Exploring BigQuery Cohort Analysis

Cohort analysis is a powerful technique for understanding user behavior and engagement over time. It involves grouping users into cohorts based on a common characteristic, such as the date they first interacted with a product or service, and then tracking their behavior and retention over subsequent time periods. BigQuery, Google's fully managed, petabyte-scale data warehouse, provides an ideal platform for performing cohort analysis at scale.

Understanding Big Cohort Analysis

At its core, cohort analysis is about segmenting users into groups based on a shared attribute and then analyzing their behavior and retention over time. Some common cohort definitions include:

  • Acquisition Cohorts: Users grouped by the date they first signed up or made a purchase
  • Behavioral Cohorts: Users grouped by actions they took, such as completing a tutorial or using a specific feature
  • Demographic Cohorts: Users grouped by attributes like age, gender, or location

By comparing the behavior and retention of different cohorts, you can gain insights into how user engagement and loyalty evolve over time, identify factors that contribute to long-term retention, and make data-driven decisions to optimize the user experience.

Benefits of Using BigQuery for Cohort Analysis

BigQuery offers several key advantages for performing cohort analysis:

  1. Scalability: BigQuery can handle massive datasets, allowing you to analyze user behavior across millions or even billions of events. This is particularly valuable for large, high-traffic applications.

  2. Speed: With its distributed architecture and columnar storage, BigQuery can execute complex queries over large datasets in seconds. This enables you to iterate quickly and explore different cohort definitions and time periods.

  3. SQL Interface: BigQuery provides a familiar SQL interface for defining and analyzing cohorts. This makes it accessible to a wide range of users, from data analysts to business stakeholders.

  4. Integration with Google Analytics: BigQuery integrates seamlessly with Google Analytics, allowing you to import user behavior data and perform cohort analysis alongside other web analytics.

  5. Visualization Tools: BigQuery integrates with popular visualization tools like Google Data Studio and Looker, making it easy to create interactive cohort analysis dashboards and share insights with stakeholders.

In the following sections, we'll dive into the specifics of implementing cohort analysis in BigQuery, including how to structure your data, define cohorts using SQL, and create retention reports and visualizations.

Implementing BigQuery Cohort Analysis

Setting Up Data for Analysis

To begin implementing cohort analysis in BigQuery, the first step is to prepare your data for analysis. This involves identifying the key columns needed from your raw data, such as the timestamp and user identifier.

Here's an example of selecting the distinct date and user ID from a hypothetical users_log table:

WITH user_activity AS (
  SELECT
    DISTINCT DATE(timestamp) as activity_date,
    userid
  FROM `project.dataset.users_log`
  WHERE timestamp < TIMESTAMP_ADD(PARSE_TIMESTAMP("%Y%m%d", @DS_END_DATE), INTERVAL 1 DAY)
)
SELECT *
FROM user_activity
ORDER BY activity_date, userid

This query uses a Common Table Expression (CTE) to create a temporary named result set, user_activity. It selects distinct combinations of activity dates and user IDs, filtering for records before a specified end date. The @DS_END_DATE parameter allows for easy updating of the analysis timeframe.

Next, we'll calculate the first day each user was seen by using the FIRST_VALUE window function:

WITH user_activity AS (
  SELECT
    DISTINCT DATE(timestamp) as activity_date,
    userid
  FROM `project.dataset.users_log`
  WHERE timestamp < TIMESTAMP_ADD(PARSE_TIMESTAMP("%Y%m%d", @DS_END_DATE), INTERVAL 1 DAY)
),
user_cohorts AS (
  SELECT
    activity_date,
    FIRST_VALUE(activity_date) OVER (PARTITION BY userid ORDER BY activity_date) as cohort_date,
    userid
  FROM user_activity
)
SELECT *
FROM user_cohorts
ORDER BY cohort_date, activity_date, userid

This query builds on the previous one, adding a user_cohorts CTE that calculates the cohort_date for each user, which is their first activity date. The FIRST_VALUE function is used within a window partitioned by userid and ordered by activity_date.

Creating Custom Retention Reports in BigQuery

With the data prepared, we can now create custom retention reports in BigQuery. One approach is to calculate the number of days between each user's activity date and their first seen date:

WITH user_activity AS (
  SELECT
    DISTINCT DATE(timestamp) as activity_date,
    userid
  FROM `project.dataset.users_log`
  WHERE timestamp < TIMESTAMP_ADD(PARSE_TIMESTAMP("%Y%m%d", @DS_END_DATE), INTERVAL 1 DAY)
),
user_cohorts AS (
  SELECT
    activity_date,
    FIRST_VALUE(activity_date) OVER (PARTITION BY userid ORDER BY activity_date) as cohort_date,
    userid
  FROM user_activity
),
cohort_retention AS (
  SELECT
    cohort_date,
    activity_date,
    DATE_DIFF(activity_date, cohort_date, DAY) AS days_since_first_seen,
    COUNT(DISTINCT userid) AS users
  FROM user_cohorts
  GROUP BY cohort_date, activity_date
)
SELECT *
FROM cohort_retention
ORDER BY cohort_date, days_since_first_seen

This query introduces a cohort_retention CTE that calculates the number of days between each activity and the cohort date, as well as the count of distinct users for each combination of cohort date and activity date.

Finally, we can create a pivot table to transform the data into a cohort retention matrix:

WITH user_activity AS (
  SELECT
    DISTINCT DATE(timestamp) as activity_date,
    userid
  FROM `project.dataset.users_log`
  WHERE timestamp < TIMESTAMP_ADD(PARSE_TIMESTAMP("%Y%m%d", @DS_END_DATE), INTERVAL 1 DAY)
),
user_cohorts AS (
  SELECT
    activity_date,
    FIRST_VALUE(activity_date) OVER (PARTITION BY userid ORDER BY activity_date) as cohort_date,
    userid
  FROM user_activity
),
cohort_retention AS (
  SELECT
    cohort_date,
    DATE_DIFF(activity_date, cohort_date, DAY) AS days_since_first_seen,
    COUNT(DISTINCT userid) AS users
  FROM user_cohorts
  GROUP BY cohort_date, days_since_first_seen
)
SELECT
  cohort_date,
  SUM(CASE WHEN days_since_first_seen = 0 THEN users END) AS day_0,
  SUM(CASE WHEN days_since_first_seen = 1 THEN users END) AS day_1,
  SUM(CASE WHEN days_since_first_seen = 7 THEN users END) AS day_7,
  SUM(CASE WHEN days_since_first_seen = 14 THEN users END) AS day_14,
  SUM(CASE WHEN days_since_first_seen = 30 THEN users END) AS day_30,
  SUM(CASE WHEN days_since_first_seen = 60 THEN users END) AS day_60,
  SUM(CASE WHEN days_since_first_seen = 90 THEN users END) AS day_90
FROM cohort_retention
GROUP BY cohort_date
ORDER BY cohort_date

This query creates a pivot table that shows the retention rate for each cohort over time, allowing you to analyze user retention patterns and identify areas for improvement. The CASE statements are used to create columns for specific retention periods (e.g., day 0, day 1, day 7, etc.).

By leveraging BigQuery's powerful SQL capabilities, you can easily set up your data and create custom retention reports to gain valuable insights into your user cohorts.

Advanced Techniques for BigQuery Cohort Analysis

Using SQL Queries for Deeper Insights

SQL queries enable us to extract more granular and actionable insights from our cohort analysis data in BigQuery. By leveraging the power of SQL, we can:

  • Segment cohorts based on specific user attributes or behaviors, such as acquisition channel, device type, or purchase frequency. This allows us to identify high-performing or at-risk segments and tailor retention strategies accordingly.
SELECT
  cohort_month,
  acquisition_channel,
  month_number,
  COUNT(DISTINCT user_id) AS retained_users
FROM cohort_data
GROUP BY cohort_month, acquisition_channel, month_number
  • Calculate additional metrics beyond basic retention, such as average revenue per user (ARPU), customer lifetime value (CLV), or time between purchases. These metrics provide a more comprehensive view of user engagement and monetization.
SELECT
  cohort_month,
  month_number,
  AVG(total_revenue) AS avg_revenue_per_user
FROM (
  SELECT
    C.cohort_month,
    DATEDIFF(MONTH, C.cohort_month, O.order_date) AS month_number,
    SUM(O.order_total) AS total_revenue
  FROM cohorts C
  JOIN orders O ON C.user_id = O.user_id
  GROUP BY C.cohort_month, C.user_id, DATEDIFF(MONTH, C.cohort_month, O.order_date)
)
GROUP BY cohort_month, month_number
  • Conduct funnel analysis to identify drop-off points in the user journey and optimize the onboarding or checkout process. By tracking user progression through key milestones, we can pinpoint areas for improvement.
SELECT
  cohort_month,
  COUNT(DISTINCT CASE WHEN milestone_1 = 1 THEN user_id END) AS reached_milestone_1,
  COUNT(DISTINCT CASE WHEN milestone_2 = 1 THEN user_id END) AS reached_milestone_2,
  COUNT(DISTINCT CASE WHEN milestone_3 = 1 THEN user_id END) AS reached_milestone_3
FROM cohort_milestones
GROUP BY cohort_month