Supply metrics focus on the performance and engagement of drivers. These indicators ensure that the platform maintains a healthy and reliable driver base to meet rider demand.

• Active Drivers: Measures the number of drivers who have completed at least one ride during a specific period. This metric helps assess supply availability and driver participation levels.
• Driver Utilization Rate: Evaluates how efficiently drivers spend their time on trips compared to idle or waiting time. A high utilization rate indicates effective use of driver resources.
• Driver Retention Rate: Tracks the percentage of drivers who remain active over time. It provides insights into engagement, loyalty, and satisfaction among the driver community.
• Driver Cancellation Rate: Reflects how often drivers cancel rides after accepting them. High cancellation rates may point to issues such as long wait times, poor matching, or pricing dissatisfaction.

Data Sources: Driver app event logs, GPS tracking data, and trip lifecycle records. Real-time data streams from the mobile platform are stored in a central warehouse for analytics and reporting.

Demand metrics measure user engagement, satisfaction, and the overall strength of customer demand for rides.

• Active Riders: Indicates the number of riders who completed at least one trip within a defined time window. It helps assess platform adoption and usage trends.
• Trip Requests: Captures the total number of ride requests received from users. This metric reflects overall demand volume and helps identify peak usage periods.
• Completed Trips: Represents the number of rides that were successfully completed. It serves as a core measure of service throughput and reliability.
• Rider Retention Rate: Shows how many riders continue to use the platform over multiple periods, offering a view into customer loyalty and satisfaction.
• Rider Cancellation Rate: Highlights the percentage of rides canceled by riders before pickup, which can indicate dissatisfaction with wait times, pricing, or driver availability.

Data Sources: Mobile app interactions, trip databases, and user engagement logs. Insights are derived using cohort analysis and behavioral segmentation.

These metrics assess how effectively the system matches riders with available drivers and how efficiently operations are conducted.

• Match Rate: Measures the percentage of rider requests that successfully receive a driver match. A low match rate can indicate an imbalance between demand and supply.
• Average Wait Time: Tracks the average time between a rider’s request and the driver’s arrival. It directly impacts user satisfaction and service quality.
• Pickup Time Variance: Reflects consistency in pickup times. High variance suggests operational inefficiencies or uneven distribution of drivers.
• Geographic Demand Heatmap: Provides a spatial view of demand and supply concentrations. It helps optimize driver deployment and incentive strategies.

Data Sources: Processed in near real time using event-streaming technologies and geospatial analytics platforms.

Financial metrics measure the platform’s economic performance, profitability, and cost efficiency.

• Gross Bookings: Represents the total value of fares collected before any deductions. It shows overall market activity.
• Net Revenue: Indicates the actual revenue retained by the platform after paying drivers and applying discounts. It reflects financial health and sustainability.
• Cost per Ride: Measures average operational costs per trip, supporting efficiency analysis and budget control.
• Customer Acquisition Cost (CAC): Captures the average marketing expense incurred to acquire a new customer. This helps evaluate marketing effectiveness.
• Customer Lifetime Value (LTV): Estimates the total revenue expected from a customer over their lifetime. It helps assess long-term profitability and user quality.
• Driver Incentive Spend: Represents the total cost of bonuses and promotions given to drivers. Monitoring this helps ensure incentive programs are cost-effective.

Data Sources: Integrated from payment systems, ERP modules, and marketing analytics platforms, with automated reconciliation through ETL pipelines.

Experience metrics assess the quality of interactions between riders and drivers and help maintain service standards.

• Average Driver Rating: Measures the overall service quality as rated by riders. This serves as feedback for driver performance management.
• Average Rider Rating: Reflects driver perceptions of rider behavior and reliability.
• Complaint Rate: Indicates the percentage of rides resulting in complaints, helping identify areas for service improvement.
• Incident Rate: Measures the frequency of safety or operational incidents, ensuring the platform maintains high safety and trust standards.

Data for these metrics comes from feedback systems, customer support records, and safety monitoring tools. Sentiment analysis and text classification models can be applied to detect recurring issues or emerging trends.