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Analyzing Conversion Funnel with Databend

Funnel analysis measures the number of unique users who has performed a set of actions, and we use it to see drop-off and conversion in multi-step processes.

In Databend, it's easy and performance to do it using WINDOW_FUNNEL FUNCTION.

Step 1. Databend​

1.1 Deploy Databend​

Make sure you have installed Databend, if not please see:

1.2 Create a Databend User​

Connect to Databend server with MySQL client:

mysql -h127.0.0.1 -uroot -P3307

Create a user:

CREATE USER user1 IDENTIFIED BY 'abc123';

Grant privileges for the user:

GRANT ALL ON *.* TO user1;

See also How To Create User.

1.3 Create a Table​

Connect to Databend server with MySQL client:

mysql -h127.0.0.1 -uuser1 -pabc123 -P3307 
CREATE TABLE events(user_id BIGINT, event_name VARCHAR, event_timestamp TIMESTAMP);

Prepare data:

INSERT INTO events VALUES(100123, 'login', '2022-05-14 10:01:00');
INSERT INTO events VALUES(100123, 'visit', '2022-05-14 10:02:00');
INSERT INTO events VALUES(100123, 'cart', '2022-05-14 10:04:00');
INSERT INTO events VALUES(100123, 'purchase', '2022-05-14 10:10:00');

INSERT INTO events VALUES(100125, 'login', '2022-05-15 11:00:00');
INSERT INTO events VALUES(100125, 'visit', '2022-05-15 11:01:00');
INSERT INTO events VALUES(100125, 'cart', '2022-05-15 11:02:00');

INSERT INTO events VALUES(100126, 'login', '2022-05-15 12:00:00');
INSERT INTO events VALUES(100126, 'visit', '2022-05-15 12:01:00');

Input table:

+---------+------------+----------------------------+
| user_id | event_name | event_timestamp            |
+---------+------------+----------------------------+
|  100123 | login      | 2022-05-14 10:01:00.000000 |
|  100123 | visit      | 2022-05-14 10:02:00.000000 |
|  100123 | cart       | 2022-05-14 10:04:00.000000 |
|  100123 | purchase   | 2022-05-14 10:10:00.000000 |
|  100125 | login      | 2022-05-15 11:00:00.000000 |
|  100125 | visit      | 2022-05-15 11:01:00.000000 |
|  100125 | cart       | 2022-05-15 11:02:00.000000 |
|  100126 | login      | 2022-05-15 12:00:00.000000 |
|  100126 | visit      | 2022-05-15 12:01:00.000000 |
+---------+------------+----------------------------+

We have a table with the following fields:

  • user_id - a unique identifier for user
  • event_name - type of the event
  • event_timestamp - timestamp which event occurred

Step 2. Funnel Analysis​

Find out how far the user user_id could get through the chain in an hour window slides.

SELECT
    level,
    count() AS count
FROM
(
    SELECT
        user_id,
        window_funnel(3600000000)(event_timestamp, event_name = 'login', event_name = 'visit', event_name = 'cart', event_name = 'purchase') AS level
    FROM events
    GROUP BY user_id
)
GROUP BY level ORDER BY level ASC;
tip

The event_timestamp type is timestamp, 3600000000 is a hour time window.

Result:

+-------+-------+
| level | count |
+-------+-------+
|     2 |     1 |
|     3 |     1 |
|     4 |     1 |
+-------+-------+
  • User 100126 level is 2 (login -> visit) .
  • user 100125 level is 3 (login -> visit -> cart).
  • User 100123 level is 4 (login -> visit -> cart -> purchase).