There has definitely been a lot of smoke around the new version of Google Analytics known as App + Web or Firebase analytics for web. The new version brings on board a new data model where we will track everything tied to events rather than sessions. This doesn’t mean no sessions; they still exist but are calculated differently. This property setup is not limited to just GA360 users, even the standard GA users can leverage this new update.
Some of the advantages of the new measurement model are:
• Unifying application and website data.
• Ability to calculate total unique users across mobile and application platforms.
• Cross-platform reporting.
• Solve cross device attribution issues.
• Enhanced tracking.
• Advanced analysis.
• Integration with BigQuery.
If you are excited and want to have this property up and running refer to Krista Seiden’s blog Step by Step: Setting up an App + Web Property or if you want to be aware of the limitations of working with this property at this moment then refer to this post Why You Shouldn't Only Use Google Analytics App + Web Just Yet .
In this post the major emphasis will be on the exploration plot in the advanced analysis reports. These reports are fancy in terms of adding additional functionality to the existing GA standard reports where we are limited to analyse not more than 2 dimensions together and also when we need to conduct cross- tab analysis slicing and dicing segments, columns or rows.
Looking at the below GA standard report we were restricted in pulling more than 2 dimensions side by side for our analysis. We need to toggle across reports or build segments for further analysis.
On building the same report on the new property we were able to easily build a cross-tab and monitor source and medium performance across devices.
If you are perplexed looking at the metrics in this report, then don’t be Google have got rid of some old metrics and have re-calculated them differently for instance engaged sessions are users on your website/application for more than 10 seconds.
Refer to this blog How to query and calculate GA App + Web event data in BigQuery to deep dive into understanding more about these new dimensions and metrics.
Nuts and Bolts:
Wide range of visualisation at our disposal:
We can leverage the available donut, line, bar, scatter and Geo charts to make better sense of the data we are analysing. Use the + icon to add additional visualisations, at this stage we can have a maximum of 10 visualisations.
Each visualisation provides its own tab settings which we need to configure to get meaningful plots. As indicated below select the respective plots and configure them respectively.
While I was using line plots, I came across this amazing setting of anomaly detection. Anomaly detection is a technique we use to identify outlier data, events or observations which show deviance from majority of data. Here we are given an option to train the data for maximum of 23 days to identify outliers in data.
For instance, if we measure the engaged sessions across source for mobile traffic and train anomaly detection for 10 days. The model has identified an anomaly for a particular source and provides actual versus expected values.
More the merrier rows, columns and segments:
For us to use segments, dimensions and metrics in the cross-tab plot we need to add them to the variables section. As shown below click on the + icon to add, by default there are some pre-defined segments, dimensions and metrics available at our disposal.
We can use the pre-configured segments or create dynamic segments we’ll get to this in a bit. But if you are curious to know about the available dimensions and metrics refer to this Google doc Reports, dimensions, and metrics.
Now that we have the required dimensions, metrics and segments its just the matter of dragging them across to respective sections to create the plot. At this stage we can add a maximum of 4 segments, 5 row dimensions, 2 column dimensions, 10 metrics(values) and 10 filters.
While adding row dimension we have an ability to nest rows. To nest rows just toggle to yes under Nested rows option. For instance, below we can see how organic and cpc mediums are nested under Google source. This makes it super easy to analyse data.
If it’s getting tougher crunching those numbers on the cross-tab ,then we get 3 additional options to view data i.e. bar chart, plain text and heat map. By default, the bar chart options are selected for all charts. Once we have the values selected toggle between the cell type options to choose the appropriate option.
We can use filters to restrict data being analysed in the cross-tab. Drag the required dimension or metric from the variables section or select them from the available list. It is important to keep in mind subsequent filters chosen follows AND logic which means both the conditions must be true for data to populate.
Benefits of using exploration plot
As mentioned above it is now possible to build segments within the analysis hub. Just navigate to the variables section and click on the + icon. A display screen similar to the one below is available.
We are provided with 2 options either to use custom segments or suggested segments. Use custom segment when we need data filtered by user, session or event. Suggested segments provide easily usable templates to filter data by demographics, technology and acquisition.
This above segment is similar to standard segments in universal analytics but with some noticeable changes. First let's chase the similarities, we are still provided with conditional group and sequences as well as a summary chart indicating aggregated sessions and users available on using this segment.
Notice now we have no single option to toggle between include or exclude users we are provided a separate section to define exclude user criteria.
On the top if we click on the person icon, we get to choose between 3 condition scoping i.e. across sessions, within the same session, within the same event.
The description section comes handy, when we have more than one user in the organisation accessing this segment. Additionally, we can build an audience from within the segment by specifying the membership duration in days.
Dynamic controls on cross-tab data:
Right click on the filed on which you want to filter data on. Here for instance on choosing direct we get options to include, exclude, build segment or audience based on direct traffic.
On clicking on the view users options above, a segment with direct traffic is created and a user explorer report becomes dynamically available with users who came to the website through direct source.
On selecting any user from the user explorer report above a new user activity report is generated indicating events, the user completed in the chosen time period.
These type of above dynamic controls makes it easier for conducting granular level of analysis of the available data.
Beware of the caveats:
Maximum data range you can look back for analysis is 3 months. There is no update on this currently, but I am sure this will be fixed in near future.
While creating a data stream we choose a data stream name which is available as a dimension but at this stage the field populates stream id as its values.
With App + Web still in beta there are a lot of expected developments in the upcoming months. So if you are an organisation which runs applications on Google analytics for firebase then it is recommended that you set up this new property but if you are organisation with website data stream then, it would be advisable to set up these property in parallel to your existing property.