 Hey there, Chad Bonser here for High University Libraries. I've got several hundred, I think about 300 business students, researching the prospect of opening a company in the retail apparel or retail apparel accessories industry in a particular market. This video shows you how you can use SimpliMap, a database to understand market trends in a particular location, looking at things like percent of people who buy a particular item of clothing or shop at a particular store, and also looking at the retail market power of that location. So once again, I show you how to use SimpliMap to find that data to better determine whether you should open that business in that particular location. So here you go. When you first get to SimpliMap, you have the option to sign in if you desire. You can sign in as a guest, but I actually encourage you to create an account with them, just using your Ohio email address. The reason being is once you create an account and sign in, it will actually remember what you're working on last time. So when I log in here, it's going to show me, you know, the last time I was using this, I was working on a number of doors bought by county, evidently, in, I guess, Ohio, is what I was working on here, something like that, something crazy looks like. And then, oh, it looks like I had overlaid the door stores in, that's kind of funny, in each area there. So you can see the kind of things you can find, and these are all, looks like doors, you know, stores that sell doors and windows looks like. So anyway, when you first log in, you'll have the option to do a new map or a new tabular report, or you can go over here and say, I want to compare variables in a table, that's basically this report, or you can make a map or something like that. I'm actually, I usually just go up here and just click on new tabular report and do standard report. That gives you the option to basically do a big old spreadsheet looking thing with different variables here. So we're going to use the example of suits, men's suits and men's clothing for my example, and I'm just going to compare a few cities here. So we're going to go under variables here, and one way you can go in is just go in and do a search for variable if you don't know what the variable is, and we're just going to search for suit and do a search here. And so here we have, looks like we find a few discontinued variables to start with. Let's scroll past those. That may happen on occasion, but then you can get down to percent of women who bought a blazer or jacket, percent. Let's see here if we scroll down some more. Percent men's apparel bought a suit in the last 12 months. That looks pretty cool. So let's just use that variable. And if we scroll down here, we see there's other things we can look at as well. So I'm going to close out that, just kind of show you here. Let's do locations. And let's say we want to just look at cities in Ohio. So we'll just do cities, and let's just do Ohio. And let's do, let's start out with Athens. Sometimes it can take a little bit to load the city there. Let's do Athens. Let's do Kahana. Let's do Bexley. You kind of get the idea, and it's a good way to kind of compare cities. And here we have percent of people by city who bought a suit or projected to have bought a suit in the last year. It looks like there's more people who bought a suit in Athens compared to USA. And that may make sense. We've got a lot of college students who are going to go on job interviews. So that may be a useful thing to look at there. Another really useful variable to look at is the retail market power of an area. So there's a section down here called retail market power. We're going to go there. And this allows us to look at the potential for expenditures in a particular area. If we scroll down here, there's actually menswear. And we can go in and say, either use all variables or I'm going to do, I'm just going to do, see how these are out of order here. I'm going to do household average, use this variable. Retail sales, household average, use this variable. I'm going to do expenditures for menswear, dollar value, use this variable. And retail sales for menswear, use this variable here. And when I close this out, this is going to be added to my table in the order that I added in there. So this shows you, if we look at here, look at Athens, Ohio, for example, this says there are $21 million in sales in Athens, Ohio for menswear. That's the retail market power there. But the community actually spent $17 million. So there's not really much of a gap there. So it's not really worth opening a shop here in Athens, Ohio. Now if we look at Bexley, Bexley has a retail market expenditures for menswear at over $7.2 million. But there was only $274,000 in sales in that area. So if I was going to open a men's shop, that might be a good place to look because there is an opportunity gap to fill there. If you go under the action here and look at the view metadata, this actually explains kind of in detail what I just told you here. So it says here we have an opportunity gap appears when expenditure levels for a specific geography are higher than the corresponding retail sales estimates. Okay, so these are all estimates now. But again, based upon that, we can look at Gehana and see, we can use it as the household average level. The average household spends $1,147 on menswear. And it looks like Gehana only has a household average of sales of $177. So again, Gehana would be a good place to open a men's shop too, based upon that data. Okay, now these cities here were kind of chosen at random just because I chose, you know, basic cities. I don't want it to in Ohio. If you want to kind of identify target areas by using data, you might go up here and do what's called a ranking. So if we do a ranking here, and if we just do locations, and let's just do, I'm going to do states. All right, and we're going to do, let's say we still want to move to Ohio somewhere, we'll move a business to Ohio, but we don't know quite where yet. Okay, so we'll just could do Ohio and states and we'll do variables. Okay, and let's go back under and search for suit again. And this time, instead of the percent, I want to maybe get the number of people who bought a suit in the last 12 months. So if we scroll down here, let's see if we can't find that number scroll through here. So here we have number who bought a suit in the last 12 months, let's use this variable. All right, and we will close that out. And so this is going to go in and it looks like it's found congressional districts. Let's change that to either you could do cities, you can do zip codes, whatever. So here we do cities, and this gives us obviously Columbus, Cleveland, Cincinnati are big towns. So those are going to be the highest number, but it gives you a way to kind of rank. If you want to, you can change this to how many rows you need to kind of identify a particular area. So here we have numbers of people who bought suits in the last 12 months by city and it's a ranking there. So that would be a good way to kind of identify the cities that you want to compare in this report right here when you're looking at the different variables there. Okay, you can also go up and do a map. And so if you wanted to go under and do, let's do locations again, and I'm just going to do recent, since we've already looked at Ohio, and let's say use this variable here. All right, and let's go or use this location, excuse me, and let's go into variables. And I'm going to do recent again, and potentially find percent who bought a suit. All right, so let's use this variable. And this allows us to go in and look by specific geographic area in a mappable way to figure out, you know, what percentage of people the population bought suits compared to everybody else. And if you're like me, you're not from Ohio, you know where these counties are, you can go up here and just under change display options and shows the map labels so you know what counties you're looking at. If you don't like the OSU kind of Pepto-Bismol template, you can go up under edit legend here. And you can change this to whatever color scheme you want to here, or you can change this and really make your map either really pretty or if you're like me and not a graphic designer, really nasty looking and kind of change your map that way. These maps here can be saved as a map image, as a PDF or an image file for your PowerPoints. Likewise, these standard reports we looked at can actually be downloaded as an Excel file to manipulate further in Excel or whatever your file of choice. So again, this is how you look for a retail market potential and identifying places where you might want to potentially open your clothing or apparel business. Hope this video helps you understand how to use SimplyMap. We only use like a couple of different variables and the sky is really the limit as far as what you can find in SimplyMap to better understand your local market. You can find all kinds of census data, household income, all that kind of good stuff. So once again, it's just a kind of a snapshot of what you can find in SimplyMap so hopefully you can use this database to really understand your local market. So you need more help with this database or any others. Look for the contact link on the business blog. We've got to help anyway. I can't take care and best of luck with your research.