Pradipta, Sonny and Purnama, James (2011) Profiling-Based Mobile Advertisement as A Marketing Strategy For GPS-based Online Traffic Map. Bachelor thesis, Swiss German University.

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Abstract

The emergence of mobile computing is inevitably followed by mobile advertising: advertising that target mobile device, such as feature phones, smartphones and tablets. However, the majority of mobile advertising is still relying on the traditional approach: to send an advertisement to as many people as possible, in hope that some of them will be interested in the advertisement and in turn buying the promoted product or service. The problem with this method is that each people have their own preferences, so that kind of strategy won‘t get an optimum result. Furthermore, with traditional strategy there is very low chance of people getting the right advertisement in the right place. This research proposed a new advertisement system that combined a location based service with user profiling system. In this study, the advertisement system is integrated to an online traffic map mobile application, which also has an RSS feed reader feature. The system will learn user interest by using web mining to analyze browsing history that is taken from the RSS reader. When the user tries to generate a route in the map, the system will automatically fetch advertisements that are located along the route, which suited user interest. This proposed system has successfully created a mobile advertisement that is highly effective. Moreover, it has gained positive feedback from user by being accurate, beneficial, and non-obtrusive. Better reception of advertisement from user will lead to the increasing rate of advertisement success.

Item Type:	Thesis (Bachelor)
Uncontrolled Keywords:	Marketing Intelligence; User Profiling; Web Mining; Mobile Marketing; Location Based Service
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) > G109.5 Global Positioning System H Social Sciences > HF Commerce > HF5410 Marketing H Social Sciences > HF Commerce > HF5801 Advertising Q Science > QA Mathematics > QA76 Computer software > > QA76.91 Data mining
Divisions:	Faculty of Engineering and Information Technology > Department of Information Technology
Depositing User:	Astuti Kusumaningrum
Date Deposited:	16 Mar 2021 09:19
Last Modified:	16 Mar 2021 09:19
URI:	http://repository.sgu.ac.id/id/eprint/1114

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