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postgraduate thesis: Item-level RFID-based customer shopping experience enhancement

TitleItem-level RFID-based customer shopping experience enhancement
Authors
Issue Date2014
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Yang, Y. [杨雅星]. (2014). Item-level RFID-based customer shopping experience enhancement. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5388029
AbstractTo survive and thrive in the customer-oriented global market, retail companies have to make persistent efforts to provide customers with satisfactory shopping experience enriched by leisure process, interaction for merchandise information and personalised assistance. In traditional retail stores, customers’ needs cannot be fully satisfied due to difficulties in locating target products, out-of-stocks, a lack of professional assistance for product selection, and long waiting for payments. The relative visibility and traceability of individual items provided by the radio frequency identification (RFID) technology is helpful for enhancement of customer shopping experience (CSE). However, current RFID applications for retail business tend to be limited to inventory control and replenishment, with few implementations for CSE enhancement based on collection and analysis of real-time RFID data. To mitigate these limitations, this research project develops RFID applications for real-time collection and analysis of customer shopping behaviour (CSB) data in retail stores. Artificial intelligence (AI) is incorporated for data analysis to facilitate business decision-making and proactive individual marketing. Accordingly, an item-level RFID-based customer shopping experience enhancement (IRCSEE) system is developed to provide customers with leisure shopping process, interaction for merchandise information and personalised guidance for enhancement of CSE in apparel retail stores. The IRCSEE system incorporates RFID hardware devices installed in an apparel retail store to interrogate RFID-tagged apparel items to obtain data for subsequent sales processing and analysis. It is characterised with a programmable data format for unique identification of individual apparel items, together with a suite of software modules to control the RFID hardware devices at different locations of the apparel retail store for real-time collection of product information and CSB data. Moreover, an innovative fuzzy screening (FS) algorithm of AI techniques is developed to analyse the RFID-collected CSB data and the corresponding product information for generation of apparel collocation recommendations to provide customers with intelligent and personalised assistances in product selection. The algorithm considers not only the static fashion expertise, but also the dynamic customer preferences for collocation, such that the recommendations are more effective and adaptive for enhancement of CSE in the fast-changing apparel retail industry. The IRCSEE system is validated in an emulated RFID-based apparel retail store. Experimental results demonstrate that with appropriate RFID hardware settings, the proposed system is effective to help enhance CSE in apparel retail stores by providing customers with leisure shopping process, interaction for merchandise information and personalised apparel collocations. Furthermore, the approaches for collecting real-time CSB by RFID technology and analysing such data by AI techniques can be conveniently adapted for many other products to improve retail business management in general.
DegreeMaster of Philosophy
SubjectConsumer behavior
Radio frequency identification systems
Stores, Retail - Management
Dept/ProgramIndustrial and Manufacturing Systems Engineering
Persistent Identifierhttp://hdl.handle.net/10722/208567

 

DC FieldValueLanguage
dc.contributor.authorYang, Yaxing-
dc.contributor.author杨雅星-
dc.date.accessioned2015-03-13T01:43:57Z-
dc.date.available2015-03-13T01:43:57Z-
dc.date.issued2014-
dc.identifier.citationYang, Y. [杨雅星]. (2014). Item-level RFID-based customer shopping experience enhancement. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5388029-
dc.identifier.urihttp://hdl.handle.net/10722/208567-
dc.description.abstractTo survive and thrive in the customer-oriented global market, retail companies have to make persistent efforts to provide customers with satisfactory shopping experience enriched by leisure process, interaction for merchandise information and personalised assistance. In traditional retail stores, customers’ needs cannot be fully satisfied due to difficulties in locating target products, out-of-stocks, a lack of professional assistance for product selection, and long waiting for payments. The relative visibility and traceability of individual items provided by the radio frequency identification (RFID) technology is helpful for enhancement of customer shopping experience (CSE). However, current RFID applications for retail business tend to be limited to inventory control and replenishment, with few implementations for CSE enhancement based on collection and analysis of real-time RFID data. To mitigate these limitations, this research project develops RFID applications for real-time collection and analysis of customer shopping behaviour (CSB) data in retail stores. Artificial intelligence (AI) is incorporated for data analysis to facilitate business decision-making and proactive individual marketing. Accordingly, an item-level RFID-based customer shopping experience enhancement (IRCSEE) system is developed to provide customers with leisure shopping process, interaction for merchandise information and personalised guidance for enhancement of CSE in apparel retail stores. The IRCSEE system incorporates RFID hardware devices installed in an apparel retail store to interrogate RFID-tagged apparel items to obtain data for subsequent sales processing and analysis. It is characterised with a programmable data format for unique identification of individual apparel items, together with a suite of software modules to control the RFID hardware devices at different locations of the apparel retail store for real-time collection of product information and CSB data. Moreover, an innovative fuzzy screening (FS) algorithm of AI techniques is developed to analyse the RFID-collected CSB data and the corresponding product information for generation of apparel collocation recommendations to provide customers with intelligent and personalised assistances in product selection. The algorithm considers not only the static fashion expertise, but also the dynamic customer preferences for collocation, such that the recommendations are more effective and adaptive for enhancement of CSE in the fast-changing apparel retail industry. The IRCSEE system is validated in an emulated RFID-based apparel retail store. Experimental results demonstrate that with appropriate RFID hardware settings, the proposed system is effective to help enhance CSE in apparel retail stores by providing customers with leisure shopping process, interaction for merchandise information and personalised apparel collocations. Furthermore, the approaches for collecting real-time CSB by RFID technology and analysing such data by AI techniques can be conveniently adapted for many other products to improve retail business management in general.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subject.lcshConsumer behavior-
dc.subject.lcshRadio frequency identification systems-
dc.subject.lcshStores, Retail - Management-
dc.titleItem-level RFID-based customer shopping experience enhancement-
dc.typePG_Thesis-
dc.identifier.hkulb5388029-
dc.description.thesisnameMaster of Philosophy-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineIndustrial and Manufacturing Systems Engineering-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b5388029-

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