Project 2 - Supply and Demand Gap Analysis
Project 2 - Supply and Demand Gap Analysis
Aim :
The primary goal of the analysis is to identify the root causes of cancellations and non-availability of cars, leading to potential revenue loss for Uber, and to recommend actionable measures to resolve these issues effectively.
Introduction :
This dataset, modeled on data that Uber analysts handle, simulates real-world scenarios of cab service issues, such as driver cancellations and car unavailability. These issues negatively affect both customer experience and Uber’s revenue. The analysis will explore patterns and causes using exploratory data analysis (EDA) and visualization.
Problem Statement :
Uber faces challenges that lead to potential revenue loss :
• Driver cancellations: Drivers decline requests, disrupting customer plans.
• Car unavailability: Customers are unable to secure a cab, especially during peak hours.
By analyzing the data, the objective is to identify the root causes of these issues and recommend steps to mitigate them.
Methodology :
1) Data Cleaning and Preparation:
o Identify and address data quality issues.
o Ensure date and time fields are in the correct format.
o Derive additional variables such as time slots (e.g., early morning, evening) for detailed analysis.
2) Exploratory Data Analysis:
o Use visualization to analyze request frequencies, cancellations, and availability issues.
o Examine supply-demand gaps across time slots and locations (city to airport, airport to city).
3) Insights Derivation:
o Identify patterns contributing to the supply-demand gap.
o Propose hypotheses for identified problems.
Insights :
1) Key Problems Identified:
o High frequency of driver cancellations during peak hours.
o Significant unavailability of cars, especially for trips from the airport to the city during late evenings.
2) Time-Based Patterns:
o Early morning and late evening show the highest supply-demand gaps.
o Airport-to-city requests face more availability issues than city-to-airport.
Recommendations :
1) Operational Improvements:
• Introduce incentives for drivers to accept trips during high-demand time slots, particularly late evenings.
• Increase fleet availability at critical locations (e.g., airport pickup zones).
2) Demand Management:
• Implement surge pricing effectively to manage demand during peak hours.
• Provide customers with accurate wait-time predictions.
3) Data-Driven Decisions:
• Use real-time demand forecasting to allocate resources dynamically.
• Monitor driver behavior and provide targeted training or support where needed.
Conclusions :
The analysis highlights a significant supply-demand gap for Uber trips, especially during late evenings and for airport-to-city trips. Addressing these issues through operational changes, driver incentives, and better demand management strategies can enhance customer satisfaction and minimize revenue loss.