Cohort Analysis Framework Builder

Description

## Cohort Analysis Framework Builder

Generate build cohort analysis frameworks for customer retention and behavior tracking.:

**Input Data:** [DATA_SOURCE — describe your data source]
**Analysis Goal:** [GOAL — what question are you trying to answer]
**Output Format:** [FORMAT — report/dashboard/script/notebook]
**Technical Stack:** [STACK — Python/R/SQL/Excel/BI tool]

**Analysis Framework:**

### 1. Data Assessment
- Source identification and access method
- Data volume and format evaluation
- Quality check: completeness, accuracy, consistency
- Schema understanding and documentation

### 2. Methodology
- Select appropriate analytical approach
- Define metrics and calculations
- Set up reproducible analysis pipeline
- Document assumptions and limitations

### 3. Implementation
```python
# Step 1: Load and prepare data
import pandas as pd
import numpy as np

# Step 2: Apply analysis methodology
# [METHODOLOGY_SPECIFIC_CODE]

# Step 3: Generate outputs
# [VISUALIZATION_AND_REPORTING_CODE]
```

### 4. Visualization
- Choose appropriate chart types for each finding
- Apply consistent styling and branding
- Add annotations for key insights
- Ensure accessibility (colorblind-friendly palettes)

### 5. Insights and Recommendations
- **Finding 1:** [DATA_INSIGHT] → **Action:** [RECOMMENDATION]
- **Finding 2:** [DATA_INSIGHT] → **Action:** [RECOMMENDATION]
- **Finding 3:** [DATA_INSIGHT] → **Action:** [RECOMMENDATION]

### 6. Next Steps
- Additional analysis opportunities
- Data collection improvements needed
- Monitoring and alerting setup
- Stakeholder presentation preparation

**Variables:** Replace all [BRACKETED] placeholders with your specific data and requirements.

**Quality Checklist:**
- [ ] Data sources documented
- [ ] Methodology is reproducible
- [ ] Results validated against known benchmarks
- [ ] Visualizations are clear and labeled
- [ ] Recommendations are specific and actionable

Free to copy and use. Compatible with Claude 4 Opus, GPT-5.

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