“How first-party data delivers real business value”—with Mia-Malaika Lange (BARFER’S)

Recorded in German—English translation of the original conversation.

Intro

Maria-Lena Matysik: My guest today is Mia-Malaika Lange, Head of Marketing at BARFER’S. BARFER’S is an online shop for BARF.

And now things get a little nerdy right away 😉 For all the non-dog owners listening: BARF feeding is a feeding method in which dogs and cats are fed raw meat. So in short: BARFER’S sells pet food. But as you can imagine, it’s a more complex feeding method than simply dry food. That will become important later.

But back to Mia. Mia originally comes from brand marketing—usually not a sooo data-driven marketing discipline, actually. But she has spent her career mainly in startups and scale-ups. And that’s exactly where she learned early on: data creates insights and added value—also in and for brand marketing.

At BARFER’S, she is now responsible for several marketing channels—from brand and content to performance and new customer acquisition. And over the years, someone who originally came very strongly from the creative brand world has become a pretty enthusiastic data user.

This episode is about how BARFER’S uses first-party data to better understand its customers and make marketing more relevant. We talk about why customers are surprisingly forthcoming with this data in particular, how BARFER’S then uses the information—and, very concretely, what business value this first-party data has for BARFER’S marketing.

Here we go …

Interview

Maria-Lena Matysik: I’m very happy to have you as my guest on Analytics Sprechstunde today, Mia.

Would you maybe describe the marketing, or rather your marketing focus, at BARFER’S? Is it very content-oriented, very performance-driven? How would you describe it? What’s important to you?

Mia-Malaika Lange: In marketing, two things above all are important to us. On the one hand, that’s new customer acquisition. We have a product that naturally has a relatively high retention rate. But on the other hand, it’s also relatively difficult to get new customers excited about the food. I don’t know how many dog and cat owners are listening, but you know it yourself as a dog owner: you go a bit by the principle of “Never change a running system.” Once you’ve found something your dog does well on and also likes to eat, you’re actually not that willing to switch.

Maria-Lena Matysik: I can confirm that.

Mia-Malaika Lange: That actually shows in studies, too. Last year I saw a study according to which 44 percent of dog and cat owners would rather switch stores than brands. So if Fressnapf delisted your four-legged friend’s favorite brand, you wouldn’t buy something else at Fressnapf instead, but would probably look elsewhere to get your beloved food brand again. My hypothesis has always been that it’s because our four-legged friends can’t talk to us and we don’t consume the product ourselves. That means as a pet owner, you have to rely a bit on what comes back to you as an experience.

Accordingly, we put a lot of emphasis on new customer acquisition—and then have a very reliable customer base once we’ve been able to convince someone of our product.

On the other hand, we have a very educational approach, because BARF feeding isn’t quite that easy. Compared with dry food, for example, where you just have to tear open the bag, BARF feeding is—honestly—a bit more complex. It’s frozen food that has to be stored in the freezer. And we see that we have to explain so, so much to customers so that they even understand how the product needs to be handled. That’s the second big focus in our marketing, and we concentrate on different KPIs: with the educational topics, it’s really more about reach and engagement. We measure new customer acquisition very strictly by customer acquisition cost and the number of new customers.

Maria-Lena Matysik: If you zoom out for a moment: which types of data do you use in particular in marketing? Sure, web analytics data, maybe—can you go through them a bit from one end to the other?

Mia-Malaika Lange: Yes, sure. I think we use everything that’s available to an e-commerce shop, and that’s actually quite a lot. On the other hand, we also notice, for example, how much data loss we have in our own stores when people no longer order through a customer account there. Of course, we try to capture the data there as well and to show customers the benefit of the customer account, so we can gather data there too.

But on the web, we can basically measure everything. It starts with sessions and goes all the way to the smallest details in the orders. And of course we also see through which entry point the customer arrives. We track that in marketing attribution: what are the channels and what influence do they have? Because the product needs so much explanation, we’re talking about a relatively large number of touchpoints before the first conversion.

It’s not an impulse purchase where you see an ad, click on it and think: Wow, I’ve always wanted these shoes, I’m buying them now. Instead, you engage with the topic for quite a long time. That’s why it’s very important for us to see which channel has which influence, so we can evaluate the channels better. If we only went by last click, for example, that would distort the whole picture. At the end of the customer journey, you’re perhaps more likely to be in the pull channels—but there has to be some channel that works as the introducer.

Maria-Lena Matysik: And of course you also use CRM data to reach existing customers. But what you also often use—we already picked this apart a bit in our prep call—is data that your customers give you themselves.

Mia-Malaika Lange: Surprisingly willingly.

Maria-Lena Matysik: Exactly. Would you tell us a bit about it? What do you ask your customers? What kind of data is it? And how did you come to collect it?

Mia-Malaika Lange: What’s really interesting—and maybe that’s also because we can all only communicate so-so with our four-legged friends: while we, especially in the DACH region, are very picky, I think, about the privacy of our own data, we’re very eager to share when it comes to our four-legged friends’ data.

We’ve set up a funnel, the BARF calculator. Its primary purpose is for the customer to get a feeding recommendation at the end. That means they enter their four-legged friend’s data, and at the end we can use this data to calculate how large the required daily amount of food is—and of course also derive from it how much you need to order, depending on the period you want to order for. But we can also give you a product recommendation, for example which products you perhaps shouldn’t feed because your four-legged friend is allergic to them, or the like.

What was really interesting in developing this funnel: we proposed asking quite a lot of questions. We developed the calculator with an external provider, and no matter who we talked to, everyone said: “Nope, you have to cut down the questions. You’ll have so many drop-offs. That’s way too many questions, the funnel is way too long.” But I insisted a bit, because it’s not a mechanic we just made up. We’d seen it a lot in the market, especially in pet food.

And it proved true that practically no one drops out of this funnel. People go through it pretty much straight up to the double opt-in—whether it’s about the date of birth, allergies, the breed or other things. As a pet owner, you’re very willing there. And it makes total sense: because we can’t talk to our animals and can’t choose the food according to our own taste, you have to rely on the product recommendation. And of course you then want to provide as much data as possible to get the best possible result in the recommendation and the calculation.

Maria-Lena Matysik: It’s also totally plausible for the user: you need this information from me to be able to recommend the right thing to me.

Mia-Malaika Lange: Absolutely. And of course that’s worth its weight in gold. We can then also use this data in CRM. And I really have to say: a subject line with my dog’s name in it hits differently than just “How is your dog doing today?” For me it then says “How is Selma doing?”—and I definitely want to click, because of course it matters to me that Selma is doing well.

Maria-Lena Matysik: Yes, absolutely, I can relate. So what do you do with all the data the users give you in the BARF calculator?

Mia-Malaika Lange: Actually, we do all sorts of different things with it in all sorts of different departments. In marketing, we can use it very well to personalize communication. That doesn’t necessarily have to be only in CRM. You can also use it wonderfully on-site when the customer is logged in and, for example, ask whether Selma might need a snack. Selma would say yes—but as a two-legged creature, you still have to think it over.

On the other hand, we also use the data to analyze purchasing behavior a bit. If we know how heavy your dog is, we also know how large its needs are. We can compare that with your shopping cart and see whether you’re actually buying the right amount or whether you might be feeding something else to cover the daily requirement. We can then also do a kind of upselling: we’ve segmented some customers and written to them: “Hey, we have the feeling you’re only partly feeding BARF—here are the benefits of going full BARF.”

You can do a lot of cool things with this data and of course also derive in which customer segment there may still be room to grow. As I said, we didn’t invent BARF feeding. It comes from a bubble in which larger dogs tended to be more common. You can also see that a bit in our dogs’ median weight: it’s 20 kilos. And I don’t think that’s the median weight of dogs in general. There are still lots and lots of small dogs, of which we simply don’t have that many with us yet—and we can take direction from that in marketing, too. Because of course you can feed BARF regardless of whether the dog weighs 5 kilos or 45.

Maria-Lena Matysik: Totally interesting what that ultimately says about your target group, too—what you can conclude from the type of dogs or cats about what kind of people they are. I could well imagine that people who keep a 50-kilo Dogue de Bordeaux are wired a bit differently than people with a Chihuahua.

Mia-Malaika Lange: Definitely. But both animals can be fed BARF—and both need to be convinced by us.

Maria-Lena Matysik: Totally, yes. Super interesting. Could you maybe put your finger on exactly where the business value lies for you in the end? You’ve already touched on it in the different areas—but how would you sum it up concretely?

Mia-Malaika Lange: That’s difficult and at the same time not that difficult at all, because in our marketing, no gut decisions are made. That’s why it’s actually relatively easy when I think about it: the business value is huge. With data, we can simply back up everything, and we can reformulate gut decisions into hypotheses, which we then test to see whether they work.

Data is really the core of all marketing decisions. It’s always part of it. You can also tell that I think I grew up with this mindset and don’t run campaigns just because I find them somehow nice or cool, or because they’re particularly emotional—even though, of course, that always works with us pet owners.

But I think we do sometimes reach the point where we see in the data: something isn’t worth it. And even if it’s a passion project, we sometimes have to say: Okay, kill your darlings. It’s cool and fun, but it doesn’t deliver the return on investment we have in mind. And you can only prove something like that by also tracking what you test.

Maria-Lena Matysik: When you say you don’t make decisions based on gut feeling—that’s a sentence a lot of heads of marketing would like to say, too. But from my perspective, the reality is that in many marketing teams, a lot still happens along the lines of “We’ve always done it this way” or “Looks good, feels good.” What would you say: what are you doing right, or what can other marketing teams learn from you to get there too?

Mia-Malaika Lange: I think one of the biggest things we do right is that we try to ensure a kind of data democracy. We use a tool that really everyone has access to—whether you’re a designer, Head of Marketing or a student employee. Everyone has access to the tool, and everyone should basically be able to look things up there when they have a question on their mind. And we really do try to encourage the team to actually do that and to check in regularly.

For example, we always start our weekly kickoff in the marketing team with a quick look at the numbers. For me, it’s not at all about being super performance-driven; what matters to me is that everyone on the team always knows: How did last week go, where do things stand right now, where might we need to make adjustments, and what are the current priorities?

And I think because I went through such a tough data school myself, I try to pass that on a bit—I don’t feel like it did me any harm. But you always have to give your colleagues the data that’s relevant to them. For example, it doesn’t make sense to talk to a designer about hard SEO data, because that’s simply not their area. What does make sense, though, is looking with a designer at the reach and engagement of social media posts that are about designs, or at which flyer performed better. Sometimes we also run A/B tests with package inserts—and then design is a relevant component again.

Accordingly, you have to make sure that everyone gets access to the data they need, but also isn’t distracted by data that isn’t relevant to them or that they have no influence over.

Maria-Lena Matysik: Super interesting. I have one last question—I’ve actually never asked it on the podcast before, but it came to mind while I was preparing: What would you say is currently the biggest unsolved tracking or analytics problem for you in marketing? Where would you say: this could be better, or we don’t yet know exactly how to pull it off?

Mia-Malaika Lange: That’s a good question. I think the biggest unsolved data problem for us is not getting lost in the data and staying focused. As I said, it feels like we collect everything. You can look into everything, and sometimes you might then tend to analyze everything, too, and look for the answers in the data everywhere. The question of how we prioritize which data we analyze and where exactly we look isn’t necessarily a problem yet—but it’s an area we’re working on.

Maria-Lena Matysik: Thank you so much, Mia! That was really super, super interesting.

Mia-Malaika Lange: My pleasure. It was a lot of fun.

Outro

Maria-Lena Matysik: What did I find particularly interesting about this interview?

First-party data works particularly well when the customer gets direct added value in return. A classic example from lead generation is “email address in exchange for an e-book.” But there, the added value is actually forced—you could just as well provide the e-book without an email address. With BARF feeding, on the other hand, it’s genuinely necessary for the data to be there. Otherwise, customers risk buying and feeding the wrong food and possibly even harming their pet.

And I also found it interesting how much business value lies in this data. It’s far more than a basis for personalization—even though personalization shouldn’t be underestimated! With this data, you can:

• better understand the target group: Who exactly are they? How do they think?
• build customer segments and address them separately and in a targeted way,
• identify upselling potential,
• and discover new growth potential within the target group.