Google Analytics is a very powerful free tool used by millions across the world, Google Analytics 360 (formerly Google Analytics Premium) is it’s paid and even more powerful bigger brother. BigQuery, on the other hand, is a fast, scalable and reasonably priced enterprise-level data warehouse for analytics at any scale.
In this article, I am giving you a few reasons why you should consider upgrading to Google Analytics 360 and an option to seamlessly integrate with BigQuery is a pretty good starting point.
Why Google Analytics 360?
As a website owner, marketing manager or even an analyst, you might ask yourself “Why should I start paying for a tool for some extra features when I’m probably not even using all of those available in the free version?”. And that is a totally valid question, in fact, I’ve asked this myself for quite a many times.
Support and Training
Google Analytics is an advanced tool and some customizations require a rather good set of technical skills. Also, reading some reports might not come naturally to those new to digital analytics.
When using the free version, all you have is Google Product Forums and communities such as Stack Overflow. While those options are better than nothing and way better than most free tools, you sometimes have a more complex request or just need an answer fast. With Google Analytics 360, you will have an access to the dedicated support team.
The support team will help you with any upcoming questions, during the setup process or while implementing any changes or updates. Also, they provide an implementation audit to make sure your setup is solid.
Besides support, the paid version also provides a training program with some really good resources for learning how to take your marketing efforts to the next level with Google’s 360 suite.
Data Volume Capacity
The data volume limits might not be an issue if you’re running a small website, e-commerce store or a blog but once you start getting more traffic you might easily hit the limits of the free version.
The following table will give you an overview of the different limits that apply to both free and 360 versions of Google Analytics.
|Data Processing & Limits
|Google Analytics Standard
|Google Analytics 360
|Data Volume Capacity
|Up to 10 million hits* per month
|Up to 20 billion hits* per month (0.5 billion for first pricing tier)
|Max Data Rows
|3,000,000 rows per export
|Custom Dimensions and Metrics
|20 of each
|200 of each
|Views per Web Property
|Max 200 per property
|Max 400 per property
|Maximum Rows in Data Exports
|Less than an hour – Max 4 hours
* Hits = PageViews + Events + Social Actions + Commerce Hits
As you can see, the differences are huge. Although, many businesses might never reach those limits but when you do, your data will be much less accurate! So, if your website is anywhere close these numbers, I recommend you occasionally check it. Don’t worry too much about missing it, though. Google will notify you once you “qualify” for their premium product anyway.
Logically, the premium version of Google Analytics also comes with some great features that are not included in the free one.
One of the main reasons why companies decide to upgrade is the list of tools that seamlessly integrate with the 360. Here’s a list of the most popular ones:
- DoubleClick for Advertisers
- DoubleClick Bid Manager
- DoubleClick for Advertisers
Another great feature has to do with data freshness and accuracy. When using the free version of Google Analytics you don’t have to wait long to get irritated by the sampling, 360, on the other hand, allows you to access unsampled data as well as exporting raw hit-level data. 360 is faster, too, on average the data is 1-2 hours old, maximum 4 hours.
Combining Google Analytics 360 and BigQuery
BigQuery is a Google Developers tool that lets you run super-fast queries of large datasets. It can crunch through terabytes in seconds and petabytes in minutes.
You can export session and hit data from a Google Analytics 360 account to BigQuery, and then use a SQL-like syntax to query all of your Analytics data.
When you export data to BigQuery, you own that data, and you can use BigQuery ACLs to manage permissions on projects and datasets.
Take a look at this guide to learn more about connecting the two.
Turning the Integration into Money
While the list of possible use cases is endless, here’s the one that helps you really to make more money.
Step 1 – Connect multiple data sources
BigQuery allows you to import data from basically anywhere. Use that option to pull data from Google Analytics 360, your mailing list, accounting, CMS & CRM and from any other source that would make sense.
Step 2 – 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.
Step 3 – Predict and target future perfect customers
By leveraging the data you already have in BigQuery, 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.
Google Analytics and it’s paid big brother are amazing tools but to keep up with today’s competition you probably need more.
In case you are new to data analysis, I recommend you start with something easier. Perhaps combining, analyzing and visualizing data from Google Analytics and CRM using Google Sheets and Google Data Studio. But once you have enough data, using way more powerful tools such as BigQuery definitely pays off.
Have a great idea or use case for Google Analytics 360 or BigQuery? Let us know in the comments below.