Todd Foley: Hello, and welcome to the CDO Magazine interview series. I’m Todd Foley, CDO and CISO with Lydonia. I’m joined today by Ellen Webb, Vice President of Shopper Analytics and Insights at PepsiCo. Ellen, thank you for joining us today.
Ellen Webb: Oh, happy to be here.
Todd Foley: You know, it’s always interesting as we talk to different practitioners in our industry to understand what led them to their particular role. I was talking to someone the other day who started as an actual scientist before they got into data science—from Petri dish to pie charts, I guess. I’m really curious, tell me about your current role as Vice President of Shopper Analytics and Insights at PepsiCo and maybe some key milestones that led you to that position.
Ellen Webb: Yeah, sure. Happy to do it. So, the team that I lead today leads the development and deployment of commercial insights, capabilities, and digital products. So, what exactly does that mean? Translated into non-industry speak, it’s really about how we’re accelerating growth for PepsiCo and through our retailer partners using analytics and insights. We have a relatively small team, and we work to do as much as we can to help scale them throughout the organization so we’re making analytics accessible—another way to talk about it. At the end of the day, we’re ensuring that we’re bringing the shopper perspective through the shopper lens clearly. Something I was actually just talking with my team about earlier today is really driving actionable impact. Because, as we think about analytics and insights, sometimes that can seem a little bit wonky and nerdy, which is great—we are allowed to be nerds and encouraged to be that. But it really needs to be in service of actionable impact across teams, certainly across our PepsiCo portfolio.
Todd Foley: We are very, very close to the business, right? Your mandate is to have an influence on PepsiCo customers and to help drive that business in an immediate way. So, it’s not back-office data; it’s something forward-facing, very immediate. I think that’s not always common in different roles in our industry. What led you to a position where you were taking the arcane data—well, not that it’s that arcane—and applying it in such an immediate way?
Ellen Webb: I would say throughout my career, I’ve been drawn to roles that really balanced left brain and right brain work. So, what does that mean? I really love to get into, and to just completely nerd out with, the data and analytics; it’s something that’s always been a passion point for me. I’m a very curious person. But I also really love the right brain part of it—bringing it to life, together with human insights and strategy, to get someone, whether it’s an internal or external customer, to take action and do something with that. From a functional perspective, that’s meant customer-facing roles like category management, which is where I grew up. I spent much of my early career there. I’ve also had more internal roles like sales strategy and business planning. Most recently, I got into insights and analytics. From a channel perspective, I’ve worked across both retail and away-from-home, so I’ve had just a ton of learning there. So, if you think about the difference between buying things in a store versus being out at an airport, a hotel, or a business cafeteria, the way shoppers engage at those points is very different. Most recently, I added digital commerce and space transformation, really rounding things out from an omni-channel, end-to-end experience. Each of the roles I’ve had has given me different experiences and learnings that have prepared me for this role. To your point, having an insights and analytics team that is closer—not a back office—we’re definitely not a back office. We’re part of our commercial organization, and it’s really been helpful for our team to become better insights and analytics professionals because they’re in the room hearing the problems to solve, which I’ll talk about a little bit later. But really, seeking to hear about the big challenges and how we can use data and analytics to do that. In some cases, it’s the least sexy of things, like when we hear people are still doing things in Excel and it’s highly manual. Our team can come in and say, “Hey, we can take and make things more efficient so you can get to speed to action a lot more quickly.”
Todd Foley: That’s brilliant. I mean, that breadth of experience really makes your value proposition unique, right? You’re able to lead your teams in a way that’s informed by your experience with the business and how you engage with it. And I think you’re also talking about the breadth of applying analytics. You mentioned space transformation, which I’m assuming has nothing to do with NASA, right?
Ellen Webb: Unfortunately, no, it does not.
Todd Foley: So, you’re working not only on what to sell, how to sell it, but how to present it even in a retail location. Is that accurate?
Ellen Webb: Yeah. At the end of the day, it’s really about how you take the strategy and the insight all the way down to in-store execution and making sure that, at the end of the day, when the shopper is standing at the shelf, whether it’s in the store or online, they have the options that they’re looking for. There’s so much science and data and analytics that goes into that, that most people probably don’t realize. A lot of the reason behind what we do is to make sure that we’re meeting the shopper where they are and providing the things that they’re looking for. Certainly, helping our retailer partners is critical in that, because, at the end of the day, they’re the ones that own that last moment of choice.
Todd Foley: I think it’s fascinating, too, because you’re talking about applying sophisticated analytics and machine learning to things like physical placement of products. At the same time, there’s been an evolution across all industries from a traditional brick-and-mortar model to all kinds of digital engagement and online transactions. How do you merge those two things together in an impactful way?
Ellen Webb: Yeah, I would say it starts with the data. And the data, as you know being in this industry, data is incredibly valuable, and I think it’s something that, when we talk about data, the first thing we always make sure is that we’re working in conduct with our data privacy, responsible use of information. It’s a privilege, not a right, to have all this data. It’s a privilege to have it. We take the utmost responsibility to make sure that we’re using it in the right ways. That said, as you think about the data sources, we have everything from open-source data, first, second, and third-party data, to proprietary household data, on top of a ton of data that we have that gets created through our DSD or direct store delivery network. If you think about all those disparate data sources, the work that goes into actually connecting them and making them into something that is actually valuable is incredibly labor-intensive. That’s where machine learning comes in. I think it’s the least kind of interesting part when people talk about AI and machine learning, but as you know, the data has to be in a place, in a way that is connected to do something with it. That’s a place where we’ve really prioritized that work, so our teams are not spending their time and energy around data engineering. We have a team that helps us with that. It’s critically important, but my team is really focused on what we’re actually going to do with that data. I started off the answer to my question by listing off a lot of different data sets we have, but we’re very mindful in determining what are the data sets we need to be able to answer the questions we have. There are certainly data sets we’ve said no to, and it’s not because they’re not accurate or interesting, but because they’re not relevant to our business. We’re really mindful of making sure we’re not muddying the waters to the point that it will prevent us from being actionable with the insights. I think that’s one of the challenges for people in this business. And a lot of us are very curious and would love to just go down wormholes and find out things for the sake of it, rather than being purposeful about what we’re trying to answer and how to do that in the best way.
Todd Foley: Well, you talked a lot about how many different data sources you have, right? And how you’re leveraging things like machine learning to try and reduce the burden that any lean team has just managing, cleaning, preparing, and blending all the traditional fun. But you’re also really talking about doing it in a guided way, right? It sounds like you’re applying the business insight to the process of sorting through and weighting that data in a way that is very much integrated with what the desired business outcome is and what types of insights you’re pursuing. I think that combination, tying things in terms of what you’re trying to do so directly with how you’re managing that data, is key. First, it’s a tremendous amount of work, and it requires strong skill sets, both technically and from a business perspective. How do you work with your team to build those teams, maintain those teams, and how do you have them balance the technical aspects of it, whether it’s machine learning or just data prep, with what you’re trying to do at any given point in time?
Ellen Webb: So really what we do, you mentioned process, and that’s really central to how we effectively pull out these actionable insights. It really starts with the problem to solve. As we think about the problem to solve, it’s rooted in our PetViz approach, which is about how we partner with our retail partners in a way that is insights-filled and performance-driven. When I use the word partnership, it’s really important because it’s a collaborative approach. If you think about our retail partners, I just talked through all the data we have. Our retailers know their shoppers better than we ever will. That is their sole purpose and who they are serving. But what we can do is help bring an outside perspective. If you think about a shopper at any given retailer, that retailer is going to know what they do in their four walls, whether physical or virtual. We can take that and say, “Okay, here’s how your shopper behaves when they’re not inside your store,” and really bring light to that. As we have these conversations, this process or approach is about getting clarity on the problem we’re trying to solve. It helps us be purposeful in the data. It helps us be purposeful in the solutions we’re creating. How do we get at granular growth for our customers, together with them. As you talked about, where we sit in the organization, I would say that is something that is unique in terms of where my team sits because we’re having these conversations directly with the customers, listening for things that sometimes aren’t explicitly articulated. We then say, “Okay, I heard you say this, can we understand a little bit more about that?” So, there’s a lot of discovery that happens on the front end before we do any work. Sometimes the answer might not be in analytics. Sometimes the answer might be in some primary research that we have, or it could be third-party information that we have to help answer that. So, we’re really looking at, again, what’s the problem to solve that we have, and then what is the right tool in our toolkit to answer that in an effective manner? I think it’s just really that partnership approach. In many cases, it’s an iterative process. We’ll hear one thing and start to dig into it and say, “Okay, we found this, does this feel like it’s the right thing or not?” That partnership has been critically important in how we operate with our customer partners.
Todd Foley: You’re talking to my mind from the perspective of having a very mature data and data governance environment. To be able to do that in an agile way, you must have a pretty comprehensive data catalog and data inventory to be able to identify when you do or don’t have something useful or whether you’re going to use these third-party sources. Then, to have the business requirement or the problem you’re trying to solve drive your work around how you assemble that data and those data sets and how you go after trying to get to those answers. That’s a place a lot of customers I work with would be jealous of. How do you look at your own environment from that sort of maturity scale? Are you, as I think you are, at that high level, or is it always possible to do more?
Ellen Webb: Yeah, I mean, I think it’s always possible to do more. We’ve got great partners in our strategy and transformation team that help us do a lot of that governance. Thankfully, that’s not necessarily on my plate to manage, but we certainly get to benefit from that. What we do is really a lot about, as you think about it, there’s the data, but then there’s the solution that it’s driving. So, I talked earlier about being able to scale some of these solutions out to our organization. That’s a lot of what we’re working through too—organizing the analytics solutions we have around some of these core problems to solve. Are you seeking to drive more trips? Are you seeking to build baskets? What are those high-level business questions that here are the first two or three solutions that you might want to go to? That starts the conversation for those who might not be as familiar with it. A lot of that we go through as an iterative process to say, “Okay, we might be able to look at it this way, but maybe this will work.” You have to be willing to be agile with it and say, “Okay, based on the problem to solve and the set of conditions surrounding that, we can help narrow down what the best way to approach it might be.”
Todd Foley: I think you have some unique challenges, right? You alluded to them earlier when you were talking about helping retailers understand their customers beyond the experience within their four walls. You have these data sources coming related to physical activity and purchase history, plus the whole online and digital aspect of things. I think you referred to it earlier as the “fidgetal” experience. Is that right?
Ellen Webb: That is right.
Todd Foley: What does “fidgetal” mean?
Ellen Webb: Yeah. “Fidgetal” is really about the idea of blending physical and digital spaces. If you think about that, this is not something entirely brand new. It’s not a mind-shattering idea, but it’s also not anything super far off. It’s really happening now. The core of the definition of the word “fidgetal.” If you think about what that means for us as an industry, it’s really about that fidgetal experience when considering the strategic use of both digital systems and data to improve not only the online or digital commerce experience but also the in-store experience or the pickup experience. So, every single shopping touchpoint a shopper has across both online and in-store experiences and making that as seamless and just natural for them. What you see on the shelf in-store should start to come together and really reduce friction and frustration for shoppers. I think we’ve all had experiences where the experience is not the same, and you want to make sure that what you’re seeing online is what you’re experiencing in-store. You want to make sure those experiences are synonymous and work together to elevate the entire experience.
Todd Foley: There’s just so much data. It’s almost data exhaust, right? Everything you do when you’re shopping or acquiring a product, whether in-store or online, being able to mine that effectively with the huge volume of telemetry, I imagine, must be as much art as science. How do you ask your teams to pursue that? How do you give them direction around these things? Or is it always the ideal of “let’s make the customer’s experience a positive one, and let’s use that to drive the behavior we’re looking for”?
Ellen Webb: Yeah, I think that the shopper’s positive experience is always, you know, my team’s remit is to look out for the shopper. And so that is the core of what it is that we do. And if you think about, you know, talking about these digital shoppers, they’re the majority. So, you’ve got 63 percent of shoppers grocery shopping both online and in-store, which is about two and a half times growth since 2020. So, this is a, you know, nearly ubiquitous behavior and will continue to grow. So, it’s something that I think at this point is not optional. At the end of the day, if people have a good experience, they’re going to reward you with their loyalty. And so, it’s really, you know, making sure that, again, that experience of online and in-store is seamlessly blended. And part of that does come down to data streams. So, what do we know about the online experience compared to what’s happening in terms of how people shop in both of those environments? Because they aren’t necessarily the same, they are a unique and connected tissue between that to make sure, again, we’re making logical leaps within that space.
Todd Foley: But I think you’re in a unique position because of the data you look at and the way you’re looking through it to kind of, you know, be able to talk about sociological trends, right? I think the markets have changed so much over the last few years, impacted certainly by COVID and everything after that, as we’ve seen different shifts in where people live, you know, how often they go to the office, all of those things, which must make an impact on their shopping behavior as well. Your position and your team’s work allow you to document that and project what it’s going to be, I would think.
Ellen Webb: Yeah, we are. We do a lot of that. And I think that’s another, you know, as you think about, you know, the stat I gave you about 63 percent of shoppers doing both. And I think there are some things that oftentimes, you know, in the insights world, you have to break out of what we call the sample size of one. It’s what my experiences and what your assumptions are. And you kind of have to check that at the door and say, okay, what is the data telling us? And, you know, Gen Z is a really interesting one in the, you know, the physical space because they really, really like, obviously the online experience, but they value going in-store to, you know, find new things. And so, there’s a lot of, I would say, interesting tension points that I think bust some of the myths in terms of how we think about different demographic groups, for sure.
Todd Foley: With so many changes in customer behavior being, at least looking over the last couple of years, relatively volatile, how do you approach answering questions about what’s going to happen in the future if you can’t base it on historical activity? How are you dealing with answering the questions that don’t have our data to do reporting on?
Ellen Webb: That is a million-dollar question. Usually, my snarky answer is I don’t have a crystal ball. You’d be amazed, though, how many times I think, you know, I was, you know, in this space during COVID and the number of questions were like, what’s going to happen? What can you tell us what’s going on? And so, I think that for us, it’s really about how do you take a measured approach and say there’s a range of possibilities? And then there’s also, within some of these things, there’s going to always be faster movers and slower movers and people who kind of wait to see. So, I think being broad with what we think might happen, and really, thinking through the implications of each of those things through our business is really important. So, I think you get into dangerous situations when you confidently say, I think this is going to happen because the likelihood of you being accurate in that is pretty low. But saying, okay, here’s a range of possibilities. And then, more importantly, thinking through what are the next potential implications for that if each of these things happen. Because I’m sure this is the case in every business, but it’s hard to think past what’s happening right now, immediately in front of you. And it’s really important to kind of take that pause and say what might happen. So, I think that’s one of the things that, as I’m in this role, it’s probably one of the most challenging things, but it’s also one of the things that I love. When you don’t have imperfect information, you kind of have to make a leap and say, okay, based on the data that we have or the things that we think could potentially happen, here’s what that looks like.
Todd Foley: I think you always also have that North Star of knowing that if you can improve the consumer experience, you’re going to be able to do good things, whatever the trends might be, right? So, you don’t have to have a crystal ball, necessarily.
Ellen Webb: Although it would be nice on occasion.
Todd Foley: I’d like that. That’s, I’m waiting for the machine learning model that’ll tell me everything that’ll happen in the future. And Ellen, thank you so much for joining me today. For those of you listening, please visit cdomagazine.tech for additional interviews. But Ellen, it’s truly been a pleasure. Thank you.
Ellen Webb: Well, thank you so much.
