gets nutty, right? It's all the information you have around those, around customers and it might be your financial data, who your customers are, who's spending what they're buying, what products they use. It might be information about where you've seen people. Did you run into them at a trade show? What conversations have you had with them? What salespeople have touched them? If they're already a customer, who works with them on the team? They've been to your website, traffic analytics, newsletter information, tons, right? There's all this all... The ecosystem of data right now is limitless in complexity.
Adam Kerpelman:
And I think in this context, so to chase the way that we broke it down in our previous conversation that I will link to, I think there's what we call your data. Right? So you get your CRM, email lists, personas, marketing, demographics, all the stuff inside of your company. That's kind of like, here's what we know about our users, about our customers, about the potential market, et cetera. We're not talking about that data in terms of how do you manage that data. That stuff is fairly manageable in the world of CRMs, right? We already told you the answer to that, get a CRM, that mostly handles a lot of that stuff. The hard part is what do you do with the other two bubbles on are VENs marketing data graphic that we did before, which is campaign analytics, which I think we'll get to.
Adam Kerpelman:
And then the other one, which I think on that one, we called vendor data. But we've realized, that's not really the way to say it. It's more like marketing intelligence, I think.
Brian Jones:
Yep.
Adam Kerpelman:
Which is the campaign analytics is stuff you're doing and the data you're getting back. And there's definitely ways to manage that data that we'll talk about. The other one is marketing intelligence, right, which is taking your data and essentially fattening it by taking it to places where we can use other sources, intent, technographics, lead gen, the kind of stuff that we do inside of NetWise's platform to essentially fat in that dataset. Right? We frequently in our own conversations say, cool, we can take that list. And then we can go forex the size of the targets that we can use across channels with that just by running it through our platform, right? But that's a good example of what marketing intelligence is, right.
Brian Jones:
And even, I think people, especially us because we're in the data space, right. We're in the market intelligence space. So we're working with a lot of other companies that provide sort of ancillary information to fatten up your marketing data. But let's not forget about all the hard manual work that goes into curating information. Right? If you go out as a marketer and research potential customers in your target market, that's market intelligence, right? That was hard earned information. You went out and read about companies and looked at news sources and found people and company names and contact information and demographic, right? That's the hard, old school way of doing that. So there's again, just endless amounts of market intelligence and then campaign analytics, like you said, that's where the real explosion is. Right, and that's different for every single company because every company is using different platforms, right?
Brian Jones:
If you even just look at websites, there's so many different ways to host a website and then every single one of those systems has its own unique ways of tracking information and how you build that stuff out. So tying all this together and managing all this information is wildly complex. And it's not only important as a modern marketer, but you have to do that or you can't be competitive anymore, both as a business and as a marketer, within your company, as an employee who wants to progress their career, right? You need to be building towards things and it's, data-driven targets that you're looking for.
Adam Kerpelman:
So when you break it down that way, then I feel like the thing to hit before we come back to marketing intelligence and how it's the amplifier on a lot of this stuff, then we should talk about campaign analytics first.
Brian Jones:
Mm-hmm (affirmative).
Adam Kerpelman:
So campaign analytics is ultimately you take your data, you take all the stuff you're talking about. You take market research, not necessarily marketing research, which I think is a distinction we want to draw and you go run a bunch of ads, right? You do whatever you're going to do in your campaign stuff, ads, outreach, whatever sort of demand gen behavior you're taking part in. And because we're talking about digital platforms and data-driven approaches, you get data back from all of those things, that data is sort of a hot mess. Right?
Brian Jones:
Sort of?
Adam Kerpelman:
Yeah, sort of. I'm trying to be generous to the platform I use everyday.
Brian Jones:
Data's like someone poured a barrel full of hot caramel sauce on your company.
Adam Kerpelman:
And said, this is delicious if you can make it into the right shape.
Brian Jones:
Just scoops some up, just have some it's made of sugar.
Adam Kerpelman:
Yeah. So yeah. So what exists in terms of ways to manage that data? Because literally on the Venn diagram, we have a bunch of acronyms that I am just barely beginning to understand.
Brian Jones:
Yeah. You very quickly are... You're beholden to the tech stack that you choose, which is such an engineering term. I know we come back to this a lot in it, it happens to be because I'm CTO at our company, but the tech stack is really critical for your marketing operations. Right. And sales and marketing, right? It's what all these systems operate on. And it dictates what the data looks like, how it flows, how it ties together. And for all of this, the story comes back to how can you link it to other data, right? For instance, website traffic, what do you do with it? Most of it is anonymous. You can't do anything with it. You don't know who the people are. It's just visits to your web server. And so you see IP addresses. Maybe if you have campaign conversion tactics going, you might get an email address.
Brian Jones:
Now, all of a sudden, do you drop a cookie on that person's website or on their computer. Now you know if they come back, now you have an email you can link to your CRM. If you had your IP address, you have to go to a marketing intelligence company to match that, maybe to a company if you're lucky. And so there's a lot of complexity, but it all comes back to figuring out what are the signals I'm getting in? How do I link them ultimately to a person and at a company if you're B2B. And so that's the real tactic here and that's why everything always comes back to that because if you can't link it to a person or a company, it's noise, right. It's not helpful to you as business.
Adam Kerpelman:
Right. So, what has emerged... Well, so to start with, the data that you get back, first, it comes out of the platforms, right? So if you're using Google ads, they feed you back some information that's coming out of that, but it's not necessarily helpful. Especially if you're talking about an amplified scale. If you're just running a local shop and you want to run some Google ads, you can probably learn what you need to know coming off of Google's built in platform. But I think you quickly scale up to a size where it's hard to read, what's coming back in from Google's ads or Facebook's ads or LinkedIn's ads. And then to make it all make sense together as part of a campaign. You can tag that all and say, oh, this is all part of the rocket fuel campaign. But it still doesn't necessarily like trying to pull signal out of that data still requires other extra work.
Adam Kerpelman:
And so on the campaign analytics side, you start to... You already have platforms that are trying to help with that. Right? That's what the DSP or the DMP, CDP, all that stuff is right. So, what are all of those intermediaries doing? Because as I look at this graphic, you have socially of web visitors, you have the ad platforms, sure data's coming back from them. But like I said, it gets to be a mess, once you look at all the platforms and everything else. Those other providers, that's where they fit in. Right? Well, this is trying to sort of organize the mess of trying to do this at scale.
Brian Jones:
Totally. All of the modern data infrastructure that exists in marketing exists to service companies, as soon as your sales process is more complex than someone lands on my site and clicks, buy and buys a thing, or does a thing, as soon as you have more than one step, right? That's not, I converted the person when they landed, which essentially is everyone, right? I know you can simplify, e-commerce impulse buys to, it's just get them there and get them to click buy. But even that there's more complexity to if you're doing it right. So as soon as you have nurturing as a concept, as you have email newsletters, as soon as you have other forms of media, podcasts, videos, and your multi-channel marketing emails and social, it's so complex that you need centralized systems to bring that information in, standardize it, cleanse it, merge it all together so that you can look at hierarchies and be like, okay, this subset, I know are these people at these companies and these are targets and I need to address them in certain ways, they're going to go off to this part of the company.
Brian Jones:
We're going to have sales talk to them immediately. Then you see these other groups where it's okay, these are just, I think these people are at this type of company. Let's run some campaigns against that broad swath because we see their behavior. Then you have a bunch of anonymous noise and you can still do stuff with that, right? You might still, if you've dropped a cookie, you can keep targeting that person when they come back. But you don't actually know anything about them. So there's layers and layers and layers of how you can slice up information that you have. And you need technology platforms to do any of that-
Adam Kerpelman:
And just fake nonsense.
Brian Jones:
Tons of fake nonsense.
Adam Kerpelman:
The amount of traffic on the internet, that's still just fake. It's mind boggling, just spam and bots and crawlers.
Brian Jones:
It's especially mind boggling because if you haven't worked on those technologies, I can tell it feels abstract. When you tell people that there's a lot of fake traffic. They understand like, oh yeah. Most web traffic is fake and you're like, yes. It's most web traffic, fake is not quite the right word. It's like some things physically happening, but it's computers communicating with computers, right? The scale of it is hilarious. Right. Have you ever looked at a web server? It's all nonsense.
Adam Kerpelman:
We have a whole episode of our other podcast, I should link to that's about search engines and the idea that Google has just an entire copy of the internet on their server, but better organized. And that's basically what they have. So I think before we move on from that aspect, right. Campaign... Well, the last thing I want to add is I think one of the reasons it's hard to get your head around is because we're used to sequential sort of funnel thinking, right? Step one, then step two, then step three and then convert. But in a digital marketing and multichannel ecosystem, it's not really like that. There's step one and then there's two possible ways to behave at step one and each step then has sub steps. And so when you go from one layer of complexity to three layers of complexity, it's not three times its complexity cubed.
Brian Jones:
Right.
Adam Kerpelman:
Right? It's exponential increase in complexity every new layer that you add.
Brian Jones:
It's essentially infinite in a modern marketing stack. Right?
Adam Kerpelman:
Yeah. And so like, as soon as you add one layer of complexity, one factor of complexity, you're already out of the realm of human. It's like, you just jumped from algebra to calculus, right? Human brains can't do it without extra tools anymore. And so, just so we've done it before we move on from campaign analytics, DSP, DMP, CDP, what is all that nonsense stand for?
Brian Jones:
DMP is data management platform.
Adam Kerpelman:
Okay.
Brian Jones:
And it's essentially, I think that's kind of the old school marketing term for what CDPs are now. DMP was like a platform to bring all your data into and organize it and centralize it. CDP is customer data platform I think is what it stands for. And it's kind of the new version. It speaks more to the idea that the customer is at the center of this marketing information. I don't... Maybe DMPs were not marketing specific back in the day or something, but generally... Sorry, what was the DSP?
Adam Kerpelman:
DSP.
Brian Jones:
DSP stands for demand side platform and it's actually what a marketer uses to run programmatic advertising and display advertising and other stuff too, I guess. But that one's funny because as soon as you hit that one, my instinct was to say that's a different concept, but they're not because what makes everything so complicated in our market is that many of these companies are some or all of these things at once, right? So you'll be a CDP that also has a DSP bolted on, or you'll be a data provider that also has a CDP or you'll be a place where you'll activate Facebook ads and digital ads. But can't do email. Again, the complexity of what your tech stack is just to start, what tools you pick to use immediately starts to define the complexity of the data that you need to process and understand to go to market.
Adam Kerpelman:
Right. Well, and so I think what's interesting as we talk through this, just the way that you've been talking about it, you can kind of see that ways that we could fat in our Venn diagram thing, which is really, it feels like there's a progression of software eating this stuff, which is it started with, okay, let's clean up your Rolodex because that's a hot mess and nobody knows what's going on in CRM. And then all of these platforms, like the DMPs and CDPs that you just mentioned emerged because it was like, okay, we need to manage all this customer behavior data over here, that's about your prospects and your campaign stuff. And now we get to the next piece, the marketing intelligence, which I think is the part that when I look at that list, the list on this graphic, which we're going to have to post at this point, because I've referred to it so many times.
Brian Jones:
Redo it, post it.
Adam Kerpelman:
They are all the newer technologies, right? So this piece where NetWise lives is part of sort of a tech stack that feels like the newer thing, which is we can use outside data and marketing research, essentially, marketing intelligence to create a feedback loop here where it isn't just about, you have your data, you have what's coming back from the campaign. So, you know what's working and you can make database decisions and do whatever. Now, because of the way that this stuff all interlinks and is in meshed, you can amplify your campaigns in an interesting way. It is, I think the simplest version is like, you go on Facebook and they have lookalike audiences, right? That feels like the crudest version of what we would call marketing intelligence. But what Facebook is providing there is they're saying, well, you put in your audience, we can, based on our data set, our proprietary dataset, take a guess at an extra 10% of people that probably match this profile and might also want your product.
Brian Jones:
And what's interesting there, and this is the crux of where, where we come in with our kind of thesis is, and I say our, as our business, but also kind of me and our tech team, that lookalike audience is really designed to spend more of your money. Right?
Adam Kerpelman:
It's Facebook's lookalike audience.
Brian Jones:
Yeah. And so, one of the major complaints that marketers have these days, especially as more and more marketers become more data savvy and they understand all the underlying information that powers these systems. They don't want Facebook's black box guiding their marketing spend. If you're spending 10 grand a month or hundreds of grand, or even millions of dollars a month on ad platforms, you need to know what's going on. You need to know what data level decisions are being made by a system and you want control over it. Right?
Adam Kerpelman:
Right.
Brian Jones:
So you can, the promise of all these systems to auto optimize everything is just not there. It's my professional opinion seeing all the systems, they can't do it yet because there's too... There's not enough signal to feed into the platform to make the decisions yet.
Adam Kerpelman:
And also there's just a problem of incentive alignment. Right. Which is just a really hard thing to do, We talk about it constantly in my weird crypto calls. But when you... You always have to back up to, okay, but what incentive does this push forward? And if you don't think about it the right way, then you end up with a system that maybe doesn't do what you think. The thing that makes Facebook money is people clicking on the ads. And I can tell you from my experience, lookalike audiences are really good at getting people to click on your ad, but they don't drive conversions. Well, this is where the... So they make Facebook money, but it costs me money. And they make me feel like I'm getting more leads, but none of those leads convert. So whatever they're doing, that they call a lookalike, isn't actually matching up people with the intent to purchase my product. It's just matching people that are the same gender and age and race.
Brian Jones:
Exactly.
Adam Kerpelman:
And geographic area.
Brian Jones:
This is exactly why we don't want Facebook to both own the ad platform and the media that people are consuming, right? This is the problem because their incentive for their ad platform is to make money by pushing it into people's media feed. So if you go to Facebook and use a lookalike audience, I'm not saying it's untrustworthy. I don't think they're just there trying to take people's money blindly. There's tons of tech behind it. It's very advanced with great engineers there. But if you go there, their incentives are just misaligned. Right?
Adam Kerpelman:
They're clicks, not conversions.
Brian Jones:
Exactly. And if you go to an ad tech company where they don't own the media, so their ads need to perform. So the black box magic needs to show up in your campaign, right? It needs to show those conversions.
Brian Jones:
So there's, the interest are just better aligned if you separate these things. And so, but ultimately what I think sophisticated marketers really want is they want the reveal, right? If you go into display advertising, you go to a DSP where marketers are familiar with programmatic advertising for five or six years now, if not a few years longer, there have been just floods and floods of like bullion categories you can select to do your marketing. You can build your campaigns by selecting segments, demographic, firmographic, all these segments about consumers and business people. And they're just very limited for what modern marketers are trying to target.
Brian Jones:
You want to target more explicitly and even if there's a category on there that you explicitly understand, if you look at the 10 different vendors that represent the same category, they'll have wildly different numbers of people in their audiences. Some will have more people than there are in that space, right. You'll click on marketer and they'll have 50 million people in it. There aren't 50 million marketers in the US. So you know immediately that, that stuff's BS, right? And so it all comes back to revealing the underlying information and people being responsible enough in their careers to become data savvy, which is what all marketers are doing.
Adam Kerpelman:
Right. That's what we're here about, what I was going to say the thing about revealing that is, when you... The strength of the idea of the lookalike audience is that's how you can go and use data to fatten your data set instead of having to use the traditional things, right? The new leads came in, it's not, we don't have to Glen Gary, Glen Ross things anymore and fight over the new leads. We can go fatten our lead set by using big data practices like this. But Facebook doesn't let the data minded, the data-driven marketer see enough. And so I personally get in there and I'm frustrated that I can't see all of those check boxes that you're talking about because to me, every single one of those check boxes is a test parameter for something that I might want to run. And so I want to know if my ads convert better for people with iPhones, then Android, or things like that, that Facebook won't tell me.
Brian Jones:
We're battling up against the human impulse and kind of the philosophical argument for, or against artificial intelligence. Do we need to know what our intelligence is doing to make decisions? And the answer really is yeah, kind of. Right? A lot of people will be like, well, if we don't know how the artificial intelligence is making a decision, how will we know if it's making a good decision? And part of the argument there is, well, you don't really know how you're making your decisions either. You just think you do. So we don't really know how humans are making their decisions either. So you need to be able to extract data that you can look at and at least have an intelligent conversation about.
Adam Kerpelman:
Yeah. It really gets to the emergent field of, I think what we're calling marketing intelligence at this point. And then, it lets us talk about one of the cool things in NetWise, which is the, bring your own data, what we call BYOD, bring your own data.
Brian Jones:
Data party in the data-basement.
Adam Kerpelman:
But before we hit that real quick, ultimately what you're talking about, when you say tech and graphic firmographic, all that kind of stuff. It's not just about... Ultimately it's about bring in data, use that data to get better signal, but also to grow your signal and to grow your understanding of what's happening in there.
Brian Jones:
So what a lot of these data platforms, I'm trying to use a phrase there that's not already been capitalized on as a marketing phrase to brand an area of product, but a customer data platform data.
Adam Kerpelman:
See last week's episode, rebrand it.
Brian Jones:
What all these centralized data management platforms for managing data are trying to do is allow you to bring in information that you want to centralize and link to other things, right? And again, it usually comes back to linking to people or businesses or leads or sales opportunities. It's all about people in businesses, right? So what is challenging with these platforms is one, they tend to be extremely expensive. Two, they tend to be extraordinarily complex there. And three, you tend to need your whole company's involvement to get stuff up and running, right? You need to bring this platform in, you need a company-wide initiative to like tie all your systems together, which means you need company-wide coordination and cooperation and budgeting and spending, and they're challenging.
Brian Jones:
And then you get a system set up and it's six months, a year later, you've got all this stuff in it, but it doesn't do some things that then the on the ground people trying to do work needed to do. And so we ran into that constantly here, helping big customers integrate our data into their big platforms. They're always like edge cases and they're always people who need something out quicker than it can it, through the hierarchy at a company that's large enough to have something like this, or they need it done in a way that it can't be done the way it was set up. So what we ended up doing as our strategy is we didn't want to build a platform that needed your whole company to buy in. We didn't want to try to, as a business model, we didn't want to try to capture and sequester your information as a marketer.
Brian Jones:
We didn't want to try to capture and sequester your business information and become the platform that you're stuck with because you've done all this. So instead we built a really simple process for you to just quickly bring data that you want to execute on right now into our platform, blended into our ID graph, which then is people in businesses, in the U S pretty much everybody. And then a person who's running a campaign right now and has some interesting information that's helping guide them, whether maybe it's website visitor information, maybe it's technographic data they bought from someone else, maybe it's info from their CRM. It's like past prospects that were great customers. You can then bring that into our platform and do all of that with it very quickly, yourself, you don't need a big integration. You don't need your engineering team. You don't need your data science team to sign off on it.
Brian Jones:
You don't need all the other departments of your business to coordinate on a big, expensive platform. So the BYOB concept, it's named funnily, right? Oh, fun BYOB. It's like a party in the data-basement, but really we meant it to feel light. It's supposed to help people who are doing the work of marketing, use data more efficiently instead of executives at a company force a platform on people. So it's just kind of a different psychology around helping people use data, as opposed to burdening it with an aggressive business model.
Adam Kerpelman:
Right. And we're back to that thing of talking about our own platform, but I will again refer people to the previous episode, which it really is about a bigger thing here, which is about to get even more relevant with the sort of cookie list stuff happening. But right now, it's a disservice to the ability of companies of a certain size to compete that they have to go live in Facebook's black box, unless they have the money for this high-end way of looking at this stuff to have the data science teams and stuff like that, which even those cope companies are frustrated at how slow it moves. Then you're just stuck with Facebook lookalike audiences or Google's expansion algorithms, and things like that, that just sort of promised to reach more people, but don't really... And whether that promise is met aside, they don't feed back enough to the data-driven marketer to let them feel like they're actually doing anything other than checking a box and then waiting three months to see if there's a noticeable difference and hoping that it works because they trust granddaddy Facebook to help them.
Adam Kerpelman:
And that's not really the world like that we want to live in, in terms of optimizing advertising as a practice.
Brian Jones:
No.
Adam Kerpelman:
And that's, that's really what we hit for a full half hour last episode, about the democratization of data, but this is just an offshoot of that. Part of what should be democratized is also that idea of marketing intelligence, right? The idea of lookalike audiences and stuff like that. So in our platform super easy to do, but also, if you work at any of these other marketing intelligence side companies, you should do that too.
Brian Jones:
Right. I think that's the future of this, right.
Adam Kerpelman:
Right.
Brian Jones:
The data's too big and it's too complex to silo all this stuff. People want stuff more available.
Adam Kerpelman:
We're betting the company on it.
Brian Jones:
Yeah.
Adam Kerpelman:
Yeah. So I think, that's kind of, I hope, for the dear listener, that that was a good rundown of kind of the... It's hard for not to be messy because it's one of those things that like, as this data has popped up, other adventurous entrepreneurs have gone, here's the thing we could build, but it's a ecosystem that evolves so fast that one of the things NetWise did effectively was actually sat tight through five different waves of this is the next thing, to get to where we are now, where it's kind of like, okay, we've sort of hit the infinite part of the cycle.
Adam Kerpelman:
So the next new thing is just going to look like a slightly different version of the same thing that we're all kind of talking about here. And it's going to cruise toward openness and democratization, like you're talking about, which to me, feels like the point to move, to make the moves-
Brian Jones:
Totally agree-
Adam Kerpelman:
That we're making. But ultimately, that's our rundown of how we got here and ultimately the power of all that kind of stuff.
Brian Jones:
BYOD.
Adam Kerpelman:
BYOD, FTW. Thanks for hanging out for another one. This has been the data driven marketer. If you enjoyed what you're hearing here, like, subscribe, kind of do whatever that is. Give us a review, reviews on iTunes, still go a long way on here. And do they have reviews on Spotify? I don't even know.
Brian Jones:
I don't know, either. I don't think so.
Adam Kerpelman:
Well if they do-
Brian Jones:
Speak for themselves.
Adam Kerpelman:
Anyway. It's been the data-driven marketer.
Adam Kerpelman:
I'm Adam.
Brian Jones:
I'm Brian, stay cool as cucumber.