To have more flexibility in setting up definitions, Google Analytics users can make use of regex. Learn how to use it for filters, goals, segments, audiences, content groups, and channel groupings.
- [Instructor] Google uses Regular Expressions…so that we can have more flexibility…when creating definitions.…With Regular Expressions, or Regex, you can create filters,…goals, segments, audiences, content groups,…and channel groupings.…Regular Expressions can then be used to match…as much as possible.…For example, let's say you want to create the new filter…that would exclude the site data from all of your employees.…
In this case, you could use a regular expression…to exclude all of the IP addresses,…that belong to your employees.…If, for example, the IP addresses have a range,…as indicated here on your screen,…then instead of manually entering all of those IP addresses,…you could create a regular expression…such as the one you see at the bottom of the screen.…That will actually match all of them.…
It is better to keep regular expressions simple, if you can,…as simple as possible, so that others,…who need to interpret, then modify them,…will do so more easily.…Next, you need to learn about how to speak Regex.…
Note: This course picks up where Google Analytics Essential Training leaves off. If you have questions about how Google Analytics works straight out of the box, see that course for more information.
This course is part of a Learning Path approved by the American Marketing Association.
Gain the skills you need to become an AMA Professional Certified Marketer (PCM) in Digital Marketing by using the industry-leading courses and resources in the Learning Path. Take the AMA certification exam to show that you have what it takes to lead the digital transformation.
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- Doing more with analytics
- Understanding user intent
- Setting up site search
- Using content grouping
- Building customized team reports
- Setting up operational dashboards
- Fault reporting with segmentation
- Conversion rate analysis
- Creating a custom attribution model