First, we're going to derive the preference partworths with no marketplace simulation. We'll inspect these results. Afterward, we're going to then bring back in all of that marketplace simulation information and conduct actual market simulations. Here's the initial setup for our conduit analysis. Notice that we have a selection for the actual conduit design, and we're going to be estimating consumer preferences or utilities based on the actual product ratings. We're selecting the estimated from product ratings option here because we actually presented the actual product to people and had them score them. By conducting this analysis, we'll then derive preference partworths. In other words, how much each individual person valued each individual level of each attribute. When we run the model, the first thing we get is aggregated results. Even these aggregated results, even though it's not going to be our focus, we do actually see some interesting insights. On average, we can already see the most preferred product. On average, people prefer a 48-can option, with seven days of cooling, strangely a 90-day limited warranty instead of a one-year-no-questions-aest warranty, and a hard-top cooler. But even while we fixate on these averages, the other results in the summary table reveal to us that there was definitely heterogeneity and consumer preferences. Notice the rather large standard deviations that indicates to us that different people valued these different combinations of attribute levels quite differently. And even here, we can see that there had to be at least one person who placed enormous value on a 36-can option rather than the 24 or 48-can option. This chart shows the same numbers that we saw previously, just the averages. Again, take a look at this little green spot here for the one-year-no-questions-aest in 90-day limited warranty. What could be going on? First, notice that people's preference score, as a result of the warranty, is relatively low compared to the other three attributes, so it doesn't seem like it matters so much. Also, the difference between these two options is relatively small. This might just be due to error in perfect data, or if this is actually real, perhaps consumers saw the one-year-no-questions-aest warranty as a signal of poor quality. This pie chart shows us the relative importance weight of each attribute. Here we can really see that the material or the hard-top, soft-top choice had the biggest driver impact on someone's overall preference for the cooler. Size and cooling length also had an important role. Warranty, on the other hand, regardless of which version of the warranty we presented, had a relatively small impact on someone's overall preference for the cooler. The next set of results, the detailed preference partworths, the most important key outcome of our conduit analysis. Each one of our 85 respondents now has a beta weight for every one of the combinations of attributes and levels. Let's open this up in Excel, which you can easily do in ingenious and work with these parameters. Here's a little snippet screenshot from an Excel spreadsheet that I slightly adjusted based on the ingenious output. Notice I added a series of dummy codes along the top of each one of the attributes and levels. This is my way of flipping the switch, if you will, for a particular product combination. Right now, we're looking at a 48-can, 4-day, 90-day limited warranty, hard-top cooler. For Respondent 1, we simply plug in zeros and ones as appropriate to simulate the version of cooler that I just show in the Excel spreadsheet. We solve it all the way through, which is the equivalent is using the sum product function here. We find that this person most prefers this particular version of the cooler. In the ingenious output, all consumer preference scores will vary between 0 and 100, 100 indicating the version of the cooler that they most preferred and 0, the one that they least preferred. So what have you learned so far? Well, first, knowing consumer preference patterns is extremely valuable. We can take individual level models of our market, because we have them for all 85 individual persons, and we can now sort, aggregate, simulate with this data. The simulation that we're about to conduct in ingenious is just meant to help simplify some of the more basic forms of analysis we might do. If you need to do something more complex with your simulation, you need to build your own spreadsheet. Let me illustrate some of the things that we've already learned. Here, I've plotted the betas for hard-top cooler for all 85 respondents. Notice that we can see that most people prefer a hard-top cooler. In other words, the beta weight is something greater than 0. On the other hand, we also know that about 21% of our participants prefer a soft-top cooler. That's because the beta for the hard-top cooler was 0. Further, we also know that every single person's preference is scaled between 0 and 100. That tells us that for about 27% of our market, over half of their entire preference score is driven by their preference for a hard-top feature. In other words, hard-top really matters for this particular market. A simple pivot table reveals some insights. In the insight, we have the average utility score for this most preferred version of the cooler, but the cross tab actually splits the results by household income and gender. In general, all gender and income combinations are pretty enthusiastic about this optimal version of the product, which isn't a surprise. But we can really see here that relatively higher income women are extremely enthusiastic about this optimal combination. This type of insight can be useful for marketing, marketing, communication design, promotion, packaging, labeling, and so on. Before we dive into the ingenious simulation features, I need to emphasize one more time. If your needs for simulation are beyond ingeniousess capacity, you can simply build your own spreadsheet. There's nothing that's happening with an ingenious that couldn't be done with an excel. Let's give a concrete example of this. By default, ingenious can only simulate introducing one new product into the market editor. However, what if your company was willing to introduce two versions of a product, such a light and elite version of a product at the same time? It is absolutely possible to simulate this condition and estimate how changes to market share will be impacted across the entire space, but you'd have to construct your own spreadsheet.