本研究主旨為考量消費者需求偏好情況下求解廠商最佳價格促銷組合之策略 。本研究引用102年2月動能行銷平台研發計畫之問卷資料,研究對象為曾於本研究個案之服飾網站進行購買的消費者。 本研究採用兩階段的模式來求解廠商最大獲利下之最佳價格促銷組合,首先,藉由二項羅吉特模式建立衡量消費者購買意願之個體需求模式,所考慮的變數有折扣方式、折扣幅度、折扣頻率、免運費額度與生活型態等。接著再將折扣方式、折扣幅度、與免運費額度作為決策變數,並以廠商獲利最大為目標,採用基因演算法求解各種假設情境下(不同之定價策略與折價卷使用機率)之最佳價格促銷組合。 本研究之研究結果分為消費者購買偏好與最佳價格促銷組合兩個部分,分述如下: 一、 消費者購買偏好: 影響消費者購買偏好的變數依序為折扣方式、折扣幅度、折扣頻率與免運費額度。 二、 最佳價格促銷組合: 1. 折價卷使用機率高時,建議廠商以直接降價的方式進行促銷;折價卷使用機率低時,則以折價卷的方式進行促銷。 2. 折扣方式為直接降價時,建議廠商以不打折的方式進行促銷。 3. 本研究所提出之求解架構,可以求出各種情境下的各種價格促銷變數的最佳設定值。
This study aims to discover the strategies of firms’ optimal price promotion mix by considering preference of consumer demand. This study adopts questionnaire data in Feb. 2013 of SBIR. The responders are consumers with the experiences of purchasing products on the specific clothing website. The model was conducted in two phase to obtain the optimal price promotion mix under firm’s profit maximization. First, we used binary logit model to build the discrete demand model of customers’ purchase intention. The discount methods, discount level, discount frequency, shipping fee free with certain expenditure and lifestyles. Afterward, we apply variables as decision variables such as discount methods, discount level and shipping fee free with certain expenditure to construct the optimization models under firm’s profit maximization in various scenarios and solved by genetic algorithms. The empirical results show as following: 1.Customer's purchase preference: The factors influencing customers’ purchase preference are discount methods, discount level, discount frequency and shipping fee free with certain expenditure. 2.Optimal price promotion mix: (1)Direct price reduction is suggested when customers executing coupons with high probability, while coupons is suggested when customers applying coupons with low probability. (2)When firms apply direct price reduction as discount method, we suggest firms do not discount. (3)The solution framework proposed in this study is able to resolve optimal values of price promotional variables in various scenarios.