Mining customer pattern across multiple time-series-segments of traditional and e-Business market channels in China
Item statusRestricted Access
Embargo end date10/07/2020
Li, Peggy Heng Wah
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The objective of this thesis is to investigate customer pattern across multiple time-series-segments of traditional and e-Business market channels in China. Most customer behaviour studies were done in the context of the US and European markets, but limited attention to the China market, which is actually playing an increasingly influential role in the global market (Larson, 2014). Meanwhile, China is a highly fragmented market comprising a wide diversity of ethnic races with geographical and economic differences (Tsang et al.,2003, Zhang et al., 2008), there leaves a room for researchers to study the disparities of different customer behaviours across China. To carry out the study, a novel research methodology was designed for running quantitative analysis in the Big Data of China market, by applying the scientific data mining solution of C5.0. Research analysis is conducted with a leading retail company in China, which provides a data sample of 7 million customers, out of company’s total customer base of more than 166 million. Data mining was conducted in three main streams, namely (1) Regional disparity of customer behaviour across the fragmented markets in China; (2) Regional customer behaviour analysis from product perspective; (3) Regional disparity in technology adoption of different shopping channels in China. This thesis constitutes inter-disciplines of Marketing and Information Technology, with research focus on investigating different market segments and shopping channels; which were identified from the Marketing literatures particularly about Cognitive Dissonance Theory (Festinger 1957, Sharma 2014) and Involvement Commitment Model (Beatty 1988; Lee, Cheng and Shih 2017). Along with that, Information Technology of data mining was implemented to run the Big Data, by introducing the Unified Theory of Acceptance and Use of Technology (UTAUT) Model as an analysis framework (Venkatesh 2003; Khechine, Lakhal and Ndjambou, 2016), together with the application of Traditional Cluster Typology and McKinsey Cluster Typology for analyzing market segmentation. By utilizing the strength of relevant research disciplines, highly reliable data analysis results from the Big Data can be obtained. Through analyzing the Big Data of China market, this thesis brings theoretical contributions of identifying customer behaviour pattern in the fragmented markets of China across different time and distance dimensions; locating the factors that affect customers’ technology adoption of online channel; and adopting a novel research methodology that implements scientific data mining for producing highly reliable analysis findings. By that, it provides a more comprehensive understanding of customers’ purchase behaviours, to fill up the identified research gaps and questions.
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