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Rossman Store Analysis

I collaborated on a team project to analyze Rossmann's sales data, identifying trends and providing actionable insights to optimize business strategies. Using Power BI and Excel, we explored sales patterns from August to September and developed an interactive dashboard for decision-making.

🔗 Here’s a link to the full interactive dashboard in Tableau Public where you can explore the results:

Rossmann Store Sales Dashboard

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Key Steps in the Analysis

1. Data Preprocessing:

  • Cleaned and transformed data in Excel, including encoding state holidays, promotions, and school holidays into binary indicators.

  • Organized datasets in Power BI using a star schema for efficient analysis.

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2. Exploratory Analysis and Visualization:

  • Analyzed sales trends across store types, assortments, promotional periods, and holidays.

  • Built interactive Power BI dashboards to visualize insights and support decision-making.

3. Machine Learning Classification:

  • Used PyCaret and XGBoost to classify daily sales into categories such as "Very Low," "Moderate-High," and "Very High."

  • Incorporated predictions into the dashboard to support future sales planning.

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Business Insights and Recommendations

Promotional Strategy Optimization:

  • Promotions significantly increased sales across all stores, especially during peak periods.

  • Recommendation: Enhance promotional effectiveness by aligning with high-traffic periods and tailoring offers to customer preferences.

Seasonal and Holiday Planning:

  • Sales increased during holidays and promotional events, requiring strategic inventory and staffing adjustments.

  • Recommendation: Plan inventory and staffing based on seasonal and holiday sales patterns to maximize consumer spending.

Store Type-Specific Strategies:

  • Store Type A performed well across all categories, while Store Types B, C, and D showed variability in sales performance.

  • Recommendation: Focus on maintaining high performance in Store Type A, Optimize Store Type B’s strategies to improve middle-category sales, Apply successful strategies from high-performing stores to boost Store Type C and D sales, especially in lower categories.

Continuous Monitoring and Adaptation:

  • Real-time monitoring is crucial for adapting to market trends and customer feedback.

  • Recommendation: Implement a feedback loop to adjust strategies dynamically based on live sales data.

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