Brian Eberman Joins Elias Crum on The Marketing Technology Podcast

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Head shots of Host Elias Crum and guest Brian Eberman

Next stop on Brian Eberman’s podcast tour, Brian stops in to join Elias Crum on “Marketing Technology Podcast” to discuss the customer centric approach and the importance of an omni-channel approach.

Listen to full episode here.

Included In This Discussion:

  • How Zeenk defines “Customer Centric” analytics and how to use it to optimize your business
  • How to predict the churn probability of your customers and leverage to better retain them
  • The benefit of having an omni-channel brand that is both DTC and has a presence on Amazon
Brian Eberman quote about the 80/20 rule and how business need to optimize their business around the 20% of customers that drive 80% of their business

0:00:00.0 Elias Crum: Welcome to this brand new episode of the Marketing Technology Podcast. This podcast is hosted by Mark Van Horek and myself, Elias Crum, and brought to you by Marketing Guys, the Martech agency based out of the Netherlands. Welcome to this new episode of the Marketing Technology Podcast, on which I today have Brian Eberman, who is the CEO at Zeenk. Welcome, Brian. Can you introduce yourself?

0:00:24.0 Brian Eberman: Hi, Elias. Great to be here. Glad to be on your podcast. Absolutely. So I am Brian Eberman. I have been with Zeenk for a little more than a year. We are an e-commerce analytics company that provides customer-centric analytics to help brands grow. I’m here in lovely Boston, which looks like we’re going to have wonderful weather this weekend. I’ve been here in Boston for quite a few years, about 40 years, and I’ve been a tech executive involved in data, data science, data analytics for about 30 years.

0:00:57.0 EC: The tech industry is pretty big in Boston, isn’t it?

0:01:00.0 BE: Oh, yeah. It’s gone through a number of changes over my career, right? But it’s centered around obviously the large universities that are here, MIT, Boston University, Boston College, and Northeastern. But it’s gone through its changes. When I first graduated with my graduate degree, Digital Equipment was the big employer. I actually worked there as a research scientist, and then that changed out as HP became… I started to own my company, and the internet wave started to hit. Today, if you walk into Kendall Square, where MIT is, you’ll find a giant Google building, a giant Microsoft building, some Facebook people, and then an enormous number of biotechs.

0:01:45.2 EC: Yeah, and I think there’s also a lot of martech companies there, right? Isn’t HubSpot there? And Drift, I think Drift’s headquarters is there.

0:01:53.0 BE: HubSpot was founded here, and that’s actually one of the ways we think about our company is thinking about how HubSpot works. But yes, HubSpot was founded, Drift was founded, Wayfair, huge e-commerce company. It’s a good diverse set of technologies. Once you’ve been here a long time, it is interesting how small the community actually is, though.

0:02:13.0 EC: Love it. Love it. So let’s dive into analytics. That’s the topic for today. So Zeenk is mainly into, or particularly into e-commerce analytics and data sciences. The obvious question here would be, you know, there’s Google Analytics, there’s all kinds of analytics suites. What makes you guys different, and why do you specialize in e-commerce?

0:02:37.0 BE: So let’s make sure we understand what I mean by e-commerce. By e-commerce, we mean any company selling direct to consumer where they have some kind of store, right, where they’re selling directly to their consumer. They may also be selling on Amazon or Walmart or other channels. And we work to support them across the Amazon and their direct to consumer channel. So that’s the first place that we would be different than in Google Analytics. So we think Google Analytics is a great tool. We think it’s great. It’s very complimentary. But we think it’s challenging for companies to really gather all the data that they need from all the different places they’re spending money and get insights into where they’re really generating their profits, both from a product perspective and a user perspective. And we help them do that.

0:03:26.0 EC: And well, I think you’re spot on here because that’s the main thing, probably that is one of the most important things within owning a successful e-commerce business is know where your profits come from. How do you, what are some of the key KPIs that you focus on? So what are some of the KPIs that your customers are looking into first before looking into others?

0:03:57.0 BE: So we gather all the data from their sales and marketing channels and help them understand, and pull that together and help them look at how they’re performing on their various marketing channels down to a very detailed level. That’s the first thing that people care about. They care about attribution. They care about advertising content performance, how well it performs on certain ads and how their various strategies are working. So we are a very flexible platform that allows people to pivot the data very quickly and decide and figure out how to match their strategies to the analytics that they need. But what we really distinguish ourselves is that we pull in, we build models, data science models of the brand’s customers.

0:04:47.9 BE: So we give them insights on the customer lifetime value of every single customer on their system that’s ever bought from them. The likelihood that that customer is still a customer at the current moment and what it costs them really to acquire that customer. Both of those, all those things require machine learning kinds of modeling, even the cost of acquisition. And so then knowing that this is what we mean by customer-centric analytics. Customer-centric analytics is actually not a term that we coined. It’s a term from the Wharton School of Business. There’s a professor there who’s been talking about it quite a bit from the perspective of larger e-commerce firms and other firms about how it really matters to understand the 80-20 role. 20% of your customers are generating 80% of your profits. But typically companies, especially in the consumer space, because they’re dealing with so many people, just deal with the overall averages. And if you can get down, if you can focus your lens down to smaller pieces and really down to the individual, you can find some really interesting insights and you can organize your business around your best customers.

0:06:00.0 EC: The e-commerce landscape is pretty broad. So there’s all kinds of direct to consumer brands online. There’s pop and mom shops. There’s of course, up to Walmart, who’s probably selling directly. And everything in between. So from from which company size or maybe a revenue size, does it matter to have these metrics in place?

0:06:31.7 BE: So I’ll talk about revenue in a minute. It’s really about the number of sales and the number of individual customers you have. And then I would also distinguish between a number of different types of models of business. And there’s different levels of complexity to those businesses. So as you said, there are something like 2 million stores on Shopify. There’s something like 6 million merchants, individual merchants on Amazon. We focus on those individual third party sellers, right? Not the Walmarts or the Wayfairs or the Amazons of the world. We think of those as marketplaces on which these third parties are selling their products. So to put those guys to the side, there are companies that they’ll get a customer, the customer will buy pretty quickly or not buy. The majority of course don’t buy. And a very small percentage of them will buy again. You know, call it 10 or 15%, right?

0:07:33.0 BE: So these are companies that care a great deal about the cost of acquisition against the average order value, right? Because that’s what’s really driving their business. The second purchase, the retention of customers is really not driving their business very much. Then there are companies that sell a product that these tend to be consumable products or products that have add-on components where they’re going to sell. They’re not subscription businesses, but they’ll sell the same product or similar products over and over again. So food, you know, anyone in the food and beverage industry is definitely in that space.

0:08:11.5 EC: Cosmetics…

0:08:12.8 BE: Yeah. Exactly. Anybody who’s in personal care, you know, so hair products, beauty products, clothing tends to be in that space. Right. So there you really, really, really care about who’s really driving your business. There are subscription companies, right? And subscription is a little bit different because, and our models do support subscription because there you know you’re going to get another purchase, what you really care about is whether you’re going to lose the customer. Right. And that can be, you can start to detect that by the behaviors you’re seeing from the customer. And then finally, I would call there’s a number of e-commerce companies that are marketplaces, but not the scale of the ones we’re talking about the Wayfairs or the Walmarts, you know, they tend to be vertically focused marketplaces, not all of them, but a number of them, so like a company that resells beauty products or resells a number of dress, you know, or fashion clothing. Right.

0:09:13.4 BE: Those companies also care a great deal about repurchase, but it’s repurchase of a different product generally. Right. But the customer relationship really matters. So to answer your question, our, you know, our chart models, our lifetime value models matter the most when the customer, when the repeat purchase is 30% to 40% or more of your total revenue. And then you have to have enough data. Right. So typically we find that, you know, a lot of these companies are selling, you know, let’s call it a hundred dollar average order value. Right. So if you have a $5 million business, right, you’ve got 50,000 customers buying a year. That’s plenty of customers.

0:09:55.0 EC: Absolutely. Absolutely. So coming back to the main focus that you have, you’re mainly focusing on e-commerce or you’re solely focusing on e-commerce at the moment, I guess. How is analytics different in e-commerce when you compare that to, for example, manufacturing or retail?

0:10:18.0 BE: So, you know, e-commerce is purely a B2C business, right. So you’re talking about a large number of people and you’re trying to get insights across that customer set and figure out how to craft your marketing strategies and your product strategies and your pricing strategies is to maximize profit. Right. And you’re dealing with aggregates. And so you need some kind of, you need analytics that works at the aggregates and go down to the individual and then re-aggregate in interesting ways. Manufacturing is primarily a B2B business and most of the ones I’ve been involved in anyway. And so they have a small number of important customers and they know who they are. Right. And they usually have a target account list that they’re working to expand. And so they’re where they might really, they might really care a lot about lead scoring to know where to focus their sales and most of their sales efforts. They care a little bit less about the conversion, right, because a lot of that turns into how the sales team interacts with the customer and how their support teams and delivery matter, right, because manufacturing is typically a, you know, especially if it’s contract manufacturing, it’s a support relationship.

0:11:38.1 BE: Retail is a lot more closer to us and we’ve had a number of conversations with companies as you go to the larger end, if you go to the 100 million, 200 million sort of annual GMV, where they have an e-commerce presence and they have a retail presence. And there we think there are a number of interesting problems that we could work on with them around the incrementality and essentially the reinforcement between the retail presence and their e-commerce presence. Because, you know, there’s obviously an effect if you’re getting a marketing exposure by seeing a retail facility and the convenience may really help drive e-commerce. So we have a deep causal analysis capability. We’ve done a lot of causal modeling in the past for very large companies. And we’re very eager to work with a larger brand where we can do geographic based causal modeling studies for them and help them understand how those two are interacting.

Image of Brian Eberman quote "We tell companies that aren't on Amazon that if you're not on Amazon you're advertising for your competitors."

0:12:37.0 EC: Wow. So talking about that retail where you have like an offline and online component, and you’re obviously coming from that online e-commerce component side of the business. I would be interested in learning more about that attribution that you just mentioned, because probably you’ll need to measure some offline touch points in your distribution or in your attribution model, right? Because the customer journey is partly online and partly offline, how do you measure those offline touch points?

0:13:11.0 BE: So we would need plan of sale data that we could link. You know, minimum we would want plan of sale data by the store and product, right? We don’t necessarily need to know that it’s the same person, though that would be an even deeper level of analysis. But we can do things around using media mix modeling kinds of techniques. So look at the impression volumes that you’re generating in a particular geo and link that to the point of sale information that’s coming through your retail store and vice versa, right? Look at how the point of sale data is driving e-commerce sales for people that are in that same geo.

0:14:00.9 EC: Okay. Okay. Yeah, that makes sense.

0:14:04.7 BE: We do something similar today actually for Amazon. So if you’re driving Facebook, TikTok, other kinds of impression media, right? Most impression media is going to give some kind of brand exposure, right? You may be doing performance marketing with your impression media, but it’s still displaying your brand, right? There’s a very strong effect where you can see people basically like, Hey, I’m prime on Amazon. I’m just going to see if this product is available on Prime, right? They go on Amazon, they see your brand on Facebook, on TikTok, they go to Amazon, they type the name of your brand, and they choose to buy it or not based on their reviews. This is a known effect in the e-commerce world. We tell companies that aren’t on Amazon that like, if you’re not on Amazon, you’re advertising for your competitors. But we can measure this using media mix modeling and have shown that it can be a pretty significant effect depending on the ratio of your Amazon to your direct to consumer revenue.

0:15:08.0 EC: Love it. Love it. So we are almost at the end of the interview, and there’s one question that I would like to ask you because, as said, during your introduction, you’re a year into Zeenk now and you’re a CEO, so you probably have seen a lot of analytics cases and case studies, and you’ve worked with customers. If there would be one thing that listeners should start doing when it comes to putting a model together or maybe analyzing a specific process in e-commerce? What would be your number one thing or number one tip for them? What would they need to do, start doing tomorrow, and are most of the e-commerce shop owners not doing?

0:15:54.0 BE: Well, I think the most important thing, let’s focus on the companies that have multiple purchases. They need to know what the lifetime value is of their customers. Most people I talk to, especially as you get very small, and I’ve done this myself in the past, you can make a model of customer lifetime value with a spreadsheet. You can say, I’m going to take the January people who bought first in January, and then I’m going to track down how many of them bought in February and March. It’s a fair amount of work, but it’s doable. The problem with that is it’s great for telling you as a business owner how you’re doing as a business, you know, on my, whether my customer is really worth, is it going up or down? Is my lifetime value to cost of acquisition ratio going up or down? It’s not very actionable, right? There’s not much you can do with that. So I think a core thing I would recommend, and you know, you can try and do this with Google Analytics, you can try and do it with BigQuery, where we would help people is try and go deeper. Who is driving that? Why are they driving that? Is it a particular product that’s driving that? Is it a particular sequence of product purchases that leads to higher lifetime value? Right. And then how should you adjust your marketing strategy to maximize that outcome? And how should you run tests to prove causally that that is actually what matters?

0:17:21.0 EC: That makes sense. And that’s a great tip, Brian. Thank you very much for being on the Marketing Technology Podcast. I will share a link to your LinkedIn profile. So it’s Brian Ebermann at Zeenk. And I will also put a link in there to your website, of course. So should people have any questions, they can they can reach out to you on LinkedIn or send an email to you on the website, probably. And with that, I would like to thank you very much, Brian.

0:17:53.1 BE: Elias, it’s been great talking to you and I enjoyed the conversation. 0:17:56.7 EC: Thanks for listening to this episode of the Marketing Technology Podcast. If you enjoyed this podcast, please leave us a review on your favorite podcast platform or iTunes. Also, if you want to be a guest or know someone that should be a guest to our show, shoot me an email on Thank you for listening.