Impara A/B Test Analysis in SQL | A/B Test Analysis and Window Functions

A/B testing is a fundamental technique for evaluating product changes by comparing two or more variants. In product analytics, you use A/B tests to measure the effect of a new feature, interface change, or marketing strategy. You randomly assign users to different groups—typically group A (control) and group B (variant)—and compare their behavior to determine which version performs better.

The main metric used in A/B testing is the conversion rate, which tells you the percentage of users who complete a desired action, such as making a purchase or signing up. Another important metric is lift, which measures the difference in conversion rates between the groups.


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-- Calculate conversion rates for groups A and B
SELECT
  u.ab_group,
  COUNT(DISTINCT u.user_id) AS users,
  COUNT(DISTINCT CASE WHEN e.event_type = 'purchase' THEN u.user_id END) AS converters,
  ROUND(
    COUNT(DISTINCT CASE WHEN e.event_type = 'purchase' THEN u.user_id END)::NUMERIC
    / COUNT(DISTINCT u.user_id) * 100, 2
  ) AS conversion_rate_percent
FROM users u
LEFT JOIN events e ON u.user_id = e.user_id
GROUP BY u.ab_group
ORDER BY u.ab_group;

To analyze A/B test results, you first calculate group-level metrics. For each group, you determine how many users there are, how many converted, and what percentage of the group that represents. The SQL query above joins the users and events tables, counts the number of unique users per group, and counts how many of those users performed a "purchase" event. By dividing the number of converters by the total users in each group, you get the conversion rate for both A and B.

Once you have the conversion rates, you want to compare them to see if the new variant (group B) outperforms the control (group A). The difference between these rates is called lift. Calculating lift helps you quantify the impact of the change.


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-- Compute the conversion rate lift between groups B and A
WITH group_conversions AS (
  SELECT
    u.ab_group,
    COUNT(DISTINCT u.user_id) AS users,
    COUNT(DISTINCT CASE WHEN e.event_type = 'purchase' THEN u.user_id END) AS converters
  FROM users u
  LEFT JOIN events e ON u.user_id = e.user_id
  GROUP BY u.ab_group
)
SELECT
  (b.converters::NUMERIC / b.users - a.converters::NUMERIC / a.users) * 100 AS lift_percent
FROM
  (SELECT * FROM group_conversions WHERE ab_group = 'A') a,
  (SELECT * FROM group_conversions WHERE ab_group = 'B') b;

When you interpret A/B test results, you look for meaningful differences in conversion rates between groups. If group B's conversion rate is higher than group A's, and the lift is positive, the change may be beneficial. However, you also need to consider statistical significance — whether the observed difference is likely due to the change or just random chance. Statistical significance is usually determined with additional statistical tests, but SQL helps you quickly compute the key metrics needed for deeper analysis.

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Sezione 4. Capitolo 1

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              12345678910111213
            
-- Calculate conversion rates for groups A and B
SELECT
  u.ab_group,
  COUNT(DISTINCT u.user_id) AS users,
  COUNT(DISTINCT CASE WHEN e.event_type = 'purchase' THEN u.user_id END) AS converters,
  ROUND(
    COUNT(DISTINCT CASE WHEN e.event_type = 'purchase' THEN u.user_id END)::NUMERIC
    / COUNT(DISTINCT u.user_id) * 100, 2
  ) AS conversion_rate_percent
FROM users u
LEFT JOIN events e ON u.user_id = e.user_id
GROUP BY u.ab_group
ORDER BY u.ab_group;


              123456789101112131415
            
-- Compute the conversion rate lift between groups B and A
WITH group_conversions AS (
  SELECT
    u.ab_group,
    COUNT(DISTINCT u.user_id) AS users,
    COUNT(DISTINCT CASE WHEN e.event_type = 'purchase' THEN u.user_id END) AS converters
  FROM users u
  LEFT JOIN events e ON u.user_id = e.user_id
  GROUP BY u.ab_group
)
SELECT
  (b.converters::NUMERIC / b.users - a.converters::NUMERIC / a.users) * 100 AS lift_percent
FROM
  (SELECT * FROM group_conversions WHERE ab_group = 'A') a,
  (SELECT * FROM group_conversions WHERE ab_group = 'B') b;

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 4. Capitolo 1

A/B Test Analysis in SQL

1. What is the purpose of an A/B test?

2. How is conversion rate calculated in SQL?

3. Why compare metrics between groups A and B?

A/B Test Analysis in SQL

1. What is the purpose of an A/B test?

2. How is conversion rate calculated in SQL?

3. Why compare metrics between groups A and B?