Sales Analysis of a Clothing Retail Shop
Syed Fatik Islam
Project Description
This project focuses on analyzing sales trends, product performance, and supplier transactions using DBF (Database File Format) data, aiming to provide actionable insights for optimizing sales and supplier relationships. Key aspects include:
Sales Trend Analysis:
Monthly sales data visualization to identify seasonal trends and fluctuations.
Cumulative sales tracking for monitoring long-term growth patterns.
Sales forecasting using linear regression modeling to predict future performance.
Product Performance Analysis:
Identification of top-performing products based on quantity sold and revenue.
Highlighting underperforming products to inform inventory and marketing strategies.
Category-level insights to optimize product portfolios.
Supplier Performance Tracking:
Monitoring credit and debit transactions over time to evaluate supplier relationships.
Identifying trends in supplier performance to improve financial efficiency and negotiation strategies.
Monthly Sales Trend Bar chart
Monthly Sales Percentage Change
Monthly Sales Line Chart
Outcomes and Benefits
Sales Optimization:
Clear understanding of peak sales periods and underlying causes.
Forecasting to align marketing strategies with future demand.
Product Portfolio Insights:
Data-driven decisions on inventory management and marketing efforts for underperforming items.
Enhanced focus on top-selling products to maximize profitability.
Supplier Relationship Management:
Financial visibility into supplier transactions, enabling better credit and debit handling.
Identification of top-performing suppliers for stronger partnerships.
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Posted Dec 23, 2024
Analyzed sales trends, product performance, and supplier transactions using Python, pandas, and matplotlib.
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