I am writing a series on filters for GA. This is the first actual implementation post I am doing in the series. This one is called “easy implementation,” because it is just implementation of the three “out of the box” filters I wrote about in Part 2. In Part 2, I only wrote about what they do; here, I write about how to make them work for you.

So first, go to the Filter Manager, found in Analytic Settings. And to make this as easy as falling off a wet rock, here is a picture of it:

Notice how you have to click on the white text which has a charcoal background to create a new filter (the top border), not on the blue underlined Learn More. And the next screen will be similar, you have to click on the white “Add filter,” which is on a charcoal background. Hard to see, easier after you have done it hundreds of times.

Easy Filter #1: Exclude all traffic from a domain. Remember that you are excluding traffic based on the visitor’s domain, and that domain has to be an ISP, before you start this. Here, let me be more clear: you can’t use this filter to exclude all the traffic from Google — it will only exclude the traffic from people who work at Google. Don’t remember this? Well anyway, your screen looks like this:

All you need to do is give the filter a name (and it just needs to be descriptive so that you’ll remember it). Then, use the drop down box to choose “Exclude all traffic from a domain.” Only the final part might be hard — the domain. (In fact, I would love to get email/comments from anyone who actually uses this functionality.) When you screen out a domain, and it is an isp, you are going to exclude a *lot* of traffic. Did you really want to exclude everyone from Verizon? From Microsoft?

Well, let’s say that you do. You can keep your RegEx simple, and merely type in Microsoft. Regular Expressions are greedy though, and will include Microsoft.com and Microsoft.ca and anything else. If that’s what you want, great, and if not, make them specific: microsoft\.com or if you are really compulsive, microsoft\.com$. Need to understand how to use the characters that make up the Regular Expressions, like the backslash and dollar sign?

Easy Filter #2: Exclude all traffic from an IP address. This is a much more selective way of excluding traffic. You’ll do it the same way as above: First you give the filter a name that is meaningful to you, then you choose “Exclude all traffic from an IP address” from the drop-down box, and finally, you create the regular expression that defines your IP address(es). You might just have one, like this: 66.249.72.68, in which case, you’ll write it like this 66\.249\.72\.68 , thereby turning the magic dots into simple everyday dots. Or maybe, you have a range of IP addresses - you can still get them into that one box by using Regular Expressions. So if you own IP addresses 72.77.12.26 through 72.77.12.40, inclusive, you’ll write ^72\.77\.12\.(2[6-9]|3[0-9]|40). If this is complete gobbledygook to you, the way it was to me a year ago, check out this post on Regular Expressions - the same example is used there.

Easy Filter #3: Include only traffic to a subdirectory. So it’s the same drill. First you give the filter a name that means something to you, then you choose “include only traffic to a subdirectory” from the dropdown box, and finally, you create a regular expression that defines the subdirectory. For example, if I only wanted to see traffic to this blog, which is now at http://www.lunametrics.com/blog, I could do it a few ways, but the safest would be like this: ^/blog/

Why? The carat ensures that the directory has to start with the word blog. And because there is a slash after the word blog, the RegEx will stop working at that point — it won’t pick up another random directory calling, for instance, blogging.

And now how do I apply these easy filters? You created extra profiles, right? So use the bottom half of that Filter Manager screen and choose which profile to add the filter to. Alternatively, you can just start with a profile and create a filter for it. (But you still have to find the white text on the charcoal background to Create a Filter.)

Check your filters the next day to see if they are working correctly. If you pull in only the new day’s data, you’ll be sure that you don’t have any unfiltered data. Do they make sense? Compare them to unfiltered data over the same time period - does the difference make sense?

Robbin
Lunametrics

We already talked about what the out of the box filters do in Google Analytics. Why/when does anyone use custom filters? (Note: I really will circle back and talk about how to actually create them. )

Most of the time, people use custom filters, because most of the time, the out of the box filters don’t do enough. All you can really do is exclude visitors from the data, and create a filter that includes a specific section of your site, only. But as soon as you want to do more, or flip one of these on their heads (eg. INCLUDE only the people from your company, so that you can see their internet activity — that’s what eBay did) — well, as soon as you want to do that, you need something stronger. Enter custom filters.

Here they are again, but this time, with explanations of what they do.

1. Include. You can use an include filter to include just about anything you want. Include means, you include what you specify and nothing more. It works like the green plus sign on the little filter box that goes with almost every report in the GA reporting interface. However, you might have multiple include filter on a profile. Example: You have one include filter that says, include everyone who lives in Seattle and another that says, include everyone who uses Firefox. In order to be included in this profile, visitors have to clear both hurdles, so you only see data about Seattlites (Seattlans? Seattle residents?) who uses Firefox.Remember that include filters are going to be one of your best friend.The comments from the last fourteen posts on this blog got lost after we did the conversion, so you can’t see one important comment on this topic. The commenter, Ben, asked, can filters be used to separate marketing campaign traffic such as paid search from the standard traffic? They can and should be used that way.In fact, you can create an include filter to include only Campaign Medium = cpc|ppc (that way, you get the traffic whether it is coded as “cpc” the way that Google does, or “ppc” the way that you might have done it yourself.) More on this when I do implementation posts, coming soon.
2. Exclude. Exclude filters work the opposite from include filters (this is actually not so trivial.) If you have two exclude filters on a profile, the data will be excluded if the data should be excluded by either of the filters. So if we have the same two filters as above, geography=Seattle and a second exclude filter, browser=Firefox, the visitor will be filtered out if either condition is met.
3. lowercase and UPPERCASE. These are the same thing, just opposites. You can use them to rewrite your Google Analytics data in lowercase or UPPERCASE. But why would you want to do that? (I never use this one, so I had to ask.)Helen at Google gave me a great answer:
   “There are a few examples. It’s more than making URIs pretty, but also aggregating the stats for URIs that are identical though they may have varied cases. A good example of why URIs may vary in case are when the published URI is camel-case for readability, but others may get there using all lower-case. [Robbin: When she writes "camel-case," she means, like this: www.LunaMetrics.com, instead of www.lunametrics.com.] Perhaps more likely inconsistencies may be page titles or manual tags (campaign values). Finally, another great example: keywords. I may want to see all the visits driven to my site by “socks,” “SOCKS,” or “Socks” rather than seeing those results in 3 separate lines.”

I love that keywords example, because I see the problem so often.

4. Search/Replace. Google actually does a really nice job with this explanation. They even give two different examples. So I will let you read their explanation. Here’s a short version: You use these filters when you want to make one value look like another value. So you might have a strange URI that you want to be able to read and understand.
5. Advanced. I don’t want to do this one here, it deserves a post all of its own, after the implementation posts that I will write. And I can’t wait to get to it.

Robbin

Filters are hard when you don’t know why to use them, when you don’t know how to use them, and when you don’t know which ones to use. I’ll start with the end, which ones to use when. Even understanding what you have available can be confusing. Note: even though this may seem like an easy topic, there are some interesting things here.

At the “top level,” are three predefined filters plus the ability to create custom filters:

1. Exclude all traffic from a domain
2. Exclude all traffic from an IP address
3. Include only traffic to a subdirectory
4. Custom

Then, within custom filters, there are five other kinds of filters:

1. Include
2. Exclude
3. lowercase

,

4. UPPERCASE
5. Search/Replace
6. Advanced

But wait! you are saying. What about Lookup table? Sorry, it is not supported at this time. Don’t you think the idea of showing something you can’t do is ridiculous? But no one asked me.

In any case, not all custom filters are advanced filters. (I can’t wait to get to Custom Advanced filters, but that is not today.)

I put the first three in boldfaced to point them out. They are the most predefined (or if you like, out of the box) filters that we have, and the ones I will write about today.

Now, let me go through them and talk about when you would use them. (By the way, Michael Harrison at ROI Solutions wrote a nice summary of filters for GA in January.

Exclude all traffic from a domain and exclude all traffic from an IP address are two different ways to not include traffic. Mostly, I see them used to get rid of your own traffic, your web developer’s traffic, your website marketing firm’s traffic. So why use one versus the other?

Well, I’ll use me as an example. Although my company is lunametrics.com, our domain is really our ISP, Speakeasy. And I don’t want to exclude all Speakeasy traffic, because that means, I will have excluded everyone else from there.

So IP address would seem to be a better choice. (To get your IP address, type “what is my IP address?” into Google, and you’ll probably get the answer in the first hit.) On the other hand, every time I log in at Starbucks, my IP address changes. And my domain still wouldn’t be LunaMetrics, it would probably be T-Mobile. Or something like that.

So on the off chance that I haven’t already given enough publicity to this workaround: my favorite answer for a small company is to use this workaround, called “Count me Out!.

However, the Count me Out! workaround is not where you want to go with large companies. It’s hard to get 25 people to go to a site and type in a specific word (I even had a customer who reprogrammed it so that the word was already there and all they had to do was hit “enter”, and I still don’t think I have everyone out of the data. Imagine doing it with a 25,000 person company?) With a large company, you might as well get the IP addresses and take them all out of the data. (But wait, you are thinking, they are a large company, can’t I just use domain name? Well, do a domain lookup [type in “domain lookup” to get a tool), and give the tool one of the big company’s IP addresses. Do you get their company name or do you get Verizon?)

Finally, in the category of “out of the box” filters is: Include only traffic to a subdirectory. That enables you to see just the traffic that is going to just a part of your website. You could also achieve the identical results with a custom include filter, but that one has to wait until tomorrow.

Of course, you should try all your custom filters on separate profiles, like I wrote about earlier in the week. And a separate profile is just about required for the include all traffic to a subdirectory, because if you put it on your good, “prodution” profile, you will lose all the information about the traffic not going to that subdirectory.

Many thanks to Nick and Helen for their help. They didn’t read any of this, so the mistakes are mine, mine, all mine. Coming next: when to use custom filters.

Robbin
LunaMetrics

I promised to write about Google Analytics (in this video). But first, I want to talk about profiles and Regular Expressions, because they will make your work so much easier.

Profiles. So you’re learning about filters, and you’ll probably make some mistakes. Join the crowd. But why make mistakes on the data that you’ve been using for a year now? Keep that “production data” holy, and experiment on a sandbox profile. Even if you think you are an expert at GA, always have at least once sandbox profile, and preferably two.

(Need to understand what profiles are? Well, certainly, you can use a profile within an account to measure a second website. But here, we aren’t talking about profiles for a new website, we’re talking about profiles for the same website. This is one of those concepts that is hard to understand at first, but is trivial once you get it. The idea is, you have multiple copies of your web analytics, all measuring the same thing, and if you set them up exactly the same, they will look exactly the same. However, you don’t have to set them up the same — you can keep one as your “good” copy, and the others can be used to learn. Need to learn how to configure a second profile?)

Having two clean (i.e. no filters) sandbox profiles will help you in a variety of ways: First, you don’t need to worry that the other filters on that profile are messing you up somehow. Second, they both start (one with and the other without the filter) at the same time, so when you write me and ask me why your filter doesn’t work, I promise I won’t ask if you chose a time period that pulled in unfiltered data. Third, since you won’t have taken yourself out of the data (because most people use filters for that, all except those who build special cookie workarounds), you can test it yourself doing all the strange things you’d like to check out.

Regular Expressions. Most filters require regular expressions. Now that I’ve gone through fourteen posts on Regular Expressions (RegEx) for Regular People (and specifically, for GA), I will be referencing that data. And if you already know it, you’ll think that this filter stuff is easy, easy.

Robbin