Transcript
Speaker: It's the hottest course of study in business schools. It's one of the hottest job categories out there. It's business analytics and middle market companies are playing catch up. We'll find out what they're doing and what they should be doing on the next episode of The Market That Moves America.
Speaker: Welcome to The Market That Moves America, a podcast from the National Center for the Middle Market, which will educate you about the challenges facing mid -sized companies and help you take advantage of new opportunities.
Speaker: Today's podcast is about how middle market companies are harnessing the power of analytics and what it's doing for their business. I'm Tom Stewart. I'm the executive director of the National Center for the Middle Market at the Ohio State University Fisher College of Business. We're the nation's leading research group studying mid -sized companies which account for a third of private sector employment and GDP and the lion's share of economic growth.
Speaker: It is the market that moves America. The National Center for the Middle Market is a partnership between Ohio State and Chubb. I've got a special guest with me today. Ralph Greco is a senior lecturer in the Department of Management Sciences at Fisher, and he's also the director of the Nationwide Center for Advanced Customer Insights. Ralph, good to have you. Thank you, sir. Glad to be here.
Speaker: Ralph, let me start here on campus. And on a lot of college campuses, analytics is becoming a very, very, very, very, very hot field in business schools. How hot?
Speaker: Well, it's one of those, I think, it has taken off since probably 2013, maybe 2014. We at Ohio State created our own undergrad data analytics program. We're the first. We were one of the largest. There are now schools all across the United States that have an undergraduate program in analytics. So it's hot in that way that a lot of schools responded to requirements from companies.
Speaker: to grow and to get folks into this field. So the recruiters are coming saying, have you got anybody who knows analytics? And the business schools are saying, not this year, but by next year we will. And also part of it is that
Speaker: The recruiters were doing it, and I think the students themselves were smart enough to start seeing that that's where the jobs were. But it's funny in terms of, so let's pretend five years ago, six years ago, 2013, 2014, I'm just using that date because that's when I got hired here, so that's an easy date for me to remember. So we have all these programs all kicking off, all starting.
Speaker: We have lots of folks going on data analytics. Now there's a drive for what are you doing for machine learning, artificial intelligence, right? All the things you're starting to read about more in the journal or the times or whatever it is, those students are starting to want to be in that program.
Speaker: When you talk about the companies saying, we want students with these skills, who are there? Banks? Financial services? Health care? Manufacturers? Is it across the board? Where's the epicenter of demand, if you will? I think it's going to vary by area. So as an example, we're in Columbus, right? Who's driving the need for analytics? Retailers.
Speaker: So not picking on anybody, but if it's L Brands, whoever it is, Chase. You have 18 ,000 people working at Chase here in Columbus. They're going to drive the need for analytics. If you and I were in San Antonio talking to the folks at the University of San Antonio or UT San Antonio, well, there's lots of insurance companies down there.
Speaker: So they're driving the need for it. If we're in another city like Pittsburgh, it might be metals and manufacturing and other companies driving analytics. So it's all dependent on where you are geographically and the companies that are trying to pull from those students. So what you're saying, in effect, is if you then spread that out across the map and you look for—
Speaker: an industry heat map, you wouldn't find it. It's basically across industries, although I presume financial services and retail are probably maybe the brightest red on the heat map. Yeah, they really are. It's interesting. Well, again, that's our example here. But I think if we looked across the board, given the sheer quantity of folks that work in those industries, yeah, they're going to be big ones. But that doesn't say that health care isn't driving it like crazy as well, too.
Speaker: It's dependent on, so if you and I were to drive to Cincinnati, and again, Xavier University. It's a very long boring drive. It's only two hours, but it's a very long drive from here to Cincinnati. So if we were to go to Xavier, they have a data analytics program, they're doing work with Kroger.
Speaker: in Parker and Gamble, right? So I think the schools are starting to spend more time with the industries that they're close to, and the industries realize that the students want to stay maybe in the towns that they went to school at. That's bad English, but you know what I mean? They want to stay in Columbus, so they want to work in an industry that's here in Columbus. And there's a response to that.
Speaker: I mean, one of the things, of course, analytics is, among other things, about precision. So what precisely is analytics? Ah, so we should define it probably up front. Let's use INFORMS, the Institute for Operations Research and Management Science, INFORMS. Their definition, basically, is that analytics is a scientific process to transform data
Speaker: into insights that allow you to make better decisions. And I've kind of, you know, their definition might be a little cleaner than that, but I'm just kind of summarizing there. So imagine the key phrases we had in there. It's a scientific process. It is transformation of data into insights and is using those insights to allow a company to make better decisions.
Speaker: It doesn't mean you have to use it to make a decision. You can just determine at the end of all the work you've done, you may not use that data, all the insights may not help you, but in a lot of the cases, it will allow you to make a better decision. So it's an action -driven process, right? We start with a question in mind and we'll go away with it. Give me an example of a question.
Speaker: Well, it might be. Let's pick on something here. Maybe ticket sales are starting to go down at the stadium. So how do we drive people to come see Ohio State games? Which you didn't think would be a question you asked 20 years ago. Maybe not one we're asking now, but a lot of colleges, this is well known, are having attendance problems. So how do we get folks to come to college football games? And so that's a question we start with. And part of it is,
Speaker: What's our competition? Is it driven by what's on TV? We start asking ourselves a lot of those components. Then you look at some of the things people are trying to figure out. Alabama, now just University of Alabama has an app that sits on the student phones that tracks their attendance. And if they leave, they get docked. They don't get docked. They just don't get signed up for the SEC championship game or whatever it is.
Speaker: We're trying to figure out how to do it now. We have split season tickets here, three game series that you can buy for a high state. Men's basketball is the same thing. So these are great questions. Other questions might be, is there a better form of transportation to get product from point A to point B?
Speaker: So let me think about it. One of the things I think about, I remember that in 2006, Tom Davenport published sort of a famous article in the Harvard Business Review called Competing on Analytics. And he wrote, he called these companies that compete on analytics, he called them analytics competitors.
Speaker: He said, analytics competitors do all these things in a coordinated way as part of an overarching strategy championed by top leadership and pushed down. Employees hired for their expertise with numbers or trained to recognize their importance are armed with the best evidence and best quantitative tools. As a result, they make the best decisions big and small every day over and over. That was 2006, 13 years ago.
Speaker: What took so long? Well, the best part is I think Devin ports are in like six more books, right? It's true. It's true. He's got a new one out on AI. Yeah, that's exactly right. But I think what happened is he started talking about it. It was still a back office. It was the quants, right? So keeping up with the quants, he wrote that book in 2013. And part of it was people were still calling him quants.
Speaker: Right. It was like, do you really want to even see them face to face? Do we want to just slide pizzas under the door kind of stuff to them and have them do their work and then give us an answer? And what happened, I think, is 2010, maybe, the economist Ken Kukie was the guy that wrote the article about big data and all of a sudden started to put a name to it. So we had another buzzword. We had a buzzword and suddenly people started flocking. Well, what happened also is from 2000, you think about technology in 2006 and then technology even in 2010 and technology today.
Speaker: So we didn't have the technology in 2006 to answer some of the questions that we can answer today. Machines weren't fast enough. We didn't understand how to put data in an environment that allowed it to be utilized the way it is today. So when we start talking about big data, about 2010, IBM, everybody started coming in there. You had this cool convergence of technology.
Speaker: Speed, storage, all that stuff all kind of coming together. So it took a while. I think we were ahead of ourselves, like driving outside our headlights faster than our headlights kind of conversation. But it then, from 2010, 2013, 2014, then took off like crazy. The other thing I'm thinking about, and this is where we get to our work here at the National Center for the Middle Market.
Speaker: If you go back to Tom's piece, and he talks about Amazon and Marriott, and they're all big companies, right? And in a case of Amazon, they've got 13 trillion SKUs that they're measuring, and Marriott has however many rooms that they're trying to sell. So they have big businesses over which to deploy these.
Speaker: But now as you think about a company that is a relatively small retailer or a single location manufacturer or some of these other smaller companies, what are you seeing about the way in which they are able to use these techniques to up their game?
Speaker: Well, it's funny, because there's a couple of different ways. One is, some companies don't even build their own. Think about Shopify. If you and I wanted to start selling a product today, we could go completely online. Somebody already has the environment set up for us. We just utilize it. So I can do my performance on someone else's platform. Yes. Yeah, yeah, yeah. We just utilize everything. I think the second thing is that as people are becoming more aware of analytics, they're going to use it in different pieces and parts.
Speaker: Let's be clear, there are certain small companies that may never need analytics, right? They might need it from how do I make sure that my customers know where I am, right? So it's social, it's social analytics, it's how do I get through the very clean process to sell them something very fast? There's analytics in there.
Speaker: But in this bad example, but if we're bronzing baby shoes, we may not need a lot of analytics in terms of the manufacturing component of it. The customer service piece, yeah. So depending on where the customer is or the company is, it might determine what kind of pieces and parts you're looking for. So people are getting smarter in terms of because of the pressure they're seeing from an Amazon, right?
Speaker: How do I get out to my customer? How do I see who my customer is? How do I see what they want? How do I look? Whether it's social media, get some feedback on the things that they're looking for. That's great things that any size company can do today. How do I interact with that customer?
Speaker: and make that a smooth, clean, right? So customer service can also be analyzed. So an example there would be, just making it up, somebody goes online and visits my website and completes a transaction, and somebody else goes online and visits my website and puts something in a shopping cart and doesn't complete the transaction.
Speaker: and after a while I got 100 of each of those, I would like to know what's the difference between the 100 that complete the transaction and the 100 that did not. And I might want to find out whether that's my fault, there's something in my user interface, or I might want to figure out whether it's a different kind of customer, like where did that customer come from, maybe that customer came from an advertising campaign that was clearly not bringing the right customers. These are the kinds of things that I could do
Speaker: that I couldn't do before. That's part of what I'm asking. What can I do that I couldn't do before? But it's kind of interesting, right? So you take the second example, which is the one you just brought up. So somebody goes onto a website and they put in the items and they make an order. Cool. If they don't and they kind of set the card aside,
Speaker: Ten years ago, we'd be like, oh my goodness, what's going on? Then people became, you know, they're dangling, right? It's a phrase, right? So now they know that if I leave it there long enough, you might throw 20%, 30 % at me. So now you have people that— Oh, Mr. Stewart, I see you've got something in your cart you want to come back and— Yes.
Speaker: Nobody's ever done that. I never have. Maybe I always complete the deal. But part of it is also, if you go to a lot of websites today, the moment you click on it and they've never seen you, it's like, would you like 20 % off for your first order? That's right. That's true. Because our deal, their analytics are saying to them, I need to get you today because you might just be looking at my site and you're looking at 20 other sites.
Speaker: meaning if I'm looking for a leather briefcase and there's, you know, Buffalo Ridge or some other company and there's a guys out of Idaho, they're all, they're thinking Ralph's only here for a short period of time, looking at my website. Here's 20 % off. But they're presumably also behind that.
Speaker: have some other math that's saying that if Ralph buys that first time, he is enough more likely to come back that it's worth me giving, 20 % is probably 40 % of my profit, right? So it's worth me giving up that piece of profit because I've got numbers and not just a seat of my pants feel that says actually that discount is a discount that's a four.
Speaker: Right, because if I came in from a Google search, which just means I'm just looking, I didn't come in. Now, if I came in through their Instagram site, they might be like, ah, that's a different person, right? We've seen a picture of them. Yeah, he's seen. But if I'm just coming in from search, they might think I got seven other people that I'm searching for. Yep. OK, I'm immediately, what I like about that is they're immediately thinking about how can I compete to get Ralph to spend more time with us.
Speaker: We've got some numbers from middle market companies that kind of blew my mind. We asked a bunch of companies, how much are you doing analytics? We defined it for them just in case they didn't know whether they were doing it, right? And the first of the companies that say that it was extremely important
Speaker: were growing at an average annual rate of 10 percent, which was more than twice as fast as companies that said it was not very important. In between are the somewhat important groups. But 10 percent for those who said it's very important, and under five for those that said, we don't care. That was not a surprising, it's a big number. It's a big difference. We also asked them about their biggest challenges.
Speaker: And one of the group—one group we asked the companies that are saying, we're not doing this, we asked them what their biggest challenges were. And obviously, one of the biggest challenges that they said was, we don't see a business case. We don't see that 10 percent, 4 percent. But it wasn't their biggest challenge.
Speaker: Their biggest challenge was that their number one challenge, these are the people who aren't doing analytics. Their number one challenge, they said, was pulling data together. Does that surprise you? No, not at all. Not at all. It's funny because if you think about where we were, let's go back to 2010, where we started talking about big data. And everybody ran around thinking, OK, we need to start collecting data. What are you using it for? We don't know, but let's start collecting it.
Speaker: We need haystacks. There must be a needle in here somewhere. That's exactly right. And we're oversimplifying. But a lot of people said, we have all this data. Let's do something. Let's now find a problem for it to solve, which is almost backwards. And so I think what's happened is that many companies started collecting data and didn't have a real thought process in terms of metadata. What am I supposed to do with this? What's the architecture? What am I really collecting? And what you're seeing now is people hiring VPs of data, finally.
Speaker: And there's a lot of young, really aggressively growing companies even here in Columbus that are just starting to acquire VPs of data, which means somebody's job to understand what they have, what it's worth, how valuable is it, how vulnerable is it? I mean, all the stuff you should be doing, right? And so think about that, that their biggest challenge is putting and keeping it and maintaining it and cleaning it in the boring stuff.
Speaker: There's a job title that's just been created in the last four or five years, referred to as a data hygienist. So think about that job. So we're flossing the data. Yeah, we've got to polish it. But bar of it is, I have to keep it clean. And I can't allow people to say, I got this new data set. I just want to combine it with all the other stuff we have. Now we got just junk, because you brought in a crap data set. You never really cleansed it. You didn't see what it was. And you threw it in. So if you imagine a pawn, now that we just don't
Speaker: a 50 -gallon barrel of crap into. It's hard to - On top of the oil drums that are already in there. But it's hard to get it out. Once you get it in, right, you came right to filter it back out as a real pain. And so a lot of companies realized maybe we went overboard on collecting data and just shoving it all together. And part of it also was, again, small companies
Speaker: or looking at maybe what's a good example. So companies that use like a form to fill out as people are doing work. So they may have said to themselves, you know, let's collect this kind of information from our reps out in the field. Here's our new form. It's, you know, it's a drop -down box, all that good stuff.
Speaker: And then after a year, they realized, this isn't the information we really wanted. So for us to go back, now we have to change the app that it's running on. We have to go back through and re -train the sales force. Yes, about what this means. And we're not going to impact your paycheck, in quotes, right? Or whatever. But we need to use this new dropdown system. So even making minor changes?
Speaker: is really, it's funny because one of the things we don't talk about a lot and we need to do more of, especially with smaller companies is change management. How do you handle from an executive side to the second layer to individuals working, change is a huge piece and how can we help them do it?
Speaker: So one of the things I'm thinking about is, as I was thinking about this data mess, the metaphor that I had was, it's been a wild party and you walk in the next morning and you just look at all the pizza boxes and everything, you just close the door and walk on. But we also see that 10 % versus less than 5 % growth. And you think, OK, I've got a pond full of whatever it is. I've got that party. I've got a mess.
Speaker: How do I start? Do I start by cleaning up, or do I start by getting low -hanging fruit where I can get results? Or is the answer to that question yes? Well, no, but it's a great question because
Speaker: You want quick returns, like anything else, right? You want to go a generation. And by the way, I'm spending money. I wanted to see some of it back. That's exactly right. That's the first thing. Second thing, though, is, and it's funny because we could say that, you know, I can say sitting here, hey, let's make sure we get the data clean. And it's the stupid analogy of, hey, we're driving a car, but can you change that left rear tire for me, Ralph? And I'm not stopping.
Speaker: Yeah. Right? Good luck. And so part of it is that a lot of companies have to figure out a way to do this simultaneously, which is we're going to continue moving forward, but we're going to continue to cleanse the data and refine it, which means all the decisions we made previously based on that other data are now suspect. And we need to relook at those decisions if we haven't made them yet or understand were we right or wrong based on the data that we had. And a lot of times we can't blame ourselves. We just say, that was the data we had.
Speaker: Now we have new data. How do we make this decision the second time? Same or not the same? It seems to me that there are lots of things you can do with analytics from workforce optimization, production line optimization, all kinds of things. But it seems to me that on the revenue generating side,
Speaker: They're like three. I can understand demand overall. Who's buying my stuff, right? Who are my customers? I can understand individual customers better. I'm selling to Ralph. How can I get Ralph to buy more than that one briefcase? Share of market, share of wallet. And the third thing is I can get my pricing better. I mean, those are three ways, right? More sales to new customers, more sales to existing customers, or better, you know,
Speaker: higher pricing. If I'm going to start, remember I got a room full of pizza boxes. If I'm going to start, would you suggest in general that I go for new customers, share of market, share of wallet, or pricing? So let's think this through. So for us to figure out new customers, we have to figure out who's buying and why they're buying our stuff today. And that's not an easy question.
Speaker: If you and I were to walk into a Kroger and people are making decisions on which eggs they're buying at any given moment, it may not be anything that analytics can do. It is and it isn't. There's been studies even done here. But it's a second level problem. Yes. But it's funny because we've done work. Faculty at Ohio State have looked at this and frankly, when you looked at even 50 or 60 variables they pulled, they couldn't find an answer.
Speaker: Right. Which is really very funny. So that's the first thing. So I think part of it would be is if you can work on the pricing component that you brought up. Right. Which is how do I get my pricing so that when I do get you I win. Right. That I need to understand if I'm in a competitive marketplace what is the difference between my price and their price if all customers are the same and they're viewing us all the same.
Speaker: All the suppliers saying, how can I make it so that my price is better in any given moment? I think that's part of it. Part of it also then is looking at all the components that make up the products. Is there a way to look at that costing to understand how to drive it down so you finally have to drop my costs? I can still make the same margin.
Speaker: I think there's a lot. I'm just thinking about this. You start with pricing and costs, it's cousin, and that can get you a little bit of money. Then you probably go back to the customers you already have because you're going to learn more about them. That's probably easier than learning about the customers you don't have. In the process, all the while, you're picking up a pizza box here and throwing away an empty Coke can here and there. Slowly cleaning up the data and paying as you go.
Speaker: So we're just about out of time, although we could keep going on this forever and ever and ever. Let me end with where we began. Classrooms are full. Hiring demand is insatiable. The number one challenge middle market companies say their face is talent. Aside from showing up in your class, what's a company to do to find talent?
Speaker: And this is going to be interesting, right? Because it depends, I think, where you are. And the reason I bring this up is articles are coming out now talking about smarter cities. Cities that have large college bases, students don't want to leave those cities. So Columbus, growing like crazy. So if you're a company here in Columbus,
Speaker: It's easier for you to find talent than if you and I were in Jackson, Ohio. No offense to Jackson, right? Part of that says you're going to have to figure out a way to get something to come out to you. So think about if you're in Orville and you're making jam, you're going to look for maybe a college closer to you, or you're going to try and find students that want to go back home.
Speaker: that are from that part of the world and want to go back to that area because they have family and they're willing to live there. But a lot of it's going to be dependent. That's number one. Number two is you have to be on campus. You have to be around here. You just can't show up to a career fair and try to find somebody.
Speaker: Because nobody knows your name. So unless your name is Google or Zillow or something like that, and if you come screaming in looking for somebody, it's going to be really hard for you to find somebody. And so particularly for mid -sized companies who lack that employer brand, getting in the face of students where
Speaker: who have the skills you need is going to be critical because, by the way, there's negative unemployment and analytics, so you're going to have a heck of a hard time stealing from anybody else. That is correct. So you better get them when they're young, get your name out there, and be more proactive. I hate to use the targeting word, but be more proactive in getting these folks. Well, or the other thing you would think about doing, even for some of these smaller companies, is going to high schools and demanding a high school program.
Speaker: so that the students that leave the high schools that don't want to go to college have some skills, and we're having conversations with local high schools about that, or you help sponsor somebody to get a degree in analytics. And so now you're sponsoring and scholarshiping somebody knowing they're going to come back and work for you. So you're going to lock in. I mean, right now, it may not be here five years from now, but we're in this weird talent.
Speaker: pool kind of hunt where everybody wants them as soon as they can get them. And so you have to think about how you can go and lock somebody in that. So let me let me wrap up. And first of all, I want to thank Ralph Greco from the Fisher College of Business and the head of our analytics program here. Thank Ralph. Thank you so much. And what I hear us saying is number one,
Speaker: this is hot, I mean, analytics is hot and it's hot across industries and across geographies and it's where people are applying computing power to sift through data in a scientific way, right? To get insights in order to make decisions that are more precise and better decisions than they could make before and it's happening all over. That those decisions, that it is,
Speaker: Those who are not doing it are lagging behind in performance with those that are doing it. And that mid -sized companies in particular are finding that these tools can help them compete with the Amazons or the big players. I mean, it's not, whatever your industry is, you are competing against an Amazon, a Marriott or whatever, are they bone void now, whatever that combination is. You are competing against those people and you need to optimize
Speaker: on the revenue side, optimize on the employment and cost side so that you can do this. And you got to get started. And probably the best way to get started is on your two things. Number one, looking at your pricing. And number two, getting some talent in there that can do this work with you. Yeah. Yeah. I think that's a great way to end it on, I think.
Speaker: It's a self -inspection that companies have to do, right? Which is, who do we have? What is our skills? What are we capable of doing? Which is hard in some cases, right? If there's only 40 of you, it might be quicker. If there's a thousand of you, it's gonna take a little while, but you have to see what you're capable of doing, because part of it might be is you have the talent
Speaker: In your company, you just need to, I hate to say it this way, it's a terrible, you need to put a shine on that apple, right? And so we need to make sure there's, schools like us need to provide folks with the ability to get themselves educated and trained. Because the one thing we can't do here, and we'll leave it at this, is we can teach analytics. We cannot teach business acumen.
Speaker: We can't teach your business that you've been doing for the last 20 years like you can. The interesting thing is with all these things, it's costs, competitors, and customers. It's always those things. With analytics, you have opportunities to be more precise and effective in all three of those areas. That is correct. For that and for this insight,
Speaker: picture of what's going on with analytics for the middle market companies. I want to thank Ralph Greco, and I want to thank you all for listening to The Market That Moves America. Never miss a new episode. You can subscribe to the podcasts on iTunes, Google Play, Stitcher, or wherever fine podcasts can be found. Or you can subscribe and learn more about us at our website, middlemarketcenter .org.
