Key Metrics
1,898
Orders analyzed
61%
Revenue from top 5 restaurants
21%
Faster weekend delivery
6
Actionable recommendations
The Challenge
Food Co., a food delivery aggregator in New York City, needed to understand demand patterns, optimize operations, and enhance customer experience. Raw transactional data was available but hadn't been systematically analyzed to address strategic questions around revenue concentration, operational efficiency, customer behavior, and peak demand patterns.
Approach & Methodology
Applied a structured EDA framework with four analytical layers:
- Summary Statistics: Initial distributions and key metrics
- Univariate Analysis: Individual variable distributions, top restaurants, cuisine popularity
- Multivariate Analysis: Cross-variable relationships and correlations
- Correlation Analysis: Quantitative assessment of numerical variable relationships
Analyzed 1,898 orders across 9 attributes using Python, Pandas, NumPy, Matplotlib, and Seaborn.
Key Findings
Revenue Concentration
Top 5 restaurants generate 61% of revenue, with Shake Shack alone accounting for 21%. Bottom 6 restaurants contribute less than 5% each. This concentration presents both opportunity and risk for business strategy.
Interactive Visualization
Revenue by Restaurant
Order Patterns
American cuisine dominates with 584 orders, especially on weekends. 29.2% of orders exceeded the $20 threshold. 14 distinct cuisine types served, indicating diverse customer preferences across the platform.
Interactive Visualization
Orders by Cuisine Type
Operational Performance
Average food prep time of 27.37 minutes. Weekday deliveries average 28.34 minutes versus weekends at 22.47 minutes. This represents a significant 21% faster weekend performance. 10.53% of orders exceeded 60 minutes total time.
Interactive Visualization
Delivery Time: Weekday vs Weekend
Customer Insights
Top customer placed 13 orders, indicating strong repeat behavior. Average platform rating of 4.33/5 demonstrates good overall satisfaction. However, "Not Given" was the most common rating category, revealing a critical feedback gap opportunity.
Recommendations
Six actionable recommendations delivered:
1. Weekend Delivery Optimization
Leverage the 21% faster weekend performance by expanding weekend capacity and promoting time-sensitive services. Investigate root causes (staffing, routing, kitchen efficiency) and implement best practices to weekday operations.
2. Tiered Pricing for High-Value Orders
Develop dynamic pricing strategies for orders exceeding $20, capturing additional margin from higher-order-value customers while maintaining competitiveness.
3. Loyalty Program Targeting Frequent Customers
Create engagement programs for repeat customers (top customer: 13 orders). Focus on retention through personalized offers and exclusive benefits to drive lifetime value.
4. Menu Diversification
Promote underrepresented cuisines to balance revenue concentration. Develop partnerships with emerging cuisine categories to attract new customer segments and reduce platform dependency.
5. Kitchen Efficiency Improvements
Target 27+ minute average prep time through process optimization, staffing models, and technology. Even 5-minute reductions compound across thousands of daily orders.
6. Targeted Marketing During Peak Periods
Implement demand-side pricing and promotions during identified peak periods to maximize utilization, reduce idle capacity, and improve unit economics.