E-Commerce Sales Dashboard: Data Analytics Case Study

Analyze e-commerce sales data to identify trends, customer behavior patterns, and revenue optimization opportunities. Build interactive dashboards using data visualization best practices.

## Background

An e-commerce company has collected 6 months of sales data across multiple product categories, regions, and customer segments. The management team needs actionable insights to optimize inventory, marketing spend, and customer retention strategies.

## The Challenge

Your task is to analyze the dataset and create a comprehensive analytics dashboard that answers:

1. Sales Trends: Which products are performing well? Which are declining?
2. Customer Segments: Who are the most valuable customers?
3. Regional Performance: Which regions show growth potential?
4. Seasonal Patterns: Are there predictable sales cycles?

## Data Sources

Based on the Data Analytics tutorial, you should consider:

### Primary Data
- Transaction logs (CSV format)
- Customer demographics
- Product catalog with categories
- Regional sales data

### Analytics Types to Apply
- Descriptive Analytics: What happened? (Sales summaries, trends)
- Diagnostic Analytics: Why did it happen? (Root cause analysis)
- Predictive Analytics: What will happen? (Forecasting)
- Prescriptive Analytics: What should we do? (Recommendations)

## Tools & Techniques

Refer to the tutorial for guidance on:
- Data cleaning and preparation
- Exploratory Data Analysis (EDA)
- Visualization selection (bar charts, line graphs, scatter plots, heatmaps)
- Dashboard design principles

## Deliverables

1. Executive Summary (1 page)
- Key findings
- Top 3 recommendations

2. Interactive Dashboard
- Sales trends over time
- Regional performance map
- Product category breakdown
- Customer segmentation analysis

3. Technical Report
- Methodology
- Data cleaning steps
- Statistical tests used
- Limitations

## Evaluation Criteria

- Clarity of visualizations
- Depth of insights
- Actionability of recommendations
- Technical rigor
- Dashboard usability

Dataset Provided:
- E-commerce transactions (6 months, 50,000+ records)
- Customer demographics (age, location, segment)
- Product catalog (categories, prices, costs)
- Regional data (5 regions, 20 cities)

Data Quality Notes:
- Missing values: ~3% in customer age field
- Some duplicate transactions (need deduplication)
- Date formats inconsistent (need standardization)
- Currency: All in USD

Suggested Tools:
- Python (pandas, matplotlib, seaborn)
- Tableau or Power BI for dashboards
- Excel for initial exploration
- SQL for data extraction

Analytics Framework:
1. Data Preparation
- Clean missing values
- Remove duplicates
- Standardize formats

2. Exploratory Analysis
- Summary statistics
- Distribution analysis
- Correlation matrix

3. Visualization Strategy
- Time series: Line charts
- Comparisons: Bar charts
- Relationships: Scatter plots
- Distributions: Histograms
- Geographic: Maps

4. Dashboard Design
- KPI cards at top
- Trends in middle
- Details at bottom
- Filters on left/right

Recommended Tools:
- Python: pandas, plotly, dash
- Tableau Public (free)
- Power BI Desktop (free)
- Google Data Studio (free)