Travel the World: Analyzing and Predicting Booking Behavior using E-mail Travel Receipts

TL;DR

This paper analyzes a large dataset of travel receipts to understand and predict online booking behavior, providing insights that can enhance customer experience and marketing strategies in the tourism industry.

Contribution

It introduces a comprehensive analysis of the largest travel receipt dataset to model and predict booking times, offering actionable insights for industry applications.

Findings

Identified key factors influencing booking timing

Developed predictive models for booking behavior

Provided insights to improve marketing and customer experience

Abstract

Tourism industry has grown tremendously in the previous several decades. Despite its global impact, there still remain a number of open questions related to better understanding of tourists and their habits. In this work we analyze the largest data set of travel receipts considered thus far, and focus on exploring and modeling booking behavior of online customers. We extract useful, actionable insights into the booking behavior, and tackle the task of predicting the booking time. The presented results can be directly used to improve booking experience of customers and optimize targeting campaigns of travel operators.