PROJECT SUMMARY

NAME

Uber supply demand gap - data analysis project

CONTEXT

You may have some experience of travelling to and from the airport. Have you ever used Uber or any other cab service for this travel? Did you at any time face the problem of cancellation by the driver or non-availability of cars?

Well, if these are the problems faced by customers, these very issues also impact the business of Uber. If drivers cancel the request of riders or if cars are unavailable, Uber loses out on its revenue.

GOAL

The aim of analysis is to identify the root cause of the problem (i.e. cancellation and non-availability of cars) and recommend ways to improve the situation.

DATA

1 csv file (6745, 6) : Uber Request Data

Techniques and libraries used :

• Data acquisition : CSV import with Pandas
• Data preparation : Cleaning and Manipulation techniques with Pandas
• Data analytics : Time series analysis
• Data Visualization : Identification of patterns, trends and key insights with seaborn and matplotlib
• Business Recommandations : to solve issues and improve services.

CONCLUSION

Observations:

• Completion Rate: Only 42% of airport/city requests are completed due to a significant supply-demand gap.
• Time Dependency: This gap varies significantly by the hour of the day.
• Critical Periods: Most revenue loss occurs during two critical periods: morning rush (5 to 10 AM) and evening rush (5 to 10 PM).
• Supply/Demand Fluctuations: The supply-demand gap flips between morning and evening rush hours.
• Impact on Service: Rush hours lead to increased non-availability and cancellations.
• Non-availability: Insufficient drivers in high-demand areas during rush hours.
• Cancellations: Drivers prefer more profitable requests during rush hours.

Inference:

Morning Rush Hypothesis:

• Demand Pattern: Most people head to the airport, fewer to the city.
• Driver Allocation: More drivers go to the airport, causing non-availability for city requests.
• Profitability: City-to-airport trips are less profitable due to potential empty return trips, leading to cancellations as drivers wait for more profitable city-to-city requests.

Evening Rush Hypothesis:

• Demand Pattern: Most people head to the city, fewer to the airport.
• Driver Allocation: More drivers go to the city, causing non-availability for airport requests.
• Profitability: Drivers prefer more profitable city trips over airport trips.

Expanded scope

Possible Solutions to Fill the Supply-Demand Gap

1. Incentives for Drivers:

• Dynamic Pricing: Increase fares during peak times to attract more drivers to high-demand areas.
• Flexible Work Hours: Provide bonuses or higher rates for drivers during peak times.
• Driver Pooling: Create a pool of part-time or on-call drivers for peak times.
• Cancellation Penalty: Set a threshold for maximum cancellations to discourage drivers from canceling requests.

2. Enhanced Communication and Training:

• Real-time Updates: Improve communication with drivers about high-demand areas and potential earnings.
• Training Programs: Educate drivers on maximizing earnings and efficiency, especially during peak demand periods.

3. Data-Driven Strategies:

• Predictive Analytics: Utilize data to forecast high-demand periods and proactively inform drivers.
• Improved Algorithms: Enhance ride-matching algorithms to minimize idle time and maximize efficiency.
• Geofencing: Create virtual boundaries around high-demand areas to ensure driver availability during peak times.

4. Customer Management:

• Promotions: Offer discounts to customers booking trips during non-peak hours to spread demand.
• Education: Inform customers about the benefits of pre-booking rides for better planning.
• Car Sharing: Allow car sharing when driver availability is low.

5. Strategic Partnerships and Adjustments:

• Local Partnerships: Collaborate with businesses, hotels, and airlines to maintain a steady flow of requests.
• Seasonal Strategies: Adjust strategies based on seasonal demand variations to ensure driver availability.

Possible Solutions to Better Understand the Problem

1. Data Collection and Advanced Analytics:

• Collect detailed data on trip requests, completions, cancellations, and driver availability.
• Use advanced analytics to forecast driver shortages and create heat maps for critical areas.

2. Customer and Driver Feedback:

• Conduct surveys to understand customer and driver pain points.
• Analyze booking patterns to identify trends and improve demand predictions.

3. Driver Behavior and Performance Analysis:

• Study driver preferences and factors influencing acceptance or cancellation of requests.
• Analyze the economic impact of different trip types on driver earnings.

4. Operational Analysis and Audits:

• Review current processes and systems to identify inefficiencies.
• Implement real-time monitoring to better manage peak demand.

5. External and Competitive Analysis:

• Examine case studies, external factors, and competitor strategies to identify best practices and innovative solutions.