BigQuery User Export: Insights and Personalisation

User Data Export creates two essential BigQuery tables:

Pseudonymous User Identifiers and Engagement Data: This table contains data that connects user engagement with pseudonymous identifiers, ensuring user privacy.

User IDs: This table stores user IDs, enabling organisations to link user data with their unique identifiers.

Key User Data Insights

The exported user data provides a wealth of information, including:

User Audience Classification: Understanding which audience segments users belong to is crucial for targeted marketing campaigns.

Predictive Metrics: Predictive metrics, such as user churn rate, estimated revenue, and more, offer valuable insights into user behaviour and potential revenue streams.

Last Active Timestamps: Knowing the last active date of users can inform the timing of marketing efforts and engagement strategies.

Limitations to Consider

It’s important to note that User Data Export in BigQuery contains data that has already undergone extensive processing by Google. Therefore, disparities may exist when comparing user data from this export with event-based user data.

Benefits of Using User Data Export

Enhanced User Segmentation

User data export enables precise user segmentation based on behaviours or attributes, such as targeting cart abandoners with tailored campaigns.

Predictive Analysis

Access to BigQuery user data supports predictive modelling for sales forecasting, identifying high-value customers at risk, and real-time fraud detection.

Audience Insights

Segmenting users by demographics, geography, and device type provides deeper insights for more effective marketing campaigns.

Improved Personalisation

Combining user data and predictive metrics enhances marketing personalisation, delivering more engaging user experiences.

ROI of marketing campaigns

Integrating Google Analytics 4 data with BigQuery allows efficient budget allocation, optimizing marketing strategies for maximum ROI.