 Hey there, Chad Bonsia here for High University Libraries. I still have hundreds of students who are researching the amusement park and theme park industry. And they are still looking for some deep diving and some demographic data, looking at things like the types of people who attend theme parks and visit theme parks and things like that. In a previous video, I did an example of looking at Simmons and also looking at Mintel to look at demographic data for people who visit theme parks or consumers of theme park brands, things like that. In this video, we're also going to look at Simmons as well, but we're going to take a deeper dive looking at the cross tabulation function, creating cross tab tables in Simmons to get really advanced data and get custom data and custom variables for our project. It's a little bit advanced, a little bit longer video, but it does take you into the nitty gritty of how to find that kind of information. Whether you are researching the theme park and amusement park industry or you're doing some other consumer product, this is a great video to kind of give you an idea of the kinds of things you can do with Simmons in order to find demographic data as well as create custom variables in looking at that data. So let's get into Simmons. All right, so here we are at Simmons and in previous videos, I've actually shown people how to go to Essentials and Quick Reports to create this demographic profile thing. That's kind of the baby steps into using Simmons. For the purposes of this video, we're actually going to go back to cross tab here and we're basically going to create a table of demographic variables that we can look at to kind of tell our story about our product or our consumer. Now my preference in using this is actually to click on Dictionary here. I like this view better. Your mileage may vary of course, but I'm going to go to Dictionary here and basically what I'm going to do is kind of browse over here on the left hand side, browse the variables that are available and then add them to columns and rows. So the purposes of my demonstration here, I'm going to go under Entertainment and Leisure and I've got students right now once again who are looking at the theme parks industry. So what I'm going to do, I've got people who are looking at demographics of people who have visited a particular theme park. So I'm actually going to look at this Times Visited and in my research of using this, I find that the one to two visits gives you the biggest sample size to be able to then do cross tabulation here. Okay, so what I'm going to do is just for my example, we will do Cedar Point. We'll grab that and put it in our column here and then we will, and I'm just dragging these over here. We will do Disney World Magic Kingdom. We will drag them over there too. And you can see already how the sample size is different. Now what this is, again, the survey size of Simmons is about 25,000 people. And so this tells me that only 205 people out of the survey have been to Cedar Point in the last 12 months. So bear that in mind. So once we start getting some cross tabulation, we may get a little bit too small to get any sort of good data, but we'll see. Typically, the larger, in this case, examples, the larger national products or brands are usually better to have more data for. All right, so we'll just use that as our example. And then what I'm going to do now is go up to the top here. And I'm going to look at lifestyle demographics here, all right? So here we see, we can look under demographics here and we can say, well, let's look at, you know, we can look at people of a particular age. So we want to look at maybe people who are in the, let's do the 18 to 34-year-old age range there, okay? So we could do that. We could also do gender. And if we want to look at how many percentage people who go who are female, we could cross tab that, okay? Now, so we can go over here and grab whatever data we want to and add it to the rows to kind of get a better idea of, you know, like what percentage of people who visit Disney World in the last 12 months, one to two times, are age 18 to 34 or are female, okay? Now, something else you can do here is we could do a combination of variables. And I'm going to use this box down here below, all right? So if we go back to age here and let's do our 18 to 34-year-olds maybe. Let's see if we can find that again. All right. So if we do 18 to 34-year-olds, we can drag that down here. And then maybe we want to just do 18 to 34-year-olds with kids. So we could, we can scroll down and I believe the kids are, we've got parent or guardian of any children under the age of 18. We could look at, we scroll down some more. Children in the household, number of children, right? So we can say one or more. Let's do one or more, all right? And you'll see once I did that, this box down here, let me do that again. Let me get rid of that and let me look at this. Okay, so this is what it looks like normally, okay? Now, if we drag this down here, watch what happens. It's going to change from green to red. It's not happy anymore, okay? To make this happy, we have to go in and combine these two variables with an and because we want people who are age 18 to 34 and who have one or more kids. So let's do that. We'll put an and in there. It turns green, it's happy and we can call this, we can call this something like young parents, all right? Let's do that, young parents, all right? Or younger parents or whatever. And then down here at the bottom, we can go over here and move this to our rows down here using this icon at the bottom. Once we do that, it grabs that for us and, you know, we can continue to do this kind of stuff. Now, just bear in mind, the more times we combine variables like that, the smaller our sample size is going to be and we may run into some problems with our data set being too small, okay? But for this example, we'll just start off with this and we'll go up here and click run at the top right hand corner. And this is going to generate our cross tab here. All right. So now looking at our cross tab, what we see here in the first box is the total population of the whole survey. Okay. So that's why it says 24,804 people. That is the whole survey for the whole Simmons survey for this year. Okay. So you can see the way you'd read this is of the total population of the survey, if we use this vertical column here in the middle of the total population of the survey, 28% of the survey is age 18 to 34, 51.7% are female and 13.1% of the people who took the survey identifies young parents. Okay. If we scroll over, I can see here, this is where we're going to run into possibly some problems. We'll say this is for Cedar Point. And again, Cedar Point had a sample size of like 205 people total who had visited Cedar Point in the one to two times in the last 12 months. So if we go here and say, all right, so of those people who went to Cedar Point in the last 12 months, 55.2% of them are age 18 to 34, 50.8% are female. And here we see 33% are identifies those young parents, 18 to 34 year olds who have at least one or more kid. Now the issue here, as you can see, we have an asterisk here. And if we scroll over here on the bottom right, it says, hey, you know, the sample size is getting kind of small. So that's what it means by the cell count is between 31 and 60. So projections may be unstable, used with caution. If you get below 31, it's basically projections are likely unstable. So you're getting a point where the data is not really viable, essentially. Okay. Now, if we go over here and look at the theme parks, again, theme parks, looking at Magic Kingdom, who visited one to two times in the last 12 months. Here we see that in the vertical percent here, 18.6% of them are young parents in the age 18 to 34 year olds, and they have at least one child. Okay. 55.7% of them are female. And here we see 35.8% are age 18 to 34. But we haven't said if they have kids or not. Okay. So that's the total, total age group population. Okay. So this is a way you can kind of go in and generate your own kind of cross tabulation reports and really kind of dig into looking at your consumer. Now, if you want to, these reports can be a little bit challenging to build. So you definitely want to try to save what you build. So you can go up here and export this to an Excel file, and you'll have an Excel file that looks very similar to the table we just generated here. Okay. So good stuff here. All right. Now, you can also use the horizontal, and the way you use the horizontal section here is of those people who identify as young parents, all right. If we use, we're going to do this bottom line here. Of those people who identify as young parents, 4.6% of them have been to Disney World in the last 12 months. Okay. One to two times. Okay. So you can also look at the indexes here. Okay. So the index with these green arrows here basically says that people in this age group, the 18 to 34 year olds are basically 24% more likely. If an index is 100. They're 24% more likely than the rest of the survey population to have been to Disney World one to two times in the last 12 months. Okay. In this case, young parents are 42% more likely than the overall survey population, the total survey population to have been to Disney World one to two times in the last 12 months. So that's all kinds of different ways you can use this data to tell your story. Hope this video helped you understand how to use Simmons and create cross tabs and Simmons. Should you need more help? Check out my business guides. Good luck with your research.