Daniel (4:03)

And I think that a lot of people have this mental picture in their mind of this is who I want my customer to be. But this, this the way you're approaching it is a, we have ready people who want to buy from us. Let's get more of who we already have. Because they already shown signal internally.

Tamara Graminski (4:24)

That's exactly it. Honestly I just, I tell people time and time again just like focus on the reality of your. And so once we've done step one, we know who's coming in. Step two is trying to figure out which of those segments or clusters are actually performing well for the business. So I like to look at each segment cluster and then look at their business metrics across a variety of different metrics. And these might differ for you depending on if you're selling a physical product or an E comm product or if you're at a SaaS company. But a few that I will typically look at are conversion rates. So how easily do they become customers? Average revenue per customer or ltv? How much do they spend? How long do they stick around? Churn and retention metrics. So are they actually getting value over time and continuing to get that value? And then customer acquisition cost? Right. How expensive are they to actually acquire as a customer? And this is where that myth of like the dream Persona often falls apart. As you mentioned, people are always like, well this is who I want to be a customer. I want that big enterprise logo on my website. It might look good in a case study, but if you actually can't afford to acquire that customer and they don't get value with your product and they churn in six months, then it is not your best customer segment. And so what you're looking for here in the data is you're looking for that sweet spot, right? So someone that's easiest to acquire, that you already have coming in through the door, someone that's valuable and someone that kind of is sticky and sticks around for a bit.

Daniel (5:58)

So if you layer performance on top of volume and suddenly you can see where that real value is.

Tamara Graminski (6:09)

That's exactly it. Like I've done this before and I'll see a large group of customers, like a large segment cluster. And then when we actually look at the performance data, we see they're not valuable at all. And so it is really about kind of looking at the data and seeing what the data tells you. And what I love about this is it goes beyond opinion, right? So it's not just, oh, well, the CFO thinks this is our best customer, the CEO thinks this is our best customer. The data doesn't lie. And now we get to step three, which is when we can actually take an external view again. Right? So most people almost start with step three. We started with step one and two and now we get to step three. And so this is where we can zoom out and look at the external opportunity. Because just because a segment performs well today doesn't mean it's the best one to scale. Right? So we've looked at our own data, we see who we're winning with today, but now we want to verify that we can also get them tomorrow. So I like to ask questions like, is this a large and growing market? Will it support our long term growth ambitions, or is this a declining market? Maybe we've done a good job of capturing them, but it's not going to be a large market for a while. Can we actually win this market if we were to prioritize them? How competitive is the space? Are our competitors also targeting this ideal customer? And then of course also asking like, does this align with where our product roadmap is going, or do we need to adjust our roadmap to make sure we can accommodate this new segment? And so this step is really where those like trade offs and tricky conversations happen, especially at the leadership level. Because maybe one segment is easier to win today, but another has stronger upside in the long term. And so it's not always a clear cut decision, but at least now your decision is going to be grounded in real data that you can all look at and discuss as opposed to whoever just has like the loudest voice in the room.

Daniel (7:58)

I feel like this is such a good reminder that segmentation isn't just a set and forget it move. You actually have to gut check it regularly with the steps you just laid out 100%.

Tamara Graminski (8:11)

Especially the way that the market is changing so fast these days. Your product is constantly evolving. Like you might be shipping features every week. The market is shifting and so I always recommend that you're rerunning this analysis every six to 12 months. And then again, just like what we talked about with pricing, if something major has happened in your business, so you're launching a new product line, you're trying to actually go after a new market, something has shifted in your market. It's another great reminder to go back and kind of refresh this activity.

Daniel (8:41)

Well, I know in like, in theory this sounds really good tomorrow, but I know you always have a real world example of how you've actually executed this. So could you actually show us how you've actually used map to guide a go to market decision in the past?

Tamara Graminski (8:58)

Yes. I knew you were going to ask this, so I brought an example today from my own work actually. So one of the best examples of this was when I was leading product marketing at Unbounce and we were preparing to launch a new AI powered product called Smart Traffic. So Smart Traffic basically promised better conversion rates by automatically routing visitors to the landing page variant most likely to convert. So we were positioning it as like better than a B testing. But before we launched we had to figure out like who is this really for? And everyone had different perspectives around the business. So instead of just going on opinion, we ran the map model. And again, we started with measure volume. We had three core customer groups. We had SMBs who are already doing testing, like using AB testing. We had SMBs who weren't doing testing at all, didn't even know that testing was a thing. And then we had these big businesses that were testing. When we looked at the data, the largest volume by far came from SMBs, especially those who weren't currently testing. So we felt pretty confident that that was like a good opportunity for us to go after. But we wanted to see like, are these customers even valuable to the business? If we focus our go to market on them, will be, will we be able to be successful? So we looked at a bunch of metrics like trial to paid conversion rate, retention, FE adoption of other products. And what we saw was that the big businesses had decent performance, but they were way harder to reach. And as I mentioned, we had less of them. And so the sweet spot really were those SMBs who weren't currently testing. They had super strong conversion and activation rates. And our hypothesis was that if we could get them to adapt adopt smart traffic, they would stick around even longer. So then we moved that into our final stage, which was prioritized potential. And this is where we felt really confident because we knew that this segment was under served by existing tools and the least captured by competitors. Everyone wants to go after the bigger businesses. No one wants to be in like the SMB market. And so it was a big market aligned to our product roadmap. And we knew that smart traffic would remove the complexity of manual AB testing, which was something that this group really struggled with. So we went all in on this. Our primary segment for launch was basically these small businesses who weren't currently testing. And then we did a secondary segment for launch which was SMBs who were testing but we're doing it incorrectly. So we were trying to like move them away from the behavior of a B testing.

Daniel (11:24)

This sounded like this approach shaped your whole product go to market and product marketing strategy for this product completely.

Tamara Graminski (11:34)

It was like a big unlock from us. So we basically made sure that the messaging, the launch campaign, all of the onboarding flows, how we enabled sales, all of it was focused on making it easy for this exact SMB segment to adopt smart traffic. Even if that meant, you know, not building flows and material for those other segments that we, we talked about. We went all in on this one segment.

Daniel (11:57)

I love this example so much and honestly, it was a perfect way to bring this whole framework into life. And to recap for everybody, the map model helps you measure volume, who's already showing up, analyze performance, who's delivering value, prioritize potential, who's worth doubling down on. It helps you focus on the right people, build better messaging, and avoid wasting time on segments that won't scale. And this is sad, but this is the end of this week's go to marketplay and the final episode of the season. If you have any good feedback, please DM tomorrow on LinkedIn or me and tell us how you liked it. If you want more stuff like this on the podcast will be really helpful. And thank you for hanging out with us for 13 place because that's tomorrow's favorite number. And if, if you love this theory, also share it with a friend. Share it with someone. Tell tell us your favorite episode. We love hearing from you. So thank you so much. And thank you Tamara for coming with all the you always do this, but coming up with details and methods and formulas and you're really good at putting things down to procedure for people.

Tamara Graminski (13:13)

Thanks Daniel. It's been fun to hang for all 13 episodes.

Daniel (13:19)

Thanks so much for listening. Keep tuning in to hear more great insights from the coolest marketers from around the world. If you haven't already, make sure to subscribe and follow the Marketing Millennials podcast on Apple Podcasts, Spotify, YouTube, or wherever you get your podcast. And if you like what you hear, I would greatly appreciate you giving us a five star rating. It helps bring more marketers into our community.