How to Transform Data into a Superpower for a Tour Operator: Case Study by GP Solutions

Tech Stack

AWS S3, Apache Spark, Kafka + Spark Streaming, Apache Airflow, Power BI

Project Management

Scrum

Start Date

2024

Scope

6,000+ man-hours

Client Background

The client is a mid-sized European tour operator specializing in multi-destination adventure and luxury package tours across more than 25 countries. They operate through a complex network of channels, including their website, partner OTAs, and traditional travel agencies, and manage thousands of itineraries and customer interactions monthly.

However, their data was siloed across booking systems, supplier feeds, customer feedback forms, and web analytics tools. This fragmentation created inconsistent data and prevented them from gaining a unified view of bookings, supplier performance, and customer behavior, hindering their ability to make strategic, data-driven decisions.

Project Goals and Challenges

The client’s fragmented data ecosystem led to significant operational and commercial challenges:

Inaccurate demand forecasting: They struggled with overbooking during peak seasons and running half-empty tours during others, leading to lost revenue and poor resource allocation.
Generic marketing and low conversion: Marketing efforts were broad and untargeted, resulting in wasted ad spend and low engagement as offers failed to resonate with specific traveler interests.
Static, uncompetitive pricing: A fixed pricing model left money on the table, as it couldn’t adapt to real-time market demand, competitor pricing, local events, or even weather conditions.
Operational inefficiency: Manual logistics planning and an inability to react to real-time disruptions led to delays, suboptimal routes, and a diminished customer experience.

Our primary goal under this project was to architect a unified, modern data infrastructure and empower the client to overcome these issues. The new software solution needed to consolidate all data sources and enable real-time performance monitoring. Additionally, the client wanted to build the foundation for advanced analytics to accurately forecast demand, optimize pricing, and offer personalized tour recommendations.

Project and Its Development

Our team designed and delivered a custom big data solution that transformed the client’s data into a strategic asset. Using a Scrum methodology, our cross-functional team managed to untangle the chaotic data flows run within the client’s company and arrange the solution infrastructure to be future-proof and load-resistant.

1. Unified Data Ingestion and Storage

We established pipelines to consolidate all data sources into a centralized AWS S3 data lake.

Batch ingestion: Apache Spark jobs securely pulled data from supplier systems (XML/CSV) and internal databases via APIs and SFTP.
Real-time streaming: An Apache Kafka and Spark Streaming pipeline was implemented to capture website clickstream data and booking feeds (JSON) from OTAs in real time.
Data lake architecture: Data was organized into a three-zone lake (Staging, Curated, and Analytics) to ensure quality and governance through on-the-fly data anonymization.

2. Advanced Analytics and BI

With a single source of truth established, our data scientist developed several machine learning models orchestrated by Apache Airflow:

Demand forecasting model: Provides a 6-month outlook for package bookings with 22% greater accuracy.
Dynamic pricing engine: Autonomously adjusts tour prices based on demand, competitor rates, and availability.
Personalization engine: Recommends tours based on a customer’s booking history and similarity to other user profiles.
Operational dashboards: Power BI was connected to the analytics zone, providing real-time dashboards for monitoring supplier performance, booking velocity, and customer engagement.

Results and Business Impact

The implementation of the big data software solution by GP Solutions delivered transformative results, directly addressing the client’s initial challenges and driving significant commercial growth.

8% increase in average booking revenue: the dynamic pricing engine optimized prices for maximum yield and directly raised profitability.
22% improvement in forecast accuracy: better forecasting minimized overbookings and underutilized capacity, improving resource management.
15% lift in repeat customer bookings: the personalization engine delivered highly relevant offers, increasing customer loyalty and retention.
95% reduction in reporting time: critical business reports that once took two days to compile are now generated in under 30 minutes, enabling agile decision-making.
Enhanced supplier negotiations: with clear data on supplier performance, the client was able to secure better partnership terms and improve service quality.

This new data-driven foundation has empowered the client to operate more profitably, market more products, and create superior experiences for their travelers, securing their competitive edge in the market.

From Fragmented Data to Growth Power: a Big Data Solution for a European Tour Operator