All right, let's now take a look at some conjoint output. What does the software actually give you after people have made all these choices? Well, it gives you something that looks like that. Wow. So that's real conjoint analysis output. That's generally the format that the output comes out in. And I'd like to take this and interpret it a little bit, find out what's useful here and maybe what's less useful. The most important column in this output is right over here on the left hand side. It's called effect. Another word for that would be utility. But in effect, each one of these numbers is a utility level for each of the levels of the attributes listed on the right hand side. So here we have a golf ball example, and one of the attributes is brand. And then I have these different brand names, High-Flyer Pro, Magnum Force, Eclipse Plus and Long Shot, those are the levels of the attribute brand and their associated effect or utility is right over here on the left hand side. And you can really interpret that as a happiness level. So larger positive numbers mean more happy and then negative numbers mean less happy. Now, in most software output that does conjoint analysis, the data has to be scaled and I'm just going to point out one issue with respect to scaling because it's important as we interpret the data. The software will usually scale conjoint analysis output such that the sum of the utilities within any given attribute sum to zero. Now perhaps you can see that just by looking at this. Now, if you have trouble seeing the data on this screen, we've also put a paper version or electronic version of this in the resources, so you can grab that and look at that while you're looking at this particular segment of the course. And that's actually a very easy way to do it. Then you can look down at the data and look at the example that I'm presenting and kind of square the two, make sure they make sense to you. But if you look at these effects, over here for brand, you see 0.56, .429, negative 0.38, negative 0.608, if you add those up they will add up to zero and that is true for each of the other attributes. This is a distance attribute, how far does the ball go. And finally down here, we have a price attribute. That's what we're really interested in and those effects or utilities add to zero. Now some of you taking this course may have done some advanced work in statistics or done a lot in terms of regression analysis and you would be familiar with the term t Ratios and standard errors. If you're not familiar with them don't worry. All the examples that I'm going to do use only the effect or only the utility and that's all you need to focus in on. Conjoint analysis generally does give us output that tells us something about the t Ratio which is a measure of whether these effects are something called statistically significant. Now, why do I not think that is important here? Every output from every conjoint analysis that I have ever seen that has been done in any kind of a reasonable way has statistically significant output, okay? In fact, if you had statistically insignificant output, that would mean that the attributes that you put in the conjoint analysis were in no way related to the people's choices that they made. That's very unlikely, okay? Especially by people who have done a lot of conjoint analysis. So the practical reality here is what we have all of this output, just by focusing in on the descriptors on the right hand side, the descriptions of the attributes and the levels and by focusing in on these numbers right here, which is the happiness levels we can really do the kind of interpretation we want to do. Now, so what can we do with that kind of output? We can do a number of different things. We can determine which product people prefer. And we can do that both for the population and for different segments of the population if you've asked those segmentation questions as part of the conjoint analysis. You can look trade-offs among different possible features. You can determine the rank order of attributes in determining choice which attributes are very important and can we quantify that and which attributes are less so in determining choice. We can also, very important for this course, compute willingness-to-pay for different design changes that we might be thinking about for our product. And finally, we can do something called Propensity Modeling. For each one of these items that I have listed here, I'm going to do an example, and I'm going to start out with determining which products people prefer. So, here's how this is done in a conjoint analysis. If you have two products and you ask yourself which product would people prefer, the nice easy part of conjoint analysis, and this is one of the reasons it becomes so popular, is that it is very easy to take the data that you have available to you, those effects or utilities and determine that. And the way you do it is by listing out the levels of each of the attributes for the product that you're considering and look at its associated utility. So for High-Flyer, if you look at the data, you will see .56 and I'm having to round it. Okay. Being the utility level for High-Flyer, 5 yards further if you look down you've got negative .48, again I'm rounding, and 6.99 per pack we have about .22. And it says .21 but it says .216 so I'm just writing down .22. And to figure out the overall utility someone would have in this population for this product all you have to do is add together the utilities of the different levels that define it. That's beautiful, right? We all can do addition, and that's all you have to do to interpret this. Addition, subtraction, and once in a while a little bit of multiplication and division, but I mean a little bit, is all you really have to do to interpret this kind of output. So if we stack that product against a Long Shot, 15 yards further, 8.99 per pack and these are the associated utilities that you can see right in front of you. Those utilities associated with the levels, we can add that together and we get a Total Utility of negative .33 and that negative .33 of course is lower than .30 so that we can say on average in the population people prefer this product to this product. When this really gets interesting is when you do the same kind of analysis I'm doing right here but you look at different segments. You can find out which segments prefer which different designs of your product and which segments prefer other designs of your product.