Reviving Old-School Customer Experiences Through Modern Data Strategies, Featuring Edward Chenard

Eric Dodds 00:06
Welcome to the data stack show.

John Wessel 00:07
The data stack show is a podcast where we talk about the technical, business and human challenges involved in data work.

Eric Dodds 00:13
Join our casual conversations with innovators and data professionals to learn about new data technologies and how data teams are run at top companies. Welcome back to the show. We’re here with Edward Chenard, who has had an extremely illustrious career across both large enterprise and startups in data. Edward, I have so many questions. Can’t wait to dig in with you. Thanks for joining us.

Edward Chenard 00:42
Oh, thank you for having me. All right.

Eric Dodds 00:45
Well, can you give us a quick background, sort of, you know, just a brief overview career and what you’re doing today in

Edward Chenard 00:50
data? Yeah, I’ve spent most of my career in analytics or as a market analyst. The last 13 years, I have been building and running data teams that include engineering, data science, and analytics at Fortune 500 companies and startups . Right now, I am looking for the next big thing to do, talking to some companies right now. I think it might be getting close to being so awesome.

John Wessel 01:15
So before the show Edward, we talked a little bit about PNL ownership, or data team. So I’m really excited to dig on that topic. And then what kind of topics do you want to talk about? Yeah, the

Edward Chenard 01:26
Building teams is really important. A change in how, like, AI, is changing the dynamics of the space, as well as, you know, areas of like personalization. It’s interesting. It’s one of those things that, every few years, comes back to the surface as an important topic.

John Wessel 01:42
Yeah,

Eric Dodds 01:43
awesome. Well, let’s dig in.

John Wessel 01:45
All right, let’s do it.

Eric Dodds 01:46
Edward, I’d love to start out by talking about your experience as a data leader in the large enterprise. And one of the things that you’ve talked a lot about, you know, publicly and I’m sure privately as well, is this concept of PNL ownership by the data leader. And so the first question I have is, you get a ton of pushback on that. Well, actually, before we start to explain that concept to us, why are you so big on P&L ownership for the data leader?

Edward Chenard 02:19
Because to me, I think the data leader is really a business leader in a lot of ways. And just like any business leader, they should own a P&L, they should be looking at profit and losses. They should own the tools and the relationships around those tools that then makes them a true business leader. Because what happens is, like when I was an analyst, you’re spending your days creating the analytics reports and finding the insights needed, but then as you move up into a leadership role, that becomes a very different role. And I think that’s something that a lot of people don’t understand, is the leadership role is not really a role that represents the analysts, the data scientists, the engineers in their day to day function. They’re the ones helping to expand the market, expanding what the team does is really important, because you need to show like, Hey, this is how our team is impacting the organization. This is how we are engaging customers. This is where our money’s going and why we’re spending it that way. And if the leader isn’t able to articulate that, then the team really does not look like a serious team to the rest of the organization. Yeah,

John Wessel 03:38
So from a cost standpoint, nobody’s going to fight you as a data leader to own the cost part, right of the tooling and infrastructure. But from the profit standpoint, I’m curious to dig in on that. What are the things on that side? Because that’s harder, right?

Edward Chenard 03:52
Yeah, that’s where the fights always are. And this is why I often take a product approach to how I run the teams, in terms of, we need to build our own stuff. And I’ve done that at all the companies I’ve worked at, because we need to show like, Hey, we’re actually contributing here. So like, in my last company, we actually were, like, it was interesting. They were doing all this custom reports for customers for free. And like, we had one customer, they said, We want all our reports in cases of wine, because they were a wine because they were a wine distributor. I was like, you know, much time that’s going to take? Why the hell are we charging them for that? Right? Yeah, we could bring in like, 10s of 1000s just on these custom reports every quarter. Why aren’t we doing that? Now, all of a sudden, it’s like, Hey, I just paid for my analyst based on these custom reports being made, right? And then it was like looking into other things, like, well, now all of a sudden, they want consulting too, because they get these reports. And, you know, people in logistics often they’ll put somebody that doesn’t have the experience, and they’re learning logistics. So then you get. Of them are reporting. They’re like, I have no idea what I’m looking at. Well, what’s a consulting business? Charge them like, you know, hey, for this project, we’ll charge you 15,000 for it. Now, suddenly, I’ve got another revenue stream coming in. Or it’s like, we’ll take, you know, there are times where we’ve had to take products from the product team because they were just stretched thin. So we take the ownership of it now we’re building that product out and expanding the revenue for it, working with sales. And this is where also the dynamics of the team changes. Because, yeah, I’m just being honest here. A lot of people that work in data and analytics are introverts, and they like being in the background because they don’t want to deal with people, but I’m like, sorry, you need to deal with people because you need to understand what’s not in our database, but what’s being communicated to sales management. So I’d have OKRs saying you have to have X number of customer engagements each quarter, and that that did really help, because also they were realizing, because I’m dealing with a lot of people that are, like, in their 20s or 30s, that don’t have a lot of that customer interaction, especially the last four years where we were going remote, and I’m like, right? I’m like, Hey, I actually feel sorry for these kids who are out of school, like, when I was their age, I’m in the office, I could watch other people and see the weapons when they do things. Sure works for them. What are they doing? Zoom call. Then when that’s over, they get nothing.

06:28
Yeah, yeah.

Eric Dodds 06:29
What pushback do you get on this concept? Because you’ve said that there’s a ton of fights that develop around this, and you know, we’ve seen some of that online. What pushback do you get?

Edward Chenard 06:39
I get told that you don’t need to own it to be the leader. I’m like, Well, there’s a difference between a big C and a small c. If we’re talking like, Chief Data Officer, small c does not own the P and L. Big C does, because now they’re being told like, Hey, what are you doing to drive revenue this quarter? If you’re not being asked that question, you’re a small c as a chief data officer. And a lot of the people that don’t want the stress of that ownership are saying, why? Don’t really need to own it. I’m just going to build dashboards, or I’m just going to make sure that my uptime on my data pipelines are good. And I’m saying, Well, look, you want to have the respect of being a big chief data officer, then you need to have the responsibility too. But a lot of people want that responsibility, and that’s where the lights really come in. How

John Wessel 07:30
do you carve out that role in between, like, say, a company has a CFO and a CIO CTO. How do you carve out that role between those two positions, like as a chief analytics or chief data officer, have you seen it done?

Edward Chenard 07:46
The CFOs never have been a problem for me. They tend to be like, yeah, what you’re doing is different, yeah, yeah, the CTO CIOs, yeah, there. There tends to be friction there and with their teams. But what I like to point out is like, you know, if they’re running software engineering teams, that’s a very different mindset. Software Engineering is deterministic. It’s very much like, hey, we have our process here. We go through it. Data and Analytics is very much trying to understand what is the problem, and then the solution, and then, okay, then you start to have some similarities with software engineering after that point, but there’s a lot of work that takes place before that, and that problem solving is where I carved the space out, because the mindset of your data engineer, data scientist and analyst, is different from the software engineer, yep, and what they do and what they focus on is very different. And even the tools being used, and I think that in a lot of places, they place the data team under the CIO, C CTO, because they think, Well, they’re both working with data, so it’s the same, right? Right? But no, the mindsets are very different. Often the problems they are working on are very different, and just the way the teams communicate and get work done is very different. That whole issue is not solved, and I don’t think it’s going to get solved anytime soon, until more companies realize that really those teams are not the same. It’s like, hey, sales and marketing is the same because they talk to customers. Well, you know, if you go to any big company, you know, that’s not the case, yeah,

Eric Dodds 09:29
Over the past decade, have you seen any shift in the mindset around that? I mean, data has always been important, but I think especially, you know, just with some of these, you know, developments over the last decade, both in terms of infrastructure, right? So it’s cheaper to store a lot of data, which means you have more data to work with. You know, there’s, you know, the tooling has changed, but also the mindset around it, right? I think that there’s also been, you know. And of course, the cliche is, you know, Gen AI. Has, you know, highlighted the importance of data, right when? Of course, it’s really been important all along, but in terms of understanding the unique nature of working with data and how to actually turn it into some sort of value for the business, have mindsets across the organization changed at all? Or do you think we’re still in a similar place to 10 years ago?

Edward Chenard 10:19
It’s changed, but actually not in a good way. Oh, interesting. Okay, I

Eric Dodds 10:24
gotta hear about this.

Edward Chenard 10:25
So if you talk to, you know, like I go to, there’s like this data leaders group here, where I live in the Twin Cities, and you’ll meet up, like once every three months, and you can see, like, this big difference. And there’s also another bigger one called mini Analytics, which I used to be a part of. They put on data conferences, but once I got married, got kids, I couldn’t dedicate that kind of time. But I sometimes go to their networking events, and you’ll see that the people that have kind of been in the trenches for, you know, a number of years, I’d say, like, once you get that, like, seven year mark, you start to change your mindset, and you’re like, yeah, there’s a lot more nuance here than before. And you know, I was like that too when I was younger. You know, I used to think like, hey, if I follow the process correctly and I do the analysis right, then clearly you should listen to me, right? Yeah, you know, when I brought that up to my dad when I was starting out, he did that same laugh too. Took me years to realize why, why he laughed at me. And what he said was, well, there are times yet you’re right, but there are other times I would rather trust somebody who has 20 years experience and knows the nuances, and that’s just it. It’s like, if you’ve been in the trenches and you start to see the nuances, you start to realize, hey, some a lot of the stuff that we think is true is completely wrong. That’s what I see when I go to these meetings. It’s like the ones that have been in there a long time in the trenches, they’re like, yeah, how I thought in 2015 is totally wrong today, right? Get into the newest. That’s why I’m saying like, but when I talk to the younger kids, and, like, my last job, I had some of them critique me, saying, like, hey, you know Edward’s looking at the strategy. And he’s looking at, like, talking to customers, and I just want to know, like, hey, how do I, like, employ this model here? And I’m like, well, is that even the right model? You went on Google, looked it up and decided, well, I’m going to pick that one on the list because I like it. And I’m sitting there saying, Well, does this fit our customers needs? Does this fit the company’s needs, how, who’s going to maintain this, and who’s currently maintaining this? You haven’t answered any of these questions yet, yeah. And if the thing is, like, the deep thought, just, it’s kind of lacking, and I don’t blame anybody for that. It’s just kind of like our use of technology, and, like, you know, you know, you go out there and there’s Tiktok videos, YouTube shorts, this shallow thinking and this ability to think deeply is just diminishing over time, and you can see it in the books out there too. I rarely buy a new book on data and analytics because they’re just so shallow. I’m rereading my old books from like a decade plus. I’m like, why am I getting so much more information, better thought in this book that’s from 15 years ago, that a book that came out six months ago that, in theory, should have more details in it, because we’ve had more experience over that time period, right? Yeah,

John Wessel 13:39
I think there’s another vector you mentioned, wrong thinking, right thinking. I think there’s this other vector of like, I’ll call it like, usefulness. Like, hey, what can we safely ignore? And how can we focus on the right things? Because I think people can really get caught up in things like, oh, well, we’re right, this is wrong, this is right. And the question might actually be like, Is this even relevant or useful? I’ve seen that a lot, yeah, yeah, yeah.

Edward Chenard 14:04
And that goes back to like, well, who are you really doing this for? And unfortunately, I’ve seen, like, particularly data scientists doing this where, yeah, they’re really padding their resume. You know, I’ve gone into companies where it’s, like, I remember when deep learning was a thing, and all sudden, like, you know, Isaac ch Robson also had a bunch of data science. Was like, we got to do deep learning. Like, why? You know, the problems we’re solving here do not require that. And then he was like, well, you then, you like, start talking to him. They’re like, Yeah, well, you know, I heard some guy who knows somebody, who knows this person making seven figures, doing deep learning, like I’m attached to that. It’s just

Eric Dodds 14:47
Yeah, yeah. I’ll never forget really early on in the show. I mean, this is like three years ago, probably, that was probably one of our first handful of guests. Brooks can probably tell us. He can look it up. But it was the CTO of this company called bookshop, which is sort of like a it’s an online book retailer, and they do a number of different things that are kind of cool, anyways, different from Amazon, different from Amazon. Yes, no, they like give money to independent bookstores for, oh, it’s so many, it’s really neat. And actually, it’s like, pretty gigantic now, I think, Huh. Anyways, we’re talking with this guy about their stack, and he’s like, I’m going to be really honest. Like, it’s really boring. Like, it’s very boring. It’s pretty simple. Like, that’s it, you know, like, we have a couple pieces here. And, like, you know, like, there’s one really hard problem, Episode Eight, september 30, 2020, there we go, Rex. Thank you very much. Mason Stewart’s actually a guest, great episode, if you want to go back into the archives. But he’s like, you know, we have one really challenging long tail data problem to solve around some sort of classification ID or something, which is data that they got from, you know, book classification, some public, you know, classification system that’s just a nightmare to deal with, right? Because of all the, you know, it’s a public data set and whatever, all whatever those things are. And he’s like, that’s kind of hard, but, like, we figured it out. And he’s like, it’s boring, but it does the job extremely well, you know, and that’s what the business needs. So anyways, that really resonates, and it just always reminds me of that, like, you know, I’m not gonna, you know, I’m not gonna do something fancy, because we don’t need it. Like, we just don’t need any fancy things. Yeah,

Edward Chenard 16:32
you know, when we built the personalization platform at Best Buy, you know, we were, like, updating our catalog, like, every 20 minutes. And for most products, that’s a total waste of time. So when you get the new Apple, whatever that comes out, yeah, that’s what’s useful. Or during, you know, like holiday season, what’s funny when I went to target their personalization team was, you know, struggling on some stuff, and they were like, we’re gonna go to Solid State Drive servers and update every six seconds. I’m like, What the hell for up to a minute, you know instant results. I was like, Well, okay, but your customer works in human time, so they’re not going to make a decision in six seconds . Do they buy that beach towel or not? And the cost that you’re going to put into that is just not worth the squeeze. So it was just like, you know, but they wanted that. They were so into the tech, they were in love with the tech, not the customer experience, yeah, well,

John Wessel 17:37
and I think you just made a great case for the PnL ownership right there, because, because, if you have P&L ownership, then that matters to you directly, right? Like the cost benefit. But if it’s, if you’re just part of a group, part of a cost center, then it’s like, yeah, I don’t know, this is how much it costs, yeah? Like, you know,

Edward Chenard 17:52
It was funny. So I’m very much into telling my teams, like, everything I know when I said, when I was at Best Buy, and we’re going over the roadmap. And of course, I told the team first, and one of the developers, he just raised his hand. He goes, What’s ROI? And why do you keep mentioning it? Hey, I have an MBA so, you know, hey there. Yeah, I’m thinking like, well, everybody knows what ROI is and why it’s important, right? Because, you know, it was drilled into me at school, but here I am dealing with somebody who maybe he took a bit, you know, general business class in school, and that’s it, and so explaining it to him, and he’s like, Thanks, now I actually know why we’re doing what we’re doing here.

Eric Dodds 18:37
Oh, yeah, that’s huge. Can we dig into the Best Buy project a little bit. So that was, you know, before the show, we were talking about how there was very little in the way of, you know, sort of true personalization, and then it became an extremely large source of revenue. But just take us to the beginning. When you say personalization, I mean, that’s such a hot topic. And, you know, marketing, you mentioned that it comes up every couple years. Of like, okay, personalization is, like, the new thing, right? And it’s like, well, like, it’s been around, you know, since before computers, yeah, since the beginning of time, you know, since

John Wessel 19:13
at a cafe, somebody wrote your name on a cup. Yeah, it

Edward Chenard 19:16
I’d always tell a team. I was like, we’re actually bringing what’s old back like we’re trying to recreate when the shop owner knew their customers knew, like Mrs. Smith comes in on Tuesday and always makes herself a chocolate. Yeah, yep, that’s what we were recreating. So personalization to me, I actually had somewhere I’ve got this presentation, like, 18 ways to personalize. But you know, for most companies, it’s recommendation engines, which isn’t true personalization, you know, the email marketing stuff. And then what we were really driving towards was we actually had three levels of personalization, which was crowd driven for. Zona driven and true one to one personalization. When I started at Best Buy it 2011, okay, they were using rich relevance. And basically what happens with a lot of the algorithms is they get what we call flatlining. You’ll see this period of revenue growth, and then all sudden, it just flat, flat. So okay, so best buy it, hit that and they were like, Hey, rich relevance, help us get more and rich relevance like, hey, our stuff’s all proprietary. So they were like, Hey, okay, we’ll just do our own stuff. Then, right? Yep, oh, that’s how I got hired to run that. Now, when I started, it was literally just a team of me working. I worked in a group called Emerging Technologies separate from it that matters down the road. When I got there, it was like, what are you building? I was like, Well, I’m going to go out research what everyone’s doing and come up with my own approach to how we should do this that’s best for Best Buy. So I was able to, like, reverse engineer other companies at the time, the guy who ran Amazon’s personalization platform had a really big ego. I found a forum where he liked to hang out. I intentionally started saying stuff wrong so he would correct me. He literally, like, told me how personalization is run at Amazon.

John Wessel 21:27
That’s amazing. That is really funny. And

Edward Chenard 21:30
So what I did was, was looking around and I realized, hey, we need, like, some kind of big data thing to play with, like, you know, MongoDB, you know, react, RabbitMQ. And then settled on Hadoop because that was the only thing that did not break when we threw our test dataset, which was to throw the holiday testing dataset at it and see if it jumped. Yeah, that was the only thing that didn’t. So we built a really simple stack. Now, at this point, I got in as a business analyst and a database engineer to help me build this, and we went over to the data center in, you know, across the street, and just on commodity hardware, built it out ourselves. Now, there wasn’t like, you know, any Coursera courses or stuff that nobody was out here, you know, we couldn’t even get like, you know, Hortonworks or Cloudera reps to come out to visit. I basically bought a book on Amazon by this professor at Stanford on building big data sets, and that’s what I use to build it. Nice, wow. We got it working, and I was like, Hey, I’m going to do all open source. That was sacrilege at Best Buy at the time. You don’t do open source. But I said, Hey, I’ve got stuff that works on commodity hardware. The data center is about to get rid of all these servers. Why do you just give me the servers so you don’t have to pull them out? Data Center guys were like, Yeah, we’re all good with that. And then it was like, No, you can’t do that. You can’t use open source. So they brought in SAP Teradata to bid on it. It was actually kind of a good thing for me, because these guys are at 20, 30,000,018 20 months just to build the big data structure. And I just came back and said, Give me one quarter. We’ll be ready for production. Wow. And I said, Give me half of what they’re asking for. So they did. And yeah, we were ready in 60 days. Had our first algorithms out there, you know, by the end of that quarter. And as we started and gave me the most restricted view of how I can credit a sale to our products. In the session, you had to have bought the product, so if you came back, like, two weeks later and bought it, oh,

Eric Dodds 23:50
wow, that’s really rigid,

23:52
yeah, and

Edward Chenard 23:54
hired some data scientists and again, well, now we’re into 2012 and I’m like, I have no idea what data science is. You have to help me figure that out, and we were doing that. Now it’s the year of the three CEOs at Best Buy. So in 2012 they had three CEOs, and that’s important, because they basically were ignoring us. I was like, hey, everyone’s worried about what the next CEO is going to be interested in. I’m just going to build this. And, yeah, I hired my own team because it wouldn’t help me. So I basically hired a bunch of contractors to come in to build it for me, and we were just cranking stuff out every two weeks, which was unheard of at Best Buy at the time. They were, you know, a sprint for most teams for four to six weeks. We’re doing it too. We got the process down where we could build, test, launch an algorithm, a machine learning algorithm, in one month. Wow. Well, and then we just start putting them out there. And I go to the call center and I see like, hey, they could use these algorithms too. I go to Best Buy for business, Geek Squad, even the distribution part of the business, we’re just spreading our algorithm. Everywhere, and we’re collecting all this data. We actually got to the point in 2013 that we collected more data than the rest of Best Buy combined. Really,

Eric Dodds 25:09
yeah,

25:10
wow. And what,

Eric Dodds 25:14
Edward dug in a little bit to like you say, you deploy these algorithms, and you talked about those three different types of personalization. What was the algorithm doing? And I think it was, you know, a crowd.

Edward Chenard 25:26
We had a crown persona and one to one, right? So to get rid of the cold start problem, we were using the crowd driven one. So when you go online, you know, people who bought this also bought that type of algorithm, yeah, what that would do is give us a pattern so back then, we could tie it to a device, and we’re saying, Hey, I don’t even know who this person is, but I’ve created persona profiles based off of different behavioral patterns I’ve seen, and I’ll match them in and start recommending products based Around the persona. So we use the corporate persona because, you know, I talked to the teams that built that in the UX team and CX teams, and I was like, they did a great job. They’d go out, follow people, and sit down with them. I mean, it’s kind of weird. They’re sitting there, like having, like dinner with these people in their homes, not the type of thing for me. But

Eric Dodds 26:21
They did the real work.

Edward Chenard 26:24
That’s yeah. So I was like, Hey, I’m gonna use these as our personas, because they did great work. And, you know, then they would bring us in because they had what we call, we nickname it, the interrogation room. Best Buy has one of these rooms, like, you know, you seem like the cop shows the one way mirror thing. Somebody asking questions. It was nicknamed the interrogation room. You could sit there and like, watch, like, how people would interact with the algorithms, and see how their responses are. And it was great. So we would figure out, like, the customer journey once and see, like, Hey, where are the points where they’re dropping out, and what algorithms might help them to stay in and get to that purchasing point and make the purchasing decision. Because when it comes to electronics, your average person looks at like, 10 different websites before they make a purchase. Sure, our own research was like, you could put Bob’s electronics on the Best Buy. Nobody cared. What they were interested in was the product they were buying. Now we were also like, hey, how do we make it? How do we take it from a commodity purchase to an experience? So that’s why the platform was called the experience platform, because we were trying to make it so that the act of purchasing was an experience in and of itself and a positive so that when we do the follow up remarketing, trying to get you to buy the accessories, you’re going to be like, hey, it was a good experience buying the main product. I’m going to go buy the accessories there too. And then on the one to one, we were trying to get to that like, Hey, who are you buying for? You know, if it’s not for yourself, what events are happening? Like, if you’ve got students in your life, hey, August, we’re hitting you up with the back school stuff, all that sort of thing. Yeah, you know the Christmas thing, anniversaries, making it a true we’re looking at your past purchases. We’re looking at your current searches, and we’re trying to figure out your life events to see like, Hey, what’s going to be hitting you? Because most people, except for appliances, their purchasings tend to be pretty much the same across all product lines. Appliances are different because, hey, if your refrigerator goes you’re just going in and saying, Hey, I need a refrigerator. Interesting remodeling. You might be looking at it for months before you make a decision, interesting

Eric Dodds 28:47
and so. But if you think about something like audio equipment, or, you know, you know, a TV, or something like those behaviors, your purchasing behavior and research behavior tend to be the same,

Edward Chenard 29:00
yep. And then just to look at you, then you have to look at like, little nuance things too. So this was a fun one. The analytics team was struggling because New Hampshire, which has the lowest population, when you look at Maine, New Hampshire, Massachusetts, during Black Friday and Cyber Monday, they would have the most sales in the store, but only on the borders. So you look at Portsmouth next to Maine and Nashua next to Massachusetts, those were the highest performing stores. And you know, they’re scratching their heads, and I’m sitting there going, well, you know, I grew up a few years in New Hampshire as well. They have no sales tax.

John Wessel 29:46
Yeah, that’s what I was gonna guess.

Edward Chenard 29:49
I’m like, everybody’s crossing the border because they just save themselves 10% just by crossing the border. But we were sending them emails, hey, go back to the Nashville Portsmouth store. Or like, if you bought a DSLR for the training classes, take their home address and then map it out to the nearest Yeah, oh, now it’s like, hey, well, it’s closer, so it’s more convenient. Or, like, when we were looking at locations, so somebody who’s in South North Dakota, they’ll try two hours to go to a store because they kind of have to, yeah, right? Atlanta, Georgia, they won’t drive five miles because of the traffic. So, like for the call centers, if you’re talking to somebody in North Dakota, you say, Well, hey, go to Bismarck. Go to Fargo. If you’d like to pick that up today, for somebody who’s in Atlanta, if it’s more than five miles, say, Hey, do you want us to ship that to your house? Yeah, interesting. Would it come if you’re using that, that that location in, like, the environment in which they live in, like, what your recommendation is?

Eric Dodds 31:00
Was it hard to so there’s the algorithm piece, and there’s getting, there’s, it’s getting those things right, you know, where it’s like, okay, you’re solving this problem around, you know, you know those cases that you just talked about to create some sort of great experience for someone, but then you have to get that, you have to sort of put that data to work in that okay, there’s probably a website component to it because it’s in session. There’s probably some sort of messaging component around email, but it sounds like Best Buy was a really interesting environment at the time. Was it hard to go to those teams and say, like, hey, you need to actually overhaul your email campaigns to use the outputs from this algorithm or to work with the like user, you know, website team or user experience team,

Edward Chenard 31:48
yeah. So that’s where, like, all the corporate politics comes into play. Because, you know, again, like I said, a year of the three CEOs, everyone’s like, vying and jockeying for position. I’m really an upstart. So there are established teams, like the.com team that runs the main website. I mean, this is a team that takes up like two or three floors in Sure, yeah. And here I’ve got like a small section on one floor that, like, you know, if you blink when you walk past us, you totally miss us. So, yeah, it’s but it’s learning like, hey, what motivates them? So, like, when emerging technologies we, you know, me and a couple of others, we actually migrated on became the omni channel team, because we went from just purely digital to digital and physical and there, you know, like with the stores. Now, if you talk to the stores, they’ve got two, three year road maps. So they’re like, Oh, great idea. Yeah, we’ll implement that, you know, cheap, yeah. What I would do is like, hey, I need to test something out here and go to the store managers. Like, hey, I’ve got something that can help you make more money than the store manager in town over, they’re like, Yeah, I’m listening.

John Wessel 33:05
Yeah, that’s great. So what would be an implementation for a store? Because we all think, I think of like the.com and the website, obvious implementations. But what would be a store? Well,

Edward Chenard 33:15
the like, the vending machines you probably saw on like airports that would Oh, yeah, okay, the one

John Wessel 33:21
thing that was, what the stock in the vending machine, right? Yeah, we’re deploying this stuff, but,

Edward Chenard 33:27
you know, the actual stores themselves. The thing that was really interesting was the tablet experiment. So a lot of people, you know, again, talking to this, you know, the customer insights team and so on, they give us feedback, like, people don’t trust the high school college kid on this $10,000 electronics they’re about to buy. It’s like, Well, hey, give them a tablet that gives them, like, the reviews, the specs, you know, everything you’d want to look up. So then the employee can be like, Hey, don’t just listen to me. Here’s all the information. Customers loved it. They loved the CFO and hated it. Oh, she just thought people were gonna, like, steal the tablets. It’s like, Oh, okay. Oh, they’re formatted for us. And, you know, it’s not like you just walk out with and just start using it, because, you know, it was stripped down to just the best buy application, yeah. And so that was one of those things where I was like, great idea, but, yeah, it died because somebody higher up had an opinion, and she was just too stubborn to change her mind. Wow, sure. Like the fact that the customers and the employees loved it. The other one was, we had this big like, so when Best Buy had the small format stores that you would see in malls, we put this, this kind of kiosk machine in there. It was basically like a big touch screen TV. Yeah, I’d go to the store managers. They’d be like, don’t get rid of this. We like this. This helps us a lot. Again, they were like, finance was like, Nah, too expensive. That’s one of the reasons why the small format stores died because they were putting costs ahead of the experience. And my logic was, if you create the right experience, you’ll create enough revenue to cover the cost, as long as you’re managing the cost, but don’t sacrifice the experience just for cost savings. So

John Wessel 35:28
So in that case, what was the experience? Then you said, there’s like a touchscreen, and like customers would interact with.

Edward Chenard 35:34
So because it’s a small format store, it didn’t have a lot of products. So what they would do, the employees would go to the touch screen, work with the customer, figure out exactly place the order, and then they

Eric Dodds 35:45
could or, or, yeah, okay, yeah. Super interesting,

Edward Chenard 35:51
yeah. You know, minor things, like we also did the in store pickup and curbside delivery. Sears and Krogers were, like, the only ones doing that before that. But they say things like, like, if you went to a Kroger, it was like two hours before your food would be already right? Wait a minute, what’s this experience like for most people? And it’s like, carry out pizza. Yeah, yeah, right. Let’s order some pizza and go pick it up. How long does it take to be ready? About 20 minutes. So why can’t it be ready in 20 minutes for us too? Yeah, our target. When I went to Target, they were just like, doing, like, a brain dump out of me to, like, how to do that there too. I’m like, they hadn’t gotten that in place before lockdowns. That would have been a very different experience for the target.

John Wessel 36:38
Oh, Target’s really good. I think, in my opinion, they’re one of the best at that experience. Yeah,

Edward Chenard 36:45
yeah. And they basically just learned that by, you know, that’s how I hit Open targets. They basically, like, threw a number out. I couldn’t say no to

Eric Dodds 36:53
There you go. And to close out with the Best Buy story is amazing. But of course, we have to talk about AI before the show’s over,

John Wessel 37:00
we would have failed everyone.

Eric Dodds 37:03
But what, how do, what’s the conclusion of the Best Buy story? So, you know, obviously, some things worked, some things, you know, died, but what was the impact on the business? Oh,

Edward Chenard 37:14
I mean that the whole data science, data engineering, personalization, one when Jolie came in, who was the guy credited with the turnaround, you know, I get asked to, like, present what we’re working on. And they’re just like, you know, the executive team’s like, Is this live? I said, Yeah, everything I’m showing you is live. They’re like, Oh, you’re the first person to show us something that’s live. Wow. So, the whole idea of two week sprints took hold. Open Source. Lots of other teams started doing that. The idea of focusing on the customer was my takeaway, not most people’s takeaway, but to me, I was like technology for the sake of technology is a waste of time, and I saw many fail because they did that. But if you focus on the customer and their customer experience, that’s what really drives it, and then technology becomes the easy part in that, because people are not easy. I mean, most people make an emotional decision, then look for a rational excuse for it, but the end result was that the whole group was bringing in over a billion dollars a year when I left, wow. I mean, from going from almost nothing and one person,

Eric Dodds 38:33
incredible, and no wonder, you know, it’s not under target for one of you. Well, it’s

Edward Chenard 38:39
not just the technology. It was also the way you manage things. Like I said, I use a very emergent strategy approach. I’m very proud that a number of people who have worked with me have gone on become VPS C suite managing directors, because they were in an environment where I allowed them to think critically, solve problems and get polished in terms of how they present themselves.

Eric Dodds 39:07
Love it, all right. We have to fit an AI about data, all right. John, what? Ai question? Yeah.

John Wessel 39:18
Edward, so I think we talked about this before the show. We talked about software development, the history there very deterministic. And now and then we start talking about data, and how data and data teams are less deterministic, right? They’re dealing with fuzzier problems and fuzzier outcomes. And then AI comes in, and you’ve got a bunch of like, historical, deterministic technology teams being said, Hey, implement this AI thing, right? And I think it’s safe to say it’s often not going well. So my question to you is: And sure, there’s probably going to be some changes on, you know, on the traditional, you know, deterministic side, anyways. And like traditional, it. So my question to you is, our data teams may be better suited for some of these AI implementation, AI problem solving, because they’re used to a less deterministic working style.

Edward Chenard 40:13
Yeah, I do think so. For me, data teams should be problem solvers first and foremost, whereas I know a lot of software engineers will say they are too, but the way I see the teams work are very different. A good example is when I was at Best Buy, we were asked to be part of the beta test for a new version of Power BI, and they brought it in. They brought in my team. We’re sitting there asking questions left and right, and it is just like, next step. Okay, next step. I’m like, No, aren’t asking questions. That, to me, was the difference in the mindset right there. It’s for us, when we’re given a problem, it’s Hey, is this even the right problem we’re solving? Was this frame, that’s where we start out with. And you know, Scrum, process, Kanban, they’re great at a certain point, but in the beginning, there’s really a big difference between software engineering and data and analytics. And when it comes to the data engineering side, you see some people like, oh, well, data engineers should really be in it. And it’s like, well, it depends on the org, but if you’re solving problems on the analytics side, you need those data engineers sitting day to day with your analysts and your data scientists. And when it comes to AI, like I mentioned before, I do not use open AI or copilot or Gemini, I experiment with Claude, but the way I use Claude is really conversational. Whereas I find a lot of people, when they start using AI, they’re just like, do this. Now, do that. We have an ongoing conversation on various threads, because to me, that’s the best way to use it. And I think people that work in data and analytics, they’re used to that, asking questions, why the answer, having that conversation, going back and forth to find the answer. So that mindset, I think works quite well if you use AI correctly, yeah,

John Wessel 42:18
yeah. I think that makes sense. But since AI is becoming so prevalent, would wouldn’t you think that maybe that deterministic mindset, at least to an extreme, is not going to work for anyone, like, long term,

Edward Chenard 42:30
yeah, but at the same time it’s so entrenched, it’s going to take to go away? Yeah?

John Wessel 42:36
And there’s and there will be years and years of life, work to do, and things for people to do, of systems that are deterministic and need people to work on them. And so I don’t think that’s going away overnight.

Edward Chenard 42:48
And I think there are fields, you know, like, you want something deterministic when it comes to, like, your finance management, yeah, definitely, you know, you’re in healthcare. So there’s always going to be that space. And, like, you know, when I hear people saying, Oh, we’re going to use, like, you know, AI to be like, a replacement for nurses, I’m like, yeah, I hope I never end up in that hospital. Yeah. I mean, I use it, and I’m just like, if I use it for things that I’m familiar with, I just wanted to help me speed things, yeah, so that I understand what the output is. But if it’s something I’m not familiar with, how do I know to call it out when it’s wrong, right? That’s what I see people doing. Or they just get lazy, and they don’t really look at the responses, you know, like, I’ve used it, yeah, when it came out, and I was using it for, like, you know, writing cover letters and, hey, make my resume adapted to this job. And, you know, I ended up with PhDs from Stanford working at Facebook or Google, which I never have, wow. I’m like, No, I don’t want you to make stuff up. I want you to facts, like, use the keywords that’s in the job description, right,

43:58
right? Yeah,

Eric Dodds 44:00
super interesting. Well, I think we’re at the buzzer here. But Edward, one more question for you. You mentioned that you’ve been rereading some of your data books you know from a decade plus that are a decade plus old. Do you have a couple book recommendations for the audience on the ones that you know you return to most often.

Edward Chenard 44:23
I can tell you the one I’m reading right rereading again, the connected company, very interesting one on like, it’s by Dave Gray. I like the book because he uses a lot of different concepts so I believe I read this back in 2012 or 13 concepts. I like it. They’re still relevant today. I would see another one here that I liked. I actually met this guy about the same time, the intention economy, by Doc surles. So I read this one over the summer. Again, if you’re looking at personalization, I think it’s a great book. There’s a companion book that’s more technical, called the live web, written by a professor down. The University of Utah, but it’s a great book for like, hey, how do we actually create an economy that’s much more driven by the consumer? And I think it’s actually very relevant today, whereas, you know, we see companies that are really shareholder driven where, you know, they’ve got they had record profits, but it wasn’t good enough, so they lay a bunch of people off. And I’m like, that’s not sustainable guys, right? You know? And I’ve been having these conversations with a lot of people, it’s like, hey, the younger generation in their 20s, they don’t want to be doing like, what we’re doing 20 years from now. They want to have a work environment that’s much more for them, and much more, you know, satisfying in terms of giving them a rich life. It’s not all about like, Hey, I’m just here so some shareholder can make money. Yeah, I think it’s a great book talking about like, hey, how could you do that? Actually, I’ve been talking to the founder guy. We actually went to the same school together. Thunderbird, and he has started a company bronze. He started a company called Hiring Hire Humans, where putting a lot of those concepts in place, about, how do you improve the hiring process? So that’s something we actually were shocked about this morning. Those are the kind of companies I’m looking forward to see, like, becoming coming out there and driving things, because I things change, and I’m looking at those things up like, hey, how do we adapt to, you know, the younger generation and what they’re looking for, not even the younger generation. I mean, I want to work remotely. I’d like to go work at a ski resort on my computer. I don’t want to have to sit in the office all the time. So I think it’s just new mindsets coming in. So I’m looking for, you know, those books. I look for these other books. Let’s see here. I don’t have it with me, but some of the books are just like, how do you engage with customers in different ways? How do you look at, how do you look at the perceptions people have about what it is you’re creating. So I, like I said, that whole product mindset I do bring to the table. So looking at everything is like, you know, going back to this conversation on hiring humans, it’s like, hey, the job is a product. The person’s a product in some way. So how do you do it? How do you make that work better? Ben and I were just talking about, it’s like, well, most people don’t know how to actually hire people and interview them. True. That’s part of the process of what’s broken. So that’s how I spend my time, what I look at, what I’ve just found is, like, these older books just give me the details and information I need more than the stuff that’s coming out today.

Eric Dodds 47:39
Love it. Well, we’ll try to put those in the show notes for this show and in the upcoming newsletter. Edward, this has been an amazing conversation. What an incredible journey that you’ve had, and excited to see where you land next once you get in there and you know, and start causing trouble like you did at all these other companies. I would love to have you back on and hear about

Edward Chenard 48:00
It sounds good. Thank you for having me.

Eric Dodds 48:02
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