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

Tools used in this project
Journey Metrics: Comprehensive Analysis of Maven Rail's Sales and Service

About this project

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.

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