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đźš• Rider Trip Analysis Dashboard (Power BI)

Business Problem:
The transportation company lacked insight into rider behavior, booking patterns, and operational
inefficiencies. They needed data-driven recommendations to optimize vehicle deployment, enhance
customer loyalty, and increase profitability.

Solution & Analysis:
Using Power BI, I developed an interactive dashboard that analyzed 100K+ trips across key metrics:

  • Peak booking days and hours (time analysis)
  • Most/least used vehicle types
  • Farthest and shortest trip patterns
  • Preferred payment methods
  • Location-based performance

Key features included:

  • Dynamic drill-through views (by time, location, and vehicle)
  • KPI cards (avg booking, distance, trip time)
  • Slicers, bookmarks, dynamic parameters, dynamic titles and page navigation for seamless storytelling.

Insights:

  • 60% of rides occurred during the day, with peaks between 3–4PM
  • RiderX was the most used vehicle; RiderPay was the most popular payment method
  • Bookings spiked between the 22nd and 26th of each month
  • Weekend usage was highest, especially Saturdays and Sundays
  • The farthest trip recorded was 144.1 miles (Lower East Side → Crown Heights North)

Recommendations:

  • Reallocate vehicle supply to peak hours and weekends for increased ride fulfillment
  • Launch “End-of-Month Rewards” campaigns to boost loyalty
  • Promote premium long-distance packages for high-margin routes
  • Incentivize use of RiderPay with cashback/partnerships
  • Prioritize high-demand vehicle types (RiderX & RiderXL) through driver incentives and
    forecasting.
  • Gather customer data to understand their ride behaviors based on demographics

Outcome:
This project demonstrated how real-time trip data can inform fleet strategy, promotional planning, and
customer engagement, leading to:

  • Reduced idle vehicle time
  • Better resource planning
    Enhanced customer satisfaction
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