Profiling mobile business customers for mass customization

• Main Author: Davari, Reza
• Format: Thesis
• Language: English
• Published: 2010
• Subjects: HE Transportation and Communications; QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/11397/6/RezaDavariMFSKSM2010.pdf; http://eprints.utm.my/id/eprint/11397/

Nowadays, traditional services are being replaced by mobile or M-business that is more efficient, faster and accessible. To enable M-business operators to service many customers efficiently but with the impression of a personalized individual service, a method called mass customization is used. For this service to work, detailed information about each customer is needed and is achieved by customer profiling. The big challenge is how to profile M-business customers who have very short attention span and want to quickly conclude a transaction on their mobile device to avoid expensive air time charges and restriction to their mobility. Currently, M-business companies do not have sufficient strategic information about their customers to correctly target them for mass customization. To answer this question, research was conducted in Iran and Malaysia to determine what technique is most suitable for profiling. Various on-line psychographic profiling methods are available and three methods, namely Big Five, Neuro Linguistic Programming (NLP), and ProScan were found to be most suitable. Big Five was found to be the best method but requires customers to answer 40 to 120 questions. NLP on the other hand, only requires customers to answer a minimum of 10 questions. The number of questions to be answered matters significantly in a M-business service. This was confirmed by a survey conducted in Iran and Malaysia, on the willingness of the respondents to answer profiling questions. After NLP was chosen, another survey was conducted to determine the different NLP profiles of M-business customers. This information was used to design and implement a prototype system for a mobile news service that is able to profile customers by NLP and then mass customize news messages either in the form of text, audio, or interactive multimedia messaging system.