How Accurate is Your Google Analytics Location Data?

We all love Google Analytics, right?   It gives us an abundance of data, but it can be hard to sift through it and know what to truly focus on.   In my recent blogs, we’ve looked at the hidden potential of your analytics and we even took a closer look at bounce rates.   This time we’re going to continue our journey and look at the location metrics.  On the surface, location data is awesome!   Of course you’d like to know where your readers are located.   But how accurate is it, and how much weight should you put into these stats?   Like everything in Analytics, you have to know the caveats and the limitations of the data.   And you have to know your products and your users.

How does Google track user location?

Google Analytics tracks user’s location based on their IP address.   That means users are tracked based on where their internet connection is, not necessarily where they are.   Let’s look at an example.   I’m at my desk… Kolbeco, 1676 Bryan Road, Dardenne Prairie, MO.   I pulled up two tabs on my computer – our website ( and our Analytics account (the realtime location report to be exact).   Immediately I can see myself pop up on the location map.   It’s pretty close; analytics is showing me in O’Fallon.    Then I opened the website on my phone.   Within seconds a second pin appears on the map, but it’s not in O’Fallon.   My phone is registering in St. Louis.   Not a huge difference, but why are they different at all?   My desktop is using our office wifi and my phone is using my data plan.   One user, one office, two different internet connections, two IP addresses and two locations in Analytics.   I know that O’Fallon to St. Louis isn’t a huge difference – 30 miles give or take – but in your local marketing strategy 30 miles can be a lot.


How much weight should you put into user location?

Now that we’ve looked at the “how,” let’s complicate things a bit more.   Even if the reported locations were 100% accurate, we live in a mobile world.   We move around, we browse on-the-go and we can cross the country in a few hours.   That’s why it’s so important to know your users, especially if your mobile traffic is heavy.

Earlier this year, my family and I went on a cruise.  It was a cold and snowy day here in St. Louis when we packed the car and headed to New Orleans the day before we set sail.   We checked into a hotel for the night when I realized that I had packed my camera, memory cards, and lenses, but no battery charger and only one battery teetering on a 27% charge – certainly not enough life to get me through a week.   It was 11:30pm and we were supposed to be on the ship in less than 12 hours.   With no time to go shopping, I turned to Amazon.   I placed an order late that night for pickup at our first port of call – Miami.   We got off the ship three days later in Florida and headed straight for an Amazon locker 5 minutes away.   This may be a shameless plug for Amazon Prime, but I couldn’t have asked for a more convenient solution.

In this scenario, I live in St. Louis, but Google saw me in New Orleans and Miami.   To Amazon, which location is more important; St. Louis (where they get most of my money), New Orleans (where my order was placed), or in Miami (where the transaction was completed)?   Now, I know better than that – Amazon knows me and my every need.   But think about your business; if you were basing your marketing strategy on my Analytics location, where would you spend your budget?   This may not have been a normal situation, but it’s important to keep these scenarios in mind while looking at your location reports.   They’re not always what they seem.

Like everything in Analytics, you have to understand the metric before you can understand the data.   Location is no different.   Think of it as a guide; it’s a glimpse into your audience, but don’t take it with 100% accuracy.   Look for trends and focus on understanding your customers.   In the end, you need to know your products, know your users and use the Analytics data to support your decisions – not to make your decisions for you.