Target Audience Analysis and Buyer Persona Creation

Data-driven segmentation frameworks for constructing actionable buyer personas and mapping user search intent journeys.

Speakers: Sevda Yurtvermez, Onur Durdağı

12 Nov 2024

Video Transcript

⏱ 14 min read• 2,473 words

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— Hey everyone, I'm Sevda from Zeo. Welcome, Onur.

— Thanks for having me. Hi, everyone. So.

— Right.

— I'll hand it over to you.

— Welcome back. We kicked off the digital funnel strategy in an earlier PPC Talks episode. What a digital funnel strategy is. Why it matters. What we actually do in performance marketing. We covered the basics there. In our second session, İlker and I talked brand awareness. Why it matters. What you can do to grow it. Which campaign types we lean on. That was the intro. Today Onur and I are digging into audience analysis and persona building. Why do you even need audience analysis? Where do we pick the right audience and build it into campaigns? And what do we do to hit our goals? Let's get into it.

Onur, you've been on the PPC side, performance marketing, for years now. You've run a lot of brands. When you take on a brand, what do you look at first in audience analysis? Which tools do you lean on? Let's kick things off with that.

— First off, hi again, everyone. So, I've been at Zeo for about a year and a half. I work as an executive on the performance side. Digital performance, specifically. Hopefully this recording is useful for everyone. And hopefully we get good feedback on it. So, there are a lot of moving parts in advertising. In digital advertising, I mean. One of the big ones is audience and persona building. These personas are the core pieces. Whether it's the Meta panel, Google Ads, Yandex, or LinkedIn. They help us build out those spaces. They help us optimize and strengthen them. They keep all the moving parts on the right track. And they get us to our target audience the right way.

If I had to name two or three big themes in advertising. One of them would be audience and persona building. Age brackets matter a lot. So do demographic splits. And those splits shift by industry, too. You have to position yourself correctly alongside your brand in that industry. Think of yourself as the customer. Or as the user. Which space are you in within that persona? Which demographic? What tastes and needs? There are a ton of splits around where you sit in the market.

Let's start here. There are a lot of professions out there, as you know. By industry, you've got everything from architecture firms to ready-to-wear and fast food. All kinds of businesses. Every one of them has positioned itself in its industry. They've all staked out a spot in the market. We need to approach these building blocks based on the dynamic we've set up for our own brand. So what is that dynamic? Say we're running ads for an architecture firm. Say we're building an ad in the real estate space. You have to look at your market benchmarks. Build the persona so it matches what people in that sector actually expect. Then organize the target audience around that.

That's how it works with in-market audiences. It's about reaching users with the right habits in the market. We also have places in Google Ads and Meta for similar audiences. In Google Ads we can even run a demotion campaign now. Mm-hmm. But on Meta we can build lookalike audiences heavily. If we go deeper in the Google Ads panel, it's the same idea. With custom audiences, we position our sector to follow people's digital footprints. What are those? Take a fast food chain as an example. You look at which similar brands people follow. You trace their footprints like an investigator. You actually discover it through research. You follow their digital traces and feed that into the panel, matched to the persona.

What is our persona? Ready-to-wear sector, I said ready-to-wear, sorry, let me correct that. Ready-to-eat sector, fast-moving consumer goods, fast food. We need to define that persona so it matches fast food items people consume quickly. Here too, we can add users who visited similar brands right from the panel.

— Yeah. Exactly. It also helps to go after custom intent audiences there. Onur, like you said, when you take on a brand. You look at age and gender. If the brand has extra market research it already analyzed. You pull that offline data in. And if there's historical data online, it gets easy. You can reach the right audiences on age, gender, interests, and habits. But when we think about a brand starting from zero, it's like you said. You target the custom intent audiences of brands doing similar work. And it helps to keep the interest targeting a bit broader as you go.

With custom intent, you can build more than one audience. One for people visiting specific web sites and competitors. Another for users searching specific queries. That makes collecting the data much easier. It helps on the processing side too. Some brands say let's narrow the interests down and get more precise. But the smaller we make the data, the harder our job gets. So we also go after closely related audiences. We run AB tests there and look at what results we get. That's how we steer our brands in this area.

So you don't have to stick to one-to-one point flows. In apparel, don't just target e-textile people. Women's wear, men's wear, kids' wear. It pays off big to also work audiences built around their other interests. In our own campaigns, we see it too. Audiences we never expected often deliver better returns.

— Yes. Uh, I totally agree. There's one more thing here. On the analysis and reporting side, back in the Google Ads panel. Uh, we can easily see where affinity and habit signals sit well. We can see which data is actually giving us a response. So based on those analyses, we can make extra exclusions later on. That goes for in-market and custom intent alike. Then we optimize the campaign. That's an important piece. Uh, and on the app side too, uh. These custom intents have a lot of breakdowns.

I think you should fill that in as well. If the market has users on an app, uh. In the same sector, running on the same, uh, portfolio. Add those apps in there. Add them through similar, uh, applications. I think that's the far more correct move. And here we can add website breakdowns. These need to be competitor websites. We already have one.

— Mm-hmm.

— If we've done the photo analysis too. It makes our job easier before we even get to the brand. We end up with a list of competitors. We add their websites from that list. That's how we build out custom intent. Building personas correctly from those signals matters too. I think it pushes our ad performance way forward at this point.

— Absolutely. Also, why does correct audience analysis matter? I wanted to touch on that a bit. Now, with these evolving dynamics. Google has a lot of campaign models that are more automated. More AI-powered, really. And for the AI on that side to work better, our manually built audiences matter. The more we feed the content with the right data, the better it runs. That goes for AI-powered campaigns like PMax and demand gen. It also positions audience signals more accurately for our target audience. And in demand gen campaigns, plus similar audience-focused campaigns, it'll run more efficiently that way.

So when we take over an account. The first thing to look at is audience and market research. And building the right personas. It's genuinely one of the most important stages.

— Right. Um, and there's another thing here. While we read the panel signals correctly from the metrics, um. Whether or not we built similar audiences, um. If we fed in a custom intent on top of that in-market pool. Um, then for that campaign, um. We can break out the custom intent segments in the conversion column panel. We analyze the data coming from custom intent. And if we see it performing well in the reports, um, a separate one. That happened to me on a few campaigns. Because researching the custom intent and rebuilding it alone in a different campaign can be really efficient.

So we set up the legs almost like an A/B test. If that custom intent gave us positive signals, we split it out. Then we widen that bridge in that space. We grow the signal coming from there. I'd say it produces really positive metrics for the campaigns.

— Yes, absolutely. Once that work is done, we run an optimization. Which audience gave us more return in the account? Um, which side should we spend the budget on? Let's do the budget optimization. It also guides us on building new audiences along those lines.

— Yeah. Um, I can add a footnote here too. We get feedback from Google reps now and then. Um, when we build those personas. Our Google team sometimes tells us which segments overlap with our sector. Which industry, which market, which space. I'd say that makes our job easier. Um, because the competition out there is insane. It keeps growing. Motivations and challenges differ by sector. To get past those challenges, you have to uncover little tricks. Pulling fresh data out of those tricks matters a lot. That's how you strengthen the campaigns. And I'd say Google reps can help here too.

— Yeah. Support from that side matters to us. But honestly, the most important support has to come from our brand. They know their own services and products best. They know the target audience best. The more detail we get from them, the better. We pull all that knowledge together and get a stronger result. But some brands enter the market from scratch. No data at all, no market research done. There we lean on our own industry experience. Which audiences should we go after? Which campaign models can we use?

Plus, like you said, it really helps us in meetings with Google reps.

— Yeah. Getting feedback matters. Feedback from the customer, from the market. Then you line that up against the brand's own read. Compare the two. That's what surfaces better results here.

— Right. And sometimes the results are wildly different. A brand does its own upfront research. They come to us with one audience breakdown. Then we look at the real data we're pulling in. Totally different picture. Take the diamond space, say jewelry. A brand comes in with women as the main audience. But the data says men buy these products more. Sure, women browse them. They add to cart. But the converting sales come almost entirely from the male profile. You get some surprises on that side. Still, the closer we get to the right audience upfront, the better. Start with a tightly related audience. That's how you end up with better data.

— Yeah. Also, every panel gives us different segmentation tools. Google has household income, for example. A lot of countries don't use it. Some do. There's a wide scale, from 10% to 50%. In some countries we can pick a household income tier off that scale. Yeah. Abroad, this actually hits the target audience much harder. In Turkey it doesn't move the needle much. You just can't get that granular here, unfortunately. Unfortunately, how the data gets used varies under GDPR too. That's why it isn't available. If we talk about Meta a bit, same story there. You go from a broad, major field down to something more minor. You follow people's actual digital footprints. Then you target the ones who overlap with our brand.

For those people, we go into interests. Those interests need signals that match our brand. So it could be customers already engaging with us. If some data from the web site is hooked up, that helps. If people are into apparel, you're reaching folks who shop for clothes online. From that approved audience we can diversify further. Lookalikes, similar audiences, or remarketing. Advantage Plus is what Meta recommends now. It makes things easier. The goal is letting AI measure the algorithmic dynamics more smoothly. Then it connects you with the audience. Unlike Google Ads on that panel. I'd say those kinds of dynamics are developing there too.

Age breakdowns matter a lot. Just like the example you gave earlier. Shopping habits shift by gender. They shift by where people are located. And by how people actually use the web. So yeah, that's where things get interesting.

— Yes, absolutely. There's a common perception on the meta side. People say they get more conversions there than from Google. They say they sell more there. But when we dig in, we see something else. On the meta side we reach different audiences with the brand. Then those people come back and buy through Google. So you build your audiences on meta with the same care. And you pull real data out of it.

— Right. You connect those two channels. You make them feed each other. And it's not just two channels. LinkedIn can create the same opening. So can Yandex. That makes the audience way more efficient.

— Other channels like Twitter and TikTok work the same way. It comes down to who our audience is. And what product or service we're offering. We find the right channel for that. Then we plan the work around the relevant audiences from the start.

— Yes. Exactly. Persona insight. The insight we pull from a panel. The panel itself carries its own insight. On LinkedIn we can reach B2B more easily. It has its own tools for that, like Sales Navigator. So the odds of hitting B2B are better there. On Google and Meta we reach broader audiences. Those tools work better for brand awareness at scale. We build the persona internally from those odds. We get feedback from the brand. We build it with them. The closer the persona matches, the better. We feed the panel signals based on it. We line up how we segment with that persona. That gives much better results. Sometimes an audience we tried by pure chance performs well.

There's no fixed motto here. What matters is reading the panel well. Pulling something useful out of those numbers is the main thing. So using the panels efficiently matters a lot. I'd say that's the critical part.

— Yeah. Great stuff, Onur. Anything extra you want to add or flag?

— Uh, nothing from me. I think I touched on most of my notes. I hope I gave people listening and watching something useful. Love and respect to everyone.

— Alright, see you next time on PPC Top then. Take care of yourselves.

This transcript was generated using AI speech recognition and verified against subtitle style rules.

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