Apprendre Advanced Metrics with Window Functions | A/B Test Analysis and Window Functions

As you deepen your skills with window functions in SQL, you unlock the ability to calculate advanced metrics that are essential for product analytics. These metrics include moving averages, percentiles, and retention curves, each providing a unique lens into user behavior and product performance. Moving averages help you smooth out short-term fluctuations and highlight longer-term trends in your data. Percentiles allow you to understand the distribution of values, such as purchase amounts or event frequencies, across different user groups. Retention curves reveal how well your product retains users over time. By mastering these advanced calculations, you can uncover richer insights and make more informed product decisions.


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SELECT
  event_date::date AS day,
  COUNT(*) AS daily_event_count,
  AVG(COUNT(*)) OVER (
    ORDER BY event_date::date
    ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
  ) AS moving_avg_7d
FROM events
GROUP BY event_date::date
ORDER BY day;

This query calculates a 7-day moving average of daily event counts. The logic works by first grouping events by their date and counting the number of events each day. The window function AVG(COUNT(*)) OVER (...) then takes the average of the current day's count and the counts from the previous six days, giving you a smooth trend line. This moving average is valuable for trend analysis because it reduces the noise from daily fluctuations, making it easier to spot meaningful changes in user activity.


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SELECT
  ab_group,
  PERCENTILE_CONT(0.9) WITHIN GROUP (ORDER BY value) AS p90_purchase_value
FROM events
JOIN users ON events.user_id = users.user_id
WHERE event_type = 'purchase'
GROUP BY ab_group;

Advanced metrics, such as moving averages and percentiles, provide deeper context than simple counts or sums. Moving averages can reveal underlying trends that are not obvious from raw daily data, helping you identify periods of growth or decline. Percentiles, like the 90th percentile of purchase values, show you how top-performing users behave compared to the rest, which can guide targeted feature development or marketing efforts. Interpreting these metrics correctly is crucial—they can influence decisions on product improvements, A/B test evaluations, and resource allocation. By leveraging window functions for these calculations, product teams can move beyond surface-level analysis and make data-driven decisions with greater confidence.

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Section 4. Chapitre 5

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              12345678910
            
SELECT
  event_date::date AS day,
  COUNT(*) AS daily_event_count,
  AVG(COUNT(*)) OVER (
    ORDER BY event_date::date
    ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
  ) AS moving_avg_7d
FROM events
GROUP BY event_date::date
ORDER BY day;


              1234567
            
SELECT
  ab_group,
  PERCENTILE_CONT(0.9) WITHIN GROUP (ORDER BY value) AS p90_purchase_value
FROM events
JOIN users ON events.user_id = users.user_id
WHERE event_type = 'purchase'
GROUP BY ab_group;

Tout était clair ?

Merci pour vos commentaires !

Section 4. Chapitre 5

Advanced Metrics with Window Functions

1. What is a moving average and why is it useful?

2. How can percentiles be calculated using SQL?

3. What insights can advanced window functions provide to product teams?

Advanced Metrics with Window Functions

1. What is a moving average and why is it useful?

2. How can percentiles be calculated using SQL?

3. What insights can advanced window functions provide to product teams?