A Big Data Analysis of the Mobile Website Browsing Behavior and Broadcasting Advertisement
| Autor: | Chiu-Ying Ling, 凌秋英 |
|---|---|
| Rok vydání: | 2016 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 104 With the growing trend of smartphones and online advertisement, advertisers are most concerned about is how to broadcast advertisement to the target audience accurately. This study used big data analysis, and developed the classification model of customer groups. With a large number of browsing behavior from the domestic mobile website, this study used data mining algorithms such as cluster analysis and decision tree. Through the results of this study, when the web visitor enter the domestic mobile website, the domestic mobile website can apply the classification model of 20 customer groups to analyze the browsing behavior and characteristics of the web visitor, the web visitor will be classified in one of the model of 20 customer groups, and be broadcasting the advertisement with utility marketing way. This study used logical categories and scientific analysis to identify the classification model of 6 customer groups with high accuracy rate from the original classification model of 20 customer groups, then the classification model of 20 customer groups presented accuracy rate is 27%. Although it is not a perfect accuracy, 27% of accuracy rate of classification model of 20 customer groups indicates that we can capture a web visitor’s characteristics accurately in four of the visitors. If the domestic mobile website apply this classification model of 20 customer groups, they can find the target audience with an easy way in web world, seizing the valuable visitor can save money and time costs for them is very helpful. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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