Have you considered to link GA4 and BigQuery?

If not, the time is now to consider to link Google Analytics 4 and BigQuery.

GA4 allows users to retain user-level and event data for a maximum of 14 months. Only 360 users can extend this period up to 50 months, and this only accounts for event-level data. But beyond these periods, data loss becomes inevitable.

Linking your GA4 properties and BigQuery offers numerous benefits

Unlimited Data Retention

Link GA4 and BigQuery to store and retain all your data indefinitely, going beyond GA4’s 14-month limit.

High-quality data

Access raw, unsampled data for the highest quality reports and analytics. We’ve written an article to increase understanding in GA4 sampling and thresholding.

Advanced Analysis

Enable advanced analysis techniques for user journey analysis, attribution modeling, Customer Lifetime Value (LTV), retention and churn predictions, and more.

Holistic View of Data

Combine your GA4 data with other data sources in BigQuery for a comprehensive view of your KPIs and user journeys. Think of matching your Search Console data and GA4 (server-side) data in BigQuery for example.

Flexibility in Data Handling

Pre-process data and adapt it to your specific business context. BigQuery provides integrated tools such as Dataform to Extract, Transform and Load the data, expanding on initial data handling capabilities.

Custom Attribution Models

Create complex sales funnels and build your own attribution models to evaluate each advertising channel’s contribution to sales.

Integration with Other Platforms

Feed data back into GA4, such as Google Ads or Search Console data, or other cloud services data.

Automated Reporting

Save time on manual tasks with automated and scheduled reporting.

Seamless Integration with Looker Studio

Use BigQuery as a data source for Looker Studio, ensuring your dashboards have all the information needed and load quick.

And remember, the sooner you link GA4 and BigQuery, the better.

Why as soon as possible?

In short, once the GA4 data export is turned on, the data will start extracting to BigQuery. However, this does not account for data prior to the moment this export has been turned on.

The date range needed to extract meaningful conclusions from GA4 data, or any analytics data, varies widely depending on several factors.

Factors include E.g. the volume of traffic, the nature of the business, seasonality, and the specific objectives of the analysis.

General guidelines you can follow

Short term analysis (days to weeks)

For high-traffic websites, even short-term data can reveal insights about campaign performance, daily traffic fluctuations, and immediate user reactions to changes on the site or marketing initiatives.

Medium-Term Analysis (months)

A few months of data can provide insights into monthly trends, help identify seasonal effects, understand the impact of specific marketing strategies over time, and track progress towards quarterly goals.

Long-Term Analysis (Quarters to Years)

To fully grasp long-term trends and shifts in user behavior, you need to examine data over several quarters or years.

Analyzing this data is essential for evaluating the effectiveness of SEO strategies. It also provides a solid foundation for making informed decisions on website redesigns or significant changes in marketing strategy.

In GA4, user and event data can be retained for up to 14 months, offering the option for 360 users to extend this period to 50 months for event-level data.

This setup allows for the identification of trends and insights within just a few months of data collection. Yet, for strategic decision-making, such as annual planning or evaluating long-term SEO effectiveness, it’s beneficial to have access to a year or more of data.

Having a more extended data history provides a deeper understanding and more significant insights, aiding in more informed, strategic decisions.

So, by enabling it today, you’ll be ready to extract relevant insights ASAP.

Read how to turn on the GA4 export to BQ here.

How are you making the most of your data?

Share insights in the comments!

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