Gangwaze has collected over 4 million cruise pricing data points across hundreds of ships over the past year, with price updates recorded every 15 minutes. This rich dataset offers a unique opportunity to develop actionable insights for two primary customer groups:

• Cruise Travelers & Travel Advisors – Helping identify optimal booking windows and pricing trends by cruise line, season, itinerary length, and more.
• Financial Market Analysts & Institutional Investors – Surfacing macro trends in cruise pricing and demand to inform equities trading and broader market assessments.

We are seeking a data team to conduct a comprehensive analysis of this historical dataset and build a scalable analytics framework. This framework will enable Gangwaze to generate real-time and retrospective insights for our users and partners, leveraging our live Supabase (PostgreSQL) data repository.

Key Objectives:

1. Understand Pricing Behavior

• Identify lowest-price timing trends before sailing across different lines, ship classes, seasons, and itinerary types.
• Compare price volatility and booking behavior between premium vs. budget cruise lines.

1. Support Decision-Making for Travelers and Agents

• Provide data-backed recommendations on when to book and which sailings offer the best value.
• Highlight price drop frequency, average savings by waiting, and booking risk exposure.

1. Enable Market Trend Analysis

• Surface directional indicators in cruise pricing by brand, ship class, and global region.
• Support third-party analysts with aggregated metrics and trend lines suitable for financial forecasting.

📊 1. Strategic Analysis Report or Dashboard

• A comprehensive report (or interactive dashboard) containing:
• Booking Window Analysis: Days before sailing vs. price curve behavior.
• Seasonality Trends: Price trends across calendar months/quarters.
• Cruise Line Comparison: Pricing behavior by brand and ship class.
• Itinerary Analysis: Price patterns based on cruise length, region, and sailing type (e.g., repositioning, short cruise, etc.).
• Historical Volatility & Price Drop Frequency.

🔧 2. Modular Analytics Framework

• A documented, repeatable analytics process (SQL-based) that includes:
• Query templates to extract:
• Last-known price before sailing
• Price drop counts and thresholds
• Average price by dimension (line, length, region, etc.)
• Guidelines for connecting to and querying live Supabase PostgreSQL tables.
• Data model or transformation layer recommendations for structuring time-series cruise pricing efficiently.

🔄 3. Future-Proof Integration Plan

• Instructions or working SQL scripts (or PostgreSQL functions) to:
• Extend the framework to newly ingested data.
• Set up scheduled queries using Supabase Cron or any equivalent PostgreSQL-compatible technologies.

Tools & Environment:

• Database: Supabase (PostgreSQL)