Last May Google announced that they’re giving Google Analytics Premium users ability to access raw session and hit level data. The access to that data is done through Google’s BigQuery tool which enables you to query through massive data sets to answer business questions. With the right approach, having huge volumes of unsampled data can be of incredible value and can provide you with deeper insights of user’s behavior and answer business questions that cannot be otherwise answered using the usual Google Analytics reporting interface or API.
10 Benefits of Using BigQuery
- Querying through massive data sets only takes seconds.
- Google Analytics connected to BigQuery enables you to answer more, deeper business questions in more detail.
- Analyzing massive amounts of traffic data will be done in the cloud. That means that you will not need to get any additional computer hardware to process this much data.
- Applying the right data mining algorithms on the raw data can help you discover patterns of user’s behavior that are otherwise hard to notice in standard reporting.
- As a premium user you’re entitled to $500 monthly credit which covers a big chunk (and in many cases all) of the data storage and processing costs associated with using BigQuery with Google Analytics Premium. More information below.
- It is easy to import custom data sets and use them with your Google Analytics data. Making Google Analytics data richer with your custom data sets becomes very easy when you bring both together in BigQuery.
- The BigQuery API will give you access to unfiltered hits collected from your custom application. With the API you will be able to use the data in your own dashboards, visualizations, analysis, and more… For example, Tableau Software has a connector to BigQuery, making it painless to use this powerful data visualization software to understand your granular digital analytics data.
- With hit based data you can analyze what is happening on your website on a very granular level (second by second, filtering by dimensions…), sequence of interactions down to a particular session level.
- BigQuery is integrated in visualization tools like Tableau, that way billions of rows of data can be processed (on Google’s side) and visualized on your computer in seconds.
- With BigQuery you can access individual user’s hits (anonymized data), which can help you personalize your website for the next time they visit it.
7 Questions You Can Answer Using BigQuery
- What was the second by second response to our TV ad that was shown at 3:41PM? How long did it take till people started coming in from social media? Second by second visualization of the traffic buzz.
- What events played a role in a funnel leading to a goal conversion?
- How many active visitors were on our website on Monday at 11AM compared to the Monday from a week prior?
- What is the most common users’ second interaction on our website?
- What was user with a specific user id most interested in during his last visit? (now lets personalize our website according to their interest)
- What are the most read articles in our blog based on how long it took to write it? Table with the times to write a post is stored outside Google Analytics.
- What is the average time between two user’s interactions when user spends more than 5min on the website?
As a Premium user you are entitled to a $500 per month credit towards using BigQuery. Costs for BigQuery are based on the amount of stored data and the amount of data processed and therefore varies from account to account, but with the $500 you will be able to do a lot!
Cost of storage is $0.026 per GB, per month. A single hit in the sample Google Analytics table in BigQuery takes around 165B* of space. Meaning that in 1GB of storage you could store around 6 million hits or that 1 million of stored hits will cost around 0.004$ a month. To see how many hits your Google Analytics property receives per 30 days use our hitcheck tool.
Processing cost is $5 per 1TB. If we refer to the same byte to hit ratio as before. With $1 you will be able to process around 1 billion hits. But writing queries smartly you can process tens of billions of hits for the cost of a dollar. For more visit BigQuery pricing.
*Based on an example dataset for Google Analytics in BigQuery which has 383 hits and takes up 62.1kB. Your actual data volume will vary depending on the complexity of your data model. Factors such as URL length, referrer data, and custom variables or dimension use can significantly impact the volume of data used per hit recorded.