It’s easy to filter people out of your Google Analytics — in other words, to choose not to count visits by some computers. Why would you want to, and whom should you filter?
In general, you should filter out all the people who visit your site as part of your work, rather than as customers or external visitors. You want your analytics to show the people who are coming as possible customers, patients, and clients. You want to know how they find your site, how they interact with the site when they get there, and what causes them to convert (or not).
Sometimes people tell us that they don’t want to filter out their own visits because it would be “depressing.” We understand that. After the filtering takes place, you may have lower traffic figures. However, it is not that you had higher traffic before. It’s just that you had inaccurate data. If you made decisions based on bad data, they were probably bad decisions.
For example, if you check in at your website daily in order to read your blog or get sales information, you’ll have an extra 20-30 visits from the town where you live. When you examine your data to see where your visitors are geographically you’ll get an inaccurately high number in your town.
So we want to stop counting people who are not customers. However, there may be situations in which you will want to keep the data from some group of workers. This came up in discussions about a site we’re getting ready to launch. The owners, a software company, have testing being done at one facility. The people at that facility are not paying customers, but the information about how they use the website is valuable.
Fortunately, we can identify this group by the town they’re in — a small town with limited traffic to the client’s website. If that didn’t work, we might find that we could identify them by their network, or we could set up additional analytics profiles so we could examine the data with and without them.
It’s all about the data. You want to make sure that you’re measuring the information you need to measure in order to make data-driven decisions about the actions you should take.