Use cases for Google Analytics raw data

Google Analytics reports provide aggregated data of visitors, such as total pageviews. In many cases, you will need raw web/mobile visitor data at an individual visitor level and individual hit (pageview or event) level.

For Google Analytics standard accounts (free accounts), SkyGlue can export Google Analytics raw data to databases, BigQuery or Amazon RedShift.

Raw Google Analytics data export to BigQuery (for selected one view) is included as part of the paid Google Analytics 360 subscription. BigQuery is harder to use than standard databases. SkyGlue can help you export Google Analytics raw data to standard databases or Amazon RedShift.

The following are some common use cases for Google Analytics raw data.

1. Integration with other data sources

You can join GA data against data from other sources, such as database, data warehouse, mailing list, accounting, CMS & CRM and from any other source that would make sense.

Some specific use cases include:

(1) “I have a database that contains all the metadata about each article posted on my site and would like to see the bounce rate, conversion rate and new visitors generated by author and topic.”

(2) “On a weekly basis, for each of my logged in customers, I want to see the top 5 products that they viewed but did not buy and add that information into their record in our CRM.”

(3) “Integrate online data with traditional offline data such as transactions in a brick-and-mortar store.”

2. Powering business dashboard

You can use the raw data to create business dashboard with data visualization tools such as Google Data Studio, Tableau.

Using raw data in conjunction with other tools such as Tableau and Python helps Skyscanner execute analysis much more quickly and efficiently than before. “While in the past it was tricky to get a fully unsampled report based on specific segments of users flowing directly from Google Analytics Premium into a Tableau dashboard, now it is simply a matter of writing the query, creating a connection in Tableau to automatically refresh the data daily, and publishing this dashboard to the rest of the company.

3. In-depth data analytics with more powerful queries

No limitations: Even though Google Analytics has many pre-defined reports and you can create custom reports in your GA portal or using its published APIs, you will find limitation in GA query APIs and reports, including data sampling, query dimension/metrics limits, etc.

With raw Google Analytics data, you can run queries with unsampled data, large date range, unlimited number of dimensions and metrics.

Easier and faster: It’s easier to query databases or BigQuery than to query Google Analytics API. It will speed up your workflow and enable you to gain greater insight more quickly.

Below are some sample queries you can run over raw data:

(1) Answer the question: “From 2010 to 2013, which sections of my site had the most volatility in daily traffic volumes?”

(2) Answer the question: “Of the visitors to my site that used a voucher code, how many originally discovered my brand from a voucher code site and how many left the checkout process and returned within 10 minutes with a voucher code? Which codes were used in each case?”

(3) Understand the performance of individual site functionalities/features. See how interactions with those functionalities/features affected conversion rates.

(4) Understand how users interacted with your website over time.

(5) Use Cohort Analysis to estimate customer retention on the basis of their acquisition date and create report in Google Data Studio. (In Google Analytics, the cohort analysis report is limited to a maximum of 3 months of customer data.). You can process the GA data, store them in staging tables, and finally populate the cohort analysis report each month with new and updated data.

4. Data Cleaning and transformation

Clean/Modify data: The data stored on Google Analytics servers cannot be changed. With raw data, you can clean data or modify data.

Below are some specific use cases:

(1) A query can be written to eliminate any additional characters on page titles and then automatically run to generate reports with clean page titles.

(2) Create a new “page group” column to group pages by page titles.

(3) Remove duplicated transaction data due to implementation errors.

5. Marketing campaigns for top customers

The top 20% customers brings 80% of your revenue. You need to identify and retain top customers and create more top customers. You can use the following two steps to create personalized marketing campaigns for top customers using your raw data.

(1) Create a persona for the “perfect customer”:

Combining all the data you have, create a persona for the so-called “perfect customer”. Most probably it is the person who has spent X amount of money in period Y on your website.

The variables X and Y should be set so that only 5-15% of all customers match the criteria.

(2) Predict and target future perfect customers

By leveraging the visitors’ raw data, you can set rules to detect visitors who might yet not qualify as the “perfect customer” based on variables X and Y but do share similar traits with those who already qualify.

Now, all you have to do is to create campaigns targeting especially those who have the highest potential for becoming a future “perfect customer”.

This method will help you raise the ROI of your ad spend by a lot and will provide you with a ton of new data. The data you can use to fine-tune your algorithms and make it even more effective.

6. Data Science and target customers by clusters

Data scientists and analysts can use raw data to create data models and update data models using new data collected everyday. This type of automated user assignment is often used by digital marketers looking to identify and group users based on common interests.

For instance, a pipeline can be created to automatically add new users to a specific user cluster on the basis of their online behavior.

Customer profiles can be developed based on the exported data and can be used to build personalization platforms, customer scoring, customer segmentation and lifetime value models.


SkyScanner use case