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:**
```python
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:

```bash
python scripts/analyze_business_data.py path/to/business_data.csv output_report.json
```

The script will:
1. Automatically detect data structure (revenue columns, date columns, categories)
2. Calculate statistical metrics (mean, median, growth rates, volatility)
3. Identify trends and patterns
4. Detect weak areas and underperforming segments
5. Generate improvement strategies based on findings
6. Output a structured JSON report

**Output structure:**
```json
{
  "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:**
1. **Current State**: What the data shows about business performance
2. **Weak Areas**: Specific problems identified with severity levels
3. **Root Causes**: Why these issues exist (use business frameworks from references/)
4. **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:
1. **Strategic Initiative Name**: Clear, actionable program name
2. **Objective**: What this strategy aims to achieve
3. **Key Actions**: 3-5 specific, prioritized steps
4. **Expected Impact**: High/Medium/Low
5. **Timeline**: Realistic implementation timeframe
6. **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:**
1. **Revenue Trend Chart**: Line chart showing revenue over time with growth rate overlay
2. **Category Performance**: Bar chart sorted by revenue contribution
3. **Volatility Analysis**: Box plot or standard deviation visualization
4. **Weak Areas Heatmap**: Visual representation of severity and impact

**Example code for revenue trend:**
```python
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",