Google Analytics 360 + Google BigQuery for Predictive Digital Marketing

Google Analytics 360, the full featured website traffic analytics tool, has been integrated with BigQuery. That means you can have full-resolution Google Analytics logs automatically imported to your BigQuery project several times per day. You can fully utilize this data to realize predictive digital marketing - with better insight into your customers' activities.

You may be asking yourself the question: "I already have Google Analytics 360, why do I need BigQuery?"BigQuery offers a fully-managed data analysis service that's fast and scalable for big data analytics. With BigQuery you can gain additional customer insight by combining multiple data sources and performing advanced statistical analysis, operating on your unsampled Analytics data which enables hit-level analysis and still returns your results in seconds.

Note: This article provides a high level overview of how to get started with the Google Analytics 360 + BigQuery integration. For a more in-depth Code Lab and Implementation Guide, check out the tutorial.

Fully managed, scalable and customizable platform for Predictive Digital Marketing

Imagine a world where you can optimize your digital marketing activities such as product marketing decisions, digital ads, and direct mail delivery - and have them based on deeper insights into your customers. You could find the customer who recently visited your web site, has a certain amount of historic spend with your e-commerce solution or in your physical stores, and has shown behaviour similar to previous high-profit customers. You may want to focus on such high ROI customers to realize an effective "more-with-less" digital marketing strategy.

This may sound hard to put into practice, but in reality, many companies already have sufficient data within their Google Analytics logs, corporate databases, or CRM systems to realize this aspiration. The only missing piece is a way to link and correlate those data sources in a scalable, timely, and cost-effective way to extract intelligence for optimized digital marketing.

Now, Google delivers the missing piece: Google Analytics 360 and Google BigQuery integration. By automatically importing unsampled logs from Google Analytics 360 to Google BigQuery, you can easily write SQL queries to correlate your website visitor activity with other valuable business data such as point-of-sale records, online purchase history, and user sign-in logs. Using this combined insight into your customers, you can then generate customized Ad Remarketing data for Google AdWords and DoubleClick.

Benefits of Google Analytics 360 + Google BigQuery Integration

Unsampled, detailed Analytics logs automatically imported to BigQuery

The integration allows you to automatically import unsampled, hit-level Google Analytics 360 logs to Google BigQuery, the Google-scale and Google-speed fully managed query engine, several times per day. This feature allows you to easily track every single activity of millions of users and execute in-depth, ad-hoc analysis in seconds, without being worried about the scalability and availability of the data management platform.

Correlating Analytics logs with corporate databases/CRMs on Google BigQuery to create the customer list of the future

The power of BigQuery is in the ability to import, join, and correlate every single row of massive user activity logs from different sources with each other to extract valuable intelligence from them. In addition to Analytics logs, BigQuery integrates with other Google Cloud Platform services like Cloud Dataflow, and supports third party toolsfor importing a wide variety of business data from existing corporate databases and CRM systems. By linking Analytics log data with your website registration forms, shopping carts, inquiry forms or any customer interactions, you can easily correlate those transactions with customer website behavior to answer questions such as "who are the customers who have a certain spend history in the past and not visited our site for some time?", and "what was the average customer spend by source media (Search Ads, Social Ads, e-mail, organic search etc.) in the last marketing campaign?" These insights can increase the ROI of digital marketing dramatically.

Accelerates scientific data analysis for Predictive Digital Marketing

Standard tools for data scientists such as Google Data Studio, R, Tableau, and the Hadoop ecosystem are the best friends of BigQuery and can be tightly integrated with each other for sophisticated data analysis beyond simple correlations and table joins. One example is a technique known as Audience Extension, used in digital marketing to find potential customers who have a certain quantitative similarity to existing high-profit customers in terms of on-site behavior and various customer attributes. BigQuery can quickly aggregate and filter massive datasets for in-depth regression analysis with R. In short, Analytics + BigQuery + R provides an excellent platform for data scientists to realize a new predictive digital marketing approach.

Getting Started with Google Analytics 360 + BigQuery

In this section you will learn about the general process to take full advantage of the Google Analytics 360 + BigQuery integration. When you feel you have a good understanding of the overall flow, you can try your hand at the tutorial that will walk you through all steps using an example scenario. At a high level, you will perform the following three steps:

Setup Google Analytics 360

To setup your Google Analytics 360 account with automatic integration to BigQuery, follow the instructions below. This may take a few days depending on what services you are already subscribed to. You may also need help from someone with administrative access to your website.

• Sign up for Google Analytics 360: Submit the signup form and our sales representative will walk you through the subscription process. After completing the process, you will receive a notification from the sales representative that you have successfully been signed up for Google Analytics 360
• Configure your website to use Google Analytics: If you're not using Analytics on your website already, you need to enable it
• Create a new project and open the BigQuery Browser tool: Open the Google Cloud Platform Console and press [Create Project]. Open the project and click [Big Data] - [BigQuery] menu to launch the BigQuery Browser tool and take note of your Project ID
• Reach out to your Google Analytics 360 Account Manager to submit a BigQuery export request with your Project ID: Your account manager will take care of your BigQuery export request and will give you a monthly credit of USD $500 towards usage of BigQuery for this project
• That's it! You will see your Google Analytics logs imported into your project several times per day, with BigQuery tables named ga_sessions_YYYYMMDD

Link Google Analytics and CRM data

Google Analytics provides detailed information about your website visitors, including what page was visited, which browser they used, and how long they stayed on each page. You can run reports and learn a lot about your visitors by simply using the reporting that is made available out-of-the-box, but you can obtain much more insight if you combine this data with other information that is stored within your own internal databases, such as customer demographics and what purchases they have made before.

To achieve this goal, you need to have some way of linking the website visitor with a specific entry in your internal database. Google Analytics provides a unique identifier called the Client ID for each visitor, that can be used to tie two databases together. It is important to note that you should never save personally identifiable information (PII)in Google Analytics.

Analyze the Data

After you have signed up for Google Analytics 360 + BigQuery integration, you will automatically see the Analytics logs become available in BigQuery. However, you will need to import your internal data sources to BigQuery using one of several options to quickly and easily load your data.

Once the data is loaded into BigQuery you can clean it up based on your specific requirements. In particular, it is important to choose which relevant variables you will include in your analysis, and which should be excluded. BigQuery includes many powerful functions that may be sufficient for you to create a remarketing list. However, if you want to perform more advanced statistical analysis like regressions, you can use third party tools that integrate directly into BigQuery to run the regression.

Finally, when you have completed your detailed analysis and generated a list of prioritized web visitors that you'd like to reach out to, you can import that list of Client IDs into Google Analytics and build an audience for your new Remarketing Campaign. In the screenshot above, you can see an example of creating an audience that will only include visitors who have a conversion probability of 75% or more based on your statistical analysis. To find out how you can do the same, follow the detailed tutorial on how to create a remarketing list with predictive analytics.