 Hi, I'm Chad Boninger, Business Librarian for Ohio University Libraries. I make business research tutorial videos for my students at Ohio University and beyond. In this video, I'll show you how to analyze sports fans and consumers in your local market. We'll use a database called Simply Analytics to find this information. If you're not affiliated with Ohio University, check with your local library for access, as this is a subscription database. To find information about the local sports market, including sports fans and consumers, as well as demographic and economic information for the area, we'll use a database called Simply Analytics. And when you first arrive at the database, you have the opportunity to create an account. I would definitely encourage you to create an account, because it will save what you're working on last time. So the next time you log in, you'll be presented with the same content that you're working on before. For Ohio University students, simply use your Ohio email address and then whatever password you want when you create your account. I'll go ahead and sign in. And when I sign in, it actually takes me to what I was working on last time, which was actually the setup for this video tutorial. Here we see what I was last working on, which is a cross-tab of Atlanta sports fans with some demographic data, as well as some restaurant consumer information. We're going to build something similar to this report. If you look at the top of the page, you'll notice that we're using the Nielsen Scarborough Cross-Tabs data set. The Nielsen Scarborough data is similar to Simmons data, which we've used in other databases and other videos. But the data in Scarborough tends to offer better information that is unique to the local market. The size of the Simmons survey tends to be around 25,000 people over the age of 18 across the U.S., whereas the sample size for Scarborough data is around 210,000 people. Because the sample size for Scarborough data is so much larger, this allows us to get some really granular information at the local level and find information for variables that are unique and specific to local markets. We're going to be able to use this information to get some great, detailed information about sports fans in the local market. For the purposes of this video, I want to start fresh with a blank slate, so I'm going to click on New Project. Now, typically I would add a location here, but you don't necessarily have to do that for the Nielsen Scarborough Cross-Tab data. We'll select location on another page. I'm just going to click Next, and I'm just going to click Create Project without seed variables. This takes us to this blank page here where we get a new view, and we're going to scroll all the way down to the bottom right and select the Scarborough Cross-Tab table and click on the Create button. On this page is where we can select a DMA to work with. The DMA stands for Designated Market Area, and typically this is a city and the surrounding area, and usually it's associated with the television broadcast area. In Simply Analytics, there are a little bit over 140 DMAs in this list to choose from. For my example, I'm going to choose Atlanta, Georgia, and we're going to use Atlanta, Georgia to better understand the professional sports teams in the area. Now, we have to look at some data next, and that's going to require us to click on the data button up here in the top left. And probably the easiest way to start here is just to search for some of your sports teams. I will search for Braves, and you can see this is going to bring up all kinds of information for people who listen to Braves on the radio, watch them on TV, or even attend Atlanta Braves baseball games. Clicking on the variable will add it to our data. I'm going to repeat the process by looking for information about Hawks, as well as those who attend Falcons games. So now that we have our sports teams covered here, let's add some demographic data. This time, instead of searching, I'm just going to go over here and click on the buttons and browse by variable. So let's first start looking at just general demographics for men and women who may attend those sports games, and then possibly choose some specific age demographics for both genders. You'll notice here on this page, you have the option to associate the variables with either a column or a row. My personal preference is actually to put the sports teams at the top, and then put the demographics on the left hand side as rows. Once you're satisfied with how you have it set up, you can click done to display your cross tab. In addition to the demographic information here, we can also add additional consumer information, which is very useful if you're working on a sports sponsorship project. For example, we can add restaurant information here to see how many people who attend Braves baseball games also eat at certain restaurants. As an example, we can search for something like Applebee's and add that to our variables. I also want to show you that you can browse the content as well. Simply click on data folder and then click on the Nielsen Scarborough cross tabs folder. This allows you to explore the full range of all the data available in the Simmons cross tab data sets. As an example, if we scroll down to restaurants and look for, for example, quick service restaurants using the past 30 days, here we see restaurants that have to be available in Atlanta. If it's not a restaurant that's not available in Atlanta, it's not going to be displayed here. I'm going to scroll way down and choose Zaxby's, which is arguably one of the better chicken finger restaurants in the United States. Now let's look at how to read these cross tabs. The nice thing about Simply Analytics is they actually provide tips on how to read and understand this table, which is really quite nice. So we start over here looking at the total population. Again, the total population here is the total of all the people who took the survey from the Atlanta, Georgia DMA. Of that total, 21, 9% of them went to a Braves game in the past 12 months, whereas 6.5% of the total surveyed population went to a Hawks game, and 10.1% of them went to an Atlanta Falcons game. So that is using the horizontal percent. You can also use the vertical percent, and you would read it like this. Of those people who went to an Atlanta Braves game, 33.4% of them have eaten out of Zaxby's in the last 30 days, whereas only 11.8% of them have been to an Applebee's. So you can use this data to determine which companies you might want to partner with for sponsorship deals. If we look at the cell display option at the top left hand corner of the page, you'll see we're currently looking at the vertical percent index and horizontal percent. As you're looking at this kind of data, I would encourage you to change this to the sample size. This will give you an actual number of the people who were sampled for this survey. So let's explore some of the sample sizes here. If we look at the people who attended an Atlanta Hawks game, the sample size is 193. That's 193 people out of the total sample size of the total Atlanta, Georgia population for the survey of 2919 people. Now if we keep going down that column there, we can see that 93 of those people surveyed were men and 100 of them were women. Now the Simmons Scarborough documentation states when the sample size is above 70, it's generally pretty reliable information. When the data is between 36 and 70 people for the sample size, it's important to use that data with caution, okay? So the closer to 70, you're probably okay. The closer to 36, your data is going to be a little bit more unreliable. Now if the sample size falls below 36, that data is basically not usable. So you're strongly encouraged not to use that data because the sample size is really too small to make any sort of calculation. So we're looking down the sample size here, we can see that we could use the data for Zaxby's for the Hawks fans because that sample size is 79, okay? So that's above the 70 threshold. However, if we look at the Applebee's data, we see that the data is below 36. So the sample size is really too small to be accurate. So far we've just been looking at Atlanta, Georgia. If we want to look at another city, we can go up to New View, go back down and create another crosstab table and then add a new location. For the sake of time, I've already pre-run a crosstab and collected some data for the Columbus, Ohio DMA. Now what I want to point out is if we scroll down to the bottom of the page, here we see that there's variables at the bottom that are not supported by this selected DMA. That's because there's not information about Braves fans for Columbus, Ohio, nor is there any information about Zaxby's restaurants because I have never found a Zaxby's in Ohio. However, if we do look at the crosstab for Columbus, Ohio, we do see that there is data for Raising Canes restaurant, which is a pretty comparable chicken finger restaurant found in Ohio. Likewise, we find information specifically about sports teams that Columbus, Ohio DMA has access to, including the Browns, the Bengals, the Reds, and of course, Ohio State football. When you're finished creating your crosstab and you want to export the data to use in Excel or to save for later, simply go to the export button in the top right hand corner and choose the Excel format. The data that you export contains an Excel file that has both a percent view as well as the sample size view. You'll want to use both views to determine if you can use the data and how you might apply it to your particular project. In this video, I've shown you how to use Simply Analytics to find data to analyze your local consumer and local sports market and sports fans in that local market. While I've given you a good start, there are many different ways to use Simply Analytics that I didn't cover here because I simply didn't have time. If you'd like to learn more, check out my Simply Analytics Tips and Tricks Guide. There you'll find in-depth explanations and step-by-step instructions in using this very powerful resource. Good luck and best luck with your research.