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Understanding Google BigQuery: The ultimate tool for marketing analytics

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What is Google BigQuery?

As a marketing manager, you are constantly looking for innovative ways to improve your marketing strategies. BigQuery is one of the most important tools for effective marketing analysis. But what exactly is BigQuery and for what purposes can it be useful?

BigQuery is a Google Cloud Platform data warehouse service that lets you analyze large amounts of data in real time. This means that not only can you use BigQuery to store large amounts of data, but you can also analyze that data quickly and efficiently. This makes BigQuery an indispensable tool for marketing managers who need to manage and analyze large amounts of data.

Use cases for BigQuery

  • Track Marketing Campaigns: With BigQuery, you can track and analyze your marketing campaigns in real time. You can integrate data from various sources such as Google Analytics, AdWords and CRM systems and draw conclusions for an improved marketing strategy. This way you can make sure you’re making the right decisions.
  • Understand customer behavior: BigQuery also helps you better understand your customers behavior. You can aggregate and analyze data about customers interactions with your website, email marketing campaigns and social networks. This way you can better understand the behavior of your target group.
  • Optimize marketing budgets: You can use your marketing budget more effectively with BigQuery. You can measure and optimize the effectiveness of your marketing campaigns to achieve the best possible result. So you can use your budget wisely.

Problems BigQuery can solve

  • Processing large amounts of data: BigQuery is capable of processing large amounts of data in real time, making it an ideal solution for companies working with large amounts of data.
  • Data integration: BigQuery makes it possible to integrate data from different sources to get a more comprehensive picture.
  • Slow data analysis: BigQuery lets you analyze data quickly and efficiently without lag.
  • Data security: BigQuery offers high data security and compliance because the data is stored in the Google Cloud servers.
  • Cost-effective: Compared to other data warehouse solutions, BigQuery is cost-effective because it is based on a pay-per-use model and requires no upfront investments.
  • Scalability: BigQuery is highly scalable and can be easily scaled to meet the growing needs of your business.
  • Ease of use: BigQuery offers an easy-to-use interface and intuitive APIs that allow users to easily analyze and visualize data.

Why process Google Analytics data in BigQuery?

  • Processing large amounts of data: If you are working with large amounts of data, it may make sense to process this data in BigQuery, as it is a powerful platform for processing large amounts of data.
  • Advanced Data Analysis: BigQuery allows you to gain deeper insights into your Google Analytics data as it offers advanced analysis capabilities which are not available in Google Analytics.
  • Data cleaning: BigQuery allows you to clean data before using it for analysis to ensure you’re working with accurate data.
  • Data archiving: BigQuery allows you to archive Google Analytics data to ensure that data is available for the future.
  • Cost-effective: Compared to other data warehouse solutions, BigQuery is cost-effective because it is based on a pay-per-use model and requires no upfront investments.
  • Data integration: BigQuery lets you integrate data from Google Analytics with data from other sources for a more complete picture.
  • Data Consolidation: If you have multiple Google Analytics profiles, you can consolidate that data in BigQuery to get a unified view of your data.
  • Bypassing the LookerStudio quotas when using the native GA4 connector: data can be processed directly in BigQuery. This alone can lead to significantly faster evaluations. There have been quotas for data evaluations via the native GA4 Connector since November 2022. These have a very small volume, so that the great range of functions of LookerStudio can no longer be used as before. Data stored in BigQuery can be visualized in LookerStudio, even if the amount of data to be processed is too large for the native Google Analytics connector.

How to connect data from GA4 and CRM system in BigQuery?

  • Data export: Export the data from GA4 and the CRM system to BigQuery. This can either be done automatically via an API or manually via CSV files.
  • Data integration: Integrate the data from GA4 and the CRM system into BigQuery. Here you can create tables or views to merge the data.
  • Data Analysis: Use SQL queries to analyze and clean the data from GA4 and the CRM system.
  • Data Visualization: Link BigQuery to a data visualization tool like Google Looker Studio to graph and analyze the data.
  • This connection makes it possible to combine customer data from the CRM system with behavioral data from GA4 to gain deeper insights into your customers and make better decisions. Using BigQuery as the central data store also makes it possible to process and analyze data safely and efficiently.

What are the requirements to start BigQuery?

  • Google Cloud Account: You must have a Google Cloud account to access BigQuery.
    This in turn requires:
    – a Google account
    – a payment method (credit card number)
    – Company contact information incl. name, address, phone number and email address
    – Billing information including billing address and contact person
  • Knowledge of SQL: BigQuery uses the SQL language (Structured Query Language) for queries and analysis. It is helpful if you have basic knowledge of SQL.
  • Data analysis and visualization tools: You need a tool to visualize and analyze the data from BigQuery. Google Looker Studio is best suited for this.

Expected costs of Google BigQuery

The cost of connecting BigQuery to Google Analytics 4 depends on several factors such as:

  • Usage: BigQuery is priced based on the amount of data that is queried and the computing power used. The cost of GA4 depends on the amount of data sent and the number of times the data model is accessed.
  • Storage: You will also be billed for storing your data in BigQuery.
  • Data transfer: Depending on the size of your data and the traffic generated by your applications, data transfer costs may apply.

It’s difficult to give an exact price without knowing the specific needs and usage patterns. It is possible to check BigQuery pricing on the Google Cloud website and use the GA4 pricing calculator to get an estimated cost. Also note that there may be free or reduced prices for certain usage scenarios or billing models.

The GA4 pricing calculator is available on the Google Cloud website

Follow this link to Google Cloud. Here you can calculate the costs for your configuration by specifying number of events per month, number of hits and other relevant factors.

If you have additional questions about BigQuery or would like to learn more about the features of GA4, contact us. We will be happy to further assist you.

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