Sales Data Analysis Project
تفاصيل العمل
### **Project Title:**
**Sales Data Analysis and Forecasting**
### **Project Objective:**
To analyze historical sales data (515,000 records) to uncover trends, customer behaviors, and product performance. By understanding sales dynamics and predicting future demand, this project aims to improve inventory management, optimize marketing strategies, and drive revenue growth.
### **Project Scope and Deliverables:**
1. **Data Collection and Cleaning**
- **Dataset**: Sales dataset with 515,000 records, containing information on transaction dates, product categories, quantities sold, revenue, customer demographics, and geographical details.
- **Data Cleaning**: Utilize SQL and Python for data cleaning, handling missing or inconsistent data, removing duplicates, and standardizing categorical data. Ensure data integrity, especially in fields like product IDs, transaction dates, and revenue figures.
2. **Exploratory Data Analysis (EDA)**
- **Objective**: Identify key sales trends, customer preferences, and product performance to inform business strategies.
- **Tasks**:
- **Sales Trends**: Analyze sales trends over time, including seasonal fluctuations, holiday spikes, and regional variances.
- **Customer Segmentation**: Segment customers based on demographic factors (e.g., age, location, purchase frequency) to highlight high-value customers and target groups.
- **Product Performance**: Assess sales volume, revenue, and profitability by product category, sub-category, and individual product level to identify bestsellers and underperformers.
- **Churn and Retention Analysis**: Examine repeat purchases and customer churn, analyzing factors like purchase frequency, order value, and product category preferences.
3. **Data Visualization (BI Tool)**
- **Tool**: Use a BI tool (such as Tableau or Power BI) for creating interactive dashboards and data visualizations.
- **Deliverables**:
- **Sales Overview Dashboard**: A comprehensive view of sales volume, revenue trends, and customer demographics over time.
- **Product Performance Dashboard**: Visualize sales by product category and SKU, highlighting bestsellers, high-margin products, and those with declining sales.
- **Customer Segmentation Insights**: Present insights on customer demographics, location, and purchasing behavior for targeted marketing.
- **Seasonal and Regional Insights**: Analyze how sales vary by season and location to better manage inventory and align marketing efforts.
4. **Predictive Modeling (Python & SQL)**
- **Objective**: Develop predictive models to forecast sales, identify churn risks, and project revenue.
- **Tasks**:
- **Sales Forecasting**: Implement time series analysis (e.g., ARIMA, Prophet) and machine learning models (e.g., XGBoost, Random Forest) to predict future sales at various levels (e.g., by region, category).
- **Churn Prediction Model**: Create a classification model to predict customer churn based on historical purchase behavior, demographics, and transaction frequency.
- **Revenue Prediction**: Utilize regression models to project future revenue based on sales trends, product categories, and customer segments, assisting in financial planning.
- **Evaluation Metrics**: Use RMSE, MAE for regression models and F1 score, AUC for classification models, ensuring high model accuracy and reliability.
5. **Optimization and Recommendations**
- **Inventory and Demand Optimization**: Provide recommendations for inventory planning based on demand forecasts and seasonality insights to avoid stockouts and overstock.
- **Product Promotion and Marketing**: Suggest promotional strategies for slow-moving products and plan targeted campaigns for high-demand periods or regions.
- **Customer Retention Strategies**: Based on churn analysis, propose retention tactics such as loyalty programs, personalized marketing, or special offers for at-risk customers.
6. **Documentation and Presentation**
- **Documentation**: Compile detailed project documentation, including methodologies, model-building processes, and visualizations.
- **Executive Presentation**: Present key findings, recommendations, and model results in an accessible format for stakeholders to support data-driven decision-making.
### **Technical Stack**
- **SQL**: Data cleaning, querying, and aggregation for EDA.
- **Python**: Data processing (pandas, numpy), predictive modeling (scikit-learn, statsmodels, Prophet), and visualization (seaborn, matplotlib).
- **BI Tool**: Data visualization and dashboard creation using Power BI or Tableau.
### **Timeline and Milestones:**
1. **Data Collection and Cleaning**: 1 week
2. **EDA and Visualization**: 1.5 weeks
3. **Predictive Modeling**: 2 weeks
4. **Optimization and Recommendations**: 1 week
5. **Documentation and Presentation**: 1 week
**Total Duration**: ~6 weeks