Analysing Technical Performance In Google Analytics

Within the behaviour reporting area, there is an area for analyzing site speed where you can gain information on how fast your website and pages load for visitors.

This insight can help inform web development improvements and updates, such as optimizing for specific browsers as in this example and can help get information for speeding up slow loading pages.

This has an impact on the ability to improve bounce rates or identify problems such as un-optimized images or browser compatibility issues.

Site speed reports detail the pages with the slowest load time, analyzing cross-referencing bounce rates, such as in this example on the left, or devices users and geographical location, such as in the example on the right.

The detail provided in these reports are based on individual pages, as well as browser and geographic location, and they are easily cross-referenced metrics such as bounce rates and devices for greater insight.

Another key consideration to assess when improving onsite user experience is to look at devices used to access your site.

Device reports detail whether users come from a mobile, tablet of desktop and provide topline metrics for each of these so you can easily see differences in bounce rates, time on site, conversions, etc.

You can easily segment any report by device for even greater insight and analysis of user interaction and understand how each device works within your website.

This understanding can help improve layout and content on mobiles, for instance, or even give a better understanding of device usage as part of the full user journey.

Website errors can sometimes be uncovered from Google Analytics reports, such as 404 pages in content reports, identified by the title ‘/pagenotfound’. This can depend on your website and set-up, causing drops in traffic and conversions where there are server errors or from the website going offline. Custom alerts can help to notify you of these immediately.

Conversion Journey

Goal funnels, when in place, show how users move through the conversion process and how many reach each stage. In this example we can see that almost 2,500 sessions started the process, but only 559 completed it.

The funnel visualization can help to identify areas within the conversion path that have large drop off rates, as seen here in red, or possible pain points. This highlights parts of the process that could be improved on in order to enhance the user journey and increase the conversion rates. The issues, when investigated could be down to technical issues or poor content.

Assisted conversion reports provide detail on how different channels, and campaigns specifically, contributed to conversions, and what value these channels provided when compared to direct conversions.

It can help to provide greater insight to upper funnel and lower funnels campaign performance and how certain channels act as supporting channels more than direct conversion drivers.

This helps to give more credit to supporting channels, which otherwise might not appear to be valuable in contributing to goal completions. In this example, we can see how many conversions each channel assisted with and the value of those conversions, versus the amount of last click or direct conversions.

The top conversion paths report provides more granular insight than the assisted conversion reports into the most popular journeys users take to conversion. This will specifically layout the most popular steps and channels used by visitors during their conversion journey. In the example shown, we can see the top 10 conversion paths for the website highlighted in the blue box.

Understand that some channels are important from a supporting or upper funnel perspective, and others work better as a direct conversion channel, both help to better plan and operate a full marketing strategy. Attribution modelling can help to determine, based on a set of rules, how credit should be allocated to the different channels along the conversion path.

In this example, we can see and compare how the main channel groups perform as first interaction, last interaction and linear models.

By default, Google Analytics uses last-click attribution for its reports. So any report throughout the account, as well as the e-commerce and goals sections, will be providing data just on the last channels a user came through when they made their conversion. If a user, for instance, came three times through a paid search ad, before coming through an organic listing and making a purchase, the organic search visit will be credited with the conversion and the paid campaigns will receive no credit by default.

Therefore, using and comparing different attribution models can be helpful in understanding how all channels all to the conversion, and help you understand that without those previous paid ads, the users may never have come back organically and converted in the end.

The Model Comparison Tool within the Attribution reporting section helps you compare different models and what value your different channels get within each of these models.

Last Interaction: the default in Google Analytics, which provides credit to the last channel a convertor interacted with
First Interaction: does the opposite and provides the credit to the first channel a convertor interacted with
Last Non-Direct Click: this works similar to Last Interaction, but removes “direct” traffic as a channel and will give credit to the channel before a direct conversion
Last AdWords Click: this will look specifically at AdWords traffic and give more credit and insight to your AdWords campaigns
Linear: this will provide each channel in the journey with an equal share of credit
Time Decay: will provide each channel with credit, but on a time-based model, with channels closer to conversion received a higher ratio of credit
Position Based: will provide each channel with credit but it will be customized by where the channel sat in the full journey, for instance, with the first and last channel receiving the most credit and those in-between just a little.

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