Intentwise’s CEO Sreenath Reddy hosted Antonio Exsome from ProfitlogiQ for an in-depth conversation about the importance of a sound data strategy for Amazon agencies.
About Antonio Exsome
Antonio is one of the founders of ProfitlogiQ, a full-service Amazon agency. Before ProfitlogiQ, Antonio served as Head of Sales and Agencies for Facebook North Cone and Head of Agencies for Google México.
Topics Covered:
- Role of data in scaling an agency business
- Challenges with data
- Current data tech-stack
- Advice for Amazon agencies that are trying to scale
Check out the full video below:
Full Transcript
[00:00] – Introductions
Sreenath: Antonio, how are you?
Antonio: Hi, Sreenath. I’m good. How are you?
Sreenath: Good. That’s great to have you on today.
Sreenath: We’re going to be talking about the 2022 data strategy for Amazon from an agency perspective. I’m really excited about this particular conversation because Antonio has a unique background. He’s one of the founders of ProfitlogiQ, which is one of the largest Amazon agencies in Mexico. He started that business in 2018. But what’s interesting is before he got there, he worked for Google, helping a bunch of agencies. He then worked for Facebook, helping a bunch of agencies scale up. And with Amazon, he just took a path on his own.
Sreenath: So that’s one reason. The other reason is we work with Antonio & the team a lot, and they’re always data-hungry. So here we are. And just to kick things off, Antonio, do you want to tell us a little bit about ProfitlogiQ?
Antonio: Yeah, definitely. So first of all, thank you for having me. I always enjoy our conversations, especially our philosophical conversations about all the Amazon business. We appreciate the opportunity to have this conversation. So a little bit about ProfitlogiQ. I founded this company back in 2018. As you mentioned, I used to be the Head of agencies for Facebook. And before that, for Google. And one thing that I keep seeing all the time is on this digital transformation we’ve been down the road for I can’t even remember how many years, depending on who you ask. It’s either ten or 20 years that the world has been on a digital transformation. And during this digital transformation, I’ve seen different types of agencies and partners trying to find their way into creating value for advertisers and for brands.
Antonio: And my vision while creating ProfitlogiQ was to create a data-driven strategy and data-driven performance agency, which is actually what I love about Amazon. So it’s real performance. We’re not talking about CTRs. We arere not talking about impressions. We’re talking about actual sales and actual — how much purchase intent you’re driving. So that became very interesting. And probably I never worked in an agency before. And so, when I built my business, I couldn’t make or create the structure of an agency. So that’s probably why we were always bugging you guys with so much information and so much data. It’s probably because I come from the tech side of the advertising business. So data is extremely important for us, especially given that all of our strategy is based on performance and a data-driven strategy.
Sreenath: Awesome. I mean, data-driven performance marketing agency. I love that. We’ve seen you guys go from zero in 2018 to a pretty massive scale.
[02:51] – Role of data in scaling the agency business
Sreenath: So tell us a little bit about the role data has played in all of that in supporting that growth.
Antonio: Yeah. No, definitely. I mean, data for us is our main driver for decision-making. And when I say decision-making, I’m not only speaking about reporting or strategy for the clients, I think that for us it’s in our DNA. The data that we need for decision-making is crucial for us to create a sound strategy for our clients.
Antonio: We are the ones that have to provide our advertisers and our brands with certainty. We need to be able to take a look at the future. However weird this may sound, but we need to take a glimpse into the future and draw a path for our brands and advertisers with a strategy with real numbers and get there.
Antonio: But I would say that that’s only part of it. The second part of it is – it’s always reporting. We’re talking about more day-to-day stuff reporting. I believe that reporting is a way to create a lot of value, but it’s also resource-hungry, so you can spend so many resources just doing reporting and not actually driving value. I think that for us, data has to be a value driver for both our business and our brands, and ultimately, at the end of the day, we also need that data for decision-making.
Antonio: Amazon and e-com is still a pretty virgin place to be in. It’s not yet a mature market. I mean, however developed it could be in the US or any other markets, we’re still building it. And when I say we, I mean both Amazon, you, the agency like us, the advertisers, the brands, the consumers. It’s a little bit unclear yet. So this data is for us also crucial in terms of also for internal planning. We need that information even for our P&L. I was actually this morning just reviewing P&L numbers and trying to come up with budgets for 2022 and how many headcounts we’re going to increase.
Antonio: So we need a lot of data based on our business model to try to define how much Amazon is going to grow.
Sreenath: Yeah.
Antonio: How our CPC is going to behave, how much sales are we going to drive? How many accounts are we going to be able to win? And if you have one client, that could be very easy and straightforward. But once you get to the scale, it becomes harder to be able to accurately get the data you need.
Sreenath: Yeah. Let’s unpack this a little bit, right.
[05:25] – Data related goals
Sreenath: Antonio, on the data side, what does your stack—data stack look like today? And where are you going with it? Looking forward.
Antonio: Yeah. I mean, our data stack right now is based mostly on the Intentwise platform. We use the Intentwise platform for both gathering ads information, sales information, inventory, and also the organic and content side of it.
Antonio: I think the second part of our stack is the Amazon DSP and Amazon Marketing Cloud data that we gather. So what we do is that’s why we’re so data-hungry with you guys, because we tend to consume a lot of data and put it into our own stack, we intake all of that information into our own database just to try to come up with better strategies for our clients.
Sreenath: Any visualization tools you’re using today?
Antonio: Yeah. Especially, I think one thing that helped us scale. And now that you mentioned our growth in the past three years was we’re big believers in automation. And I think it’s a good moment for me to apologize for asking 25 times a day. When is Data Studio going to be ready? Which I know I did. So thank you for your patience. But once you guys launched Data Studio connector, that helped us so much because it helped us free up a lot of resources that we were spending so many man-hours and just doing Excel reporting and now being able to provide our clients with real-time information on a data studio that’s available to everyone. It actually became a driver for us to scale and let us become more efficient in our operations. So we use mostly Data Studio.
Antonio: We have tried a little with Tableau and other pieces for a more ad hoc kind of reporting, but I think that Data Studio just hit the nail on it. If I could say one of my favorite features or technologies that you have developed is probably Data Studio.
Sreenath: Awesome. Well, thank you. Again, I appreciate all the asking and pushing for that capability and the feedback along the way. So we benefit from that. Thank you so much. You touched on this a little bit, and I just have a further question.
[07:33] – Types of data been collected
Sreenath: Let’s talk about the different data types you’re collecting today, right. Like, just a quick inventory of all the different data sets you’re collecting today.
Antonio: Yeah. So what we’re collecting right now is all of the advertising information from search for both the sponsored products, Sponsored Brands Video and Sponsored Display inventory for sellers, central Data sales, retail readiness, all of the content information for Amazon agents, organic ranking. We also collect on the DSP side, all of the ad advertising information, impression, share, and the other data that comes from Amazon America.
Sreenath: Yeah. I mean, it’s varied sets of data. It needs to get connected. You need to get value out of it. And that’s what I love about you guys in terms of just how aggressive you are about collecting consumers & acting on it and assume that plays into what your clients perceive as value from you. Right? It’s not just managing campaigns and ads. It’s more than that. And that’s because of all of that data, you’re aggressively and proactively collecting and telling a story.
Antonio: I sure hope so. What I want to ultimately achieve is as a brand partner, you want to be able to answer real-world questions. And I remember back in my Facebook and Google days where most of the conversation was either on social interactions or clicks and impressions and breach. I’ve always felt that for a brand, a brand needs to know how much am I moving the needle towards purchase behavior, how much incremental sales am I getting? How many new clients I’m getting with this new strategy, even if it’s an awareness campaign, what’s this awareness campaign is going to do for my brand, either in the short, medium, or long term. And I’m a true believer in measurement.
Antonio: You cannot manage what you can’t measure.
Antonio: So that’s why we try to get all of this data and try to visualize it in order to answer the real-world questions that any brand needs to answer right now. And I think those are crucial for both advertisers and the agencies to create to come up with sound decisions on where they’re investing, why they are investing there and what their path is going to be in the future.
Sreenath: You cannot measure.
You cannot manage what you can’t measure.
Sreenath: You cannot manage what you cannot measure.
Sreenath: Love it On the data side, you touched on this a little bit earlier on the challenges, the manual aspect of all of this.
[10:07] – Biggest challenges with data
Sreenath: Perhaps… Can you just Zoom in on that and just frame up the data challenges that you have faced and/or continue to face that we should all be thinking about how to solve?
Antonio: Yeah. Definitely. So I believe that fragmentation is the first one. Amazon has so many different platforms, and they’re all isolated and just trying to get sales data from a 1P brand and try to see what was the impact on the paid search campaign and how much of the overall sales was organic and how much sales came from advertising. That’s an easy question. But it’s time-intensive. Right.
Antonio: So it’s coming up with all of these different types of reports and platforms, and it’s so time-consuming that the fragmentation is a big problem. So for us being able to get all the information into one single place with little access permissions, you get the permissions done. And boom, you’re done. And that’s a game-changer. Also in terms of infrastructure, right. So just trying to come up with Excel scripts on our own and trying to get all of that information, it just doesn’t make sense.
Antonio: I think that we need to be spending our time driving value to our clients and working on the strategy and optimizing campaigns and making sure that we’re hitting the goals and the range KPIs and not gathering all of the data and consolidating it and making sure that it’s all on the same time frame.
Sreenath: No, I absolutely do. And we have alignment on that. And a lot of our product investment, as you know, especially the Intentwise Analytics platform, is geared towards really addressing that problem at its core.
Antonio: I also think that is going to be ongoing data representation, like how we visualize the data and how we analyze the data. And I think that unfortunately, there’s not a one size fits all kind of solution. We’ve had clients who have asked us to provide all of the data and just input that into their own databases or into other data management platforms. And I think that probably will continue to be on a case-by-case basis. So I don’t think I’ll ever be able to check visualization that’s done once and for all.
Antonio: But we’re getting there. And I believe that with the technology that you’re providing data visualization and data consumption is going to be easier in the future, I hope. Yeah. But yeah, I think that’s a key challenge.
Antonio: What do you think about it?
Sreenath: One key point you already mentioned, right. Which is there is the data collection and there’s a data accessibility problem, and then there is the what story do I want to tell? How do I measure my business problem, which is very different from one advertising, one agency to the other? And that’s a real problem. And I think that it’s a problem that I don’t know, that it lends itself to some kind of canned visualizations, and that’s never enough. You have to build custom things on top. And certainly, that’s how we have defined the problem at Intentwise, which is make all the data you want accessible in an easy fashion, so you can tell your own custom story often within the tools of your choice.
Sreenath: So I fully agree with that definition of what the problem is around data and the stories that have to be told.
Antonio: Yeah.
Sreenath: So, I mean, just to further this topic, let’s talk about the future a little bit. It’s the end of 2021. I can’t believe it is—that the year is almost over.
[13:29] – Future of data
Sreenath: What’s kind of the future of data as we look forward in 2022 and beyond?
Antonio: I’m not really sure how to frame this. I would say data consolidation, maybe. And I think you framed it perfectly. So there’s several problems data collection, data storage, data visualization, but there’s also the data modeling side of it.
Antonio: So coming up with sound data model that will actually give you more insights into what’s happening. And let me use an example. So we have all of the sales data with one attribution model. We have all of the advertising data with a different attribution model. We have all of the organic ranking content. And on the other side, we have all of the DSP data with yet another different attribution model.
Antonio: So being able to come up with one single model that helps you drive the decisions on how much am I going to invest? What’s the investment breakdown is going to be? What results are we going to get with that investment? And how do I keep growing my brand?
Antonio: So I’m hoping that the future, either me, you, or both, will come up with a very beautiful beta model that will help us answer those questions in a heartbeat.
Sreenath: Yeah. Press three, four buttons. Take a vacation. You’re good.
Antonio: What do you feel about it? You’re driving a lot of that future. So, I would like to have your opinion on that.
Sreenath: Yeah. I mean, look, I think you use the word modeling, right? I guess I’m going to get a little technical here, but I think there are two aspects to it, right? One is we’re collecting all this data and you mentioned the list here, which is a pretty good list, and there’s probably more – sales, inventory, ads, reviews, content, organic ranking. I can go on and on. Even when you collect it, there’s actually a need to connect them so that you can actually extract more value from it.
Sreenath: Imagine if you pick an ASIN; one ASIN and you very quickly can access all aspects of it. Advertising to inventory to sales – all in one place. Even if you collect all the reports, somebody still has to do the work of modeling the data around it. So there is the underlying data model, the way you store and connect the data, that’s one piece and the second piece, the part you’re touching on, which is the need to now build advanced analytical models on top where you could answer a whole bunch of strategic questions.
Sreenath: One of the questions that have come to us many times is, hey, if I dial my spend down to zero, what happens to my organic sales. How are you going to answer that, right? I mean, you have this problem of multiple factors influencing an outcome called sales. And how do you know how much to move each lever to get what you want? And that’s where I think the analytical modeling that you’re talking about really has a huge role to play going forward. So fully in alignment and except that I think that the word modeling runs across the stack, the way you store the data, and the way you analyze the data.
Antonio: Yeah. No, you’re definitely right. I think you picked the perfect goal analytical modeling on how to drive value out of that data that we’re not only collecting a story, good.
Sreenath: And then the other thing about them and talking about future. If you look at what Amazon is doing, right. Like AMC – Amazon Marketing Cloud. Not everybody has access. You need to be kind of technical to go access that data, right against it. But my assumption is that you get more and more democratized and become more and more easily used. So I think there is more data coming our way.
Sreenath: The other thing and I’ve talked about this in other conversations is CPCs are up 40-45%. I know to you, this is not a surprise because you’ve seen this in the Google and Facebook world, and my guess is it will keep going up. Right.
Sreenath: The unfortunate scenario on the other side is all these shipping and logistics costs. Those have gone up. But what that does is it puts the burden on the agencies and advertisers to kind of eke out those wins.
Sreenath: It’s going to require a lot of elbow grease and fine-tune optimizations, and you can do none of that without a solid data infrastructure underneath you. So that’s another thing like it’s just getting more competitive and more challenging to make things work in a profitable manner. And one of the components is going to be you as an agency, your data agility, and comprehensiveness. So that has now become even more important, in my opinion.
Antonio: I think I totally agree with you, especially with the competition side of it. I think that it’s becoming more of a crowded place and more competitive. So we all need to get an edge, right?
Sreenath: Yeah. Absolutely.
[18:16] – Advice for Amazon agencies trying to scale
Sreenath: I think perhaps the last question for today, any parting advice, especially for an agency that is trying to scale around data and data strategy.
Antonio: Yeah. I would say that if you don’t have a data strategy for Amazon and E-commerce, I mean, you might be better off doing TV ads, right? Because you wouldn’t know otherwise what’s working. I think that my advice would be you need to know what drivers are you going to be looking at in 2022, especially with all of you mentioned rising CPCs that’s going on across the globe in every single marketplace. All the logistics costs are increasing and the competition is increasing. So 2022 is going to be more difficult to maintain your 2021 position.
We’re going to have to think hard on where we’re going to invest, why we’re doing it, and what we’re going to get out of it. So I think that creating a very defined strategy for 2022 is crucial for any brand, advertiser, or agency running on Amazon.
Sreenath: Makes sense.
Antonio: I will also add that one of the key questions you need to be asking yourself while defining this data strategy for 2022 is first, you need to figure out what data are you going to be needing? How are you going to be storing it? Where are the visualization tools that you’re going to be using it and not only you internally but also for your own clients and internal—I mean other stakeholders within your business and see how you’re going to drive value out of it and what infrastructure you’re going to have to support it, what’s your data stack going to be and what investments you need to make in order to have a clear and functional data strategy the next year.
Sreenath: Antonio, this has been super insightful conversation. The line I should remember, I will remember, I think is you cannot manage what you cannot measure.
Antonio: I’m glad you like it.
Sreenath: Yeah.
Sreenath: So with that, we’ll call this a wrap if we don’t connect before the end of the new year. Happy Christmas. Happy holidays. And thank you so much for taking the time.
