Data analysis project with python
تفاصيل العمل
This project analyzed over 100,000 global online transactions across multiple product categories, regions, and payment methods.
The analysis focused on sales performance, customer behavior, and marketplace trends.
**Key Findings:**
- Electronics and Clothing contributed the largest share of total revenue, with Electronics generating higher-value purchases and Clothing dominating in volume.
- North America and Europe led in total revenue, while Asia showed significant growth potential with rising transaction counts.
- Credit Cards were the most widely used method globally, though Digital Wallet adoption was higher in Asia.
- Strong peaks observed in December (holiday sales) and mid-year months. Weekends showed slightly higher revenue per transaction.
- Most purchases were small quantities, though high-value transactions in Electronics significantly boosted revenue concentration (top 20% of transactions generated over 75% of revenue).
**Recommendations:**
Focus marketing campaigns around holiday seasons and weekends, expand digital wallet partnerships in Asia, develop targeted promotions for underperforming categories in high-revenue regions, and use customer segmentation to optimize pricing and retention strategies.
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## Market Performance Overview
### Regional Insights
- **North America**: Highest revenue, dominated by Electronics and Home & Garden.
- **Europe**: Strong Clothing and Books category performance; higher average transaction values compared to Asia.
- **Asia**: High transaction counts but lower order values; customers prefer Clothing and Electronics with digital payments.
- **South America**: Smaller share of revenue, with strong potential in Fashion categories.
### Category Insights
- **Electronics**: High-value transactions, mostly single-unit purchases.
- **Clothing**: Bulk purchases common, driving transaction count but lower per-order revenue.
- **Books & Home & Garden**: Niche but stable contributors; Books peak seasonally around academic months.
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## Strategic Recommendations
- **Seasonal Strategy**: Launch holiday campaigns in December and back-to-school promotions for Books. Tailor discounts and bundles for bulk Clothing purchases.
- **Regional Strategy**: Increase investment in Asia (optimize digital payment integrations, targeted promotions). Expand Clothing promotions in Europe and South America to boost volume.
- **Category Strategy**: Maintain premium pricing for Electronics but introduce financing options. Bundle Home & Garden products to increase basket size.
- **Customer Segmentation**: Develop loyalty programs for Premium Shoppers (high-value Electronics customers). Offer discounts for Budget Shoppers in Clothing and Books.
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## Data Limitations
- **Missing Values**: Some records had missing Region and Payment_Method, imputed based on frequency but may bias trends.
- **Outliers**: Extreme values in Unit_Price and Total_Price were capped; some rare but valid high-value orders may have been reduced.
- **Duplicates**: Several duplicate Order_IDs were removed, which could understate repeat customers.
- **Single-year data**: Dataset covers ~12 months only, limiting long-term trend forecasting.
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## Next Steps
- **Customer-Level Analysis**: Track repeat purchases and build customer lifetime value (CLV) models.
- **Market Basket Analysis**: Identify frequently co-purchased products for cross-selling opportunities.
- **Predictive Modeling**: Apply machine learning to forecast sales by region and category.
- **Data Enrichment**: Collect demographic and marketing channel data to better understand customer behavior.
- **Continuous Monitoring**: Build dashboards with KPIs such as Revenue per Region per Month, Category Market Share, and Payment Method Conversion Rates.