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Application of data mining for identifying and predicting room bookings - window of opportunity
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This study investigates the search patterns for predicting hotel bookings. Using Expedia’s search and
purchase data, I identified the user’s booking window and which events have a higher effect on the
booking likelihood.
The tourism industry has seen exponential growth over the last two decades, much due to global socioeconomic
changes, globalization and internet massification. Portugal is finally reaping its share of profit
and continues winning “best destination” prizes year after year. It is now part of a very competitive
ecosystem where distribution plays a determinant role in whether the touristic product survives or
not. Big players like Expedia and Booking.com have taken control of a big chunk of the market’s
revenue because they understood that the large amounts of data they have enabled predicting
demand hence offering highly competitive deals.
Only by understanding the booking drivers one can negotiate better distribution deals and lower
commissions through making better sales predictions and enhancing marketing and revenue
strategies, hence the purpose of this study being to use data mining to find patterns in room bookings,
enabling this industry to become an even more important source for the country’s GDP.
Through the analysis of consumer behavior and booking times and the use of time series analysis and
machine learning, it is possible to find patterns that can be applied across not only Hospitality but other
industries as well.
By looking for the connections and the relevance of each feature on the final predictions, a new
window of opportunity will open for marketing and sales professionals.
Descrição
Project Work presented as partial requirement for obtaining the Master’s degree in Information
Management, with a specialization in Knowledge Management and Business Intelligence
Palavras-chave
Sales trends Micro moments Macro moments Zero Moment of Truth Booking Window