 multiple locations in both the US and India. What I'm gonna share with you today is a model that we've kind of created to help enable our teams to deliver impact, that is to deliver customer value both to our internal customer teams and ultimately to our retail customers in our target locations and our retail stores. But before I get into the why, how or what, I was gonna share with you a video of Melissa Ludak who's one of our BI leaders in which she talks about some of the really cool things that we've started to deliver just halfway through our reorganization, so about nine months in and some of the values that are really clear that come out in the video and some of those things are transparency. So how our data BI and engineering teams sit down with our business teams really to make the decisions together, about how our teams go into the stores and look to see how the reports and data are being used live. And that actually is a really important part of not just driving empathy, but also driving priorities about decisions that are made in the future in terms of product and what we deliver. Now I'll talk more about that at the end. The other thing in that video, and I will share the link out later, it's actually something we use for recruiting, but she talks about how we take action on getting after we get customer feedback and it's pretty exciting. Since the video was made, and that was a little over a year ago, we've actually continued to grow and learn a ton and we've had a surprising number of additional changes since then. We've moved to an open floor plan, which not a lot of people were super excited about initially, but we've actually saw some pretty incredible collaboration and innovation occur as a result. And lots of exciting things have happened as we've continued to grow and learn about how to build in customer feedback and incorporate empathy in the product design and delivery process. But I want to back up a bit to when our COE was created and where we had to start. So one of the first things we needed to do was really understand how we were going to define the work we did. We needed a place to draw some examples from that were successful and that were failures so that we could look for some behaviors and principles to either emulate or to avoid. And the tech industry at the time was definitely trending towards the product model. And we looked and we saw, this definitely worked for our platform and we could even see how our services, this worked for our services. But in all honesty, when we looked at our business intelligence work, we didn't know that this was a model that would fit. And as we were talking about this, the leader pointed out that at the end of the day, the product of our business intelligence teams was a good business decision. And he was right. The leader on the slide is Ben Shine. He's a director in our business intelligence area and he's really incredible. He's enabled a ton of innovation and agility across our organization and transparency. And I'm gonna share an example of something one of his teams is doing. And he really leads the way in terms of truly empathetic design and delivery. And so anyway, it was just really good for us to have kind of a starting place to say that no matter how we sliced it, everything that our organization is doing is some kind of product offering. And so before I get into the examples and the models that we use, I wanna share with you some of the research that I've done outside of the target in the data science and business intelligence world. So to start, how many of you in this room currently own or have owned an iPod? Yeah. So by that response, I think we can agree that the iPod is a pretty definitive example of a product success. But before the iPod came on the market in 2001, the portable music offerings at the time were in the words of Steve Jobs, crap, it's actually something he said. People had to choose between one of two things. You had to choose between something that was either easy to hold in your hand but only could hold about one CD's worth of songs or something that wasn't easy to hold in your hand but could accommodate up to a thousand songs and didn't have a great user interface. Both models, the music transfer speeds were pretty slow because you had to use USB port to get the music back and forth. All in, it was a bad user experience. So in 2001, Apple came out with the original iPod and the catchphrase was a thousand songs in your pocket. So by focusing on what the users wanted and needed, Apple delivered the best of both worlds, something you could hold in your hand or in your pocket and something that had the amount of music that people wanted. Last fall, a few co-workers and I had the chance to actually go to Apple and to their Cupertino headquarters and sit down with a few of their teams. And when we were there, we had the chance to learn from their enterprise design services team and they walked us through what this team does is they actually work with companies to design applications that will be used inside of the company either across large groups or across their entire enterprise. And they took us through the three-day design process that these companies commit to when they come in the door. And what was really surprising to me was that in these three days that the companies spend, they do absolutely no design work on the first day. The entire first day is just dedicated to talking to users. And what is more, what the design team told us is that they no longer let those companies bring in any form of proxy for the user. So no product owners, no managers for actual end users, they have to be people that will actually be using the applications once the application is built. And I asked about why and what Apple, what the team said is what they found is that companies who bring in proxies for the end users instead of actual end users are 50% less effective in what they build. And the way that they measure this is by second trips back to the Apple Design Lab. So those companies who bring in actual end users to build out the application and spend that first day with the people who are gonna be using the application once it's on the market or in the company, when they come back to the Design Lab for a second trip, they get to add an iteration or additional features to the product and actually make it more valuable and it's more useful. But companies who bring in a proxy instead, 50% of those companies have to come back to the Design Lab and redo what they tried to do the first time to make it effective to begin with. And I thought that was a pretty powerful statistic. Steve Jobs actually, there's a biography that Walter Isaacson wrote and in it there's a quote that Jobs has where he talks about the intersection between science and humanities. So really we're innovation and science meets and it's pretty powerful, right? So you can see that theme of science and innovation and humans starting all the way, like if you think about the iPod and what that was, it really started with what the users wanted and technological innovation. And you could hear it in what the experts at the Enterprise Design Labs were telling us about how important it was to stay centered on and focused on the people and the users. Another thing that Jobs says in this book that's really fascinating to me is that he was always motivated to build good products. He wasn't motivated by money. He does acknowledge that profit was important because of course it enabled the company to keep going and building great products but his motivation was to build good products. And this kind of leads me to my next point. So another thing I wanted to study, I wanted to kind of get outside of the technology world altogether and I wanted to know how does, how else does this scale? And I wanted to study some businesses. And so I studied three businesses that are all very unique. Zappos is an online shoe seller. Virgin Records is actually from the 70s. Richard Branson started it. And then Drybar, which is a salon where you can go in the US right now to get your hair washed and dried and styled. You can't get a cut, you can't get a color, you can't get anything else done. And they're all very different from one another. They're very different from anything in the tech industry aside from the fact that Zappos is online obviously. But they all had a very similar theme. So there was a need or an opportunity. There was a founder or somebody who had an idea about how to solve that need or opportunity. And then they instantly made no money. So it sounds really magical, right? But even though it seems counter-intuitive, what's pretty amazing is that it's the secret behind all of these founders' success. You see, they all started with a focus on what they were going to learn and not how they were gonna profit. And so their approach was pretty simple. They started with a hypothesis and then they designed an experiment. And they didn't take any other steps. So even though they could see this incredible vision about what it could become, they didn't fulfill that desire for self-gratification. They stayed humble and they stayed small. And I think that's really incredible. If we think about that in our world, what that would look like instead of continuing to just build features or push things out, we wait and we test and we learn before we take action to see what the next thing is that we should do. In the case of Virgin Records, Branson's hypothesis was that people would buy discounted records via mail. This again is in like the 1970s. And so his experiment, he put the ad in a magazine or newspaper. And the ad said, hey, Virgin Records, get records at 10 to 60% off of what you can get them from anywhere else. Then people ordered the records and then Branson thought, oh, crap. So he went to the local store and he negotiated to buy the records in bulk at a discount and fill the orders he had. Zappos actually started a similar way. So the hypothesis after the dot-com bus in the US, by the way, was that people would still buy shoes online. And so the founder actually started by going to the local shoe store, taking a picture of the shoes, posting the picture online, waiting for the shoes to sell. And then I sort of think he probably crossed his fingers the whole way to the store, hoping the shoes were still there. He bought the shoes and then shipped them to the original purchaser. And he made zero dollars, by the way, on this whole endeavor. He was just interested in learning, will the shoes sell? And then finally, Drybar, same thing. Alliwell was at home. She thought, I think people will pay just basically to have their hair styled. Asked a few friends, asked a few friends of friends. And then she started literally just driving to people's homes to see if she was right in doing their hair in their living room. And so what was really incredible about this to me is that none of these people were after instantly scaling. They all mandated learning before they grew. And as a result, they were able to grow with safety. And this works for us even in our world. And here's evidence. So Virgin Records, which is owned by Richard Branson. Last week, Richard Branson was 66 years old, and he had a net worth of $5 billion. Zappos was sold to Amazon for $1.2 billion in 2009. And Drybar today has seven-day locations in the US. They have their own product line, and they're looking to expand internationally. Now when these people were starting and they were making no money, rushing to local stores to fill orders that had already been promised, they maybe had a vision of becoming this way some day, but they had a lot of learning to do in between. And I honestly believe that the only reason they got to where they did is because they didn't try to fill all the features in advance. They took each step as they continued to learn. And so I just think it's a really incredible learning lesson for us about how to proceed. These founders all started with a focus on the user, a commitment to learn, a clear hypothesis, a reasonably risky experiment, and one other note there. Their experiments were logical, right? David Husman always talks about delivering 100% of 3% instead of 3% of 100%. And it's a weird nuance to understand, but it's a really important one. Alleyweb wasn't walking into people's living rooms and doing half of their hair, and then being like, peace out, I'll be back in a few iterations to finish the rest of your hair. And Zappos wasn't just shipping shoelaces. They all figured out an experiment that would be meaningful to the customer, but had reasonable risk associated with it. And that's our challenge too. How can we deliver 100% of X% that makes sense, that will provide us with value? And so what I found and what we found at Target is that this doesn't just apply to things like the iPod and it doesn't just apply to businesses that are scaling up. It also applies to data science, business intelligence, and engineering teams. And I wanna walk you through the process that we use with those teams. And I'm sorry, the resolution's bad because we messed around so many times with the cord. So the process we call as product thinking. And I try to avoid jargon, and so I was just talking to somebody and I said, if I went to a new company, I would never use the term product thinking. I would just say, hey, this is something we're gonna do. So we start by identifying our users or our teams start with identifying their users. And I would assume that most of you are familiar with what a persona map is, right? So you identify the people that you're trying to serve. We could look at any of those examples in the case of Apple and the iPod. It was anybody who had a desire to listen to music on the go, right? For Aliweb, it was people who had hair that for some reason or another wanted it styled or to look a certain way, so on and so forth. And then we spend time talking about those users. So what are the characteristics that make those users up? What defines them? And then there's a second step that was really new for us about eight months ago. And I spent time yesterday talking about empathy and I added this step in and I said, we need to develop some empathy. Empathy is, as defined by Webster, the ability for us to actually share in the world and the experiences of another person. And so to do this, it's really different from kind of stepping back as we've traditionally done and said, oh, I see you in your world. Okay, that's great. This is what I think you need and I'm gonna send it to you. Instead, we go sit with our users where they are and we try to understand their world. And so one of the things that one of our teams has done actually, we had a team deliver reports that were gonna be on handheld devices in the stores. And so they had some questions about what was gonna be useful and what wasn't. And another coach that I worked really closely with said, hey, you know what you should do? You should go to the stores and spend some time in the stores and see what they're doing. And so there's some actually really great pictures of this guy in the stores in our holiday season scanning products through. I mean, it's super busy, right? He's at the cash register, he's running on his feet doing all this stuff, trying to figure out how does this actually work when you're super busy on your feet and you're not sitting at headquarters when things are somewhat comfortable. And one of the things that they found, and I'll talk about this a little bit more too, was that they had planned to deliver about 30 metrics on this handheld device. And the people in the store were like, ah, I only use three of these, like you can take a bunch of these off. And so that's a big part of developing empathy and there's lots of different ways to do that, you can. And I think Jess talked about this maybe the other day, I don't remember who it was. You can watch people in their natural habitat. The best thing I think you can do though is go sit with them and work alongside of them because there are pains to different things that you just don't realize until you do it. UPS released Project Orion where they actually used handheld devices to tell drivers how to optimize their route and sort of allowing drivers to cruise their route. A way that they managed through the really difficult change adoption curve associated with that move was they actually had people go sit in the trucks with the drivers and figure out that change management piece and that switch there. So that's a really important part to figure out what's gonna add the most value and what's gonna be a hindrance and what isn't useful at all. And then the next part is create a hypothesis and it goes really closely with identify your assumptions. So so many times I think we jump in and we say well this is the solution but the reality is that we need to do a better job of thinking like scientists and so that sounds a lot like if we do X then Y will happen and we need to stop assuming that any of those things are facts, right? Because the next thing we're gonna do is we're gonna run an experiment to see if it's true and we have to start looking for data to actually tell us whether or not we're right. And as a part of that you need to identify your assumptions and I have a hypothesis around what's gonna solve their pain. So you've spent some time sitting in your consumer's world you've experienced some pain now you've got a hypothesis around what's gonna solve their pain or give them an advantage or whatever that is. And then you're identifying your assumptions and the reason that's really important is they're gonna either completely make your product craft if they're wrong or they're gonna really upset everything that you're doing and so you need to be aware of what your assumptions are and they're probably the first things you should work on. And then you design an experiment and that goes back to what I was talking about. Your experiments what you should be trying to deliver is 100% of the 3%, not the 3% of the 100%. So you need a skinny slice of end-to-end value. And this is really hard for business intelligence teams. A lot of times when I first started applying this in business intelligence I was on a team and we were taking data from a source system to a brand new enterprise data warehouse into a microstrategy report which for us was like cutting edge. And we had no idea how to do this agile thing so I was like okay so we have this report we have to deliver and we need team member names on it we need average hours work every single day of the week or total hours work every day of the week. How can we deliver just a skinny slice of that? And the first thought I had was okay we'll deliver the report just with team member name on it. That's completely useless, right? That is the 3% of 100%. So instead we need to flip that around and figure out what is 100% of 3% look like? What's our hypothesis? And so there is a totally different way we could have approached that in that situation. Ben Shine who's the guy that was on that slide that our product in business intelligence was a good, a high quality decision, a good business decision, leads a team and one of the things they did that was really powerful they created a dashboard for our buyers and it was intended to replace multiple tools actually. And so they created this huge dashboard using some really great Nimble data sources we have and a really great BI tool that we have and then they set up a session in which they had multiple tables and at each table they had a bunch of business users and leaders and then they put a couple of BI people at each of these tables. And the way the session went, it was a whole day or half day session, the people on the BI teams were there silent observers and they literally just watched the business users interact with this dashboard and they took notes. They didn't tell them what to do, they didn't give them any answers if they had questions. They literally watched to see where are they struggling, what's not intuitive, what's not making sense and so they used that to help them learn like what do they need to adjust before this thing goes live. And one of the biggest things that they found was that they had way too many features on the first version. People actually were saying like, this is way too many and it was really surprising to the BI team, right? They were like why I totally thought these were all things that you needed and those were assumptions they had based on other models they were looking at that actually never had provided value. And that goes back, that goes to number six, which is measure for value. If we are, and I'm gonna skip to the next slide because it's what I wanna talk about. If we're measuring for value along the way, we can start identifying assumptions before we start building the next thing. So if we're measuring for usage and engagement in return users and customer happiness and even doing correlations or regression analyses on how it ties to what we're getting at the end, we can already start informing our next steps. And then we wait to move forward to do the next thing until we actually have the results of what we delivered. And I think that's a huge secret, right? And all those cases and all of those businesses, those founders didn't start keep building out their next step until they had learned from their first experiment. And that's something we all have to stop doing. We have to stop building another card or another dashboard or another report or another algorithm or whatever it is until we've learned from the first thing we've delivered and our users have had the chance to give us feedback that's quantitative and qualitative. So with that, what questions are there? Oh, wait, one more slide. The IQs to Agility Success. And I'm gonna run through this really fast because I think we're almost out of time. So here's a few things that we've learned that are really important for our business intelligence organization to be agile or to deliver with agility. And we've got a long ways to go. But number one, you need nimble and robust tools that you allow you to explore the data, right? So we've got stuff that we've built in-house and stuff that we've bought and these things allow us to do things on the fly. So that session I talked about where we had buyers sitting at tables with the DI teams, like they were actually making changes on the fly and asking for feedback. Everyone needs to agree that data is for the masses. We have a saying in our organization that data is a utility. So think of it like water. Everybody should have it and everybody needs it. Now think about how you're gonna make it available for everybody. You need customers and leaders that are willing to ask questions and learn together. Carol Dweck has some great stuff on growth mindset. I think it's amazing. Sakya Nadella, who's the CEO at Microsoft right now, also has some really great stuff on there. I think he's leading in a really great way. And then you need a passion for learning and innovation. We have some really great stuff happening inside of Target that has kind of just organically grown up. So we have an analytics network that is completely 100% people just volunteer to be a part of it and they just innovate in their free time. Last year we had a product DNA conference and kind of the whole purpose was to study products that had failed and succeeded and then we brought in external speakers. And then we do product demystification and thinking deep dives, learning forums and weekly sharing. So that is actually it now. I think we're almost out of time but maybe if there's a question or two we can squeeze it in. Okay.