Key Business Challenges Identified
1. Inventory Management Inefficiencies
Our analysis identified significant variations in product dimensions and categories, leading to inefficient warehouse organization and increased handling costs.
Solution: Implement a cluster-based inventory management system using the 4 product clusters identified in our clustering analysis:
- Cluster 1: Small items (avg. 621g, 21.6 x 11.0 x 16.6 cm)
- Cluster 2: Very large items (avg. 16.4kg, 53.9 x 45.1 x 42.8 cm)
- Cluster 3: Medium items (avg. 2.6kg, 30.3 x 32.4 x 26.6 cm)
- Cluster 4: Long items (avg. 2.7kg, 52.2 x 12.7 x 34.4 cm)
See Clustering Analysis
Expected Impact: 15-20% reduction in picking time, 10-15% decrease in storage costs, and 8-12% improvement in inventory accuracy.
2. Pricing Strategy Optimization
Current pricing strategies don't fully leverage the relationships between product attributes, customer preferences, and payment behaviors.
Solution: Develop a dynamic pricing model based on:
- Product category price differentials (computers avg. R$1,098.34 vs. fashion avg. R$120.65)
- Payment method preferences (credit card users spend 23% more on average)
- Installment correlations (positive correlation of 0.31 between installments and order value)
See Correlation Analysis |
See ANOVA Analysis
Expected Impact: 5-8% increase in average order value, 3-5% improvement in profit margins, and 10-15% growth in high-value product categories.
3. Delivery Time Inconsistencies
Analysis shows significant variations in delivery times (average 12.56 days, median 10.22 days), affecting customer satisfaction and repeat purchase rates.
Solution: Implement a tiered delivery system based on:
- Product cluster characteristics (size/weight correlations with shipping times)
- Geographic distribution patterns (state-based delivery time variations)
- Order value relationships (higher value orders prioritization)
See Time Series Analysis |
See Descriptive Statistics
Expected Impact: 25% reduction in delivery time variance, 15% increase in customer satisfaction scores, and 8-10% improvement in repeat purchase rates.
4. Payment Method Optimization
While credit card is the dominant payment method (74%), our analysis shows untapped potential in optimizing payment options for different customer segments.
Solution: Develop targeted payment incentives based on:
- Customer state preferences (significant variations in payment methods by state)
- Order value correlations (credit card avg. R$143.87 vs. voucher avg. R$116.89)
- Product category relationships (high-value categories have distinct payment patterns)
See Inferential Statistics
Expected Impact: 12% increase in payment method conversion, 7-9% reduction in payment processing costs, and 5% decrease in cart abandonment rates.
5. Temporal Demand Fluctuations
Time series analysis revealed significant patterns in order volumes by day of week and hour of day that aren't being leveraged for marketing and operations.
Solution: Implement time-based strategies leveraging:
- Day of week patterns (Monday/Tuesday highest volume, Saturday lowest)
- Hour of day peaks (4-5 PM, 11 AM-12 PM, and 2-3 PM highest order volumes)
- Seasonal trends identified in monthly order patterns
See Time Series Analysis
Expected Impact: 18-22% increase in marketing ROI, 10-15% improvement in staffing efficiency, and 8-10% growth in off-peak sales periods.