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DataStudio

Overview​

We use a Google reporting product known as DataStudio to monitor and analyse the data we capture for user ratings. We have a dashboard that ingests data from our Big Query database table. The dashboard allows us to use filters that based on the data properties we capture allowing us to analyse our data from different perspectives. For example, you are able to filter by user journey or date or country or page location - or any combination of those depending on your aim.

We have a QA report and a production report. At first, when you implement this solution you should use the QA report to test your implementation. When you are ready you should switch over to production as is detailed in the How to setup the backend section of this documentation.

The QA dashboard can be found here: https://lookerstudio.google.com/reporting/99734c09-e379-41f3-beee-8ea49ccf5fca

The dashboard can be found here: https://datastudio.google.com/reporting/ddedfb9d-e8c4-480a-bb54-1606429e423b/page/Ug84C

Both of the above reports should be visible to anyone in the Springer Nature org.

At the time of writing the admins of the dashboard are: ben.clark@springernature.com and roland.payton.1@springernature.com

Dashboard Filters​

Here is a reminder of what the different filters mean:

  • date range: the dates you would like to view data for (blank means all dates)
  • page_location: the page url that the user was on when they used the customer satisfaction solution
  • page_referrer: the page url that the user was on previously before they landed on the page described by page_location
  • journal: the journal affiliated with the page the user was on when they used the customer satisfaction solution (if any)
  • country: the country the user was in when they used the customer satisfaction solution
  • user_journeys: the user journeys associated with the page the user was on when they used the customer satisfaction solution
  • additional_info: extra data that was component was initialised with (added during implementation) to enable that data to be isolated in reporting later
  • suspected_bot: did our solution automatically determine that the user was a bot
  • advert_user_journey: the ability to filter out users that had arrived on our site via clicking on an advert