Journey Metrics: Comprehensive Analysis of Maven Rail's Sales and Service

Project Goals: The primary goals of this project were to:

1. Analyze Passenger Behavior: Understand the purchasing patterns, preferences, and demographics of Maven Rail’s passengers.
2. Evaluate Sales Performance: Assess the revenue generation and sales volume across different ticket types, classes, and purchase methods.
3. Monitor Operational Performance: Measure on-time performance, identify common causes of delays, and assess the overall efficiency of rail services.

Business Needs: Maven Rail needed a comprehensive analysis to:

1. Enhance Customer Experience: By understanding passenger behavior, Maven Rail aimed to tailor services, marketing campaigns, and customer support to better meet passenger needs.
2. Optimize Revenue Streams: Identifying high-revenue routes, popular ticket types, and peak purchase times was crucial for strategic pricing, promotions, and resource allocation.
3. Improve Operational Efficiency: Insight into delays and cancellations would help Maven Rail address operational bottlenecks, improve punctuality, and reduce customer complaints.

Discovering and Presenting Meaningful Insights:

1. Data Collection and Preparation:

   • Aggregated data from various sources including ticket sales, journey logs, and passenger feedback.
   • Cleaned and structured the data to ensure accuracy and consistency.

2. Data Analysis:

   • Sales Analysis: Used Power BI to create visualizations such as total revenue, sales by ticket type and class, and revenue by payment method.
   • Traveler Behavior: Analyzed purchase patterns by time of day, identified popular routes, and studied journey patterns through, bar charts, and line charts.
   • Operational Performance: Assessed on-time performance with gauge charts, examined reasons for delays using bar charts, and tracked refund requests with pie charts.

3. Insights Presentation:

   • Created an interactive Power BI dashboard with dedicated pages for sales analysis, traveler behavior, and operational performance.
   • Used clear, intuitive visualizations to highlight key insights and trends.
   • Provided actionable recommendations based on data findings, such as optimizing scheduling, enhancing customer service during peak times, and targeting marketing efforts.

• Recommendations:
• Enhance Online Experience: Invest in improving the online booking platform for a seamless user experience.
• Seasonal Promotions: Capitalize on high sales periods like February with special promotions and offers.
• Credit Card Incentives: Introduce loyalty programs or discounts for credit card payments to encourage more purchases.
• Dynamic Pricing: Implement dynamic pricing strategies to optimize revenue from Advanced Standard tickets.
• Promote Railcards: Increase awareness of railcard benefits to boost sales and customer loyalty.
• Optimize Marketing: Schedule marketing campaigns around peak purchase times (5 PM and 8 PM) for maximum impact.
• Address Delay Causes: Focus on mitigating weather-related delays, especially during peak hours.