Walmart Sales Performance Dashboard – Project Report

Executive Summary

• Analyzed over 100,000 weekly sales records from Walmart.

• Integrated various datasets including store, department, and economic data.

• Built a robust insights pipeline using Python for data cleaning, SQL (DuckDB in Google Colab) for data analysis, and Tableau for visualization.

• Outcome provided actionable insights into holiday impacts on sales, store efficiency, department contributions, and sales trends.

  
  

Approach

Data Preparation

• Cleaned and standardized raw datasets into an analysis-ready format using Python tools.

• Merged diverse datasets to create a comprehensive database suited for in-depth analysis.

  
  

Analysis

• Developed SQL queries to answer crucial business questions including:

- Assessing whether holidays significantly lift sales figures.

- Identifying the most efficient stores in terms of sales per square foot.

- Analyzing which departments contribute the most to overall revenue.

- Evaluating how macroeconomic factors influence sales variations.

- Monitoring seasonal and quarterly sales trends.

Visualization

• Created interactive dashboards using Tableau to present key performance indicators (KPIs) and sales trends.

• Enabled drill-down capabilities to allow for detailed exploration of the data.

  
  

Key Insights

Holiday Sales Performance

• Holiday weeks resulted in a sales boost of 7.1%.

• Notably, over 90% of Walmart's revenue originated from non-holiday weeks.

  
  

Departmental Contributions

• Three specific departments (92, 95, 38) accounted for approximately 20% of total revenue.

• Highlighting these departments could guide resource allocation and marketing efforts.

  
  

Store Efficiency Metrics

• Top-performing large-format stores demonstrated over 2 times the sales per square foot compared to their underperforming counterparts.

• This metric serves as a benchmark for evaluating store performance.

  
  

Economic Resilience

• Sales displayed weak correlation with macroeconomic indicators such as CPI, fuel prices, unemployment rates, and temperature.

• This finding confirms Walmart's resilience to external economic fluctuations.

  
  

Seasonal Trends

• Q4 emerged as the strongest sales quarter, with clear year-end surges being predictable.

• Understanding these trends can facilitate better inventory management and resource planning.

  
  

Business Value

Strategic Planning

• Insights into holiday impact allow for focused holiday campaigns where sales lift is most significant.

• Continuous attention to core weekly sales is also necessary.

  
  

Operational Efficiency

• Underperforming stores can be benchmarked against high-efficiency leaders to identify areas of improvement.

• Operational adjustments based on these insights may enhance overall performance.

  
  

Category Management

• Prioritizing departments showcasing outsized revenue contributions can optimize inventory and marketing strategies.

• A data-driven approach can help in maximizing overall profitability.

  
  

Risk Management

• Confidence in sales stability irrespective of macro fluctuations can enable Walmart to plan more effectively.

• This resilience allows for smoother operational adjustments during uncertain times.

  
  

Forecasting and Budgeting

• Aligning resources with anticipated seasonal peaks can improve budgeting accuracy.

• Understanding Q4's sales patterns can inform future forecasting efforts.

  
  

Deliverables

• Cleaned dataset generated using Python and Pandas, ready for future analysis.

• SQL analysis workbook created in DuckDB using Google Colab for detailed data exploration.

• Excel outputs consisting of 11 comprehensive analysis sheets providing insights on various metrics.

• Interactive Tableau dashboard that showcases KPIs and sales trends for convenient stakeholder access.