2026-10-14T17:53:58Z https://api.figshare.com/v2/oai

oai:figshare.com:article/29300891 2026-09-30T11:58:01Z portal_2 item_type_8 month_year_09_2026

Online customer search and buying behaviour in the European airline market Jihao Lin (7386125) Clickstream data Online customer behaviour Customer segmentation <p dir="ltr">This thesis investigates the use of clickstream data to analyse online customer behaviour in the airline industry, focusing on customer segmentation, purchase prediction, and the impact of external shocks like COVID-19 pandemic. The first study applies Markov Chain models within the customer journey framework to segment customers based on their search and buying activities. This method offers a structured and repeatable approach to market segmentation, producing statistically robust segments that are easily interpretable for managerial decision-making. The second study uses Markov Chain models for purchase prediction, addressing the issue of missing transition probabilities in clickstream data. Two methods, Bayesian smoothing and Chapman-Kolmogorov imputation, were compared, with the smoothing approach offering better computational efficiency and prediction accuracy. This demonstrates the versatility of discrete Markov Chains in e-commerce applications, providing actionable insights in online purchasing. The final two studies examine the impact of the COVID-19 pandemic on consumer behaviour. First, a detailed analysis of website traffic highlights the strong relationship between governmental restrictions and online consumer activities, using the stringency index to quantify the impact of mobility restrictions. Additionally, a statistical changepoint analysis was performed to identify structural breaks in customer activities following key COVID-19 announcements. This retrospective analysis provides expansive insights into how consumer behaviour shifted during different stages of the pandemic. The findings underscore the significant influence of government responses on online behaviour and emphasise the need for businesses to adapt quickly to external shocks. These insights can help companies be better prepared for future global events, enabling more effective strategic decision-making and operational flexibility. Overall, this thesis contributes to the development of online customer behaviour research by demonstrating how advanced analytical methods and comprehensive datasets can enhance understanding of customer activities, providing valuable frameworks for both academic research and practical business applications.</p> 2025-06-12T12:18:58Z Text Thesis 10.26174/thesis.lboro.29300891.v1 https://figshare.com/articles/thesis/Online_customer_search_and_buying_behaviour_in_the_European_airline_market/29300891 CC BY-NC-ND 4.0