Welcome back to our course on Quantitative Customer Insight Techniques. I'm James Lenz with the University of Illinois. And today we're going to go through Lecture two as part of the module three test marketing. Again, in this module, the whole goal is for you to learn about various tools and methods to collect data and then how to use that data and make decisions based on that data. The assignment will have quizzes in it as well as at the end, you will collect data and input the data into a spreadsheet, so that you can use that data and make decisions from that data. Again, our whole concept now we went through last time is the sort of categories of new product development, the market penetrations, market extensions, product line improvements, and this product creation is new-to-the-company. If you think about what most companies when they look at their portfolio of new product development or their R and D, typically, the market penetration, I describe it more as development-type projects than research-type projects. They're focusing more on especially a lot of work of modernizing factories and so on. This is taught typically and the company is called The First Horizon, first horizon of projects. And I say most companies have about 60 percent of their R and D is spent in this area. The second horizon that companies talk about is where they categorize their product and put them as the market extensions. They call this the second horizon. And sort of is the second layer of payback. So it gives you kind of a time frame of when these projects should pay back and how long they should run. The third horizon is the going what's called North from your product portfolio, your existing core customers, but working with those customers and trying to develop new technologies to solve new problems that they have or unmet needs that they have. This is called the third horizon. So again, this type of Horizon, again, gives you a feeling of how long that technology should pay off or those projects should pay off and bring projects back into that core business. Companies put about 15 to 20 percent of their R and D into each of these areas. And the fourth area which is my favorite as I talked about, most business schools don't talk about it. It's just not talked about. It's how what are the methodologies, what are the processes, what are the techniques that work there? And when I was in many companies, many businessmen would call this the over the horizon type of projects. And so it was sort of wild and crazy and out there and not talked about. But I want to show you that today, in business, this is becoming more and more important. Becoming more important to companies is how do you address this area? And this areas is really critical to having ideas. Really driven by ideas and how ideas can be formulated and how they can help solve new problems. Another way to look at these categories is again thinking about the market and the technology. But most people and most business practice is about is market pull. Learn about the market and have it pull what it needs from you. This is relatively low risk. Very low risk because there's people, now you have to look at the market, is it early adopters, is it lead users? Who in the market is pulling you, so that you'll understand are you just addressing a small percentage of the market or are you addressing the majority of the market? Or are you addressing the laggards, the people that are sort of getting out of the market. Typically, there's not a lot of profit left by the time you get to the laggards. The laggards are not interested in, they're interested in the product but not so much in paying very much for the product. So they're interested in just using that product and near the end of the lifecycle of that product. Market push is an area that's really fun to get into. You've got a product to go try to make things happen. There's a lot of people that come out of business school and this is where they put their careers. It's a very fun activity to try to take a product and go out and push this into the marketplace and learn about this. If we start working about new technology, then there's a technology pull. Again, the pull coming from the customers or where the other extreme cases where you try to do technology push. And many places and many business schools tell you about technology push is a very difficult thing, especially difficult for large organizations to do. So typically, this is an area where small startup companies tend to do this. They'll have an idea and they push the technology and try to find a new marketplace for this. But as a result of that, they're challenged with what processes there are. As I said, when you look at new product development, the methods of new product development, do we differentiate what tools and processes and methods we use between these four different types? And today, this is not the case. If you look at four market research that we're talking about, I mean voice of the customer is one of the main activities. I'll show you a few other tools here later as well that are dealing with this approach of collecting up market information. An important part is no leading questions. Get the ideas from the customer. Ask the customer for ideas. I'm thinking this works maybe for some of these categories, but does not work for all of these categories. But this when market research has taught is taught that will fit all these types of new product development activities. If I think about another major activity associated with business is cost accounting, calculating return on investments, and we'll do this here at the end of this next course. We'll actually have you calculate net present values and return on investments and also look at a portfolio of products. But again, this is all done on the scale of all R and D activities and not trying to distinguish to you what type of category this project is specifically being done and does that methodology really works specifically for this type of new product development. Total quality programs. We talked about quality in the last module. This idea of total quality management, TQM, 6 Sigma, maybe you've heard of that. The 6 Sigma was first a practice that was developed and captured by the business schools to work about supply chain. Sort of managing the supply chain and taking out inefficiencies and uncertainty in the supply chain. And over time, especially with the encouragement from General Electric, these methodologies have moved up more and more in that whole theme of new product development in my curve that I showed these humps. It's moved from not just the supply chain activities but into the factory, into the engineering of the product, into the sales of the product, into the product support as well as even to the idea generation. And I think there are some cases where, again, you just can't manage idea generation. Sometimes it's a little bit more spontaneous and you've seen a few cases where it tends to be more spontaneous and just comes from knowledge and just having an interest and understanding where things are. It can't be very well managed. Competition. The whole concept of competition. All competition is good. It's called supplier selection via tournaments. This methodology is again taught throughout business schools and again is taught for all types of new product development activities. There's no distinguishing between which type is it. And what I've been talking about here is forecasting techniques and in the next course, you'll get in much more detail about forecasting techniques. I've studied 35 different forecasting techniques and I've actually now identified which ones fit which new product development categories better. So I want to talk about this. The approach to solving a problem is important. Now, I'll give you an example of this. I think it's a very historical, it's kind of interesting, and gives you maybe will get you to think a little bit about what type of problem you're trying to solve. And a good case is this Ptolemy and Copernicus. Maybe you haven't heard of Ptolemy but probably most of you have heard of Copernicus. Copernicus, of course, is the person that came up with the idea that the planetary system is easily described if you put the sun at the center of the planetary system. Ptolemy looked at the data and fit it and came up with a methodology for defining the movement of the planets but with the earth at the center of the solar system. But this is again I think thinking about a problem, how do you approach this problem? Is it a puzzle or a mystery? Puzzles we know quite well. Puzzles can be put together in a logical way. I mean, to me an example, the greatest example of a puzzle is Rubik's cube. Maybe all of you have tried this. I mean, it can be solved by data. You look at the cube. There are some people that can watch the cube and solve this thing in less than a minute by just watching this and seeing a pattern and they're just following a pattern and process to solve the puzzle. So they're looking at data. They're collecting data every millisecond from there and they're moving their hands and solving the problem. It relies on accurate and critical data. If you put them in a box where they couldn't see the puzzle but they couldn't, or they aren't getting very accurate data of what's going on, they're not going to be able to solve it very well. In some point, what's interesting if you've ever solve this yourself, there's this sort of one last move that's really significant fact that then solves the puzzles. There's one last thing that really gets this done. So this is how Ptolemy approached this problem. So they had lots of data. It's hard to believe this in the 200 AD, second century activity. There's a lot of data about these planets. The planets were known, not the extended ones, but certainly up to Jupiter and Saturn, they were known, they had been named, and there was a data everyday of where they were and out in the sky. So Ptolemy built a model of this. And so that on any day in the future or in the past, you could use an equation and calculate where would that object be in the night sky. And his model was accepted for over 1300 years. It's amazing activity. If you look at that model, it's very complex. You try to plot out the planetary motion. It's very complex how it had to be moving. People did this. They took his model and they drew these planetary motions. You'll see here these squiggling lines, these circular lines. This thing is going backwards and forwards in the sky. If the Earth is at the center of it, how these different planets would move around there. It's very complex. The Physics had a lot of trouble with explaining how are these things moving like this? They are going forwards, they are going backwards. What's causing them to move in such strange patterns? But this became probably the most influential scientific text of all time lasting 1300 years. But he approached the problem as if it was a puzzle. I've got data, how do I build a model from the data that I've got so that I can extract where's the future of these planets going to be? Then comes along a person by the name of Copernicus and this is often regarded as the starting point of modern astronomy. And he built a model where the sun was at the center of the universe. No longer was the earth. And at this point, one equation was able to explain all the planetary motion of Mercury, Venus, the Earth, Mars, Jupiter, Saturn. We're all being able to be explained with one equation. And so I think it's a fascinating thing, you take this book of a thousands of equations and I've had the chance to read Ptolemy's book when I was in school and went through it and did some of the calculations. It's quite laborious to say the least to work through this to say where is this planet going to be tonight. But it still was relevant. It still gives you the answer. That book does give you the answer. It is not wrong. But the Copernicus equation was so much more easier to use and gave you just as accurate of information. In fact, maybe even a little more accurate because it didn't involve so many calculations that you could have round off error or you could have mistakes being made. It's a very simple type of thing. And I describe this as what Copernicus did is he looked at this problem as if it was a mystery. He didn't look at the data that was present there, that you're trying to map an equation to fit that data. He thought about it as a theoretical environment. What could be going on? How could this be working? So a mystery, and when you think about mysteries, I think we think of Sherlock Holmes, is sort of a worldwide now character that's used to think of how mysteries are solved, how are things done. And this requires sense making a judgment instead of a data rich process. So it's very dependent on people. And most companies don't like mysteries or problems formed to the mysteries because it's dependent on the people. They're trying to use processes. And these structured methods as a way because there's much more deterministic of when can an answer come or how much is it going to cost or when can I expect the results. Whereas, I'm depending on people, it's a little more chaotic. I can't predict it very well how well it's going to get done. So there's a big difference between these mysteries and puzzles that go on. So where does the uncertainty come from? There's many different types of uncertainty. Typically, sometimes there's missing information. So maybe market research can give us this or more R and D projects or more research projects in the laboratory can give us the information that we are missing. Sometimes we have the information but we don't know if we can trust it because it was just one time it happened and we can't maybe repeat it. But sometimes we trust it but it conflicts with other information we also believe. Or we believe the information but we can't figure out what exactly it means. These are four different types of uncertainty. And so each one of these types of uncertainty or this type of a problem that you can pose and maybe you pose this type of problem related to this with your market research here, can be dealt with a puzzle. So conjoint analysis and some sort of data method can be used to address that problem. But many times you get into R and D projects where there's more, many of these are involved. So now, you've got to deal with this mystery and now you're much more dependent on the people and the skills that those people have to help solve this type of problem. So when I think of our four categories, three of them are very much can be, the problems can be defined as puzzles. And so as a result of puzzles, I can develop methodologies, I can use data methods, I can use test marketing methods that generate data and use data to address these questions and solve these problems. Working up in this upper right hand corner, I just feel like the problems are more like mysteries. There's just too much uncertainty. There's uncertainty in the technology, there's uncertainty in the customers. So here, it's very difficult to think of data rich methods. So my test marketing methods have to be a little bit different for this. How do I understand the market? And I typically have to probe and become more of a detective in this type of area than someone just collecting data. You might say this isn't important. Many companies say that area is not important. Well, what's fascinating in the last decade is this becoming very important. In fact, the largest capitalized company in the world, Apple, and now close to Amazon and Google, this is where they live. This is the types of products they do. They want to develop these new-to-the-world type products and we show what the value of this is, it creates tremendously sized companies. Where companies now are like General Motors and Ford, we think these fascinating manufacturing companies, they typically are very small in capitalization compared to these types of soft companies that really have no assets, but they've created a tremendous amount of value, and they do it by working in this area. This area of mysteries or this area of trying to find new technologies and new customers or new needs that are not out there and the results of that as we know it's created tremendous amount of business potential. The companies like Microsoft or service providers or as many today as we know, most of the jobs in the United States are service provider. And maybe around the world this will become more and more service type oriented jobs and manufacturing types of jobs or agricultural types of jobs. And again, these business practices, these ideas, these motivations can be very successful. I'm not saying they're not successful, but they're more incremental type of business growth and it's a good place to work as well. Many companies need a full portfolio of all this capability. As we know, Apple is not very good at some things, Microsoft as well would like to be more like Apple and Google at times I believe, but they're all sort of stuck with their methodologies of being successful and hanging on to that success. I took a course one time at Harvard Business School at the business school there and the course was titled The Greatest Fear is the Fear of Success. And what talks about studied centuries of governments, of organizations, of companies, even football teams and soccer teams and it shows why you could have some success but that success then leads to a chance that you will now not have that success again. There will be failure that comes after that. And there's some description of why that happens and what happens with organizations and how hard is it to stay at the top all of the time. And I think the important thing is once you get stuck in this concept of thinking about all your problems are puzzles, you are able to think about problems as mysteries and the mysteries is where the real new activities come from and the new success can come from. So with that I want to just give a little summary is the product creation type of NPD is very much a mystery. It's my favorite as I said and unfortunately mysteries require judgment instead of data rich process. So this means you're training, there's your knowledge. If you remember the opening discussion from my friend Phillip DeFrancesco, the mathematician, he talks about how do you come up with ideas and needs and you know most of his time he says he spends in a coffee shop and he meets other mathematicians talking about ideas and learning about things. Becoming more of a problem solver and understanding things. And of course, the greatest hero to many of us people about innovation and understanding is Steve Jobs. I mean, this person if you read his history and his background, he very much was focused on this new-to-the-world activities. And Apple was quite famous. Steve Jobs one time was very famous for saying, you know he had something like 30,000 employees and only one accountant. He was not interested in all these business practices. He wanted people to be up there really solving problems and coming up with new needs. And they interviewed people based on their capabilities and their passion to solve problems and address them and not to just come up with a process that was going to make things better. So with that, I leave this discussion here for a little bit more understanding and there'll be a short quiz at this and then we'll come back for the third and final lecture as part of this module.