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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/103176

    Title: 線上消費者回訪(回購)機率預測模型
    Other Titles: Online Consumer's Return Visit (Purchase) Rates Prediction System
    Authors: 蔣璿東
    Contributors: 淡江大學資訊工程學系
    Date: 2012-08
    Issue Date: 2015-05-20 10:35:20 (UTC+8)
    Abstract: 網路廣告收入及販售商品是經營網路服務業者的最主要營收來源之一,而經常造訪該網站的使用者或會員人數更是這些業者的一大資產,如何利用使用者在網路上留 下的行為足跡,作為後續提供個人化資訊或廣告效益預測,逐漸成為這些網路服務業者重視的議題之一。而本計畫主要根據消費者的瀏覽記錄與購買點擊行為,建立 線上消費者回訪(回購)機率預測模型,試著讓行銷人員能有效區分不同行為之消費者,期望可藉此進而預測消費者是否會於特定時間內再回訪(包含網頁瀏覽或是 購買商品)或再次購買商品之機率(僅購買商品)之機率,用以協助網站業者先可以進行後續的行銷策略擬定。由於消費者興趣往往會隨著時間變化而常會有所改變 或漂移,為了掌握消費者在不同時間點的興趣差異,我們結合『時間函數』與『消費者過去行為』兩個因素,能將消費者過去行為模式納入考量,並建立一新的時間 函數區分不同類型之消費者,相較於以往研究中所使用之時間函數,純粹只是衰退消費者之影響力,我們對於那些會持續來訪或是對於該網站逐漸有興趣的消費者,我們將提升其過去行為之權重,反之,對於那些未持續來訪的消費者或是對該類別興趣將轉變的消費者,我們將弱化其過去行為之權重,預期透過此系統能有效求得顧客興趣喜好及購買能力之模型,及鑑別具有不同行為之消費者,並提高回訪率預測之正確性。本計畫之研究成果將可提供給網站的行銷人員進行有效益的行銷策略擬定,增加與廣告主洽談的有效依據,以達到最高的獲利。
    Consumer market has several characteristics in common such as revisit over the relevant time frame, a large number of customers, and a wealth of information detailing past customer purchases. Analyzing the characterizations and temporal dependencies of purchase behaviors is crucial for the enterprise to survive in a continuously changing environment. The internet advertising revenues and the commodity sales play an import role in the earning origin of e-commerce. Therefore, monitoring the members5 browse and purchase records has become emphasized for the prediction of the advertisement. Effective advertising requires predicting how a user responds to an advertisement and then targeting (presenting the advertisements) to reflect users’ favor. The behavioral target leverages historical users’ behaviors in order to select the ads which are most related to the users to display. Although we would not like to provide advertisements to customers, with the same concept, we would expect to predict return visit (purchase) rates for the registered members. However, customers’ preferences change over time. In order to capture the concept drift, we propose a novel and simple time function to increase/decrease the weight of the old data to various members’ past behaviors. In this project, we will develop an Online Consumer's Return Visit (Purchase) Rates Prediction System, to effectively distinguish the different kinds of member behaviors and predict the members’ return visit rates. Our achievement can be used to assist the marketers to target the members with high return visit rates and design corresponding marketing strategies.
    Appears in Collections:[資訊工程學系暨研究所] 研究報告

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