Anna can help marketing and sales teams automate website performance analysis, drill into website visits, page views, sessions, or user trends as well as spot anomalies and outliers. However, it is important to note that the raw data from Google Analytics will require some data work to get it ready for Anna.
This article will cover:
Example insights from this use case
Recommended data structure for this use case
What sort of insights can Anna help me uncover?
We've outlined some example questions which to provide an indication of the types of insights you can expect with Anna:
Acquisition performance analysis
Visits by source this quarter
Year to Date trend in visits from mobile devices
Visitors by city this month
What’s driving trends in number of bounces
Bounce rate by source
Number of entrances by page path
Behaviour trends and analysis
Average number of page views per visit
Average visit length by city
Average time on page by page path
Top page views by page path
Exit by page path
Average number of sessions per user
How do I structure my data?
Anna requires structured, transactional data, with at least 1 measure (e.g. Spend) and 5 segments (e.g. source of visit). In addition, we recommend at least 7 months of data (at monthly or daily granularity) so you can take full advantage of Anna's Unexpected Changes feature.
For Google Analytics data it is likely that some additional steps to categorise data for Anna’s Natural Language Processing (NLP) will be required.
Example data structure
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Please refer to this article for more information about data structure.
Here are some of the typical segments we find in Google Analytics data. A segment is a qualitative value, like names or categories:
Device Category (desktop, mobile, tablet)
Previous Page path
Source / Medium (e.g. referrals from sites like LinkedIn, or from email campaigns)
A measure is a quantitative, numeric value. Some of the typical measures include:
Time on Page
Visitors (unique visitors)
Visits (number of visits)
Any recommendations on the process to extract and transform the data?
Step 1: Extract
You may need to use a specific tool / solution to help you securely and reliably move data from Google Analytics into your databases, warehouse or lake. Please reach out to us if you have any questions on this.
Step 2: Transform
Once you’ve extracted your data you may need to make some further transformational changes. This can either be done in your database itself or via creating SQL query in Anna. See here for further information.
If you have any questions, please contact us at email@example.com.