[00:00:00] Welcome to Unpacking the Digital Shelf, where we explore brand manufacturing in the digital age.
[00:00:16] Hey everyone, Peter Crosby here from the Digital Shelf Institute.
[00:00:19] For a company that has been around since 1911, Mars has a reputation as one of the most visionary
[00:00:25] companies in the industry, particularly when it comes to building global capabilities that scale
[00:00:31] and flex to serve the needs of the regional lines of business. It's not an easy feat.
[00:00:36] Celia Van Wickle, Director, Omnichannel Digital Commerce Analytics at Mars, drives building out
[00:00:42] their global digital commerce analytics strategy and works closely with the business to design,
[00:00:47] test and scale capabilities that deliver efficiency, measure results and identifies
[00:00:53] opportunities for growth. She kindly agreed to join Lauren Leavak Gilbert and me to share the
[00:00:58] art and science for enabling digital commerce analytics to every region around the world.
[00:01:04] Celia, welcome to the podcast. We are so delighted to have you here. Thank you so much.
[00:01:10] No, thank you for having me. It's a pleasure. I'm a big fan of the Digital Shelf podcast.
[00:01:17] Really excited to be here and to give whatever insight I can back to your audience.
[00:01:23] Well, you are doing some incredible work at Mars around data and analytics. And
[00:01:28] as we talk about a lot, brands have more data than they can use, but they're really not always set
[00:01:34] up to layer all those data sets together and really form a clear picture of what's happening
[00:01:40] in the business. And pulling that off, I would imagine takes a lot of collaboration internally
[00:01:46] and then a lot of collaboration to sort of create and get the impact out of it. So
[00:01:51] you have built this analytics team and so we would love to know more about your team
[00:01:57] and how they serve the business. Yeah, so specifically at Mars, I lead a global team
[00:02:04] that focuses on digital commerce and omnichannel advanced analytics activation, particularly in
[00:02:09] the Mars snacking division of Mars. So I'm actually part of a larger group collectively
[00:02:14] where our goal is to drive one demand and data and analytics across all functions,
[00:02:19] what you call the demand side of the business. So sales, commercial, marketing,
[00:02:24] portfolio, we have various folks who are supporting the one demand side and together
[00:02:29] working collectively as I think about my digital commerce agenda and my team's agenda
[00:02:34] to drive the strategic goals. So I personally guide my direct team to think about strategic
[00:02:41] digital commerce data and analytics solutions and vision and how we build best in class
[00:02:47] solutions towards these one demand goals. So we're enabling digital commerce by building
[00:02:52] business intelligence tools and analytics solutions to really give data at the hands
[00:02:57] and fingertips of sales leaders for Amazon, omnichannel leaders in Sam's Club, people
[00:03:03] leading Instacart and Uber Eats and other on-demand delivery platforms globally.
[00:03:09] And we're also working very closely, very close to your heart in the digital capabilities
[00:03:13] area around search content management and really building our own best in class solutions
[00:03:18] for digital self-measurement, what we call here at Mars perfect digital store capabilities.
[00:03:25] So Celia, a question, when you think about having a global role, does that mean
[00:03:30] say I'm a digital lead in the business on one of the brands and I want to know how to connect
[00:03:37] sales to my content on our PDP? Would they come to you and present that problem and then
[00:03:43] you would work with them? Like what is the dynamic between the global and the regional
[00:03:47] activation of the data? Yeah, so each of my people, my team actually serve a different
[00:03:52] region of the world but I actually serve globally up into a cohesive strategy. So first
[00:03:57] we start with what is the global strategy we're trying to achieve? What are our biggest
[00:04:02] bet markets, maybe our growth and activation markets and kind of come up with a plan there
[00:04:06] in terms of how we're going to globally roll out different solutions. Sometimes we'll have
[00:04:11] markets also come to us and say there's a need and we always want to try to unify it back
[00:04:14] up to that global strategy of what we're trying to achieve and how we are getting there.
[00:04:19] As a group I've been on a journey, so the group I joined was brand new when I joined in 2022
[00:04:24] and so we've been on this journey of how do we enable the regions, especially in the US,
[00:04:30] Europe and some other regions of the world and now like how do we really think about
[00:04:32] that global unification and strategy to harmonize and scale across the world from
[00:04:38] the biggest markets to some of the smaller markets, whether that's in Asia or that's in
[00:04:42] Europe or in other parts of the world. So we're really looking at that strategy today,
[00:04:47] but again it starts with global strategy first. What are we trying to achieve?
[00:04:50] How do the markets and regions enable that? And then we kind of work with those regional
[00:04:54] partners to help us activate and buy into those solutions so they can actually build
[00:05:00] the next best in class measurement for their areas as well. I think that's so unique. I mean
[00:05:05] from a global analytics function, especially if you're thinking in the digital commerce
[00:05:10] capability space, because I know I work with a lot of brands who say they have to figure it
[00:05:14] out in their team and they don't necessarily have that global support. And I can imagine
[00:05:19] you can then provide a lot of perspective around organizational data like sales or skew
[00:05:25] count or prioritization of skews. And that can create a lot of, or it can take a lot of the
[00:05:34] responsibility off the brand and then you can provide that to them while they're doing
[00:05:38] their day job. Was that kind of the premise? Like take it out of the region so they can focus
[00:05:43] on activating and you can have that broader umbrella? So I think it's about more solidifying
[00:05:51] unified strategy, but the regions still want to be enabled, right? So it's not to really
[00:05:56] alleviate them necessarily. They want the enablement. It's more to be more cohesive and
[00:06:01] purposeful about what we're measuring and how we're rolling that out across the world. So
[00:06:06] we're all looking at a performance in the same way. So you talk about it a little bit
[00:06:12] further about how we're doing that in the digital shelf, perfect digital store space
[00:06:16] specifically. But with this area, whether it's sales data or digital shelf data,
[00:06:24] every market and region has different data depth, quality or sources that they're using
[00:06:31] for. So if we can unify what we're measuring globally, what we're trying to see and unify
[00:06:38] globally, then we can bring that down and use the data that they have available to enable
[00:06:43] that solution. And if they can't enable that solution, right? We come up with,
[00:06:46] we had to work with the business to come up with a plan, like a data plan, right? To
[00:06:49] achieve that. You're all marching towards the same thing. We just talked to someone on a podcast
[00:06:54] the other day who said there was a different definition of, I think it was net revenue
[00:06:57] across the business, which makes it really challenging to define what that means and
[00:07:03] then to look at the same statistics across the board. So in terms of measuring, can you tell
[00:07:07] us what are some of the key areas that you're focused on measuring? Yep. So we, as I mentioned
[00:07:14] and we're focused a lot on digital capabilities is one core area. And so in that area,
[00:07:19] we are definitely looking at unifying, at searching content drivers across the different
[00:07:24] markets. We're starting also to introduce things like anomaly detection for content,
[00:07:29] making sure that PIM, Celsify type of integration is there from a gold standards
[00:07:34] benchmarking perspective globally where we have it. And really again, talked about earlier,
[00:07:39] unifying those KPIs. So you mentioned the net revenue for another company kind of being
[00:07:44] different, right? Well, we have share of search being different everywhere, right? Or
[00:07:47] our total health score being different everywhere because every business has different
[00:07:52] perspectives or every data source and provider has a different perspective and how they measure
[00:07:56] that. We also can look at the raw data and like assess quality issues and harmonize that
[00:08:02] data as well. So we can pick up if there's a skew off or something's missing from the data.
[00:08:07] So it really helps us understand and apply those weights equally, those KPIs equally,
[00:08:12] and really focus on what's the best in class? What's the right unified North Star,
[00:08:16] what we should be achieving from a KPI perspective. And then we execute on those
[00:08:21] performance scores across each market. So when we're doing what we're working on is that US
[00:08:26] to Europe, to Mexico, to South Korea will be measuring content search the same regardless
[00:08:32] of whatever data source they have within their ecosystem. And so that's like one of
[00:08:38] the core areas there. And then where we kind of go a little bit deeper is we have different
[00:08:42] tools. One is called search algorithm decoding. And so we're really actually going deep on
[00:08:48] different retailers search algorithms to understand the value drivers of that
[00:08:53] we tell us whether it's car for in Europe, we can actually see the drivers from encoding
[00:08:58] that search algorithm and we'll know whether it's our content presence or sales drivers
[00:09:03] or it's other types of taxonomy that we need to drive within that retailer ecosystem.
[00:09:08] And then another one that's always fun is that we also measure incrementality
[00:09:12] of our onsite paid media measurement. And so we're actually looking deep by tactic, by keyword
[00:09:19] for overall incrementality performance. So we can see that for Tesco, Amazon and others
[00:09:24] to really help the e-commerce teams along with their shop or marketing teams really
[00:09:30] understand how to optimize that mix to drive onsite sales performance. So again, there's
[00:09:35] a nuance there because it focuses on digital commerce. So we're really focused on the
[00:09:38] customers online sales perspective in terms of what we're measuring and to show that case of
[00:09:43] incrementality, but incrementality could actually be shown across different use cases, depending
[00:09:47] on what you're trying to actually measure. And lastly, the other thing is I also enable sales
[00:09:52] teams. And so one of the things we're doing with sales teams that actually connect back to
[00:09:56] our digital shelf and perfect digital store measurement is that we're actually connecting
[00:10:02] solutions end-to-end and understand those drivers. But to do that, we're first trying
[00:10:06] to drive and understand the e-commerce P&Ls for every customer from almost around the world.
[00:10:12] So those have been very hard to obtain, to understand holistic P&L. But to understand
[00:10:17] the P&L for e-commerce has been very disparate based on the data sources available. And so
[00:10:21] we're actually working a lot with harmonizing bringing in our different e-commerce POS data
[00:10:26] and connecting those value drivers and those P&L drivers, which will allow us to then go
[00:10:32] back and really drive true understanding holistically end-to-end sales P&L of different
[00:10:36] drivers such as perfect digital store or portfolio or other category drivers that
[00:10:41] maybe the customer is trying to achieve. So that's a very big undertaking that my team
[00:10:46] is doing right now, is really trying to unlock those key value drivers so we can
[00:10:51] understand the true health performance from a global perspective. That's really amazing.
[00:10:57] It's an amazing effort because part of what we talk about a lot here on the podcast is
[00:11:03] in this era of money's no longer free and digital commerce and e-commerce needs to mature
[00:11:12] and become part of an overall omnichannel profitability. I would imagine, and you can
[00:11:18] confirm this or not, but that these business shifts which is a drive towards the whole
[00:11:23] business needs to mature and everything needs to come together into ultimately, and I don't want
[00:11:28] to misstate your naming, but a perfect store for the consumer no matter where they are
[00:11:34] and no matter where their journey takes them. And I would imagine these analytics,
[00:11:39] one, are incredibly difficult to get together and normalize and pull assets or insights out of,
[00:11:46] but that the organization must be grateful to start seeing that really show up.
[00:11:51] But I was just wondering if you can dig into a little bit about how this data comes to life,
[00:11:57] particularly the sort of the P&L data to help drive towards that profitability
[00:12:02] that you're looking at. Yeah, so it could get very complicated, right?
[00:12:06] I imagine so. It depends also again on the data maturity of every market and what's available,
[00:12:12] but we start with the POS data. We need skill level data to see that. We need to understand
[00:12:18] how it maps to our cases, but then we have our own calculations for P&L and the NL KPIs,
[00:12:24] and then we make sure that we're mapping accordingly. But we need unified measurement.
[00:12:29] We need unified mapping. Sometimes we actually use our PIM data to map those skews. It's
[00:12:34] a very complex. It's actually a lot of things are in there. In fact, some of what we're
[00:12:38] finding is in some of the e-commerce POS data is very volatile. So it's accurate one month,
[00:12:43] and then next month it's not accurate anymore because the retailer may have ruined a skew or
[00:12:48] two that's not ours or our data feed system may not be capturing every statement that the
[00:12:54] retailer did. So we are constantly troubleshooting this space. And so it takes a lot of effort
[00:13:01] and a lot of mindset to really drive that clean data for the organization
[00:13:09] to even get to the P&L level. So it's very, very complex.
[00:13:13] And you mentioned something earlier with one perfect store. And so again, my area is more
[00:13:20] in the digital commerce side, making sure how we show up online is perfect. But we are having
[00:13:26] active conversations going back to that one demand point of view is how do we look at
[00:13:31] perfect execution in store to perfect execution online? How do we look at similar, but not exactly
[00:13:38] the same measurement and value drivers one-on-one. So someone at the Tesco or Walmart or wherever
[00:13:47] can see side by side holistically what is going on from how we show off both online and offline.
[00:13:54] Right. Again, very different how you measure that need offline space in the in-store space.
[00:13:59] But share of the store to share of search. These are the proxies that you could come up with
[00:14:06] and look for in terms of total execution and values.
[00:14:12] I love the idea of reverse engineering search algorithms.
[00:14:17] You may not put it this way, but I was thinking that sounds like a competitive advantage
[00:14:22] if you can figure out. And particularly you were talking about how much some of the data
[00:14:28] shifts and algorithms on especially on the big players change constantly. And if you're able to
[00:14:35] add that just sounds super interesting. And I could imagine if I were one of those leaders
[00:14:41] who was be able to have that data, I can adjust my strategy, my content strategy,
[00:14:46] my ad strategy. Like a lot of that sort of feeds off of I imagine the signals that you
[00:14:50] get from that decoding. Yeah. So no one's as fast as Amazon yet in the algorithms, but
[00:14:59] and you know, even Tic Tac I think is coming. That's like an algorithm that you can't,
[00:15:03] it's almost impossible to unlock. But the retailers are changing them fairly regularly.
[00:15:08] You know, again, we've done a lot of work deeply in Europe in this area.
[00:15:12] And we find that just looking at it at a certain periodic level allows us to kind
[00:15:17] of adjust that strategy. No organization I've seen yet, whether I talk for my own or from
[00:15:23] any other organization have I seen that you make daily decisions you can actually
[00:15:27] actually take a daily action on your own, right? Your everyone's very busy. But these
[00:15:32] algorithms they change maybe in every quarter to annual and we kind of take a look at them
[00:15:38] to make sure that we're always driving the right value drivers for that retailer,
[00:15:42] adjusting that strategy. That has AI written all over it. But that's another episode.
[00:15:49] Very interesting. And so I can imagine looking at this,
[00:15:54] this whole process end to end and all the teams that you're collaborating with and sourcing data
[00:16:00] and then getting it out into the hands of the people who need it is really a great example of
[00:16:07] building cross functional and cross potentially sometimes silo collaboration within an organization.
[00:16:14] And how do you keep that collaboration alive? How do you make that work effectively to be
[00:16:21] able to impact the business the way you do? Yeah. So, you know, one of the things in analytics
[00:16:28] where we say is best practice is really understand the business needs first. So
[00:16:33] if I'm building a digital store or sales movement, what matters to that person? What
[00:16:37] are they trying to drive? How do we leverage interviewing process design thinking skills to
[00:16:43] understand those business needs first before we go build anything? So that's really one
[00:16:48] of the things we do. But to even get to building something, right? Once we understand the needs,
[00:16:55] they have to actually have people who want to take ownership of analytics solutions in the
[00:17:01] business. So whether it's global leading the strategy, but sometimes global again,
[00:17:06] helps us set the strategy of what we still need those regional market level partners around
[00:17:09] the world to help us embed that connectivity of the data for their markets and partners.
[00:17:14] And so we need someone who has strong sponsorship and ownership of wanting these capabilities
[00:17:20] within their market and really helping us drive what we call change management,
[00:17:26] training and enable them these solutions once we build them to make sure that they're getting
[00:17:30] used. So again, we really want to deliver that. But we also need those top sponsors
[00:17:36] across each region bought into our harmonized solutions and strategy. So we're all driving
[00:17:43] towards the same thing towards the best in class markets. We might build best in class
[00:17:50] design in Europe first because they have more needs than questions, and then we figure out how
[00:17:54] to scale that solution accordingly. So again, it is a constant collaboration about how we bring
[00:17:59] in the global to the regional and guide that true ownership and anything we do, right? If we
[00:18:04] can't get the market on board with the right people, we can't build it. We won't build it.
[00:18:13] So we work really, because we all have to be on the same page.
[00:18:17] And it's so that's a common theme. I mean, leadership is always a common theme, right?
[00:18:22] Across these things and finding the human beings who are engaged by the possibility
[00:18:29] of what could happen and then seeing their way clear to committing the resources is doing
[00:18:34] something like that, which might take a little bit to pay off or something, you know,
[00:18:38] instant among all of the work that they have to do day to day, they make a decision to take this
[00:18:44] on because they see what it could bring. And I was wondering if you have a sense of the
[00:18:49] characteristics of those leaders that you've built this, this core of leaders that are
[00:18:55] willing to be sponsors with you and go on these, you know, sometimes frustrating
[00:19:02] and difficult journeys to achieve these things. What are the characteristics of those leads? What
[00:19:06] do you look for? What have you found in these people that that help you with these kinds of
[00:19:12] transformations? Yeah, absolutely. So I would find that, you know, I know that they were on
[00:19:19] a journey being more digital and data savvy within their organizations. There's some who
[00:19:23] kind of just lead that first, right? You always have kind of someone who's more passionate
[00:19:27] about it. Those are usually key partners to kind of start incubating with overall.
[00:19:32] But it really goes back to that buy in of what the analytics solutions will do for their team.
[00:19:41] We need them to be embedded into business objectives. And so we kind of look at multiple
[00:19:46] layers and factors. One of them is whether we're going to drive time savings back into
[00:19:52] the organization. The other is value, business value, whether it be net dollars on the table
[00:19:58] for having these solutions. So when they can understand those kind of drivers, right,
[00:20:04] they didn't get more bought in for their team. Actually, I think time savings has more value
[00:20:09] sometimes than the actual value of dollars. Because in the business, yes, we want to drive
[00:20:12] dollars and we will drive dollars. But we need that time from people to give them back
[00:20:19] time within the solutions that we build for them. So we need those strong partners.
[00:20:24] We also have strong partnerships and people who can understand the holistic end to end view of how
[00:20:29] measurement will drive value to the business. We need people who could help us partner on road
[00:20:35] maps to make sure that we're driving it right for the business. So, you know, it's not
[00:20:40] necessarily an e-commerce person only, but we need someone who has that more forward thinking
[00:20:44] vision, who might already be a good data enabler within their organization of what they're
[00:20:50] doing day to day to help us build that holistic strategy.
[00:20:55] And how do you see that the regions report out on the data that you provide? Because I know that
[00:21:02] it can be challenging when you're sitting in the region to share out a scorecard and nobody
[00:21:07] knows the cadence to share that out and don't know who to share it out to. And there's fear
[00:21:11] that if you share it out and the number is wrong, then someone's going to ask too many
[00:21:16] questions and you can't provide the answers to that. So I'm curious how the regions and how
[00:21:21] you as the global team helped to disseminate that into the organization in a productive
[00:21:27] action focused way, not a, oh no, my numbers are red and we did this wrong kind of way.
[00:21:34] Like I said, it comes back to how we build it with a sponsor, right? A strong product owner
[00:21:39] who's going to help us do that. So they'd be kind of like, we help them understand what's
[00:21:43] in there, what the data is telling them when we build the tools and capabilities. But then
[00:21:49] we have to actually go out with them on training the teams. So they actually become the central
[00:21:53] point of knowing everything that's in that tool, knowing what it's measuring within the tool.
[00:21:58] And so we go back to their business and able business owner to help us with that.
[00:22:03] It does take some change management on their end to get people to use it day-to-day.
[00:22:08] We will help create some thought starters and ways to use those opportunities from
[00:22:12] the data that we're seeing, like we're showing like Valor, Value and Lift. There
[00:22:16] was a tool actually that there was a question that came into the business about value of
[00:22:23] investment for one of our retailers and we had built a forecasting tool and it was used
[00:22:27] actually for different use cases. But playing around with the tool, I actually was able to
[00:22:32] answer that question for a different use case, right? And so how do we help partner with
[00:22:37] them? But it is a holistic training. Like we have serious training that we just rolled out
[00:22:41] to how do you be a business product owner with us in analytics and what's your roles
[00:22:45] and responsibilities for what you need to do. In some of our markets, we have full change
[00:22:50] management plans that business owner with us have to be bought into in terms of who's going
[00:22:55] to embed it, who's going to own the strategy with us to get it into the business. So again,
[00:23:01] we do look at that. We're also looking at usage of our tools that we're tracking
[00:23:05] every 90 days or so like how much usage are we getting? Is the right users for using it?
[00:23:11] You know, we're doing adoption surveys to understand how our tools are perceived. How
[00:23:16] do we make them better? So we're doing these different things to get people in there.
[00:23:21] But you know, thinking about some of the challenges that we have, right? It's not like
[00:23:28] this line from the movie Heal the Dreams with Kevin Costner. So they say, you know,
[00:23:32] if you build it heave, in my case, they will come and that's not ever the case.
[00:23:38] Not with data.
[00:23:41] They need to be along with you to make them come. And so again, it comes back to that
[00:23:46] strong partnership to drive and embed change management and get those solutions bought in.
[00:23:51] So for example, our digital store, we have a great person globally helping us think about
[00:23:57] the harmonization strategy. And we have really strong capability leads who are very
[00:24:03] passionate about measuring their digital self KPIs and making sure that their teams are
[00:24:09] accountable for that measurement. So it's truly a partnership. I cannot do it alone
[00:24:14] and if I build it, it just won't be there for people to come.
[00:24:19] And huge kudos to you and your team, Silly. I mean, I think this is a very,
[00:24:23] very unique approach. The way your team is built and how you're working with the regions
[00:24:27] and the amount of data you're looking at at such a high level that you're able to answer
[00:24:31] these very specific questions that will inform the overall strategy of the brand.
[00:24:37] So I really think it's a very unique team. I haven't heard a lot of teams like this in
[00:24:42] the organization. And since we have you, can we pick your brain to say, like if you're
[00:24:47] in an organization that might not have a global analytics team or you're building
[00:24:50] a global analytics team, what are your lessons learned advice, call outs to say as you're
[00:24:57] doing this, think about these things or this is what I wish I would have done
[00:25:02] when we started. Love to get your thoughts. Absolutely. So one, it's going to take time.
[00:25:09] It takes significant time, not only from my team, but it takes time from the business
[00:25:14] to help us build and embed this analytics. Right? So, you know, people need to be bought
[00:25:18] in that they're going to take sometimes 30% or more of their time. I mean, it's pretty
[00:25:24] significant to help us build it. Right? But I talk about is that value driver back
[00:25:28] to them, right? Does it save them time? So they'll get those hours back
[00:25:31] once we're done. And then, or is that value that's going to drive? Are we locking
[00:25:37] dollars for the business or other KPIs? So that's one of the first things. The
[00:25:42] other thing is that, you know, we have to build in incremental steps. So do we
[00:25:48] have the data foundation set to even measure the basics to bring the data
[00:25:54] together? People may not need right away the fancy AI model to be applied.
[00:26:00] They might just need that triangulation of data. So our internal data to the
[00:26:03] second party and third party data sources, right? And that has immediate
[00:26:07] unlock and value in itself. And then once we get that foundation set, we can
[00:26:10] start building the fancier forecasting tools and integrate driver tools and
[00:26:15] sales drivers. The other thing is that, you know, we really need to listen to
[00:26:21] the business to unlock value and understand strategy. Right? So we need
[00:26:25] those people to help us understand it. As I talked about with that, we
[00:26:28] tell our example, right? It was a random question, but we had the
[00:26:30] tools to do it. And then that tool is being used or reconsidered for
[00:26:33] other purposes. And, you know, one of the other things is that again,
[00:26:39] each market, especially as we've done our perfect digital store work for
[00:26:42] global harmonization, every market has different data sources. So again,
[00:26:46] one of those unifying APIs, can we measure everybody in the same way?
[00:26:53] So again, we're looking at it, can we use not only the same KPIs, but
[00:26:56] what's the weights? What's included in that measurement? How does that
[00:26:59] come together? How does that globally scale? Maybe smaller markets just
[00:27:03] need a scorecard view. And that's all we're going to be able to
[00:27:07] enable for them. And as well, there's, we can go deeper into the
[00:27:09] data for maybe some larger medium size markets. So we're constantly
[00:27:13] battling this. But I think the thing is to think about things in
[00:27:17] chunks and bits and pieces to give time. Data does not always work
[00:27:22] the way you want it to when you first start out. So again,
[00:27:25] do a data assessment. What is it we need to have in the data?
[00:27:28] Do we have the right data? And then what time will it take us
[00:27:32] to get to that in the right place that we need to enable the
[00:27:34] business? So these are the things that we are learning as a team.
[00:27:39] And they have had challenges for us, but we also have learned to
[00:27:44] overcome a lot of them. Very big on you that you learn from
[00:27:50] failure, you learn from mistakes. And so those are the things
[00:27:53] that we're trying to drive towards to drive this best in class
[00:27:57] solutions for the future and really do some cool things that
[00:28:00] we have in our North Star Roadmaps that we're trying to do
[00:28:03] in the future.
[00:28:04] I love that point about the data, which I was just going to say,
[00:28:07] because I think people underestimate finding the data.
[00:28:10] Where does it live? Who owns it?
[00:28:12] You can build whatever fancy model you want, but if you don't
[00:28:16] know where it lives in your organization and you don't have
[00:28:19] the right data governance, you don't have the right data
[00:28:21] dictionary, you don't know what you're defining with that
[00:28:23] data, you can't then take this effort that you're talking
[00:28:26] about, Celia, and make it a global capability.
[00:28:29] And I feel like that chunk of time you were talking about,
[00:28:32] a lot of that falls into just finding it and knowing where
[00:28:37] it is in the organization before even building this broader
[00:28:39] model. Would you agree that's the upfront time-suck?
[00:28:42] Yeah, it's been a huge time-suck. Like I said, we're
[00:28:45] building these P&Ls that I talked about. Getting the
[00:28:48] data in the right way, in the right format, and then
[00:28:51] not having it change on us is a big one that we're trying
[00:28:55] to figure out and tackle and making sure it's a more
[00:28:58] fluid process. It is a lot of enterprise learning that
[00:29:01] we're doing as we're bringing this information on
[00:29:04] for the business. And so, yeah, there are those challenges
[00:29:07] that we see there. Again, there's challenges in the
[00:29:11] measurement. There's different things that we have to decide
[00:29:13] and align on as an organization.
[00:29:16] Well, I mean, for a company that was founded in 1911,
[00:29:21] I am consistently impressed working with Mars. Just their
[00:29:28] long-term commitment to just building and testing and
[00:29:34] expanding. And your organization, as I've experienced,
[00:29:39] it has a tremendous amount of ambition and vision,
[00:29:44] but also patience at the same time to take projects
[00:29:49] like these through. And they end up really impacting
[00:29:54] the overall business in such a fundamental way.
[00:29:57] And I wonder if from the inside, does it feel that
[00:30:00] way to you? I mean, it feels like you have a ton of
[00:30:02] support from the organization to do this. I'm sure there
[00:30:05] are all the frustrations and wish I had more budget
[00:30:08] and more people and all that. But I was just wondering
[00:30:10] if it feels on the inside what it feels like from
[00:30:15] the experience of having worked with you all.
[00:30:18] Yeah, absolutely right. We're really here to drive
[00:30:21] transformational analytics solutions. They want us to
[00:30:23] globally scale capabilities to unify measurement,
[00:30:28] to have a holistic view of what's going on around
[00:30:30] the world. So they are supporting this, right? This
[00:30:32] is core and fundamental to an organization.
[00:30:37] Well, Celia, so grateful that you take the time
[00:30:41] to share this with a broader community. It's really
[00:30:44] generous and helpful. And we are really grateful
[00:30:48] for you coming on the podcast.
[00:30:50] Well, thank you for having me. And this has been
[00:30:52] such a pleasure. And I really enjoy being here,
[00:30:55] and I really hope that your audience learned
[00:30:57] something today. So thank you so much.
[00:30:59] Thanks to Celia for sharing Mar's approach with us.
[00:31:03] There's always more information on digital
[00:31:04] commerce excellence to be had at
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