Predictive Analytics + Google Analytics 360 Suite

July 08, 2016

Collective Measures
In March 2016 Google announced its new analytics package: Google Analytics 360 Suite. Find out how to utilize the six ‘predictive’ tools offered on the platform at the agency blog.

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In the world of digital marketing, there has been a lot of talk about predictive analytics – the practice of analyzing current and historical data to make predictions about the future or otherwise unknown events. Marketers have been rushing to implement predictive analytics for obvious reasons: if you can predict how consumers will act, it’s easier to get relevant content in front of them at critical points of their conversion path. This process is extremely promising and can be very powerful if implemented well, but can be dangerous if misused.

The “danger” of predictive analytics rears its head when there isn’t enough historical data available. It’s quite easy to collect data that is too specific (not a big enough sample size) or simply incomplete (not enough raw data to establish a trend). In situations like this, marketers increase the possibility of making an incorrect prediction and wasting both time and money.

For example, say you are a toy store that has only collected data from the holiday season. Unfortunately, consumer activity during the holidays is probably not an accurate representation of how consumers act in a non-peak season, which means it’s likely you won’t be able to accurately predict a consumer’s future actions. While this is a pretty obvious example, it is extremely common for brands to collect only a portion of the data and strive to make predictions only from that.


Thankfully, all is not lost! Google is here to help. In March of 2016, Google announced its new analytics package, called Google Analytics 360 Suite. It consists of six tools all working together to give a more thorough, actionable picture of the consumer’s conversion path that ultimately allows you to better target, predict, and understand your consumer.

Below is a brief description of each of the tools included in the suite. Out of the six applications included, four are brand new. They include:

  • Google Audience Center 360 (beta): A data management platform (DMP) that helps marketers understand exactly who their consumers are and find similar consumers to reach out to.
  • Google Optimize 360 (beta): A website A/B testing application that allows marketers to test a variety of web pages to see what works and what doesn’t. This allows you to customize the same site to display alternative versions to optimize towards different demographics.
  • Google Data Studio 360 (beta): A data analysis and visualization tool that allows for real-time collaboration and sharing similar to Google Docs. It also allows for data integration across the suite and other data sources. While it is still pretty young, it’s set up to become a reporting powerhouse.
  • Google Attribution 360 (formerly known as Adometry): Adometry has been completely overhauled and rebranded as Google Attribution 360, and can help advertisers “value marketing investments and allocate budgets with confidence.” That essentially means marketers can see all their advertising campaigns in one place and identify which are the most effective at driving revenue.
  • Google Tag Manager 360: A familiar program that can be used efficiently to track interaction and activity on websites.
  • Google Analytics 360: The cornerstone of Google Analytics 360 Suite. In the new package, this is the measurement centerpiece that collects data from all the other tools and other ad products, such as Google AdWords. It is also set to undergo several updates in the next few months that Google has been keeping quiet, but promises will be helpful.


The Google Analytics 360 Suite is relatively new, with several parts of it still in beta. That said, it already looks incredibly promising. The 360 Suite will help minimize the likelihood of collecting incomplete data by tracking the consumer’s whole conversion journey, rather than just one or two steps. This allows for analysts to have more confidence in their predictive modeling. When used correctly these tools and methods can work together to help data scientists make actionable forecasts that drive revenue.

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