QueryMaster-GA4 BigQuery Analysis

Example

SELECT event_name, COUNT(*) AS event_count FROM {table_name} WHERE event_date BETWEEN '20230101' AND '20230131' GROUP BY event_name;

Scenario

A user wants to analyze the frequency of specific events within their app or website over a given month. QueryMaster generates a query to count occurrences of each event, helping the user understand user interaction patterns.

Example

SELECT user_pseudo_id, COUNT(DISTINCT session_id) AS session_count FROM {table_name} WHERE event_date BETWEEN '20230101' AND '20230131' GROUP BY user_pseudo_id;

Scenario

To understand user engagement, a business analyst seeks to identify active users and their session counts over a specific period. QueryMaster provides a query that segments users by activity, offering insights into retention and engagement.

Example

SELECT user_properties.value.string_value AS country, COUNT(DISTINCT user_pseudo_id) AS user_count FROM {table_name}, UNNEST(user_properties) WHERE user_properties.key = 'country' GROUP BY country;

Scenario

A marketer aims to tailor campaigns based on user demographics, needing a breakdown of users by country. QueryMaster crafts a query to aggregate users by their country, enabling targeted marketing strategies.

Example

WITH filtered_events AS (SELECT event_name, event_date FROM {table_name} WHERE event_name = 'purchase') SELECT event_date, COUNT(*) AS purchase_events FROM filtered_events GROUP BY event_date;

Scenario

Handling large datasets efficiently is crucial for performance. QueryMaster advises on structuring queries with CTEs or partitioning to optimize query execution and manage resources effectively.