Data Analysis Skill

Description

Comprehensive data analysis capabilities for business intelligence, including e-commerce metrics, marketing performance, customer insights, and predictive analytics.

Capabilities

• Sales Analysis: Revenue, orders, conversion tracking
• Customer Segmentation: Identify customer patterns and segments
• Trend Identification: Spot emerging trends in data
• Performance Metrics: KPI calculation and monitoring
• Predictive Analytics: Forecast future performance
• A/B Testing: Statistical analysis of experiments
• Reporting: Generate executive summaries and dashboards

Tools Required

• execute_python
• shopify_get_analytics
• shopify_list_orders
• printify_list_orders
• read_file
• create_file
• convert_format
• process_json
• search_web (for market data)

Workflows

E-Commerce Performance Analysis

name: Analyze Store Performance
steps:
  - Fetch Shopify analytics data
  - Fetch order history
  - "Calculate key metrics: Total revenue, Average order value, Conversion rate, Customer acquisition cost, Lifetime value"
  - Identify trends (daily, weekly, monthly)
  - Compare to previous periods
  - Generate insights and recommendations
  - Create visual reports

success_criteria:
  - All key metrics calculated
  - Trends identified
  - Actionable insights provided
  - Visual report generated

Customer Segmentation

name: Segment Customer Base
steps:
  - Collect customer order data
  - Calculate RFM metrics (Recency, Frequency, Monetary)
  - Apply clustering algorithm
  - "Identify segments: VIP customers, Loyal customers, At-risk customers, New customers, Lost customers"
  - Profile each segment
  - Generate targeted recommendations
  - Create segment-specific campaigns

success_criteria:
  - Clear customer segments defined
  - Each segment characterized
  - Marketing recommendations provided

Product Performance Analysis

name: Analyze Product Sales
steps:
  - Fetch all product data
  - "Calculate metrics per product: Units sold, Revenue generated, Profit margin, Return rate, Customer satisfaction"
  - Rank products by performance
  - Identify top performers
  - Flag underperformers
  - Analyze seasonality
  - Forecast future performance
  - Generate product strategy report

success_criteria:
  - Complete product performance matrix
  - Top/bottom performers identified
  - Actionable recommendations

Market Trend Analysis

name: Analyze Market Trends
steps:
  - Research industry trends
  - Analyze competitor performance
  - Identify emerging opportunities
  - Correlate with internal data
  - Forecast market direction
  - Generate opportunity report

success_criteria:
  - Trend analysis complete
  - Opportunities identified
  - Competitive positioning assessed

Analysis Frameworks

AARRR (Pirate Metrics)

• Acquisition: Where customers come from
• Activation: First positive experience
• Retention: Customers coming back
• Revenue: Monetization effectiveness
• Referral: Viral growth coefficient

RFM Analysis

• Recency: How recently did they purchase?
• Frequency: How often do they purchase?
• Monetary: How much do they spend?

Cohort Analysis

• Group customers by signup/first purchase date
• Track behavior over time
• Identify lifecycle patterns
• Measure retention rates

Python Analysis Scripts

Sales Analysis Template

import pandas as pd
from datetime import datetime, timedelta

def analyze_sales(orders_data):
    """Analyze sales performance"""
    df = pd.DataFrame(orders_data)
    
    # Calculate metrics
    total_revenue = df['total_price'].sum()
    total_orders = len(df)
    avg_order_value = total_revenue / total_orders
    
    # Time-based analysis
    df['date'] = pd.to_datetime(df['created_at'])
    daily_revenue = df.groupby(df['date'].dt.date)['total_price'].sum()
    
    # Product analysis
    top_products = df['line_items'].explode().groupby('product_id')['quantity'].sum().nlargest(10)
    
    return {
        'total_revenue': total_revenue,
        'total_orders': total_orders,
        'avg_order_value': avg_order_value,
        'daily_revenue': daily_revenue.to_dict(),
        'top_products': top_products.to_dict()
    }

Customer Segmentation Template

def segment_customers(customer_data):
    """RFM segmentation"""
    from datetime import datetime
    
    df = pd.DataFrame(customer_data)
    today = datetime.now()
    
    # Calculate RFM
    rfm = df.groupby('customer_id').agg({
        'order_date': lambda x: (today - x.max()).days,  # Recency
        'order_id': 'count',  # Frequency
        'total_price': 'sum'  # Monetary
    })
    
    rfm.columns = ['recency', 'frequency', 'monetary']
    
    # Score each metric (1-5)
    rfm['r_score'] = pd.qcut(rfm['recency'], 5, labels=[5,4,3,2,1])
    rfm['f_score'] = pd.qcut(rfm['frequency'], 5, labels=[1,2,3,4,5])
    rfm['m_score'] = pd.qcut(rfm['monetary'], 5, labels=[1,2,3,4,5])
    
    # Combine scores
    rfm['rfm_score'] = rfm['r_score'].astype(str) + rfm['f_score'].astype(str) + rfm['m_score'].astype(str)
    
    # Segment definitions
    segments = {
        'VIP': rfm[rfm['rfm_score'] >= '444'],
        'Loyal': rfm[(rfm['rfm_score'] >= '333') & (rfm['rfm_score'] < '444')],
        'At Risk': rfm[(rfm['recency'] > 90) & (rfm['frequency'] > 3)],
        'New': rfm[(rfm['recency'] <= 30) & (rfm['frequency'] == 1)]
    }
    
    return segments

Visualization Templates

Revenue Dashboard

• Line chart: Revenue over time
• Bar chart: Revenue by product
• Pie chart: Revenue by category
• Heatmap: Sales by day/hour
• Gauge: Current vs target revenue

Customer Insights

• Funnel chart: Conversion funnel
• Cohort chart: Retention rates
• Scatter plot: RFM segmentation
• Bar chart: Customer lifetime value distribution

Reporting Templates

Executive Summary

# Business Performance Report
**Period**: [Date Range]

## Key Metrics
- **Total Revenue**: $X (+Y% vs last period)
- **Orders**: N (+Y% vs last period)
- **Avg Order Value**: $X (+Y% vs last period)
- **Conversion Rate**: X% (+Y% vs last period)

## Highlights
- [Key achievement 1]
- [Key achievement 2]
- [Key achievement 3]

## Concerns
- [Issue 1 and recommended action]
- [Issue 2 and recommended action]

## Recommendations
1. [Action item 1]
2. [Action item 2]
3. [Action item 3]

Error Handling

Missing Data

• Identify gaps in data
• Estimate missing values if possible
• Flag incomplete analysis
• Request additional data

Data Quality Issues

• Validate data ranges
• Check for outliers
• Remove duplicates
• Standardize formats

Calculation Errors

• Validate formulas
• Check for division by zero
• Handle null values
• Log errors for review

Best Practices

• Always validate data quality first
• Use appropriate time ranges for comparisons
• Consider seasonality in analysis
• Segment data for deeper insights
• Visualize key findings
• Provide actionable recommendations
• Update metrics regularly
• Document methodology
• Keep stakeholders informed
• Automate recurring reports

Advanced Techniques

Time Series Forecasting

• ARIMA models
• Seasonal decomposition
• Trend projection
• Confidence intervals

Statistical Testing

• A/B test significance
• Chi-square tests
• Correlation analysis
• Regression models

Machine Learning

• Churn prediction
• Customer lifetime value prediction
• Demand forecasting
• Anomaly detection

Dependencies

• Python with pandas, numpy, scipy
• Access to analytics data
• Historical data (minimum 3 months recommended)
• Business context and goals

Metadata

• Domain: analytics, business_intelligence
• Complexity: Medium-High
• Automation Level: High
• Human Verification: Recommended (interpret findings)