🚆 UK Train Rides Analysis | Power BI Dashboard
📌 Project Overview
This project analyzes UK train ticket purchases and journey performance to uncover insights related to revenue, customer behavior, punctuality, delays, and refunds. Using Power BI, I built an end-to-end analytics solution that transforms raw transactional data into actionable insights for business, operations, and customer experience teams.
The dashboards are designed for multiple stakeholders:
Executives → High-level performance & revenue visibility
Operations teams → Punctuality, delays, and route performance
Customer & commercial teams → Pricing, refunds, and purchase behavior
📂 Dataset Description
Source: Maven Analytics – UK Train Rides Dataset
Granularity: One row per ticket purchase / journey
Scope: Ticketing, journey details, delays, and refund behavior
Key data fields include:
Ticket purchase details (channel, payment method, ticket type, class)
Pricing & discount indicators (railcard, advance/off-peak/anytime)
Journey information (stations, dates, times)
🧱 Data Modeling Approach
Although the source data was a single flat table, it was treated as a fact table and remodeled into a star schema to enable scalable analysis and efficient DAX calculations.
Data model enhancements:
📅 Date dimension (purchase date & journey date)
⏰ Time attributes (departure, arrival, purchase time)
🚉 Station & route attributes
🎟️ Ticket characteristics
This approach reflects real-world BI best practices and improves performance, readability, and extensibility.
📊 Key KPIs & Metrics
The report includes business-critical metrics such as:
Total Revenue
Total Journeys
On-Time Performance (%)
Delay & Cancellation Rates
Average Delay Duration
Refund Request Rate
Revenue at Risk due to delays
Advanced DAX measures were used for time intelligence, ratios, and performance benchmarking.
📈 Dashboard Pages & Analysis
1️⃣ Executive Overview Dashboard
Purpose: Provide senior stakeholders with a high-level view of business performance, punctuality, and customer impact.
Key focus areas:
Overall revenue and journey volume
On-time vs delayed vs cancelled journeys
Refund request rate as an indicator of customer dissatisfaction
Ticket sales split by purchase channel and ticket type
Business questions answered:
How is the rail network performing overall?
Are delays and cancellations impacting revenue and customer experience?
Which ticket types and channels contribute most to revenue?
2️⃣ Operations & Punctuality Dashboard
Purpose: Enable operations teams to identify delay patterns and performance issues across routes and time periods.
Key focus areas:
On-time performance by route and station
Delay and cancellation rates
Average arrival delay (scheduled vs actual arrival time)
Breakdown of delay reasons
Business questions answered:
Which routes and stations are most affected by delays?
What are the primary reasons for delays and cancellations?
When do delays occur most frequently (time of day, date)?
3️⃣ Customer & Ticket Insights Dashboard
Purpose: Analyze customer behavior, pricing, and refund drivers to support commercial and customer experience decisions.
Key focus areas:
Ticket type and class distribution
Railcard usage and discount impact
Refund requests by journey status and delay reason
Purchase behavior (online vs station)
Business questions answered:
How do different ticket types and discounts impact revenue?
What factors drive refund requests?
Do purchase channels or ticket characteristics influence customer outcomes?
💡 Key Insights
A small number of routes account for a disproportionately high share of delays and refund requests
Peak-hour journeys show significantly lower punctuality compared to off-peak travel
Advance and Off-Peak tickets drive volume but increase sensitivity to delays
Certain delay reasons consistently lead to higher refund rates, increasing revenue risk
🛠️ Tools & Skills Demonstrated
Power BI (Data modeling, DAX, dashboard design)
DAX (KPIs, ratios, time intelligence)
Data storytelling & stakeholder-focused design
Business & operational analytics
🚀 Potential Enhancements
Predictive modeling for delay likelihood
Customer segmentation using purchase patterns
Integration with operational cost data
SLA benchmarking across routes and stations
📎 Files Included
UK Train Rides Report.pbix – Power BI report file railway - Raw sales dataset railway_data_dictionary
📬 Contact
If you’d like to discuss this project or similar analytics work, feel free to connect with me on LinkedIn: https://www.linkedin.com/in/shankar-narayanan-iyer/ or explore my other projects on GitHub.