City-Wise Passenger and Revenue Dashboard

Problem Statement

Goodcabs, a transportation service provider, aims to enhance its operational efficiency and profitability by analyzing its city-wise trip, revenue, and passenger data. The organization seeks to identify:

1. Trip patterns across cities.
2. Revenue performance and cost efficiency.
3. Passenger retention rates and satisfaction levels.
4. Gaps in target achievement for trips and revenue.
5. Key areas for improvement to ensure sustainable growth.

This dashboard provides comprehensive insights to address these needs, supporting data-driven decision-making.

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Objectives

The dashboard addresses the following key objectives:

1. Understand city-wise trip patterns, including trends by weekday/weekend and passenger type.
2. Monitor revenue generation, average fare, and cost per kilometer.
3. Evaluate passenger retention through Repeat Passenger Rate (RPR%).
4. Compare actual vs. target performance for trips and revenue.
5. Investigate passenger satisfaction through driver and passenger ratings.

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Key Features

Interactive Filters

• City Filter: Drill down into specific city performance.
• Date Filter: Analyze data by month or quarter.
• Passenger Type Filter: Segment data by new or repeat passengers.

Dashboard Sections

1. Key Performance Indicators (KPIs)

• Total Revenue: Total revenue generated across all cities.
• Total Trips: Total number of trips across all cities.
• Repeat Passenger Rate (RPR%): Passenger retention metric.
• Average Fare Per Trip: Average revenue per trip.
• Revenue Target Achievement: Progress toward target revenue goals.

2. City-Wise Trip Patterns

• Visuals:
  • Line chart of total trips by city.
  • Segmentation of trips by weekday vs. weekend and passenger type.

3. Revenue and Cost Analysis

• Visuals:
  • Combo chart comparing average fare vs. cost per kilometer.
  • Monthly revenue trends to identify seasonality and growth.

4. Target Performance Analysis

• Visuals:
  • Bar chart comparing actual vs. target trips and revenue.
  • Monthly trends for target achievement.

5. Ratings Analysis

• Visuals:
  • Scatter plot comparing driver and passenger ratings.
  • Line chart showing trends by passenger type.

6. Repeat Passenger Insights

• Visuals:
  • Bar chart of repeat passenger counts by trip frequency.
  • Line chart showing monthly RPR% trends by city.

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Findings Summary

1. Top Performing Cities

• Cities like Lucknow and Jaipur lead in trip counts and revenue with high RPR%.

2. Underperforming Cities

• Cities such as Mysore and Coimbatore lag behind in trip volumes and revenue, requiring operational improvements.

3. Passenger Retention Challenges

• Cities with low RPR% (e.g., Jaipur) need strategies to enhance passenger loyalty.

4. Cost Efficiency

• Cities like Jaipur have high fares but also exhibit high costs per kilometer, indicating potential inefficiencies.

5. Customer Satisfaction

• High ratings in cities like Lucknow reflect excellent service, while variations in ratings suggest gaps in other cities.

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Recommendations

Primary Recommendations

1. Boost Underperforming Cities:

   • Launch marketing campaigns in Mysore and Coimbatore to increase trip volumes.

2. Improve Retention:

   • Develop loyalty programs to boost RPR% in cities like Jaipur.

3. Optimize Costs:

   • Review operational expenses in high-cost cities to improve fare alignment.

4. Enhance Customer Experience:

   • Train drivers and improve onboarding processes to address low satisfaction ratings.

5. Capitalize on Top Cities:

   • Focus marketing efforts on Lucknow and Jaipur to maximize revenue potential.

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Further Analysis & Recommendations

1. Factors Influencing RPR%:

   • Investigate how service quality, pricing, and demographics affect retention rates.
   • Examine correlations with socioeconomic and lifestyle patterns in each city.

2. Tourism vs. Business Demand:

   • Analyze the impact of seasonal events (e.g., festivals, conferences) on trip volumes.
   • Tailor marketing strategies for tourism-oriented cities.

3. Dynamic Pricing Models:

   • Introduce dynamic fare pricing during peak demand to optimize revenue.

4. Driver Retention Analysis:

   • Evaluate driver satisfaction to ensure consistent service quality.

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Usage Instructions

1. Open the dashboard in Power BI Desktop or Service.
2. Use filters to focus on specific cities, dates, or passenger types.
3. Hover over visuals for detailed tooltips and insights.
4. Refer to KPIs for a snapshot of performance metrics.
5. Explore each section for detailed insights into trends, costs, and retention.

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This refined README file provides a comprehensive summary of the dashboard, integrating the problem statement, findings, recommendations, and additional analysis suggestions. Let me know if you need further adjustments!

Screenshot