CXL Institute: Intermediate Google Analytics Course Review (Week-5)

This article is written as part of the CXL Institute scholarship and covers my fifth-week of studying the Intermediate Google Analytics course from the Digital Analytics Minidegree. Previous 2 weeks I have covered about complete Google Analytics for beginner’s from 1 to 20 lessons. , and this week I will be covering Intermediate Google Analytics lessons from 1 to 5. Overall the trainer Chris Mercerhas a great knowledge on Google Analytics and his presentation is awesome.

Fifth Week Lessons and Thoughts on ‘Digital Analytics’

So far I have completed up to 5 lessons from the Intermediate Google Analytics course. I am already having an experience of using GA for a quite long and now added few more skills to my knowledge by attending this course.

Lessons Covered in Intermediate Google Analytics:

  1. Introduction to Google Analytics Intermediate
  2. Clean Data: Filtering Out SPAM
  3. Clean Data: Removing Internal Hits
  4. Clean Data: Cross-Domain Tracking
  5. Finding Answers: Funnel Tracking
  6. Finding Answers: Segments – Part 1
  7. Finding Answers: Segments – Part 2
  8. Finding Answers: Custom Reports
  9. Tips & Tricks: Dashboards
  10. Tips & Tricks: Saved Reports & Alerts
  11. Tips & Tricks: Channels
  12. Tips & Tricks: Multi-Channel Funnel Reports
  13. Tips & Tricks: Attribution
  14. Tips & Tricks: Exporting to Google Sheets
  15. Tips & Tricks: Measurement Protocol
  16. Wrap Up & Resources
  17. Final Exam – Intermediate Google Analytics

Introduction to Google Analytics Intermediate

By the time of completing this course, I have taken my Google Analytics skills to the next level so you’re better able to find the answers you need, when you need them, in a format that makes sense and clearly communicates your “data story”. It has 3 main sections and derived deeply into the lesson wise.

Section 1: “Clean Data”

Google Analytics has a story to tell you, but “dirty data” can make reading that story harder than it needs to be. In this section, you’ll leave knowing:

    • What to do when the user completes an action on an entirely different domain
    • How to tell the difference between your team your actual users
    • How to minimize “spam” in your data

Section 2: “Finding Answers”

Google Analytics can answer some key questions, but only if you know how to get to those answers. After this section, you’ll have:

    • The ability to measure step-by-step in the customer journey funnel
    • A solid framework of how to use segments (and create your own)
    • The knowledge required to create any custom report you need

Section 3: “Tips & Tricks”

This is where the real magic of Google Analytics comes alive! In this section, you’ll cover the various skills you’ll need to unlock your analysis, including:

    • Creating dashboards and other “shortcut” reports
    • Customizing your marketing channels
    • Understanding attribution in a multi-touch world
    • Using Automation to Exporting Google Analytics Data

Clean Data: Filtering Out SPAM

Referral spam may show up to administrators as either a fake traffic referral, a search term, or a direct visit. Referral spambots hijack the referrer that displays in your GA referral traffic, indicating a page visit from their preferred site even though a user has not viewed the page.

Referral spam can’t actually harm your site by triggering a fake visit (as long as you don’t click on the spam link). The problem is that marketers have to manually decipher and filter this type of traffic out of their GA data to make proper sense of it. Since we rely on GA to make major ongoing marketing decisions, clean data means everything to us.

Referral spam, also known as referrer spam or ghost spam, is created by spam bots that are made to visit websites and artificially trigger a page view. Without knowing about referral spam and how to filter it, marketers could be making weighted conclusions based on bogus bot traffic.

How to Filter Referral Spam in Universal Analytics

To get started, first create a new view. It’s a best practice in GA to test new configurations like filters in a new view, instead of in your default raw data view since changes can be permanent and mistakes can be made along the way.

  • Click on Create View on the far right-hand column
  • How Do You Identify Spam Referral Traffic in GA?
  • Select the type of view you are creating, either Website or Mobile app
  • Then give it a name, and select the same regions and time zone as your main view
  • Google will do the bulk of the referral spam filtering work for you automatically.
  • Navigate to your test view View Settings and ensure that the option to Exclude all hits from known bots and spiders is selected. By checking this off, you’ll automatically and easily be able to filter out about 75-80% of bot traffic.

How Do You Identify Spam Referral Traffic and Remove it in GA?

If you want to see if the websites that you suspect to be spam in your Referrals report actually are, first check if they’re on this list or this list of known spam websites.

  • Head over to your Referrals report, and filter by descending bounce rate.
  • Now, apply an advanced filter to only show a number of sessions over a certain threshold.
  • That number can vary according to your traffic volume.
  • You’ll have to do as much verification as you can without actually clicking through and visiting these spammy websites.
  • Once you’re sure, create your list in Notepad or Text Editor so you can paste it back into GA.
  • It is important to roll out filtering in your test view account first.
  • Navigate to your new testing view, click on Filters, then Add Filter to create a new one.
  • Give your new filter a descriptive name like Referral Spam for easy identification later on.
  • Change your Filter Type to Custom, and change the Exclude Filter Field to Campaign Source (not the Referral field).
  • Finally, paste your pre-made list of referral spam URLs:
  • Once you start filtering referral spam, you can start to see how much it was and is affecting your traffic.

Clean Data: Removing Internal Hits

Exclude Internal Traffic

Filter out traffic to your website from people on your corporate network. Most of the time, Analytics is used to track how external customers and users interact with your website, since internal traffic patterns are typically different from external traffic patterns. Therefore, we must need to exclude internal traffic in GA. When your reporting views contain hit data from both internal and external users of your website, it might become difficult to determine how your customers are actually interacting with your website.

As an example, let’s say you have an ecommerce website. Internal traffic might include stress testing that will send a large number of hits to a particular page on your website. Your reports will show a large number of hits on this page and it will be difficult to discern how many hits came from customers and how many hits came from your stress testing.

Create an IP address filter

  • If your network is IPv6 compatible and you are sending IPv6 addresses to Analytics, then make sure your filter uses the IPv6 format for those addresses.
  • If you are sending IPv4 addresses, then make sure your filter uses the IPv4 format for those addresses.
  • To prevent internal traffic from affecting your data, you can use a filter to filter out traffic by IP address.
  • You can find the public IP address you are currently using by searching “what is my ip address” on You can find out what IP addresses and subnets your company uses by asking your network administrator.

To create an IP address filter:

Follow the instructions to create a new filter for your view.

  • Leave the Filter Type as Predefined.
  • From the Select filter type menu, select Exclude.
  • From the Select source or destination menu, select traffic from the IP addresses.
  • From the Select expression menu, select the appropriate expression.
  • Enter the IP address or a regular expression. See the examples below.

Clean Data: Cross-Domain Tracking

Cross-domain measurement makes it possible for Analytics to see sessions on two related sites (such as an ecommerce site and a separate shopping cart site) as a single session. This is sometimes called site linking. To set up cross-domain measurement, you’ll need to be comfortable editing HTML and JavaScript, or have help from an experienced web developer.

To measure sessions, Analytics collects a Client-ID value in every hit. Client-ID values are stored in cookies. Cookies are stored on a per-domain basis, and websites on one domain cannot access cookies set for another domain. When you measure sessions across multiple domains, the Client-ID value has to be transferred from one domain to the other. To do this, the Analytics code has linking features that allow the source domain to place the Client-ID in the URL parameters of a link, where the destination domain can access it.

Cross-Domain tracking can be setup using 2 methods:

  1. Set up cross-domain measurement using Google Tag Manager
  2. Set up cross-domain measurement by modifying analytics.js

In the next week post I will be covering the remaining lessons of Intermediate of Google Analytics course from Digital Analytics mini degree from CXL. Feel free to come back on every Mondays and read about CXL Digital Analytics mini degree review.

Thank you!



I am Isak, founder and author of blog. A best place to have article through guest posts. I love reading (learning), sharing my skills and knowledge with all over the world using modern digital platforms.