Case study
Our client was a global organization operating in the transportation sector, providing financial and settlement services for business travel. The company was looking for ways to better understand trends in business flight activity and to identify periods of increased demand for corporate travel.
The client’s team wanted to understand how the frequency of business flights changes over time and whether it is possible to build a model capable of forecasting these changes. The objective of the PoC was to develop an analytical solution for forecasting traveler activity, which in the long term could improve marketing offer targeting and sales planning processes.
The 3Soft team developed a predictive model that forecasted the frequency of business flights based on data obtained through web scraping.
As the client did not have its own historical data, particular emphasis was placed on data quality validation and preparation for modeling. The process included extracting information from unstructured sources using OCR technology (Tesseract), followed by data processing and modeling in the R environment.
The model was deployed on the Cloudera platform, and its results were presented to the client in the form of an interactive dashboard, enabling analysis of trends and seasonality in business travel.
The client gained the ability to independently verify the model results and assess its potential. Despite limited access to internal data, the project confirmed the effectiveness of the approach and became a starting point for further development of the solution based on real historical data.
As part of the project, the following activities were completed:
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