Technical Showcases - DataLake Projects - DERTour
Data Lake for Dynamic Pricing: A comprehensive solution for a leading tour operator
Company profile
The company in the focus of this example is a major travel and tour operator with an annual turnover of 2 billion euros. It employs around 10,000 people and has offices in 16 European countries, with its headquarters in Germany. This company offers a wide range of travel services, from flights and accommodation to package tours.
Initial situation
The company's daily business is characterized by an enormous amount of data. Every day, more than 10 million travel offers are created and sent to over one million dedicated customers. These offers are based on complex sales processes and a diverse IT landscape comprising numerous systems and data sources. However, the multitude of data sources and the complexity of the processes also pose challenges.
Challenges and problems
One of the main problems was the complex and error-prone manual pricing. The company
had to set thousands of prices for different travel offers every day, which often
led to errors and inconsistencies. In addition, the company was slow to react to
market changes as it was difficult to make quick and accurate decisions.
Another problem was that many decisions were made without adequate data. In order to be able to analyze relevant information quickly and efficiently, all underlying data had to be collected in a central location.
Solution
To overcome these challenges, it was decided to implement a data lake. A data lake is a central storage location for all of a company's raw data, regardless of its structure or format. In this case, a scalable data lake was built in the cloud platform AWS (Amazon Web Services). This enabled the company to integrate and manage data from currently 20 different sources.
Data Lake Architecture
An automated revenue management system (RMS) was also implemented. This system uses the data from the data lake to make intelligent pricing decisions. It takes into account a variety of factors, such as demand, competition, season and other relevant variables. This allows prices to be adjusted dynamically in order to achieve the optimum profit.
Some of the AWS components used
Results
The implementation of the data lake and automated RMS has led to impressive results. Between 13 and 17 million price changes are now made every day for over 200 destinations. These adjustments are made automatically and are based on current market conditions, enabling a fast and accurate response to changes.
The monitoring dashboard of the daily RMS process
Another remarkable result is the daily growth of the data lake by around 100 GB. This shows how much data the company generates and stores every day. This data forms the basis for the intelligent decisions that the company makes.
Finally, the implementation of the system has led to a significant increase in turnover. Sales increased by 5-10%, with this upturn varying depending on the destination. This illustrates the importance of using data to make optimal pricing decisions and therefore increase sales. In addition, the overall data quality of the offer also increased, as some errors that were previously caused by manual price changes could be avoided.
Conclusion
The implementation of a data lake and automated RMS has enabled the company to utilize its vast amount of data effectively. This has not only led to an improvement in pricing, but also a faster and more accurate response to market changes. The resulting upturn in sales demonstrates the importance of viewing data as a strategic asset and using it accordingly.
#TravelTech #Reisebranche #Tourismus #DynamicPricing #RevenueManagement
#ADEALSystems #DataLake #DynamicPricing
