business-analytics-reporter

Business Analytics Reporter Overview Generate comprehensive business performance reports that analyze sales and revenue data, identify areas where the business is lacking, interpret what the statistics indicate, and provide actionable improvement strategies. The skill uses data-driven analysis to detect weak areas and recommends specific strategies backed by business frameworks. When to Use This Skill Invoke this skill when users request: "Analyze my business data and tell me where we're lacking" "Generate a report on what areas need improvement" "What do these sales numbers tell us about our business performance?" "Create a business analysis report with improvement strategies" "Identify weak areas in our revenue data" "What strategies should we use to improve our business metrics?" The skill expects CSV files containing business data (sales, revenue, transactions) with columns like dates, amounts, categories, or products. Core Workflow Step 1: Data Loading and Exploration Start by understanding the data structure and what the user wants to analyze. Ask clarifying questions if needed: What specific metrics or areas should the analysis focus on? Are there particular time periods or categories of interest? Should the report include visualizations or focus on written analysis? Load and explore the data: import pandas as pd # Load the CSV file df = pd.read_csv('business_data.csv') # Display basic information print(f"Data shape: {df.shape}") print(f"Columns: {df.columns.tolist()}") print(f"Date range: {df['date'].min()} to {df['date'].max()}") print(df.head()) Step 2: Run Automated Analysis Use the bundled analysis script to generate comprehensive insights: python scripts/analyze_business_data.py path/to/business_data.csv output_report.json The script will: Automatically detect data structure (revenue columns, date columns, categories) Calculate statistical metrics (mean, median, growth rates, volatility) Identify trends and patterns Detect weak areas and underperforming segments Generate improvement strategies based on findings Output a structured JSON report Output structure: { "metadata": {...}, "findings": { "basic_statistics": {...}, "trend_analysis": {...}, "category_analysis": {...}, "variability": {...} }, "weak_areas": [...], "improvement_strategies": [...] } Step 3: Interpret the Analysis Results Read the generated JSON report and interpret the findings for the user in plain language. Focus on: Current State: What the data shows about business performance Weak Areas: Specific problems identified with severity levels Root Causes: Why these issues exist (use business frameworks from references/) Impact: What these weaknesses mean for the business Example interpretation: Based on the analysis of your sales data from January to December 2024: Current State: - Total revenue: $1.2M with average monthly revenue of $100K - Average growth rate: -3.5% indicating declining performance - Revenue stability: High volatility (CV: 58%) suggesting inconsistent performance Weak Areas Identified: 1. Revenue Growth (High Severity): Negative average growth rate of -3.5% 2. Performance Consistency (Medium Severity): 45% of periods show declining performance 3. Category Performance (Medium Severity): 4 underperforming categories identified Step 4: Generate Detailed Recommendations Consult the business frameworks reference to provide strategic recommendations: Load business frameworks for context: Refer to references/business_frameworks.md for: Revenue growth strategies (market penetration, product development, etc.) Operational excellence frameworks Customer-centric strategies Pricing strategy frameworks Common weak area solutions Structure recommendations as: For each identified weak area, provide: Strategic Initiative Name: Clear, actionable program name Objective: What this strategy aims to achieve Key Actions: 3-5 specific, prioritized steps Expected Impact: High/Medium/Low Timeline: Realistic implementation timeframe Success Metrics: How to measure improvement Example recommendation: Strategy: Revenue Acceleration Program Area: Revenue Growth Objective: Reverse negative growth trend and achieve 10%+ monthly growth Key Actions: 1. Implement aggressive customer acquisition campaigns 2. Review and optimize pricing strategy 3. Launch upselling and cross-selling initiatives 4. Expand into new market segments or geographies 5. Accelerate product development and innovation Expected Impact: High Timeline: 3-6 months Success Metrics: Monthly revenue growth rate, new customer acquisition, ARPU increase Step 5: Create Visualizations (Optional) If requested, create interactive visualizations using Plotly to illustrate findings: Consult visualization guide: Refer to references/visualization_guide.md for: Recommended chart types for different analyses Code examples for creating charts Best practices for business dashboards Common visualizations to create: Revenue Trend Chart: Line chart showing revenue over time with growth rate overlay Category Performance: Bar chart sorted by revenue contribution Volatility Analysis: Box plot or standard deviation visualization Weak Areas Heatmap: Visual representation of severity and impact Example code for revenue trend: import plotly.graph_objects as go from plotly.subplots import make_subplots fig = make_subplots(specs=[[{"secondary_y": True}]]) # Add revenue line fig.add_trace( go.Scatter(x=df['date'], y=df['revenue'], name="Revenue", line=dict(color='blue', width=3)), secondary_y=False ) # Add growth rate line fig.add_trace( go.Scatter(x=df['date'], y=df['growth_rate'], name="Growth Rate", line=dict(color='green', dash='dash')), secondary_y=True ) fig.update_layout(title_text="Revenue Performance & Growth Rate") fig.show() Step 6: Generate Final Report Compile findings into a comprehensive report format. Option A: Generate HTML Report Use the report template from assets/report_template.html: # Read the template with open('assets/report_template.html', 'r') as f: template = f.read() # Load analysis results with open('output_report.json', 'r') as f: analysis = json.load(f) # Populate the template with actual data # Replace placeholders with real values from analysis # Add Plotly charts as JavaScript # Save as final HTML report with open('business_report.html', 'w') as f: f.write(populated_template) The HTML template includes: Executive summary with key metrics Interactive charts for trends and categories Styled weak area cards with severity indicators Strategic recommendations with action items Professional styling and print-ready format Option B: Generate Markdown Report Create a structured markdown document: # Business Performance Analysis Report Generated: [Date] Data Period: [Period] ## Executive Summary [Brief overview of findings] ## Key Metrics - Total Revenue: $X - Average Growth Rate: X% - Revenue Stability: [Assessment] - Weak Areas Identified: X ## Performance Trends [Insert chart or describe trends] ## Areas of Weakness ### 1. [Weak Area Name] (Severity) Finding: [Description] Impact: [Business impact] ### 2. [Next weak area...] ## Strategic Recommendations ### Strategy 1: [Name] Objective: [Goal] Actions: - [Action 1] - [Action 2] ... Expected Impact: High/Medium/Low Timeline: X months Key Analysis Metrics The analysis script calculates the following metrics automatically: Growth Analysis Average Growth Rate: Period-over-period revenue change percentage Declining Period Count: Number of periods with negative growth Trend Direction: Overall trajectory (growing, declining, stable) Stability Analysis Coefficient of Variation (CV): Measures revenue volatility CV < 25%: Stable performance CV 25-50%: Moderate volatility CV > 50%: High volatility (flag as weak area) Category Performance Revenue Contribution: Percentage breakdown by category Underperforming Categories: Bottom 25% by average performance Top/Bottom Performers: Best and worst performing categories Statistical Indicators Mean, Median, Standard Deviation for all numeric columns Min/Max values and ranges Total aggregates Business Frameworks Reference When generating recommendations, leverage the frameworks documented in references/business_frameworks.md: Revenue Growth Strategies: Market penetration, product development, market development, diversification Operational Excellence: Process optimization, resource allocation, quality management Customer-Centric Strategies: Retention programs, CLV optimization, segmentation Pricing Strategies: Value-based, dynamic, competitive pricing Data-Driven Decision Making: Analytics maturity model, KPI frameworks Match identified weak areas with appropriate strategic frameworks to provide contextually relevant recommendations. Tips for Effective Reports Start with the Big Picture: Lead with overall performance and key findings Prioritize by Severity: Focus on high-severity issues first Be Specific: Provide concrete numbers and percentages, not vague assessments Action-Oriented: Every weak area should have actionable recommendations Context Matters: Consider industry benchmarks and business context Visual Communication: Use charts to make trends immediately clear Executive-Friendly: Structure for quick scanning with clear headers and summaries Common Weak Areas and Detection The analysis automatically detects these common business problems: Weak Area Detection Criteria Typical Root Causes Revenue Growth Negative average growth rate Market saturation, increased competition, poor positioning Performance Consistency >40% declining periods Lack of recurring revenue, seasonal dependency Revenue Stability CV > 50% Customer concentration, volatile demand Category Performance Categories in bottom 25% Poor product-market fit, pricing issues, low awareness Example Usage User request: "Analyze my Q4 sales data and tell me where we're weak and how to improve" Workflow: Load the CSV: df = pd.read_csv('q4_sales.csv') Run analysis: python scripts/analyze_business_data.py q4_sales.csv q4_report.json Read results: with open('q4_report.json') as f: report = json.load(f) Interpret findings for the user in natural language Create visualizations using Plotly (refer to references/visualization_guide.md) Generate HTML report using assets/report_template.html Provide strategic recommendations using references/business_frameworks.md Expected output: Clear explanation of current business performance 3-5 identified weak areas with severity levels 4-6 strategic initiatives with specific action plans Interactive visualizations (if requested) Professional HTML or markdown report Resources scripts/ analyze_business_data.py: Automated analysis engine that detects data structure, calculates metrics, identifies weak areas, and generates improvement strategies references/ business_frameworks.md: Comprehensive guide to business strategy frameworks, common weak areas, and solution templates visualization_guide.md: Chart type recommendations, Plotly code examples, and dashboard design best practices assets/ report_template.html: Professional HTML template with interactive visualizations, styled cards for weak areas and strategies, and print-ready formatting Weekly Installs189Repositoryailabs-393/ai-l…e-skillsGitHub Stars328First SeenJan 23, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykWarnInstalled onopencode174codex173gemini-cli169cursor164github-copilot162kimi-cli149