Adobe Analytics Opening Keynote

Transcript

Hey everyone, my name is Eric Matosoff. I am the Global Evangelist focused on everything Experience Cloud here at Adobe. And I’m so excited to be here, part of Experience Makers, the skill exchange focused on Adobe Analytics. So my session today, I’m going to be digging into what we all know A for T, right? Analytics for Target. I wanted to focus on A for H, Analytics for Humans, you, me, everybody.

And now the reason I wanted to focus on this topic is because of something that we’ve all agreed on, is that this is the era of AI, right? We all agree it’s the era of AI. We’re in the middle of this revolution. I mean, there is all sorts of things. There’s chat GPT. There is Microsoft Copilot. There is Perplexity. There is Claude. There is Cursor and Replit and Adobe Firefly, and of course our own Data Insights agent and more at Adobe. But wait a second, where is all of this headed? As much as the sneaks technologies that I highlight every single year at Summit show the possibilities of AI, we need to be thinking about what is next. And now as Tim Wilson recently shared in a podcast, actually I believe he was quoting Michael Helbling and Michael Helbling was quoting somebody he didn’t even know. But as you know, the really interesting thing here is AI will take you from zero to average very, very well. That is actually inherently how these AI technologies, these LLMs, these generative AI technologies work is they scour the internet for content, content, content, pull it all in and try to present you with what the LLM thinks is going to be most valuable for you. So the question that I’m asking is how do we then get from average to awesome? And there’s a huge different ways, a wealth of different ways that we can do that. And the ways that I think of as analysts, as marketers, as data scientists that we can do that is through humans, creativity and experience. Let’s go through several of the day to day activities that we all do in the analytics world and see how these three things, humans, creativity and our industry experience continue to be and will forever be essential to effective data influenced decision making. So there’s four topics that I actually want to cover today. First is data collection. Next is data governance. Third is analytics training. And then finally, the big whopper report building analysis and data storytelling. So let’s start at the beginning. So for data collection, AI can help provide you with a really, really great start. It can get you to average super fast if you were to ask what are the metrics and dimensions I should deploy or collect based on my vertical or based on this website. But you need to take the time to understand what is the data that your stakeholders care about, and you need to be able to push them to tell you what matters, what needs to change to get promoted. Now, sure, you can use AI to help support you, but those humanistic conversations that will show they’re going to be what show your true value. There’s also plenty of other AI technologies that are out there to help you with data collection, whether it be helping you migrate from app measurement to web SDK, QA tools and more. But they all require guidance and ownership by analysts and data engineers. AI is just simply a tool and the humans are bringing it to life, nurturing it and guiding it. So you need to be thinking about ways that you can connect with the business to truly understand their needs. Use your creativity to track these in ways that no one has ever thought about. Use your experience to link them to the right EVAs, the right prompts, the right events, because you have the foresight to realize how they need to be broken down, segmented, trended and more.

Next up is data governance. Now AI can certainly help here. For example, if you’re not SQL competent, you may be able to use AI assistance to better understand and organize your data. And then with tools like solution design references, the Adobe analytics data dictionary, component analytics, or even like last used and used in data governance is an extremely human driven analytics task. Remember, without data governance, organizations are in big trouble. Data can’t be trusted and we might as well just not have data at all if we can’t trust it. Next up is analytics training. Now having access to technologies like AI assistance can be immensely valuable for better understanding the how to. But these are for specific questions, right? Like, hey, chat, what do merchandising EVAs do in Adobe analytics? Yeah, that’s a great question and you can get that answer. But is that helpful for new users of Adobe analytics who don’t even know what to ask? What about new employees to your specific implementation? They have no idea what event 215 does or when it’s set or when it’s collected. Plus, everyone learns differently. Learn through books, LinkedIn learning courses, YouTube videos, documentation, in-person courses, monthly lunch and learns. And assuming that everyone within your company learns and wants to learn Adobe analytics through AI is guaranteed to be a recipe for failure. So find different ways to bring humans into the loop because they don’t just need to be in the loop. They need to be in the driver’s seat.

All right, friends, here’s the big one. Report building, analysis and data storytelling. This is where the good stuff is. And sure, at Adobe, we’ve had AI built into analytics for just about like a decade in terms of Adobe analytics. From anomaly detection, contribution analysis, segment comparison, intelligent alerts and more, AI features have been a cornerstone of the things that we do right in our workflow for years. In fact, I go back to my very first webinar, or at least the first webinar that I can remember when I joined Adobe, which was announcing those four AI features as part of Adobe analytics and analysis workspace. Now, I spent some time chatting with Jason Thompson over at 33 Sticks recently, and he’s been evangelizing the humanistic side of not just analytics, but business in general for well over 10 years. And we talked about how the evolution of the analyst continues. AI is certainly helping to support everything I just talked about, from data collection to data governance and training. And then there’s new technologies that are helping more quickly build visualizations, build reports, and even provide data commentary. So does this mean that the data analyst is cooked? Has the data scientist gone from the sexiest job of the 21st century just in 2012 to the most useless one in 2025? I cannot say this more emphatically, no. But has the job evolved? Oh, hell yeah. As Jason recently articulated so wonderfully, human-centered analysts must develop a group of skills that no AI can replicate. I’m going to walk you through those four skills right now, kind of talk about each of them, because I think they’re really interesting. The first is statistical literacy meets psychological insight. Now, applying your statistical skill set to understanding the psychological aspects of your prospects and your customers, this means that you need to sit with your customers to better understand their needs, their issues, their wants, and you can use data along the way to find them and others.

The next key topic that Jason shared is about business acumen meeting ethical reasoning. Talk about humanistic thinking. Think about like Patagonia, right? You can still get great products, make smart business decisions, and be extremely ethical in the way that you do it. For example, just because you found a sneaky way to use local storage and IP address or something even funkier to track visitors who have declined tracking consent, it does not mean that you should be doing that.

Next up, research methodology meets storytelling craft. Now, we’re all analysts by trade. That means that we’re curious, we love slicing and dicing and splicing the data that we find, and we keep going until we find what we’re looking for. But this doesn’t always meet our organization’s need for us to tell good stories. Storytelling is a muscle, and if you don’t build it, it’ll atrophy.

Last but not least, technical competence meets emotional intelligence. Now, I want you to think about something. Close your eyes. I’m just going to have to take your word for it that you’re closing your eyes, but think about this. What’s the feeling that you get when you think about calling your internet service provider for technical support because your internet just went down? Now, I can’t see any of you, but I have a feeling a lot of you are wincing in pain just thinking about having to make that phone call. I know that I am. Instead, think about the experience that you get at an Apple store if your device isn’t working as expected. Now, even if it’s not under warranty, you can still talk to someone that listens, that empathizes, that responds and supports. That’s the difference between technical competence without and with emotional intelligence. Find a way to incorporate it into your everyday efforts.

And your day needs to consist of so much more than get a ticket, build a report, send a PowerPoint, rinse and repeat and rinse and repeat. So instead, sit with your customers to understand both what they do and what they don’t say. Do this by engaging them to fill in the gaps of your understanding of the business or to uncover strategic blind spots. Ask questions, build hypotheses, test and learn. Go way and way, way above and beyond just the data. There is something that Jason shared that I absolutely love the concept, which is creating an empathy experience in order to help teams feel what customers feel. And this reminds me of a story about Mark Zuckerberg and the Facebook app living on an Android application years ago. Now, at the time, the Facebook app worked really well on iOS and on iPhones. Everyone thought it was so smooth, so slick, worked great. But there was a challenge in that the Android app was super buggy, didn’t work well. Everyone was complaining about it, poor ratings. And what Mark Zuckerberg realized is that just about everyone in his company was using iOS, and so they didn’t have that empathy for their users that were on Android. So you know what he did? Is he forced all of the engineers, the product managers, the product marketers, all everyone, the UX designers to switch their phones from iOS to Android so that he would create that empathy. And I’m an iOS guy myself, so I can’t quite say, but I would bet that the Android experience for Facebook is much, much better these days. And now you may not be the CEO of your company. None of us are Mark Zuckerberg, but I still love this way of thinking. Do it. Demand empathy for your users. Don’t ask for budget. Go skunkworks if you have to. Make yourself a linchpin by creating empathy for your users so that everyone in your organization can get value from them.

Finally, I’ll leave you with this quote from Jason. This one is not anonymous.

Somewhere along the way, we got so good at measuring things that we stopped asking what it meant. We stopped wondering how people felt. And because of that, we lost sight of the very people we set out to understand. So don’t let yourself lose track of wondering how your customers feel. Be an analyst for humans in everything that you do. Thank you.