SQL for Funnels and User Journey Analysis

This lesson dives into using SQL to analyze user journeys and conversion funnels, crucial for understanding user behavior and optimizing growth. You'll learn to map user flows, identify bottlenecks, and build visualizations, ultimately gaining insights to improve your product or service.

Learning Objectives

  • Define and construct SQL queries to analyze conversion funnels.
  • Use SQL to track user journeys and identify key steps in a user's experience.
  • Analyze funnel metrics like conversion rates and drop-off points.
  • Apply advanced SQL techniques like window functions for user behavior analysis and Cohort Analysis.

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Lesson Content

Understanding User Funnels and User Journeys

A user funnel visualizes the steps a user takes to achieve a goal, like making a purchase or signing up for a trial. Each step in the funnel represents a user interaction. The user journey is a broader concept encompassing all the interactions a user has with your product or service. Analyzing both allows you to optimize user experience and improve conversion rates. We'll be using a hypothetical e-commerce dataset for our examples, simulating events like 'page_view', 'add_to_cart', 'checkout_started', and 'purchase'.

Building a Simple Conversion Funnel with SQL

Let's create a basic funnel. Assuming we have an events table with columns like user_id, event_type, and timestamp, we can track the progress through the funnel.

SELECT
    event_type,
    COUNT(DISTINCT user_id) AS user_count
FROM
    events
WHERE
    event_type IN ('page_view', 'add_to_cart', 'checkout_started', 'purchase')
GROUP BY
    event_type
ORDER BY
    CASE
        WHEN event_type = 'page_view' THEN 1
        WHEN event_type = 'add_to_cart' THEN 2
        WHEN event_type = 'checkout_started' THEN 3
        WHEN event_type = 'purchase' THEN 4
    END;

This query gives you the number of users at each step. You can then calculate conversion rates (e.g., add_to_cart / page_view) to identify bottlenecks. The ORDER BY clause ensures the events are presented in a logical sequence.

Analyzing User Journeys with Time-Based Tracking and User Flows

To understand user journeys, we need to track the sequence of events per user. This is where time-based analysis becomes crucial. We can use subqueries and window functions to achieve this.

WITH user_event_sequence AS (
    SELECT
        user_id,
        event_type,
        timestamp,
        ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY timestamp) AS event_sequence
    FROM
        events
    WHERE
        event_type IN ('page_view', 'add_to_cart', 'checkout_started', 'purchase')
)
SELECT
    user_id,
    GROUP_CONCAT(event_type ORDER BY event_sequence SEPARATOR ' -> ') AS user_journey
FROM
    user_event_sequence
GROUP BY
    user_id
LIMIT 10; -- Displaying a few journeys.

This query creates a user_event_sequence CTE (Common Table Expression) which numbers each event for a given user chronologically. The final SELECT statement then uses GROUP_CONCAT to string together the events in order, creating a representation of each user's flow. You can use this to identify common user paths and unusual behavior. ROW_NUMBER() is a window function that assigns a sequential integer to each row within a partition (defined by user_id), ordered by the timestamp.

Identifying Bottlenecks and Conversion Rate Optimization

By analyzing the funnel, you can pinpoint the steps where users drop off. This could be due to usability issues, pricing problems, or other factors. Once you identify a bottleneck, you can brainstorm solutions and test them. For example, if many users drop off between 'add_to_cart' and 'checkout_started', you might need to simplify your checkout process or clarify shipping costs. You can use the previous funnel query to calculate drop-off rates: (Step X count - Step Y count)/Step X count for the following funnel steps. For instance: (Add_to_cart users - Checkout_started users) / Add_to_cart users.

Advanced Techniques: Cohort Analysis (Window Functions)

Cohort analysis groups users based on a shared characteristic (e.g., signup date) and tracks their behavior over time. Window functions are ideal for cohort analysis because they allow you to perform calculations across a set of table rows that are related to the current row.

WITH cohort AS (
    SELECT
        user_id,
        MIN(timestamp) OVER (PARTITION BY user_id) as signup_date,
        event_type,
        timestamp
    FROM
        events
    WHERE
        event_type = 'signup'
), -- Identify cohort signups
cohort_activity AS (
    SELECT
        c.signup_date,
        DATE_TRUNC('month',e.timestamp) AS activity_month,
        COUNT(DISTINCT c.user_id) AS users_signed_up_in_cohort,
        COUNT(DISTINCT e.user_id) AS active_users
    FROM cohort c
    LEFT JOIN events e ON c.user_id = e.user_id AND e.timestamp >= c.timestamp
    GROUP BY 1,2
)
SELECT
    signup_date,
    activity_month,
    active_users,
    users_signed_up_in_cohort,
    ROUND(active_users * 100.0 / users_signed_up_in_cohort, 2) AS retention_rate
FROM cohort_activity
ORDER BY signup_date, activity_month;

This query first identifies cohorts (users who signed up on a specific date). Then, it calculates the number of active users within each cohort over time (e.g., monthly). The final SELECT statement then calculates retention rates, which can be visualized in a heatmap to easily understand user engagement trends. DATE_TRUNC is used here to truncate the timestamp to a month level. This is just one example, cohorts can be based on different user characteristics.

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