Corrupted analytics data can have a major impact on your analyzing, tracking, monitoring and optimizing in marketing. Today we’ll show you a set of filters to remove what internal traffic data.

what is internal traffic

At a first look, internal traffic is pretty self-explanatory and seemingly easily to understand. But Internal traffic (traffic to your website coming from your own Employee’s computers, tablets, smartphones, kiosks, POS stations) can tell a different story than customers, clients, and other web surfers coming to your site.

Common corruption causes include the Employee’s default home page being set to the company’s site, Employee resources being accessed on pages containing analytics code, the sales team using the site while talking with customers or clients, vendors accessing site resources or content, and more. Be cautious of these concerns when reviewing traffic patterns.

cleaning your data

Filtering out employee traffic can vary from account to account, however, let’s dig into some of the most common ways to filter and segment your data as well as a few “detective” ways to help find the harder to get data.

Filter Number One: Internal I.P Addresses

The most common and usually most effective way to remove your employee and internal traffic is to create a filter in your analytics that only removes users with a specific I.P. Address or that fall in a range of I.P. addresses. Two things to note with I.P. filtering: 1) Some analytics are implemented with an “I.P. Anonymizing” tag which means despite the filter, Google won’t recognize employee traffic from regular traffic and 2) Filters in Analytics only work in a forward sense, meaning that they won’t remove whatever you’re filtering from your historical data.

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how to:

  • Go to the Admin section of your analytics account
  • If you haven’t done so, create a new view, with a name like: Internal Traffic Filter
    • Tip: Always have one MASTER View for your analytics that doesn’t have any filters, applying filters is a more permanent way to filter data in Analytics so you should always keep one view unfiltered just in case you accidentally filter out the entire internet… Scared yet? Good.
  • Now that you have your new view click the “Filters” Tab under the view column
  • Click “Add Filter” and name your filter Internal Traffic Filter
  • From the drop down menus choose “Exclude”, “Traffic from the IP Addresses” and “that are equal too”
  • In the box below you’ll want to put in the I.P. of the companies wireless. Go to a site like: http://whatismyipaddress.com/ and copy that number string into the text box in analytics.
    • Tip: Check this site on multiple devices at your work. While some companies have a single I.P. address, others may have a “range” of I.P’s. For those with a range of I.P. Addresses, you can enter all of them at once using a regular expression, walk-through here.
  • Once your I.P(s) are entered you’ve set up your first filter! Keep in mind that 1) Filters only affect data moving forward and will not change historical data 2) Your new view will also not include historical data so the sooner you implement an internal I.P. filter the better.

Filter Number Two: Service Provider

The Service provider filter works great if your internal company’s ISP is Unique, often times it’s the company’s name. This filter is definitely not recommended if you are using a “standard” ISP like: Comcast, AT&T, Etc…

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how to:

  • Go to your reporting tab and click “Add Segment”
  • If you already have a spam filtering segment (which you should) click into that, if not create a new segment with “Internal Traffic Segment” as the name.
  • Click on the “Conditions” tab
  • Go to filter “Sessions” and “Exclude”
  • Click the drop down box and select “Service Provider” and “Exactly Matches”
  • In the box to the right, type the name of the company’s service provider
    • Tip: MAKE SURE that your service provider is unique to your company’s traffic and not a “common” service provider like: Comcast, AT&T, etc…
  • This will now filter out all traffic coming from devices that are coming from the internal service provider.

"Filter” Number Three: Employee Opt-Out

This method is less of a filter and more of a proactive approach to the problem, requiring your employees to download an extension that Opts the user’s browser out of sending data to analytics. While seemingly the best and easiest option, there is bound to be stragglers at your company that forget, don’t want to install, etc… So I treat this as a “first step” so to speak to getting cleaner data.

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how to:

  • Google has provided a great extension for Chrome users and a downloadable for Firefox and Internet Explorer that lets users Opt-Out of sending data to analytics. Essentially the download / extension simply blocks the JavaScript on pages that contain Google Analytics code and stop it from connecting to Google Analytics.
  • This method is great in theory but hard to control, unless you can force / beg / plead with all the employees to use the feature on each browser and device you’re bound to get a fraction of your workforce that doesn’t care, forgets, can’t figure it out etc…
  • This is best used in combination with the other actual filters so you have multiple backup systems in place to keep your data clean

Filter Number Four: Tagging Employees

Similar to the above method but with less work on the employees end and more on yours, we can set a custom dimension to tag all employees when they visit a page on your site. This works great for companies whose employees have a login page, or other resource pages on the site that only employees can access.

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how to:

  • Create or Edit an Employee Login page or page you know can only be accessed by employees
  • Implement your custom dimension after the analytics code snippet on the page but before the pageview tag
  • The dimension should look like this: ga(‘set’, ‘dimension1′, ’employee’);
  • So your full GA section in the source code will look something like the  snippet to the left
  • Go to the Conditions tab
  • Filter “Sessions” “Exclude”
  • In the boxes below choose “Custom Variable” “Contains” and then enter “Employee” in the field
  • Now any user on your site with that “cookie” attached to them will be filtered out of your data.

in conclusion

The above methods should give you multi-level / redundant filtering that should capture all of your employee’s and internal traffic and remove them from your analytics. Unfortunately, it’s not always so easy to implement all these methods, or in some cases they don’t exclude all the right data. That’s really where the Sherlock Holmes hat and pipe come out. If you’re not sure that all the right traffic is being removed start snooping around in analytics and see what you can uncover to support your hypothesis. Maybe you’re seeing thousands of visits a month come from the GEO around your office, take a look at the traffic, how often is it coming to the site? How is it getting to the site? How long are the sessions? In this case if you’re seeing Direct traffic, session frequency of 2,3,4,5+ times a month, and navigating to pages that the rest of your traffic isn’t or spending unusual time on the site chances are it’s internal traffic.

Achieve better data with a few simple steps, and make better marketing decisions because of your new business intelligence.

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