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In Google Analytics Essential Training, Corey Koberg shows how to use the Google web analytics platform to generate and evaluate information about the visitors to a web site, including data on site traffic, user behavior, and marketing effectiveness. This course covers the out-of-the-box functionality, from account creation to reporting fundamentals, and explains how to glean insights from the vast array of data available.
One of the most illuminating segments to consider about our visitors is simply where they are from, geographically speaking, when they visit our site. This segment we almost do in the real world without thinking about it. Where the web is accessible to the whole world with ease, we know that the market segments of Europe or Asia may interact with our site differently, especially if we ship products to certain places or if we have language dependencies. Google Analytics uses some sophisticated technology to determine, as accurately as possible, where the geographic location of your IP address is and records that data as part of your visit.
We navigate to the Location report in the Audience, sometimes known as the Visitor section. Here we can see a graphical representation of the map of the world, where the darker areas represent more visits. We start with a world view, and we can drill down into the country. From there, we can drill into the states or provinces, which are known as Regions in Google Analytics. And from here we can even drill into individual cities as well. Below each of these maps will be a full table that gives us all the information that's represented in the map above, down here in the table format, along with all the standard metrics, including usage, ecommerce, goals, et cetera.
For example, I can see the number of visits from each individual city and if we zoom out, I can see that California brings twice as many visits than the next nearest state. The interactivity of this report makes it very popular. But as analysts looking for actionable metrics, we need to consider what this map is really telling us and how we can get to the really interesting stuff. For example, unless you have a web site that targets visitors from a very specific region, your map is going to look very similar to this. Why? Well, what other map does this remind you of? What we see here is that very often our visitor map overlay in the country level will look almost exactly like this census map of population density.
Now this makes sense because where there are more people living, those are the places where they're more likely to visit our site from. So it's no surprise that our darkest states--California, New York, Texas, Florida, Illinois--where the largest populations live are also the darkest ones on our map. There's very little actionable about a map that looks largely the same from one web site to the next web site, and almost all of our web sites are going to look like this map. One way to get actionable analytics out of this report is to take advantage of a feature here that many folks don't even realize it's there, the ability to use other metrics to populate this map than visits.
For example, if you are interested in engagement, you can change this map from Visits to Time on Site or Pages/Visit. Here we get an entirely different map, which gives us an idea of our ability to retain and engage visitors who come from this particular area. Here we see that California is still somewhat dark, but now we see some newcomers on here, such as Montana and Vermont. Or we can switch this over to a conversion rate metric and see that the states with the most visitors don't necessarily convert the most. Or if we looked at something like a value per visit, we could be looking at places that would be very profitable for us to do cost-per-click advertising, since the value per visit may be higher. Overall there may be less population, there may be less visitors, but from a profitability point of view, it may be higher.
One very useful way to use geographical reports is to get more information about a direct visit which usually doesn't have very much information. In this case, someone who has just typed in our URL directly, perhaps after seeing a newspaper advertisement. Hypothetically, let's say we weren't getting much of any visits from Indianapolis and St. Louis regions until we advertised in two local newspapers. Now while we have no absolute way of knowing these visits came from those who saw the ads, comparing the increase in traffic with the ad publication dates, we can get a pretty good idea that this traffic was likely due to viewing these ads.
There is one more very important piece of insight we can gain from this. Not only do we see where there are bit more than a thousand visits from each of the cities; we can look beyond the visits to the value of those visits. By looking what happened during those visits, we can see how much each was worth. And we can see now that in this example the number of visits was approximately the same, but the Indianapolis ad was far more successful at getting the right people--in other words, those who spent money to come with the site. As potentially illuminating is this report is, one limitation we need to be aware of with this report is that it is by no means 100% accurate.
If you are connecting through a network, such as a corporate VPN, the IP address that Google sees may actually be located in another city, such as the headquarters of your company, rather than where you're located when you created the visit, so that can result in a false location. It's also possible Google simply was unable to determine where the visitor was located, and therefore unable to put that visitor in a particular geographic segment. Now the visits still occurred. And that needs to be accounted for, so let's simply put into a category called not set. In this case, as we look at the data table, if we increase the number of rows, we can scroll down here, and we see that the number 25 state here is actually not set. 2300 of these visitors could not be put in a particular state category.
The map overly report can be a fantastic source of information once we learn to get pass the less useful aggregate metrics and into the details, where the true insights lie.
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