Dataset Overview:

• Transactional event data (2010-2011)
• Each transaction represents a completed purchase
• Users are identified by CustomerID
• Revenue calculated as UnitPrice x Quantity

Cohort Construction:

• Cohort Month: user’s first purchase month
• Cohort Index: number of months since first purchase (Month 0 = acquisition month)

Aggregation & Metrics:

• Customer Retention (%):

  Measures the proportion of users retained in each cohort month relative to the cohort’s initial size. This metric captures repeat usage behavior and identifies early and long-term churn patterns

• Revenue Retention (%):

  Measures retained revenue relative to each cohort’s first month. This normalizes revenue performance across cohorts and distinguishes between revenue driven by user volume versus spending behavior

• Average Revenue per Retained User ($): Calculates total revenue divided by retained users for each cohort month. This metric isolates spending intensity among retained users and highlights revenue concentration effects that may not be visible through retention alone

• Month-1 Retention by Country (%):

  Measures early retention performance across top geographic segments to identify regional differences in user stickiness and value realization

Why Multiple Metrics Are Required:

Customer retention alone does not fully explain revenue outcomes.

Revenue may remain stable or increase despite declining retention if a small subset of users contributes disproportionately to total revenue.

By analyzing retention, revenue retention, and average revenue per retained user together, this analysis distinguishes between:

• Broad-based user engagement
• Revenue concentration among high-value users

This distinction is critical for understanding whether growth is sustainable or dependent on a narrow customer segment.