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Episode · 1 year ago

S2E03: How McDonald's France is Using Data Lakes to Improve Customer Experience

ABOUT THIS EPISODE

Adrien Sieg, Head of Data at McDonald’s Global Technology France, Christina Moss, Director of AWS Cloud Services at McDonald’s, and Mathieu Rimlinger, Director of Global Technology France at McDonald’s, talk about their latest technological advancements in the cloud and how McDonald’s is using data lakes to set customer expectations and improve satisfaction.

...involve solve, evolve, welcome to cloud Crunch, the podcast for any large enterprise planning on moving to or is in the midst of moving to the cloud hosted by the cloud computing experts from Second Watch, Ian will be chief architect Cloud Solutions and Skip Berry, executive director of Cloud Enablement. And now here are your hosts of Cloud Crunch. Welcome back to the podcast. Everybody very excited. Today we have, ah, a group of people from McDonald's Corporation with us today from around the world, and I've had the opportunity to work with McDonald's quite a bit. And it's fantastic. So once again, my name is Ian Willoughby. I'm with a second watch, and today I am joined with Adrian Sig, head of Data and McDonald's Global Technology France. Christina Moss, director of a Ws Cloud Services at McDonald's, and Matthew Rim Linger, director of Global Technology France at McDonald's as well. They're joining us today, and we're gonna be talking about the latest technology advancements in the cloud and how McDonald's is using Data Lakes to set customer expectations and improve satisfaction. Now, Little commentary for me before we get going into this was we had an opportunity to do? Ah, very, very interesting project with McDonald's. We call it the France Data Lake. And really, it's gonna be a fun story to share with our audience and really talking about how this technology used thio drive some change there. So welcome, everybody. Christina. Goodness. I had the opportunity to see everybody while we're recording. Just so I know the audience doesn't Good to see you. Good to see you, Matthew and Adrian as well. Welcome to the podcast. Thank you. Yeah. Oh, this is great. Yeah. Yeah. Welcome, Welcome. Now I've got some questions. I just kind of want to ask you all and what kind of dive into it if you're ready. But first of all, and I'll let anybody take this question. Let's just I'll start with it out there. How did this idea of using this data, like for this goal, really help improve some customer satisfaction? And And how did it come about? How did you kind of conceptualize this? Maybe I can start s a new training in McDonough. Written off course margin Eri speaking in a fast food industry is that it's no longer customer satisfaction. That is important, but just in our experience, which paves the way to an explosion off types and and sources off data. Consequently, McDonald said, the needed new way off storing and manipulating data and this fax involved off course in shifting from the traditional data. Where are usedto more flexible, scalable data like so what we have to keep in mind in McDonald's. We switch from a transaction centric view to a customer. Contribute as that super and more complete perspective on customer lifetime value and consequently, we we need. McDonald's need full visibility...

...into every step in the customer journey to understand what customer allies, what they don't and what McDonald's can do to improve the customer experience and, as a by product improve conversion rate and will die. Alte. Yeah, No, that's That's really interesting, Matthew. Do you have anything? You can add her? Yeah, And more generally, we knew that we were sitting on a mountain of data because we're talking a lot, A lot, a lot off that, but we're not using it. And that was quite a pity. And and I really wanted 18 months ago to start a project to the eldest, leverages data and do something about the data because we're just missing an opportunity to do wonderful thing with the data we had regarding consumer experience and any other field off data, because I believe we have hundreds of fields of data to to experiment on. And then Cristina, you're sitting at the corporate headquarters. Well, not actually physically these days. But a zoo Most of us were working from home. Is this a trend that you're seeing across the globe with all your different business objectives? Yeah, I think you know The interesting thing about McDonald's is that you don't really think about us as a technology organization, but we really are, like everything we're doing in House is really like to figure out how to improve things in the customer experience in the stores. And I do think this is a trend that is growing. They're just and it's kind of the direction we're heading more, more more now, even as this year has been progressing and I think into next year, yeah, and I have a unique opportunity to not only work with McDonald's is a client of second Watch, but also many, many other multinational and large enterprises. I can honestly say that what you all are doing is much further ahead than most of the industry. A Sfar. The way that you look at digital and it's it's a really, really exciting opportunity. I don't wanna, you know, no shameless plugs here or anything like that. But it's just it is really, really fascinating. And I see it from kind of the whole arc of your business is, well, like it's really it's about that customer experience. And I did enjoy a nice Big Mac the other day and using the mobile app it was fantastic. So I'm gonna kind of move on here a little bit. But I want to really kind understand, Like, what were some of the factors that drove you to this? You know, I I think there's there's a lot of things going on. One is you have, ah, large set of data that you're able to look at, but also the technology has been changing very rapidly that you can use. Can you kind of tell me what the catalyst waas and I'm gonna go to the France team here initially, is what what was the driver? What was the catalyst to say now is the time to do this, the technology became much more efficient and much cheaper to so we entered the opportunity to to build a system with a nisi return on investment,...

...a fast return on investment that was a key for us. We don't have to invest so much and wait many years to get a feedback and a return engagement. That was a major catalysts toe. Help us sell the project and engage the project. And after that, we had to find a good resources and the good people, the good partner to walk with. And then what kind of data are you actually analyzing? And what are some of the outputs that are associated with us? Basically, we collect any form of data from from anywhere within McDonald's, numerous data sources and and zeros from roof new numbers to show social media streams and anything in between and off course, we got a vast amount off operational data. For example, a speed of service is a case in point on its data coming from the kitchen management system. Basically that there is an explosion off sources restaurant have access to much more data and many more data sources and than ever, ever before internal external commercial correction or structure and structure. So we have a lot of data and the underlying goal off our Data Lake and take behind the saying is to reduce the effort needed to analyze or and process the same data set for different purposes and and by different application. So that's why to answer your first question, we was looking for a scalable, fault, tolerant data platform that processing architectures and framework using something very common in a in the tech world but uh, loosely coupled and distributed system and basically toe to say in keeping with Mature said Way was looking for a manage solution that scale seamlessly and put less focus on infrastructure. Thio Hello, McDonalds team to focus on what really matters so data and the resulting inside from data. So if I be complete three main type of data that we're using today for dash boarding or editing, uh, sales, obviously speed of service and consumer behavior. So the three main type of data that we're using but still there is one we are walking that I wanted Thio to put in place within the Data lake is any kind of data that comes around whether you know or don't know what you're gonna do about it. We grab it and we store it in the head. Lake storage doesn't cost much old storage and customers, so we're gonna grab any kind of data internal, external. That is that we don't know what we're gonna do about but so that when we're gonna have a use case, relevant use case about data will have it. We have a history about the data and will be more efficient and faster. So that's also a point off importance in the way we did put that are taking place. And we wanna walk tomorrow. So even though there are...

...three kinds of data today that are mainly used and and more important than the other ones, if I may say so, sales, speed of service and consumers experience, we're gonna grab everything. And we're already reading everything from I t. Data around monitoring devices, something that we're going to do next year, or ticketing or whatever and also saves etcetera, etcetera. Now, when you were getting ready to deploy this design it, you know, and then you get into the implementation phase Was there any unexpected challenges that you experienced while building this other than like how slow McDonald processes are, like our internal processes of, like, just getting things going sometimes? Well, everybody faces that problem. I think so. You know, universal universal challenge that that is definitely, definitely universal challenge. But beyond that, let's say you know, is there any like, you know, from the technical aspects, the operational side, any of the collecting of data because you mentioned lots of data sources Now that air coming in Was there anything that that you had to overcome any of those challenges? Uh, but basically, from my perspective, it's something very common. It zits rated Thio data inconsistencies because in in the world, off additional point of sales, we sells happening online, offline in dozens off a different location. Collecting and aggregating that fermented data is a fit off. A huge complexity, very huge complexity. And when you try to put in a single place that are coming from many sources, and from many data vendors from mobile labs from loyalty programs from serum tools and many, many more, other digital interface is very hard to have a single premarital. Tito draws in the entire customer journey into the own digital ecosystem because sometimes the fact you have explicit customer and engaged are missing the different digital platform and and loyalty program. So from my perspective, the challenge we face is to find the link for a given customer to to put the food puzzle together and as the big picture. And as Christina said Andi, it was a beautiful project because we work in in many locations in the world from the U. S. From India, from France, from UK, with with a lot off James. So it waas incredible Thio to put everybody on the same table and and thio to discuss with the all time. So it was an amazing project in terms off technical expect with issue we got and off course in terms off off projector itself. That is like one of the best things about, like working for a global organization, is that you really get to kind of interact with tons of different people from different places. And the one thing that's like pretty universal is a...

...common goal, right? Like everybody wants to get the things done and work towards, you know, whatever the solution is so I think that's probably the biggest challenge. Is just the the different time zones in the different places and everybody working around the clock kind of. But I think it's been good. It was really good. Yeah, it was funny. Sometimes We got some called, uh, very early in the morning with with India and very late on the evening with us. So it was very funny to work with, ah, lot off people. And, uh, and Christina teams and based in in India in in us. So yeah, it was incredible. Yeah. I think your organization really understands how to work across those global boundaries as well. Better than virtually anybody I've seen. So that's it's really interesting. And obviously we're all working remote to some degree right now, These us that are office workers. And, uh, we still have people obviously out the front lines, which we appreciate their efforts out there. But have you seen the results that you were expecting out of this project? Has it helped you meet your goals as it any unexpected benefits come to the surface that you weren't anticipating? Maybe you could kind of share some of those stories. In fact, we've reached a few goals. We've produced speed of service, dashboard, uh, something that didn't exist previously. And we're now able Thio, analyze and provide census about those those kind of data. But the most important thing is that we've opened tense off those tens of possibilities and opportunities that we didn't have previously. So this brings a lot of challenges and a lot of possibilities that we didn't have previously and and that's the most important thing. But we're to me. We are only at the beginning of the journey that we have around data and Algeria will talk about the specific goals and objectives that we did rich until now. But the first goal that would read reach from my point of view is that we've been able to to show people and show our organization that we were losing a lot of opportunities, and now we have the ability to go through on to move on into those opportunities and to to walk on new data, new possibilities and to stop walking in data like handcrafted walk on data. And that's the main goal that we achieved. We we proved the French organization that it was possible and important to invest and continue investing in data. Yeah, basically, I can deep dive into one objective. So I joined McDonald's on the very beginning off February and and mature. Ask give me one goal as the speed of service because, as you can figure out, it's the only important for fast food. Shame to know, to monitor, to have visibility and on speed of service. Eso Basically we follow our own national Operation Team toe, get...

...visibility into their business and off course logistic operation. And they can make accurate decision about where and how toe to implement changes that we make a new impact. Operational efficiency and gustatory on DFO from McDonnell Operation Team. Getting data about prep time is the first step in gaining insight that can improve other fulfillment efficiency for our listener, as the speed of service in McDonald's world is the time restaurant text to serve the customer, the clock start when the customer placed is air order in the restaurant or pull at the drive thru and the clock stop when the food is delivered to the customer. So, as you can figure out, there are a lot a lot. A lot off data toe give you some, uh, some some figures. We serve 1.5 millions off customers daily and each transaction produced maybe 20 or 30 point of data. Eso we are in the in the big data world because we collect data from prep area and kitchen and operational team Now can get to better understanding off how long prep time, text and off course in the future with Second Watch Team and the Christian A team machine learning and model prep time to provide a more accurate prediction off Our long prep time will take in the in the future. And maybe this is a supporter of big data and the objectivist off mature to monitor, to understand, toe get visibility into speeder service. And in France you can trust us. It zbig revolution toe can monitor as a different prep speed of service area speed of service. And because of of course, you can say Okay, here it's like too much time to take other Here. It's like too much time to to prep food and explaining all other some stuff like that. So here is a concrete. A result we way we have done together. Yeah, this is something we talked Thio all our customers about. And we actually don't have to talk to you guys about this because you are all down the road. But if you're not collecting your data, doing that analysis and starting to think about how to apply machine learning for whether it be forecasting predictions, wherever else it is you need to because your competitors air going to get there eventually as well. So you might as well be in the forefront of it of that digital transformation. So it's very, very exciting to see you all do that. Now that you've done this in France and it's it seems like it's a pretty good success. You're getting some data out of there. You're you're making some data driven decisions. Are you planning on doing this in other parts of your business, either today or in the future? I think to like some of the reporting and the visibility you've been ableto have is is really cool, Like some of the reports that I've seen that came...

...out of that were really nice, and you can actually, like collectively see data that you have across from a bunch of different places now in one central location is pretty cool and very helpful. I think you know, if you're trying to analyze what's going on, Yeah, and I also think it's interesting, too is because a lot of us have come from traditional I t backgrounds. But now we're starting to play into the data area. So in France, do you plan on doing any more analysis of data collecting additional data, looking at expanding this data like, yeah, we some other teams that already doing some of this and we will continue to push to roll this out. We'll continue to kind of marketed and and hopefully kind of used France is a good example to say, Look at how what they were able to collect and how this worked. Okay, so let me tell you facts twice today, a business analyst, manager, director coming from, of course, another department call me and ask me if I can provide him or air more detailed analysis, or if I can delve into transaction level at purchase tickle develop mean to increase their analytical, uh, power. Because, historically speaking, our business analysts have used, let's say, excel based modeling to gain insight from from data. Eso thanks fully advances in in big Data Analytics and concretely speaking with our data platform enables McDonald's to reap their data benefits. And we have now means to integrate, analyze our data, the transaction, uh, level to predict the next best product to execute association and the disease to customize so experience of customer to do a lot of stuff as much. You say that just before we have a lot off opportunities to come. So basically the different business lines aware off this advantage and they want to You have more data to make more, let's say, more analysis and much detailed analysis. So it zvehr e nice to see what he it will come in the in the month, maybe years to come. That's fantastic. Yeah, I'm very excited to see where you all take. This is Well, you know, the A I Revolution is coming, and it's it's interesting to see how how you've already applied it and it's working. It's really helping the customer experience, and I'd like Thio see that continue. Well, I want to thank all of you for your time today. Adrian, Christina, Matthew fantastic. This has been a really interesting project to get the watch come from conception to deployment and implementation. And I would like to say success so great work out there in the fields with this technology. Okay. I want you to think also, all the teams that helped us during this major project and helpers may make it a success. Obviously, uh, Christina, for global technology at McDonnell's, then but also all the...

...second watch people that worked with us. It's been really, really important to have you alongside with us, and you help us make this a real success. So thanks. Thanks to all of you. Thank you. I would also say thank you. I think second watchers have been a really, really good partner, and I'm looking forward to doing more of this with more people. Well, thanks for those kind words. We don't often try toe self promote too much on this podcast. But hey, we'll take it, uh, at the end of 2020. Thank you again, everybody. Thanks. Thanks. Thank you again. Appreciate your time audience. Thanks again. We really enjoyed this episode. I hope you do too. As always, please. Email is that cloud crunch at second watch dot com with any questions, comments or suggestions. Thanks again, you've been listening to Cloud Crunch with Ian Willoughby and Skip Very. For more information, check out the blogged. Second watch dot com slash company slash blogged or reach out to second watch on Twitter.

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