Ever wondered how you can exclude data that Google Analytics collects from your own computer? Ever pondered over how to re-write the way data looks in your Google Analytics reports? Ever dream about changing all of your data from lowercase to uppercase? Well, you can do every one of those things and more by using filters to manipulate your profile’s data.
The catch is that you need to write your filters in a language called Regular Expressions. If you’re not familiar with regular expressions, you may want to review the blog post that I wrote over two years ago titled “Stuck Between a ^ and a $ place“. Once you familiarize yourself with regular expressions, you’ll be able to write filters to manipulate your Google Analytics data.
If you’re an administrator of your Google Analytics account, you can click on the “Edit” link next to your profile, and scroll down on the subsequent page to the section of your profile’s settings that shows your filters. This area may be blank, so click on “Add Filter” to start creating your filter.
First, give your filter a descriptive name, like, “Excluding my IP address”. Then, select your filter type, between a “Predefined Filter” or a “Custom Filter”. Personally, I like custom filters much better over the predefined ones, even to do simple filters that you can do with the predefined type – but that’s a topic of conversation for another day.
With custom filters, you can choose from a few different options, as you can see in the image below. You can exclude data, which removes data from appearing in your profile. You can include data, which will only include the data that you enter. You can change the case of a certain data point by either lowercasing or uppercasing it. You can search for a page, keyword, source, or other data point and change what it says with the search and replace filter. And, if you’re feeling adventurous, you can write an advanced filter to change the order of, insert data behind and in front of, and do many, many other fancy things.
If you find yourself looking for ideas or needing clarification on anything, you will find a help menu below the filter creation screen, as in this example for what you should do with a visitor IP address filter:
Before you start firing away with filters as bullets into your Google Analytics profile, do note that:
– You should definitely review my Stuck Between a ^ and a $ place blog post that describes what regular expressions are and how to work with them.
– The order of the filters matters. If, for example, the first filter listed in your profile excludes Yahoo data, your second filter won’t be able to find any Yahoo data to manipulate. You can change the filter order from the main website profile settings page.
– Filters cannot be applied to retroactive data. When you apply a filter to your profile, the data will be affected by that filter moving forward, not even one second before that moment.
– Filters take approximately 24 hours to propagate (e.g. to activate) in your profile.
Two weeks ago, I made a purchase that I’ve been dying to make for a long time: I bought an Apple iPod Nano! And, for the record, it wasn’t I that broke down and bought it, it was my first generation iPod Nano from 2006 that broke down, leading to my buying my new mini-toy.
Boy, do I love my new iPod Nano! It’s 8GB, which for me is more than enough. It’s smooth, slick, and user-friendly, which is a staple of most Apple products. It even has a built-in FM radio tuner, which allowed me to listen to the end of the Yankees / Rangers game last night while biking! Yeah, I’m <3’ing it. 🙂
Naturally, being in the web analytics industry, it got me thinking about my journey from start to finish in purchasing this item, and how all of it would be tracked in Google Analytics, WebTrends, or any web analytics platform. Clearly, if I were in charge of Apple’s web analytics efforts, I could easily pull up a report within a matter of seconds which would show me the exact purchase scenario and tell me exactly what I would need to do next. Right? I mean, how hard could it be for CoreMetrics or NetInsight to show me that I:
– Saw the Apple iPod Nano television commercial five times across two different cable channels;
– Went directly to Apple’s website, found the iPod Nano section, and took a long hard look at its technical specs and color options;
– Listened to my friend ramble on in glowing terms about how great the new iPod Nano was; and
– Played with the iPod Nano at the Apple Store and purchased it (but not before discovering that the Apple Store was out of the 8GB silver, which forced me to choose the 8GB blue one).
For those of you who caught on to the sarcasm in the previous paragraph, you know that we are nowhere near being able to call up such a report from our favorite web analytics vendor. In fact, the only piece of information from the four points listed above that you would be able to access would be:
– Went directly to Apple’s website, found the iPod Nano section, and took a long hard look at its technical specs and color options
The information on the other three points is not in Google Analytics, Oracle CRM or SalesForce. The data isn’t anywhere online, other than in this blog post and in my memory banks. As far as Apple is concerned, I made a single, very-long visit to their website without purchasing something via their online shopping system. On the surface, my visit to Apple’s website could be considered largely unsuccessful, since I never performed an important action, like making a purchase on their website. However, if it wasn’t for the Apple website, I would have never visited the brick and mortar Apple store in the Boca Raton Town Center Mall and purchased the iPod Nano in person in the first place!
To further elaborate upon this point, I wanted to show you what a possible report would look like in an imaginary world where I was Apple’s only website visitor, using the four points previously outlined as purchase influencing factors. Using an arbitrary value of 100 to score a purchase for an individual visitor (myself), a possible report listing the influencing factors for a purchase and their scores could look like this:
3 TV Commercials on Channel A: 0 pts.
2 TV Commercials on Channel B: 0 pts.
Website Visit: 0 pts.
Customer (Friend) Testimonial: 0 pts.
Apple Store in-store Demo: 0 pts.
Color / Model Availability: 0 pts.
Apple Store Purchase: 100 pts.
But in actuality, as far as my particular purchase is concerned, credit is spread across the board, as all of these factors had their own contributions in their own right. I would score my purchase influencers this way:
3 TV Commercials on Channel A: 10 pts.
2 TV Commercials on Channel B: 15 pts.
Website Visit: 35 pts.
Customer (Friend) Testimonial: 5 pts.
Apple Store in-store Demo: 30 pts.
Color / Model Availability: 5 pts.
So, how does Apple (and how do you) really know when a visit to a website is a successful one? How do you really know what source is deserving of the official credit for a purchase (or a conversion)? Unfortunately, there is no perfect answer or one-size-fits-all solution to this very complex problem that all marketers and web analysts face on a daily basis. In my single example alone, I am crediting six different factors as influencers into my purchase of a couple of weeks ago. Using the first scoring model, you would conclude that the website is doing a poor job of selling iPod Nanos. In actuality, in the second scoring model, I gave it 35 points – the biggest slice of my pie – which would indicate that the website is actually doing a great job toward the purchase of iPod Nanos!
My message to you today and the point of this blog post is for you to take a more critical, less cynical approach to the data that your web analytics program is spitting out at you – especially when you are measuring conversions, sales, or purchases. Not only are there going to be multiple influencing factors involved other than what your web analytics package is showing you, but the website that you are in charge of measuring and the online marketing campaign you are running can and will have an impact at your brick and mortar, physical store location or your offline advertising efforts. It did for me.
Google Analytics has introduced a new feature called In-Page Analytics, which allows you to see your performance data overlaid on your homepage and various web pages as you navigate throughout your site. This new GA product is still in Beta but is available to all English users and has been designed to replace the old Site Overlay. In-Page Analytics can be accessed through the Content tab in Google Analytics.
According to Google Analytics, with In-Page Analytics you can view Clicks, Transactions, Revenue, Goal Value and any Goals that you have set up in your Analytics, based on certain links on your website. Bubbles with a percentage of clicks on the current page will appear next to each of the links on your page.
Just as you can use Advanced Segments (i.e. All Visits, Paid Search Visits, etc.) throughout the Analytics interface, you can segment your In-Page Analytics the same way. You can also apply filters for Visitor Type, Geography/Location, Campaign, Keyword, source or even Browser to get very specific on the type of analytic data you are trying to capture.
In-Page Analytics allows you to visually see your website at the same time as you view your website performance data. You are able to see what different visitor types are clicking on which products or services your company offers, and it is very user-friendly and helpful in analyzing website trends and visitor usability.