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Business Analytics Reporter

业务分析报告生成工具

Overview

概述

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.
生成全面的业务绩效报告，分析销售与收入数据，识别业务存在的薄弱环节，解读统计数据的含义，并提供可落地的改进策略。本Skill采用数据驱动的分析方式识别薄弱领域，并结合业务框架给出针对性策略建议。

When to Use This Skill

适用场景

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.
在用户提出以下请求时调用本Skill：
  • "分析我的业务数据，告诉我我们的薄弱环节在哪里"
  • "生成一份关于需要改进领域的报告"
  • "这些销售数据能反映我们的业务绩效如何？"
  • "创建一份包含改进策略的业务分析报告"
  • "识别我们收入数据中的薄弱领域"
  • "我们应该采用哪些策略来提升业务指标？"
本Skill需要包含业务数据（销售、收入、交易）的CSV文件，文件需包含日期、金额、类别或产品等列。

Core Workflow

核心工作流

Step 1: Data Loading and Exploration

步骤1：数据加载与探索

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
首先了解数据结构以及用户的分析需求。
必要时提出澄清问题：
  • 分析应重点关注哪些特定指标或领域？
  • 是否有特定的时间段或类别需要关注？
  • 报告是否需要包含可视化内容，还是仅侧重文字分析？
加载并探索数据：
python
import pandas as pd

Load the CSV file

Load the CSV file

df = pd.read_csv('business_data.csv')
df = pd.read_csv('business_data.csv')

Display basic information

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())
undefined
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())
undefined

Step 2: Run Automated Analysis

步骤2：运行自动化分析

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": [...]
}
使用内置的分析脚本生成全面的洞察：
bash
python scripts/analyze_business_data.py path/to/business_data.csv output_report.json
该脚本将：
  1. 自动检测数据结构（收入列、日期列、类别列）
  2. 计算统计指标（均值、中位数、增长率、波动率）
  3. 识别趋势与模式
  4. 检测薄弱领域与表现不佳的业务板块
  5. 根据分析结果生成改进策略
  6. 输出结构化的JSON报告
输出结构：
json
{
  "metadata": {...},
  "findings": {
    "basic_statistics": {...},
    "trend_analysis": {...},
    "category_analysis": {...},
    "variability": {...}
  },
  "weak_areas": [...],
  "improvement_strategies": [...]
}

Step 3: Interpret the Analysis Results

步骤3：解读分析结果

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
读取生成的JSON报告，用通俗易懂的语言为用户解读分析结果。
重点关注：
  1. 当前状态：数据反映的业务绩效情况
  2. 薄弱环节：识别出的具体问题及其严重程度
  3. 根本原因：问题产生的原因（参考references/目录下的业务框架）
  4. 影响：这些薄弱环节对业务的影响
解读示例：
基于对您2024年1月至12月销售数据的分析：

当前状态：
- 总收入：120万美元，月均收入10万美元
- 平均增长率：-3.5%，表明业绩呈下滑趋势
- 收入稳定性：波动率高（变异系数：58%），说明业绩表现不稳定

识别出的薄弱环节：
1. 收入增长（高严重程度）：平均增长率为-3.5%的负增长
2. 业绩稳定性（中严重程度）：45%的时间段业绩下滑
3. 类别表现（中严重程度）：识别出4个表现不佳的业务类别

Step 4: Generate Detailed Recommendations

步骤4：生成详细建议

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
参考业务框架文档，提供战略性建议：
加载业务框架以获取背景信息： 参考
references/business_frameworks.md
获取以下内容：
  • 收入增长策略（市场渗透、产品开发等）
  • 运营卓越框架
  • 以客户为中心的策略
  • 定价策略框架
  • 常见薄弱环节的解决方案
建议结构：
针对每个识别出的薄弱环节，提供：
  1. 战略举措名称：清晰、可执行的项目名称
  2. 目标：该策略旨在实现的成果
  3. 关键行动：3-5项具体的优先执行步骤
  4. 预期影响：高/中/低
  5. 实施周期：切实可行的执行时间范围
  6. 成功指标：如何衡量改进效果
建议示例：
策略：收入加速计划
针对领域：收入增长
目标：扭转负增长趋势，实现月均10%以上的增长

关键行动：
1. 推出激进的客户获取活动
2. 复盘并优化定价策略
3. 启动向上销售与交叉销售举措
4. 拓展至新的市场板块或地域
5. 加快产品开发与创新速度

预期影响：高
实施周期：3-6个月
成功指标：月收入增长率、新客户获取量、ARPU（每用户平均收入）提升

Step 5: Create Visualizations (Optional)

步骤5：生成可视化内容（可选）

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}]])
如果用户有需求，使用Plotly创建交互式可视化内容以展示分析结果：
参考可视化指南： 参考
references/visualization_guide.md
获取以下内容：
  • 不同分析场景下的推荐图表类型
  • 创建图表的代码示例
  • 业务仪表盘设计最佳实践
常见可视化内容：
  1. 收入趋势图：折线图展示收入随时间的变化，叠加增长率曲线
  2. 类别表现图：按收入贡献排序的柱状图
  3. 波动率分析图：箱线图或标准差可视化图
  4. 薄弱环节热力图：直观展示问题的严重程度与影响范围
收入趋势图示例代码：
python
import plotly.graph_objects as go
from plotly.subplots import make_subplots

fig = make_subplots(specs=[[{"secondary_y": True}]])

Add revenue line

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 )
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

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()
undefined
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()
undefined

Step 6: Generate Final Report

步骤6：生成最终报告

Compile findings into a comprehensive report format.
Option A: Generate HTML Report
Use the report template from 
assets/report_template.html
:
python
undefined
将分析结果整理为全面的报告格式。
选项A：生成HTML报告
使用
assets/report_template.html
中的报告模板：
python
undefined

Read the template

Read the template

with open('assets/report_template.html', 'r') as f: template = f.read()
with open('assets/report_template.html', 'r') as f: template = f.read()

Load analysis results

Load analysis results

with open('output_report.json', 'r') as f: analysis = json.load(f)
with open('output_report.json', 'r') as f: analysis = json.load(f)

Populate the template with actual data

Populate the template with actual data

Replace placeholders with real values from analysis

Replace placeholders with real values from analysis

Add Plotly charts as JavaScript

Add Plotly charts as JavaScript

Save as final HTML report

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:

```markdown
with open('business_report.html', 'w') as f: f.write(populated_template)

该HTML模板包含：
- 关键指标的执行摘要
- 展示趋势与类别的交互式图表
- 带有严重程度标识的薄弱环节样式卡片
- 包含行动项的战略建议
- 专业的样式设计与可打印格式

**选项B：生成Markdown报告**

创建结构化的Markdown文档：

```markdown

Business Performance Analysis Report

Business Performance Analysis Report

Generated: [Date] Data Period: [Period]
Generated: [Date] Data Period: [Period]

Executive Summary

Executive Summary

[Brief overview of findings]
[Brief overview of findings]

Key Metrics

Key Metrics

  • Total Revenue: $X
  • Average Growth Rate: X%
  • Revenue Stability: [Assessment]
  • Weak Areas Identified: X
  • Total Revenue: $X
  • Average Growth Rate: X%
  • Revenue Stability: [Assessment]
  • Weak Areas Identified: X

Performance Trends

Performance Trends

[Insert chart or describe trends]
[Insert chart or describe trends]

Areas of Weakness

Areas of Weakness

1. [Weak Area Name] (Severity)

1. [Weak Area Name] (Severity)

Finding: [Description] Impact: [Business impact]
Finding: [Description] Impact: [Business impact]

2. [Next weak area...]

2. [Next weak area...]

Strategic Recommendations

Strategic Recommendations

Strategy 1: [Name]

Strategy 1: [Name]

Objective: [Goal] Actions:
  • [Action 1]
  • [Action 2] ...
Expected Impact: High/Medium/Low Timeline: X months
undefined
Objective: [Goal] Actions:
  • [Action 1]
  • [Action 2] ...
Expected Impact: High/Medium/Low Timeline: X months
undefined

Key Analysis Metrics

核心分析指标

The analysis script calculates the following metrics automatically:
分析脚本将自动计算以下指标：

Growth Analysis

增长分析

  • 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)
  • 平均增长率：环比收入变化百分比
  • 下滑周期数：出现负增长的时间段数量
  • 趋势方向：整体发展轨迹（增长、下滑、稳定）

Stability Analysis

稳定性分析

  • Coefficient of Variation (CV): Measures revenue volatility
    • CV < 25%: Stable performance
    • CV 25-50%: Moderate volatility
    • CV > 50%: High volatility (flag as weak area)
  • 变异系数（CV）：衡量收入波动率
    • CV < 25%：业绩稳定
    • CV 25-50%：中等波动率
    • CV > 50%：高波动率（标记为薄弱环节）

Category Performance

类别表现分析

  • Revenue Contribution: Percentage breakdown by category
  • Underperforming Categories: Bottom 25% by average performance
  • Top/Bottom Performers: Best and worst performing categories
  • 收入贡献：各业务类别的收入占比
  • 表现不佳类别：平均表现排名后25%的类别
  • Top/Bottom表现者：表现最佳与最差的业务类别

Statistical Indicators

统计指标

  • Mean, Median, Standard Deviation for all numeric columns
  • Min/Max values and ranges
  • Total aggregates
  • 所有数值列的均值、中位数、标准差
  • 最小值/最大值与范围
  • 总计聚合值

Business Frameworks Reference

业务框架参考

When generating recommendations, leverage the frameworks documented in 
references/business_frameworks.md
:
  1. Revenue Growth Strategies: Market penetration, product development, market development, diversification
  2. Operational Excellence: Process optimization, resource allocation, quality management
  3. Customer-Centric Strategies: Retention programs, CLV optimization, segmentation
  4. Pricing Strategies: Value-based, dynamic, competitive pricing
  5. Data-Driven Decision Making: Analytics maturity model, KPI frameworks
Match identified weak areas with appropriate strategic frameworks to provide contextually relevant recommendations.
生成建议时，可参考
references/business_frameworks.md
中记录的框架：
  1. 收入增长策略：市场渗透、产品开发、市场拓展、多元化经营
  2. 运营卓越：流程优化、资源分配、质量管理
  3. 以客户为中心的策略：客户留存计划、客户生命周期价值优化、客户细分
  4. 定价策略：基于价值的定价、动态定价、竞争性定价
  5. 数据驱动决策：分析成熟度模型、KPI框架
将识别出的薄弱环节与合适的战略框架匹配，提供贴合场景的相关建议。

Tips for Effective Reports

有效报告撰写技巧

  1. Start with the Big Picture: Lead with overall performance and key findings
  2. Prioritize by Severity: Focus on high-severity issues first
  3. Be Specific: Provide concrete numbers and percentages, not vague assessments
  4. Action-Oriented: Every weak area should have actionable recommendations
  5. Context Matters: Consider industry benchmarks and business context
  6. Visual Communication: Use charts to make trends immediately clear
  7. Executive-Friendly: Structure for quick scanning with clear headers and summaries
  1. 先展示全局情况：从整体业绩与关键发现入手
  2. 按严重程度排序：优先关注高严重程度的问题
  3. 内容具体：提供具体的数字与百分比，避免模糊评估
  4. 注重可执行性：每个薄弱环节都应配有可落地的建议
  5. 结合场景：考虑行业基准与业务背景
  6. 可视化沟通：用图表直观展示趋势
  7. 适配管理层阅读习惯：结构清晰，便于快速浏览，设置明确的标题与摘要

Common Weak Areas and Detection

常见薄弱环节与检测方式

The analysis automatically detects these common business problems:
Weak AreaDetection CriteriaTypical Root Causes
Revenue GrowthNegative average growth rateMarket saturation, increased competition, poor positioning
Performance Consistency>40% declining periodsLack of recurring revenue, seasonal dependency
Revenue StabilityCV > 50%Customer concentration, volatile demand
Category PerformanceCategories in bottom 25%Poor product-market fit, pricing issues, low awareness
分析脚本将自动检测以下常见业务问题：
薄弱环节检测标准典型根本原因
收入增长平均增长率为负市场饱和、竞争加剧、定位不佳
业绩稳定性下滑周期占比>40%缺乏经常性收入、季节性依赖
收入稳定性变异系数>50%客户集中度高、需求波动大
类别表现类别排名后25%产品-市场匹配度低、定价问题、认知度不足

Example Usage

使用示例

User request: "Analyze my Q4 sales data and tell me where we're weak and how to improve"
Workflow:
  1. Load the CSV: 
    df = pd.read_csv('q4_sales.csv')
  2. Run analysis: 
    python scripts/analyze_business_data.py q4_sales.csv q4_report.json
  3. Read results: 
    with open('q4_report.json') as f: report = json.load(f)
  4. Interpret findings for the user in natural language
  5. Create visualizations using Plotly (refer to 
    references/visualization_guide.md
    )
  6. Generate HTML report using 
    assets/report_template.html
  7. 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
用户请求： "分析我的第四季度销售数据，告诉我我们的薄弱环节在哪里以及如何改进"
工作流：
  1. 加载CSV文件：
    df = pd.read_csv('q4_sales.csv')
  2. 运行分析：
    python scripts/analyze_business_data.py q4_sales.csv q4_report.json
  3. 读取结果：
    with open('q4_report.json') as f: report = json.load(f)
  4. 用自然语言为用户解读分析结果
  5. 参考
    references/visualization_guide.md
    使用Plotly创建可视化内容
  6. 使用
    assets/report_template.html
    生成HTML报告
  7. 参考
    references/business_frameworks.md
    提供战略建议
预期输出：
  • 清晰的当前业务绩效说明
  • 3-5个带有严重程度标识的薄弱环节
  • 4-6项带有具体行动计划的战略举措
  • 交互式可视化内容（如果用户有需求）
  • 专业的HTML或Markdown报告

Resources

资源说明

scripts/

scripts/

  • analyze_business_data.py
    : Automated analysis engine that detects data structure, calculates metrics, identifies weak areas, and generates improvement strategies
  • analyze_business_data.py
    ：自动化分析引擎，可检测数据结构、计算指标、识别薄弱环节并生成改进策略

references/

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
  • business_frameworks.md
    ：业务战略框架、常见薄弱环节与解决方案模板的综合指南
  • visualization_guide.md
    ：图表类型推荐、Plotly代码示例与仪表盘设计最佳实践

assets/

assets/

  • report_template.html
    : Professional HTML template with interactive visualizations, styled cards for weak areas and strategies, and print-ready formatting
  • report_template.html
    ：专业的HTML模板，包含交互式可视化内容、薄弱环节与策略的样式卡片，以及可打印格式