在台灣各項網路服務之廣告及電子商務,是入口網站主要的獲利方式,因此利用消費者在網路上留下的行為足跡,預測消費者對於廣告可能的迴響程度,進而規劃其因應行銷策略,具有其重要性。然而消費者興趣通常會隨著時間變化而改變,為了掌握會員在不同時間點的興趣差異,本研究結合『時間函數』與『消費者過去行為』兩個因素,設計一應用於消費者回訪與回購機率的基礎模型;對於不同的資料集與應用(回訪與回購率的預測)時,只需修改參數即可。我們以某知名入口網站所提供的資料集作為實驗資料,由實驗證明我們的模型確實精準找尋到高回訪與高回購潛力之會員,以供入口網站的行銷人員有效益的行銷策略,進而增加獲利。
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 members’ browse and purchase records has become emphasized for the prediction of the advertisement. Effective advertising requires predicting how a user responds to effective advertising requires targeting (presenting the ad) in ways that reflect these users’ preferences. 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 and repurchase 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 research, we will develop a basic model for predicting the customers’ return visit and repurchase rates. The basic model can appropriately modified for the different applications (prediction for the customers’ return visit or repurchase rates) Our achievement can be used to assist the marketers to target the members with high return visit (repurchase) rates and design corresponding marketing strategies.