Impara Tracking User Journeys | Event-based Data Modeling

Understanding how users interact with your product or service is crucial for effective marketing analytics. User journey analysis focuses on the sequence of events each user performs, helping you see the steps users take from their first interaction to a potential conversion. By examining these sequences, you can uncover patterns in user behavior, identify common paths to purchase, and spot where users might be dropping off before completing a desired action.


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-- List each user's events in chronological order using window functions
SELECT
  user_id,
  event_type,
  event_timestamp,
  ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_timestamp) AS event_order
FROM events
ORDER BY user_id, event_timestamp;

Marketers rely on user journey analysis to optimize funnels and improve conversion rates. By reconstructing the exact order of events for each user, you can pinpoint where users tend to abandon the process—such as leaving after viewing a product page but before adding an item to the cart. These drop-off points reveal opportunities to refine messaging, adjust campaign targeting, or simplify the user experience, all with the goal of increasing conversions and boosting marketing ROI.


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-- Extract the first and last event for each user
SELECT DISTINCT
    user_id,
    FIRST_VALUE(event_type) OVER (
        PARTITION BY user_id
        ORDER BY event_timestamp
    ) AS first_event,
    LAST_VALUE(event_type) OVER (
        PARTITION BY user_id
        ORDER BY event_timestamp
        ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
    ) AS last_event
FROM events
ORDER BY user_id;

This query identifies the first and last recorded events for each user based on event time.

It uses window functions to analyze event sequences without collapsing rows prematurely. For every user_id, the query orders all events by event_timestamp and applies FIRST_VALUE to return the earliest event_type (the user’s first interaction). It also applies LAST_VALUE with an explicit window frame (ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) to correctly return the final event_type in the user’s timeline.

SELECT DISTINCT is used to return one row per user after the window calculations are applied, and the final result is ordered by user_id for readability.

Overall, this query is useful for user journey analysis, such as identifying onboarding events, final conversion steps, or drop-off behavior in an event-based funnel.

Compito

Swipe to start coding

Write a SQL query to find out how many events each user has performed.

Use the events table.
Return two columns: user_id and the count of events for that user (name this column event_count).
Group your results by user_id.
Order the results by user_id in ascending order.

Soluzione

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Suggested prompts:

Can you explain how to identify where users are dropping off in the funnel?

How can I use this data to improve my marketing strategy?

What other user journey metrics can I analyze with SQL?

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              12345678
            
-- List each user's events in chronological order using window functions
SELECT
  user_id,
  event_type,
  event_timestamp,
  ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_timestamp) AS event_order
FROM events
ORDER BY user_id, event_timestamp;


              1234567891011121314
            
-- Extract the first and last event for each user
SELECT DISTINCT
    user_id,
    FIRST_VALUE(event_type) OVER (
        PARTITION BY user_id
        ORDER BY event_timestamp
    ) AS first_event,
    LAST_VALUE(event_type) OVER (
        PARTITION BY user_id
        ORDER BY event_timestamp
        ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
    ) AS last_event
FROM events
ORDER BY user_id;