Question (+9)

Question

[The specific question being answered]

Data Sources

• Table: [schema.table_name] (as of [date])
• Table: [schema.other_table] (as of [date])
• File: [filename] (source: [where it came from])

Definitions

• [Metric A]: [Exactly how it's calculated]
• [Segment X]: [Exactly how membership is determined]
• [Time period]: [Start date] to [end date], [timezone]

Methodology

1. [Step 1 of the analysis approach]
2. [Step 2]
3. [Step 3]

Assumptions and Limitations

• [Assumption 1 and why it's reasonable]
• [Limitation 1 and its potential impact on conclusions]

Key Findings

1. [Finding 1 with supporting evidence]
2. [Finding 2 with supporting evidence]

SQL Queries

[All queries used, with comments]

Caveats

• [Things the reader should know before acting on this]

## Code Documentation


For any code (SQL, Python) that may be reused:

```python
"""
Analysis: Monthly Cohort Retention
Author: [Name]
Date: [Date]
Data Source: events table, users table
Last Validated: [Date] -- results matched dashboard within 2%

Purpose:
    Calculate monthly user retention cohorts based on first activity date.

Assumptions:
    - "Active" means at least one event in the month
    - Excludes test/internal accounts (user_type != 'internal')
    - Uses UTC dates throughout

Output:
    Cohort retention matrix with cohort_month rows and months_since_signup columns.
    Values are retention rates (0-100%).
"""

Version Control for Analyses

• Save queries and code in version control (git) or a shared docs system
• Note the date of the data snapshot used
• If an analysis is re-run with updated data, document what changed and why
• Link to prior versions of recurring analyses for trend comparison