sales-data-analysis/.cursor/rules/ai_assistant_guide.md

AI Assistant Guide for Sales Analysis Template

This guide helps you effectively use Cursor's AI assistant to create sophisticated sales analyses.

🎯 Quick Start with AI

Basic Prompt Structure

When asking the AI to create an analysis, use this structure:

Create a [ANALYSIS_TYPE] analysis that:
1. [Specific requirement 1]
2. [Specific requirement 2]
3. Uses the sales_analysis_template patterns
4. Includes [specific visualizations/metrics]

Example Prompts

Simple Analysis:

Create an annual revenue trend analysis using the template patterns, 
with LTM support and proper chart formatting.

Advanced Analysis:

Create a customer cohort retention analysis that:
1. Groups customers by first purchase month
2. Calculates retention rates for 12 periods
3. Shows revenue retention metrics
4. Creates heatmap visualizations
5. Uses the template's cohort analysis patterns

Multi-Dimensional Analysis:

Create a product performance analysis across regions that:
1. Analyzes top products by revenue
2. Shows regional distribution
3. Calculates growth rates by region
4. Creates multi-panel visualizations
5. Exports results to Excel

📋 Template-Aware Prompts

The AI automatically knows about:

• data_loader.py - Always use this for loading data
• analysis_utils.py - Use utilities for formatting, LTM, etc.
• config.py - Use config values, never hardcode
• Template patterns - Follows best practices automatically

What the AI Knows

When you mention the template, the AI will:

• ✅ Use load_sales_data() instead of pd.read_csv()
• ✅ Use setup_revenue_chart() for charts
• ✅ Divide revenue by 1e6 before plotting
• ✅ Use config values from config.py
• ✅ Apply exclusion filters if configured
• ✅ Validate data after loading
• ✅ Use LTM patterns correctly

🔧 Common AI Tasks

1. Create New Analysis Script

Prompt:

Create a new analysis script called [name].py that:
- Follows the template structure
- Analyzes [specific metric/dimension]
- Creates [type of visualization]
- Uses template utilities

AI will:

• Copy structure from analysis_template.py
• Use proper imports
• Follow template patterns
• Include validation

2. Add Advanced Features

Prompt:

Add statistical significance testing to [analysis].py:
- Compare [group1] vs [group2]
- Show p-values and confidence intervals
- Use statistical_utils functions

3. Fix Common Issues

Prompt:

Fix the chart formatting in [analysis].py - it's showing scientific notation.

AI will:

• Add data / 1e6 conversion
• Use setup_revenue_chart()
• Fix formatting issues

4. Enhance Existing Analysis

Prompt:

Enhance [analysis].py to:
- Add export to Excel functionality
- Include data quality checks
- Add logging
- Generate PDF report

🚀 Advanced AI Prompts

Multi-Step Analysis

Create a comprehensive customer analysis that:
1. Segments customers using RFM
2. Calculates CLV for each segment
3. Identifies at-risk customers
4. Creates cohort retention analysis
5. Generates PDF report with all charts

Data Quality First

Before running the analysis, check data quality:
1. Run data quality report
2. Fix any critical issues
3. Validate configuration
4. Then proceed with analysis

Statistical Analysis

Add statistical analysis to [analysis].py:
- Calculate year-over-year growth with significance testing
- Show confidence intervals for forecasts
- Test differences between segments
- Use statistical_utils functions

💡 Pro Tips

1. Reference Existing Examples

Create an analysis similar to examples/customer_segmentation.py 
but for product segmentation instead.

2. Use Template Utilities

Use the template's export_utils to save results to Excel,
and report_generator to create a PDF report.

3. Leverage Cursor Rules

The AI automatically reads .cursor/rules/ files, so you can say:

Follow the advanced_analysis_patterns.md guide to create
a price-volume-mix decomposition analysis.

4. Iterative Development

Start with a basic version, then enhance it:
1. First version: Simple revenue trend
2. Add: Statistical significance
3. Add: Export functionality
4. Add: PDF report generation

🎨 Visualization Prompts

Create Specific Chart Types

Create a heatmap showing [metric] across [dimension1] and [dimension2],
using seaborn and following template chart formatting.

Create an interactive Plotly chart for [analysis],
saving it as HTML using the template's interactive chart functions.

Multi-Panel Visualizations

Create a 2x2 subplot showing:
- Top left: Revenue trend
- Top right: Customer count trend
- Bottom left: Average order value
- Bottom right: Growth rates
All using template chart formatting.

📊 Data Analysis Prompts

Cohort Analysis

Create a cohort analysis that:
1. Groups customers by first purchase month
2. Tracks retention for 12 periods
3. Calculates revenue retention
4. Creates retention heatmap
5. Uses examples/cohort_analysis.py as reference

Forecasting

Create a revenue forecasting analysis:
1. Prepare time series data
2. Fit trend model
3. Forecast next 12 months
4. Show confidence intervals
5. Use statistical_utils for calculations

Segmentation

Create an advanced customer segmentation:
1. Calculate RFM scores
2. Apply clustering algorithm
3. Analyze segment characteristics
4. Create segment visualizations
5. Export segment data to Excel

🔍 Debugging with AI

Fix Errors

I'm getting [error message] in [file].py.
Fix it using template best practices.

Optimize Performance

Optimize [analysis].py for large datasets:
- Use efficient pandas operations
- Add progress indicators
- Consider data sampling if needed

Improve Code Quality

Refactor [analysis].py to:
- Use more template utilities
- Follow template patterns better
- Add proper error handling
- Include logging

📝 Documentation Prompts

Add Documentation

Add comprehensive docstrings to [analysis].py following
the template's documentation style.

Create README

Create a README for [analysis].py explaining:
- What it does
- How to run it
- What outputs it generates
- Dependencies required

🎯 Best Practices for AI Interaction

1. Be Specific: Mention template files and utilities by name
2. Reference Examples: Point to existing examples when relevant
3. Iterate: Start simple, then add complexity
4. Use Template Terms: Mention "LTM", "config values", "template patterns"
5. Ask for Validation: Request data quality checks and validation

Example Full Workflow

1. "Check my configuration using config_validator.py"
2. "Run data quality report on my data"
3. "Create a revenue trend analysis using template patterns"
4. "Add statistical significance testing to the analysis"
5. "Export results to Excel and generate PDF report"
6. "Create a cohort analysis similar to the example"

The AI will guide you through each step using template best practices.

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Last Updated: January 2026
For: Cursor AI users working with sales_analysis_template