Advanced segmenting of Google Analytics, at its most base function, allows for the isolation and analysis of specific kinds of data. Utilizing advanced segments allows for the freedom to create and select redefined segments such as “Visits with Mailing List Sign Ups” or “Paid Traffic” to better suit your specific reporting needs and can be applied to both contemporary and historical data.
Advanced segments in Google Analytics
For example, a default Google Analytics account offers pre-set segments that will automatically record common visitor groups such as “Non-paid Search Traffic,” “New Visitors,” “Returning Visitors,” and “Paid Search.” However, most websites can and will have types of visitors that defy this default segmentation.
To help you gain added insight and value from your Google Analytics account; here are five extremely useful custom segments that will help you to get the most from your data and hopefully inspire you to play and create your own segments.
1. Conversions by Count of Visit
These segments will quickly become essential to marketers or anyone interested in lead generation for websites catering from the healthcare industry to ecommerce. This Conversions by Count of Visit segment demonstrates the behavior of individuals who will eventually convert after 1, visit, 2-4 visits or 5+ visits to give you a better idea of what content on your site is being most readily consumed and acted upon at different stages in the sale funnel. Feel free to alter the visits you want to count to better suit your marketing goals or KPIs!
Apply all segments to any content report and reveal the evolution of content consumption by specific visit counts. To get even more out of the data, try applying them individually for a more in depth look at your customer’s behavior.
This segment was originally suggested by Josh Braaten of searchenginewatch.com
2. Segmenting ISPs
An Internet service provider report is a fantastic starting point to investigate any strange activity you may see on your site through Google Analytics. By filtering out common ISPs you can identify individual sketchy ISPs that may be sending unnatural traffic.
In Google Analytics, under the Audience section, this segment can be applied to your network report.
This segment was created by Jeff Sauer of jeffalytics.com.
3. Blog Bouncers
Naturally, the majority of your blog traffic is going to bounce fairly quickly compared to the rest of your traffic. It makes sense, many of the visitors are there to skim an article, look at some pictures, and be on their way. This segment allows you to remove individuals who have come to your blog for one page; it does not outright remove traffic from the blog.
By applying this segment to reports that will compare landing pages against your main goal metrics to locate the best landing pages, both blog and any other page on your site.
This segment was suggested by Thom Craver.
4. Cart abandonment by Traffic Source
As with any ecommerce site, you definitely want to know why folks are checking out of the completion process early. While the answer to this question varies, one way to help answer it is by segmenting cart abandonment by traffic source.
This segment allows you to sort out abandonment from Facebook or tumblr or what have you. Make as many segments as you need to cover you primary traffic sources. Then you can apply the segments to your goal reports and discover if traffic abandons differently on a segment-by-segment basis or if there is a universal drop off in your flow.
This segment was suggested by Dan Antonson of SMC Pros.
5. Geographic Brand Ripples
Using this segment series, you can view your brand’s ripple effect throughout an geographically by isolating traffic from cities and viewing a side by side comparison. This could be useful for eCommerce purchases and potential drop-offs as a result of shipping issues to B2B marketing campaigns with prospects who have geographic concerns as part of their buying behavior.
Play with these segments to fit you key target cities, metro, area, and state. Then, collect them in a geographic report to demonstrate local trends.
Segment contributed by James Svoboda or mnsearch.org
Article by Nick Tummarello