Scenario: User Retention Cohort Analysis

How can we group users by signup cohort month and evaluate how many return to place orders in month 1, month 2, and month 3?

Production⏱ 3 Minutes⭐ 860 XP

🤔 THE QUESTION

How can we group users by signup cohort month and evaluate how many return to place orders in month 1, month 2, and month 3?

💡 WHAT IS IT?

Joining signup dates with order activity dates computes cohort month offsets for retention grid matrices.

🎯 WHAT IS IT USED FOR?

SaaS and subscription analytics measuring customer retention and product-market fit over time.

💻 EXAMPLE

df_cohorts = df_orders.join(df_users.select("user_id", col("signup_date").alias("cohort_start")), "user_id").withColumn("cohort_month", date_format("cohort_start", "yyyy-MM")).withColumn("activity_month", date_format("order_date", "yyyy-MM")).withColumn("month_offset", months_between(col("order_date"), col("cohort_start")).cast("int"))

🎯 Mission Objectives

Practice typing production-grade PySpark code for Scenario: User Retention Cohort Analysis.

✓ Cohort analysis calculation
✓ Months between signup and order
✓ Retention matrix aggregation

🚀 Start Mission

← Prev (Scenario: IoT Sensor Telemetry Anomaly Detection)Next Lesson (Scenario: High-Scale Session Dwell Time Analytics) →