This comprehensive analytics project examines ride-hailing operations using a multi-tool approach combining SQL, Excel, and Power BI. The analysis focuses on understanding booking patterns, customer behavior, and operational efficiency to drive data-informed business decisions.
Tools & Technologies:
- SQL: Database querying and data extraction
- Excel: Data cleaning, transformation, and preprocessing
- Power BI: Interactive dashboards and visual analytics
The analysis aims to uncover actionable insights across multiple dimensions:
- Identify booking trends and peak demand periods
- Analyze cancellation patterns and root causes
- Evaluate customer satisfaction and service quality
- Optimize revenue streams and payment preferences
- Understand vehicle type performance metrics
The project utilizes a comprehensive dataset containing one month of ride-hailing operations from Bengaluru, encompassing approximately 40,000 booking records.
Key Data Fields:
- Temporal data (Date, Time)
- Booking identifiers and status
- Customer and vehicle information
- Location details (Pickup, Drop)
- Turnaround time metrics (V_TAT, C_TAT)
- Cancellation tracking (Customer/Driver)
- Financial data (Booking Value, Payment Method)
- Performance metrics (Ride Distance, Ratings)
- Incomplete ride tracking and reasons
- Successful Booking Analysis: Extract all completed rides to understand conversion rates
- Vehicle Performance: Calculate average ride distance segmented by vehicle category
- Customer Cancellations: Quantify cancellation volumes initiated by riders
- Top Customer Identification: Rank customers by booking frequency for loyalty insights
- Driver Cancellation Analysis: Categorize driver-side cancellations by reason (personal vs. vehicle issues)
- Rating Benchmarks: Determine rating ranges for Prime Sedan category
- Payment Preference: Filter transactions by UPI payment method
- Customer Satisfaction: Aggregate ratings across vehicle types
- Revenue Calculation: Sum total booking value for successful completions
- Incomplete Ride Tracking: Document all unfinished rides with associated reasons
1. Overall Performance View
- Ride volume trends over time (time-series analysis)
- Booking status distribution (success vs. cancellation breakdown)
2. Vehicle Type Analysis
- Top 5 vehicle categories by cumulative distance
- Average customer satisfaction scores per vehicle type
3. Revenue Intelligence
- Payment method revenue comparison
- Top 5 high-value customers leaderboard
- Daily ride distance distribution patterns
4. Cancellation Insights
- Customer-initiated cancellation reasons
- Driver-initiated cancellation reasons
5. Rating Analysis
- Driver rating distribution and patterns
- Customer rating distribution and patterns
- Comparative analysis: Customer vs. Driver ratings correlation
- Success Rate: Achieved 62% booking completion rate
- Customer Cancellations: Maintained below 7% threshold
- Driver Cancellations: Contained under 18% of total bookings
- Incomplete Rides: Less than 6% of all bookings
Operational Excellence:
- Identified AC malfunction and driver location drift as primary 4-wheeler cancellation drivers
- Peak demand periods correlate with weekends and local event schedules
- Average ride distances vary significantly across vehicle categories, informing fleet allocation
Revenue Optimization:
- 70% of transactions fall under ₹500 value bracket
- 28% represent premium bookings above ₹500
- Payment method preferences reveal opportunities for digital payment incentives
Quality Metrics:
- Vehicle type performance ratings enable targeted service improvements
- Customer-driver rating correlation analysis highlights service excellence patterns
- Systematic tracking of incomplete rides enables proactive issue resolution
- Implement preventive maintenance protocols to reduce vehicle-related cancellations
- Optimize driver allocation algorithms to minimize pickup location discrepancies
- Develop targeted strategies for peak demand periods
- Create loyalty programs for high-frequency booking customers
- Enhance digital payment adoption through incentive structures
├── Data/
│ └── ola-data-file.xlsx
├── SQL/
│ ├── SQL_queries.sql
│ └── Create_Database_Ola.sql
├── Reports/
│ ├── Overall_Report.pdf
│ ├── Vehicle_Type_Report.pdf
│ ├── Revenue_Report.pdf
│ ├── Cancellation_Report.pdf
│ └── Rating_Report.pdf
└── README.md
This end-to-end analytics project demonstrates the power of integrated data analysis across SQL, Excel, and Power BI platforms. By transforming raw operational data into actionable insights, the project enables data-driven decision-making for improved customer satisfaction, operational efficiency, and revenue optimization in the ride-hailing industry.
Analysis Period: 1 Month | Location: Bengaluru | Records Analyzed: ~40,000