# 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. --- **Last Updated:** January 2026 **For:** Cursor AI users working with sales_analysis_template