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Google Analytics have a nifty little feature called Advanced Segments that, simply put, allows you to group your data based on a wide variety of factors. By grouping the data you can analyse different segments more closely.
This post is going to take us through the kinds of segments you can use and how to get the most out of the advanced segments. There is so much scope with advanced segments that if you haven’t used them before you will hopefully be brimming with ideas by the time you’ve read this post.
The following methods are very useful examples of segments you can look at:
Default Segments (already set up for you)
Custom Segments (See, How to Set up Custom Segments in Google Analytics)
One segment alone can’t always tell the whole story, so it’s very beneficial to also combine a number of segments to narrow your data down further and to compare against different segments, for example:
Or you could combine a number of factors to see a very specific segment, for example:
What to look for using Advanced Segments
This would then allow you to see useful information about different types of users, including the volume, interaction, conversions and pages viewed. Once you know how different users interact with the site you can then optimise the website based on what you’ve learnt works for those that convert.
It’s also very beneficial to compared segments against each other for certain data, for example:
What to do with Advanced Segment Data?
Taking this further, here are some examples of the kinds of data you can find to influence your website decisions:
Advanced Segments for Error Handling
Another good use of segments is to see data when errors have happened in your account. Use this post to check for errors in Google Analytics and then you may need to use the following:
Most Common Use of Advanced Segments
If you don’t use any of the ideas above, the least you can do is try this one. It’s my most frequently used Advanced Segment and shows which segment on the graph goes up and which goes down:
Find out using Advanced Segments:
This shows how useful they are on a day to day basis, as well as being essential for in depth analysis. If you have any questions or feedback please drop a comment below.
In today’s multichannel world, there are mountains of data which provide insights into how users have interacted with your business and their path to conversion (or non-conversion). It is important to understand performance with multichannel marketing, which can be achieved through attribution modelling. Attribution refers to assigning credit to something (a channel, touchpoint, etc.) for the role it played in the final conversion. An attribution model is a rule, or set of rules, that assigns this credit correctly to the right channel or touchpoint.
For a long time, Bing, the UK’s second-largest search engine, has been underappreciated and, in some instances, even ignored. Often regarded as the inferior search engine to market leader Google, Bing has historically struggled to appeal to many in the digital world. Most PPC analysts would give justified reasons for neglecting Bing for so long; these include the volume of traffic and the user experience just not matching up to Google. However, the validity of these assessments is now diminishing. Bing has grown and improved rapidly in the last couple of years; if you are not integrating it into your comprehensive digital marketing plan, you run the risk of missing out on a large portion of your chosen market and significant revenue.