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Episode 110

What Marketers Get Wrong About Targeting

Only a third of agencies believe their clients provide a clear target audience in their briefs. So how can we build effective targeting strategies that drive real business growth?

In this episode, Elena, Angela, and Rob tackle the misunderstood world of targeting in marketing. They explore why defining your target audience isn't just a media decision but a foundational strategic choice. Plus they examine the flaws in third-party data targeting and provide practical advice for maximizing reach without wasting budget on ineffective targeting tactics.

Topics Covered

• [01:00] Why marketers should be wary of "algorithmic hanky panky"

• [03:30] The misconception that brands grow through narrow audience targeting

• [07:00] How digital platforms introduce bias in AB testing

• [11:00] The major pitfalls of third-party data for targeting

• [14:00] Balancing targeting precision with cost-effectiveness

• [17:00] Top targeting strategies that deliver without sacrificing reach

• [20:00] The surprising success of broad targeting for brand growth

Resources:

2025 MarketingWeek Article

Today's Hosts

Elena Jasper

Chief Marketing Officer

Rob DeMars

Chief Product Architect

Angela Voss

Chief Executive Officer

Transcript

Angela: The key question marketers should ask themselves is not just can we target 'em, but is it worth it? Are you gaining real incremental value by narrowing your audience, or are you just paying more to reach fewer people with questionable accuracy?

Elena: Hello and welcome to the Marketing Architects, a research first podcast dedicated to answering your toughest marketing questions.

I'm Elena Jasper. I run the marketing team here at Marketing Architects, and I'm joined by my co-hosts Angela Voss, the CEO of Marketing Architects, and Rob DeMars, the Chief Product architect of Misfits.

Angela: Hi.

Rob: Howdy. Howdy.

Elena: We're back with our thoughts on some recent marketing news, always trying to root our opinions and data research and what drives business results. Today we're talking about targeting, which has been an important and popular topic in marketing and advertising really for decades. But today we're in sort of a weird position where we've never had more ways to target our advertising or more debate and disagreement over what kinds of targeting are worth it or just a total waste.

And I'll kick us off, as I always do with some research. And I have a perfect article for this episode because it actually inspired me to propose this topic.

This is by Mark Ritson for Marketing Week. It's titled "Ignore Anyone Who Tells You to Forget About Targeting." Targeting, Ritson says, is the beginning of strategy. It's not just a media decision or an afterthought. It's what informs everything else in marketing, product development, pricing, distribution, and ultimately communications.

Without clear targeting decisions, you can't build a coherent marketing strategy. He also points out the irony of how digital platforms have flipped their narrative. A few years ago, it was all about hyper targeting, personalization. But now the same platforms are encouraging broad undifferentiated targeting.

Just upload your creative and let the algorithms take over. Ritson sees this as dangerously convenient advice that ignores the real risks of abdicating strategic control. The article also highlights some research from professors Michael Braun and Eric Schwartz, who show that AB testing on digital platforms is often flawed. The way these platforms allocate different audiences to test groups introduces bias, which makes the results unreliable.

So even when we think we're testing effectively, the underlying targeting logic might be skewing our conclusions. He also cites a better brief study showing that only a third of agencies felt their clients provided a clear target audience in the brief. Without that clarity, everything else in the marketing mix starts to fall apart. So his message is clear. If you skip targeting, your strategy lacks direction and no algorithm, no matter how advanced can replace the foundational work of defining who your brand is for.

It's a simple idea, but one that's often ignored. According to Ritson, that oversight is costing marketers more than they realized. Alright, so there's a lot I wanna talk about from that because the topic of targeting is broad, but we often talk about it from that media point of view. So I wanted to start a little higher level with that article, and I wanna talk about the critique of algorithmic targeting.

But let's start with the idea of defining your target in general, super important part of a marketer's job, and not as easy as it sounds. So Ange, do you agree that defining a target needs more attention for marketers and what do you just think about that process in general?

Angela: I think as marketers, we do need to understand our consumers, our prospect customers, but I think the question itself reflects a common misunderstanding in marketing. The idea that defining a target is the most important thing in all that we do is kind of based on this belief that consumers are highly differentiated. And that brands grow by focusing on a narrow segment of Uber loyal, high value customers. But as we've discussed on this podcast, many, many times, decades of data says otherwise, most buyers of a brand are actually light or occasional buyers.

That's how we grow as well. They don't fit this neat persona of a demographic profile, and they certainly don't behave like marketers wish they would. So if you're investing lots of time into trying to perfectly define your ideal customer, you're probably missing the bigger picture, which is that brands grow by reaching all category buyers, not by narrowing focus.

So understanding your prospect audiences and using smart buying to maximize impressions to those audiences is great. But the strategic obsession with defining and refining a target audience, I think is often misguided. And I think research shows us that.

Elena: So, Ange, do you think that there's just too much emphasis on getting down to like a very narrow definition of a customer?

Angela: Absolutely. And I think even back to the point around algorithmic targeting, it's a mess, right? Algorithms optimize for short term, low value metrics. They sort of ignore the idea of brand building. They fragment your audience, they make your advertising invisible to people who don't already buy you, which is exactly the opposite of what a growing brand needs and is appealing, I think, to those that are going down that niche, perfectly defined persona strategy as a marketer.

Elena: And I think maybe part of the reason why marketers have been pushed more and more towards that like niche, narrowly defined persona is because you can go out in media now and target to that individual or you're told you can. Like, can you actually, and is it worth it? Those are two different things. So maybe that's what's driven that. One thing that I thought was interesting when I was looking into this sort of targeting debate, 'cause I know that for us on the show, sometimes we talk a lot about targeting in terms of media buying because that's where it could all like really start to unravel.

But originally, targeting wasn't about media because you couldn't easily target individuals in the past like you can today. You couldn't track around the internet and serve them one-to-one ads. It was more about strategy. So marketers and brands, they used it to identify the most valuable or underserved customers and then built their business around them.

So one example I found was Nike, you know, they didn't try to appeal to everyone at first. They started with serious athletes and that focus, it shaped their products, their messaging, and their advertising. Now you can see how Nike, you know, they started with that target and they've grown and expanded and they're much broader now. But today, oftentimes I think targeting, and I think I'm guilty of this sometimes too, like we reduce it to a media setting. Real targeting. It starts with that strategic choice about who your brand is for and who it's not. And I know we've talked as an agency sometimes about flipping the script around defining a target.

Like instead of focusing so much on this tight, exact customer, look at who can I really not sell to? Let's try to reach like as many people as we possibly can instead of the opposite. But another strong point that I thought Ritson made in the article, the problem was something that he called algorithmic hanky panky, which just a classic Ritson. I don't know what that would be, but it's amazing.

And he's talking about the hidden and often misleading ways that digital advertising platforms run AB tests and they deliver ads using algorithms as a part of those tests. So, Rob, why do you think marketers should be concerned about this? And maybe you could then speak to kind of the rigor we recommend if you're going to AB test your creative.

Rob: Yeah. Holy Smokey should be concerned and I think Ritson's algorithmic hanky panky should be his next book. Just think that's fantastic. But he is pointing out the different ways, like these platforms like Meta and Google often interfere with what we think are clean tests. Platforms they use the proprietary algorithm, the most two overused words on the planet, to optimize in real time. And these optimizations oftentimes skew the results.

They're often favoring one ad early and then starting to show that one more, right? Which creates a false sense of superiority over the other version of the ad. It was actually interesting, a recent study published in the Journal of Marketing actually highlighted this issue and they gave it the name "Divergent Delivery." It's when an ad performs better, not necessarily because it's more effective, but because it was shown to a more responsive audience. That undermines the whole idea.

I think even going back to your point, Angela, you're already skewing the results by force feeding ads to people who are already of that mindset versus going broader and truly understanding which creative message is more compelling to the audiences. And it really undermines the whole idea of a randomized AB test and it really just changes the validity of the learnings. If you're a marketer, not only making creative decisions, but also making budgeting decisions based on these tests, there's a lot of risk of being misled.

Marketing Architects, Angela's got a great team of nerds who love to do AB testing in media and really understanding all the variables at play. And there's a lot to consider, you know, time of day, network, the responsiveness of the ad over a certain period of time, you know how to measure. All these variables are key to a truly clean AB test that gives you full transparency. And I think when people start saying "We don't have transparency on what's the AB decision that's being made," I think that's your first indicator that maybe you should be looking at your methodology.

Elena: So Rob, I don't know if you remember, but we did cover divergent delivery on a nerd alert.

Rob: I absolutely remember it, Elena. I just like, I had flashbacks about it. Absolutely.

Elena: Awesome. So you had a little bit of like deja vu. We've been here before.

Rob: I had divergent delivery deja vu, Elena. Yes.

Elena: Yeah, it's interesting. The challenge with that, like so many marketers, they base a lot of creative decisions based on AB tests, especially on Facebook. What that study found was like Facebook, their algorithm, they're always gonna try to optimize to like, how do I get the most sales? And so they don't like hold their audiences consistent naturally when you're running ads in their platform. So you might be showing like two ads and one of 'em starts going gangbusters, but the other one, like, it's not comparative because you don't have the same audience.

Which it seems like an important thing for marketers to know. Well, so we talked about defining your target, you know, some of the issues with leaving it to a digital advertising platform and AB testing best practices for testing. And now I wanna talk a little bit about the media side of targeting. I think most of us can probably agree that understanding like who your audience is, where they live, what they do every day, what they care about, like that's important.

Shouldn't be neglected. We all know you should understand your customer, but reaching them efficiently is just a whole different thing. If you can define those people and get in front of them with all different kinds of advertising in a consistent and controlled way, that sounds ideal, but it's not the reality that we're facing. So I wanna talk about some of the issues with believing types of targeting are worth it, and even truthful about who they reach.

There are really like too many kinds of targeting to get into today, but they could include targeting by demographic, geo behavioral data, contextual targeting, psychographic targeting, lookalike audiences, retargeting and first and third party data targeting. But let's start with third party data, because in my opinion that can be quite the ball of snakes. So, Angela, what should marketers watch out for when it comes to using third party data to target?

Rob: Working ball of snakes into the language.

Rob: To quote Indiana Jones, I hate snakes.

Angela: One major issue with third party targeting and data is just poor quality, right? So it's often inaccurate, outdated based on flimsy, I would say inferences like labeling someone interested in golf because they clicked on a sports article. You're often paying to target people who aren't actually relevant. I think we've probably experienced that in the digital world, been delivered something you're like, that was weird. I don't know why that was shown to me.

Another trap I would say would be the illusion of precision. We, as marketers really love data and when data is available to us and expensive data, which gets into the third point about cost. It's intriguing to go, why wouldn't I want behavioral data sets to help me understand how to best get in front of people that are maybe already in market or are likely to be in market in the next couple of months. So, granular targeting may sound smart, but if the data is flawed, that precision is sort of meaningless, overlapping segments, inflated match rates and niche audiences that don't convert are all common issues. And then lastly, all of this data, good or bad, comes with an additional cost, which is a huge watch out in this space.

Elena: So Angela, we don't say like, never use third party data, right? Like we've tested it for clients and it's just kind of be aware of the risks of using it?

Angela: I mean, just like anything else, what's the ROI on it, there's going to be added cost to it. How can you ensure that with any targeting strategy, third party retargeting, first party contextual, demographic, whatever you're doing, do you have an incrementality framework in place to be able to ensure that whatever you're doing is actually driving incremental visits, you know, downloads, whatever the key metric might be. And then, yeah, just associating the cost of that data is an important piece in just ensuring that we're making the right decisions for the brand.

Elena: I think there's also a big difference between the type of platforms you purchase third party data through, or like when you're buying, using that, because some platforms like Facebook and Meta, like they have very advanced targeting algorithms and part of the reason is because you provide a lot of information to them about who you are, where you live, what you like, like they have a lot of data on you versus, I think we found that marketers come into TV and they're like, well I wanna use third party data 'cause they're just used to using that in digital. But it's a lot harder to know who's watching this TV set. Like where they live, what gender are they, what member of the family is watching currently. Like it just becomes a lot more inaccurate. I think we see that too with like digital display. Like anytime they don't have like that direct access to data, I think you'd be smart to test other options.

Angela: Yeah, you can download your own profile. I think Catherine had mentioned this when we had done our most recent CTV episode. Catherine Walstad is our chief media officer, and she was talking about this challenge and provided some examples of I'm both male and female. I'm young and old. I love sports and hate sports. I can't remember exactly what they were, but it's quite laughable when you see your own profile in terms of what the internet believes.

Elena: Well, another thing that comes up a lot in conversations about targeting is cost. Because it does matter a whole lot. So Angela, how do you think marketers should think about cost when they're choosing what kinds of targeting makes sense for their brands?

Angela: Yeah, lot of targeting, like I said before, sounds great in theory. Hyper-specific segments, real time behavioral triggers, lookalikes based off purchase data, your own first party data, but all of that precision comes at a price, as we were saying. So generally speaking, the more narrowly you define an audience, the more expensive it becomes to reach them, both in terms of media cost and in terms of the operational complexity and or the data sets that you're using to get access to identification of that strategy.

So the key question marketers should ask themselves is not just can we target 'em, but is it worth it? Are you gaining real incremental value by narrowing your audience, or are you just paying more to reach fewer people with questionable accuracy? So marketers should also be thinking in terms of cost per incremental reach or cost per effective exposure, not just CPM or CPC, including the cost of the data. Again, some targeting tactics look cheap on paper, but deliver ads to the same people over and over again, missing light or new buyers that actually drive growth. And all of that data comes at a cost.

Elena: I think we call it positive spill. Like obviously there's some brands who they, like you can't reach certain people, maybe you can't be in certain states, but for a lot of brands, why are we so concerned about reaching some people that might not be like the most qualified customers? As long as you're hitting all the qualified customers, like it's kind of gravy on top, especially if it's more efficient.

So I know that we specialize in TV obviously. So like I mentioned earlier, some of our specific targeting experience, it applies to streaming TV advertising because it's more of a digital medium where the principles tend to hold true. So, Angela, what types of targeting would you recommend a brand invest in?

Angela: When it comes to investing in targeting, lots of approaches to consider, and I think a good place to start would be prioritizing approaches that reduce waste without sacrificing reach and adding costs. So one option that we've seen be successful for a lot of brands, I think probably all of the brands that we work with is geographic targeting.

It's one of the most effective ways to ensure your media dollars are focused, where your product or service is actually available. Makes it a clear and efficient investment. Looking at A versus B versus C versus D counties, also, you'll see wide ranges in terms of performance across those, if you have some type of response KPI to be tracking, and so that's a great way to get started.

Contextual targeting is another smart choice, especially in streaming environments where aligning your brand with either certain content types or genres can significantly improve relevance and engagement without relying on personal data. Thirdly, a lot of folks might not think this is a great place to start in the streaming space, just given all the data access that we have.

But demographic based targeting can be useful, can be profitable, but should be approached with broader strokes, targeting wider ranges or household level segments relevant to your category rather than relying on overly niche definitions that limit scale. I think it's always important to remember that before there was streaming, there was linear.

And by the way, it was a very big space and still is a very big space where we had to do demographic targeting and it's been effective. And then I think look for nuanced proprietary approaches, something that we stood up called smart targeting offers a more advanced data driven solutions. So instead of relying on inaccurate or costly third party data, smart targeting uses machine learning to identify patterns in factors like geography, like device type and viewing behavior to predict where maybe your best customers are more likely to engage. So it delivers the benefits of precision without the pitfalls of getting too niche, offers better performance and cost efficiency by focusing on media environments that are consistently attracting those high intent viewers.

Elena: I think that's great advice, Ange, for the media side of targeting. And just to wrap us up here, I think it's important to say that that article really caught my eye because we talk a lot about like the dangers of over targeting. And I wouldn't want people to think that we're saying you shouldn't target at all, because that would be silly too. Like I think that it's nice to delineate between the strategic side of targeting, which is who's my ideal customer?

What are their pain points? How can I speak to them effectively? And the media side of targeting, which is, alright, now I have this person in mind, or this group of people in mind, how do I go out and reach them? And just understanding the reality of effectively reaching your audience in different channels. But then I'd also say that it's hard because it does feel like when we talk to brands and marketers, I think typically we can offer like some wisdom and advice by looking at like, all right, well who's that core customer?

But then who's broader than that? And like, we spend more time thinking about how far can we broaden this? And that's really how our customers grow. So it's tough. You wanna start with that understanding, but I think sometimes marketers. I understand Ritson's concern about digital algorithms and just throwing away any sort of control, like that's not smart. However, it depends on the channel. And if you wanna grow, like eventually you have to figure out how to appeal to more people. Like the biggest brands in the world appeal to a lot of people. So if you wanna grow beyond just like a niche offering, you have to think about expanding your target.

Rob: Breaks my brain though, trying to process it. 'Cause I think Angela, you did such a great job in the beginning articulating why you don't want to be so narrow in terms of your focus and that's how growth happens. Yet at the same time it's like, but we're in marketing. We have to target a particular, it's like the first question anyone asks in a marketing meeting is who's our target? So it is, it's a brain bender. And, and like you said, Elena. Yeah, it's a bit of both, right? It's definitely making sure that we're going broad enough so we can grow, but also we gotta make sure we're on to our message and our strategy. And in order to do that, you kind of have to have someone you've identified to speak to.

Angela: At the core of marketing effectiveness is reach, right? And new. I think it's whether you're talking category entry points or you're talking audiences, it's like, it's not none. It's not, we don't understand it's new and not excluding potential relevance with consumers that could be effective in helping you grow your brand.

Elena: And one thing I even struggle with sometimes is I think a common word of advice is like start small and then build, which is not necessarily bad advice, however. We have seen a lot of success with our own clients of starting broad. When we launched Hurricane and Stuffies, like traditional logic would tell you, launch those brands digitally, start narrow, figure out, you know, the core target, and then start to build. We were just like, Hey, welcome to TV, and that broad reach works really well. Now,

Rob: Yeah. Had we gone narrow first? I agree. Had we gone narrow first, we would not have grown at all. We probably would've actually stopped with the whole project.

Elena: Well, and both the products that we use as examples are the Hurricane, which you would think has a very clear, narrow target and a children's toy, which again, very clear, narrow target. And yet TV works so well for building those brands. And so I don't know, and sometimes I struggle even with that advice. It's like, well, can you afford to start on TV? Because if you can afford TV, you should be on TV. Maybe I've just drank the Kool-Aid, but it feels like if you can, you should.

Angela: Mm-hmm.

Elena: Alright, well let's wrap up with something kind of fun. What is one data point about you that would completely throw off an algorithm? And Rob, why don't you kick us off?

Rob: I have many of them. I would say one of the clearest ones is that I am a 50-year-old male who's married and has two kids in college, but I had the viewing habits of a 14-year-old girl. I am arguably an expert in The Bachelor in the Bachelorette. I like shows like the Millionaire Matchmaker. I even still watch the classics like 90210 and Dawson's Creek. So I think that would probably throw off Mark Zuckerberg's amazing algorithm.

Elena: Fun fact, when I first started working at Marketing Architects, I feel like one of the first things we connected on was a bachelor. And Rob taught me that he collects Bachelor contestants on LinkedIn, like Pokemon, and so I started doing that too, and now I have a lot of connections. Whenever there's a new season, I'm just like, all right, let's go see who I can get. I have quite a collection. If anyone's curious, let me know. I feel like I've gotten more than you.

Rob: You, I'm sure you do. I have some of the classics though. My Pokemon collection's more of an archive of classics than probably the newest and greatest.

Elena: All right. Ange, what about you?

Angela: Yeah, this one was easy for me. I thought of it right away. I think an algorithm would absolutely assume I am a DIY enthusiast, and in reality, I wouldn't touch it with a 10 foot pole. I love to watch DIY programming about updating your home and HGTV, but I'm not picking up a power tool unless it's an emergency.

Elena: I resonate with that as well.

Angela: There are experts in every field and I need to bring those people in for that.

Rob: You're good at delegation.

Angela: Right. That's it. Yeah.

Rob: The D in DIY is delegate, right?

Elena: Well, yeah, mine Rob is kind of similar to yours, which is like, I spend a lot of time at work on like with marketing research, and then in my free time I watch like the trashiest reality TV you could possibly imagine. Like, I don't like to watch anything, if it makes me think or feel sad, I'm just not. It's just not for me.

Rob: Reality TV's so good.

Angela: Did I say geographic when I started, or did I say demographic?

Elena: Demographic.

Angela: Okay. We gotta go back.

Elena: Okay. I was wondering why you recommended that.

Angela: I am gonna recommend demographic targeting, but I'm starting with geographic targeting. I don't know, I just like transposed it in my notes here.