隨著電子商務的崛起與疫情肆虐，人們越來越依賴線上消費，然而相對於傳統實體店面的購物，電子商務業者很難直接了解顧客喜好與消費的行為傾向。而根據過去研究中對點擊流數據的探討，可以看出點擊流數據對於了解消費者行為的重要性。然而，過去文獻僅針對消費者端進行分析、探討，如：瀏覽時間多寡、是否重複造訪與造訪頻率，而缺少對消費者後續行為的分析與預測。因此本研究將會納入點擊流於分析數據中，先透過潛在類別分析分類消費者集群，並透過購物籃分析深入探討各集群的消費組合，同時將理論應用到旅遊業，預期能提供業者一個景點集與跨類別的旅遊方案作為參考。

With the rise of the e-commerce, people more frequently use online shopping. However, it’s difficult for the e-commerce owners to observe consumer behavior. According to the previous research, there is lots of evidence showing the relation between consumer behavior and clickstream data. Nevertheless, the previous literature only focuses on the analysis of market segments, but lacks the analysis and prediction of consumers' follow-up behavior. Our study would analyze the clickstream data to categorize consumer types and explore the consumption pattern of each group through market basket analysis. And applying the result to the tourism industry, it is expected to provide a set of attractions and cross-category travel solutions as a reference.

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