本文選題：個性化定價 + 支付意愿　； 參考：《華中師范大學》2014年碩士論文

【摘要】：在消費者需求日期差異化的背景之下,我們進入了個性化時代,通過對消費者個人信息的分析研究,提供更加符合消費者喜好的產(chǎn)品及服務,尤其在電子商務環(huán)境下,這種個性化的服務更加方便快捷,也更加受到消費者的青睞。一方面,電子商務的個性化產(chǎn)品、個性化服務、個性化推薦等迅速發(fā)展,但是目前研究者對電子商務個性化的營銷研究,大都傾向于研究個性化的產(chǎn)品和服務的推薦,而很少涉及個性化的價格和促銷方式,使得個性化定價沒有同期發(fā)展、受人矚目。另一方面,近年來,隨著人們對個性化的追求、電子商務個性化服務的發(fā)展,個性化定價作為個性化服務的重要組成部分,越來越受到人們的關注,加上數(shù)據(jù)挖掘技術的日臻成熟,業(yè)內(nèi)對個性化定價在電子商務環(huán)境下的應用前景持樂觀態(tài)度。 本文從消費者個人行為、電子商務產(chǎn)品定價環(huán)境等方面入手,分析了個性化定價的主要步驟。針對個性化定價主要的三個步驟：測量支付意愿、搜索目標顧客、實施個性化定價,基于測量支付意愿的主流方法,結合客戶調查法與聯(lián)合分析法的關聯(lián)水平系數(shù)思想,運用聚類的方法得出每個顧客的支付意愿；隨后基于支付意愿、成本、庫存、及消費者決策系數(shù)等因素建立了個性化定價模型,并且引入遺傳算法,構造了使得企業(yè)目標利潤最大化優(yōu)化搜索算法,以此算法確定針對每一個客戶的定價方案；最后分析了實施個性化定價的合理方案,對所確定的目標顧客實施個性化定價。本文的研究內(nèi)容主要有： 第一,分析了定價的概念及目標,總結了傳統(tǒng)的定價方法,對個性化定價起源、類型及社會福利進行了分析,并指出在電子商務環(huán)境下進行個性化定價的挑戰(zhàn)及機遇。 第二,研究了個性化定價的一般步驟,根據(jù)該流程,研究了消費者支付意愿的測量方法及技術,并結合聯(lián)合分析法的關聯(lián)水平系數(shù)思想及客戶調查思想提出適合本文研究內(nèi)容的支付意愿測量方法。運用模糊c均值聚類算法對消費者進行聚類,以群體當中消費者購買歷史中同類產(chǎn)品的最高價格作為該群體的支付意愿,以聚類結果隸屬度作為每個個體隸屬于每個群體的程度,以此程度作為消費者關聯(lián)于各個群體支付意愿的水平系數(shù),計算這個程度和每一個群體的支付意愿的乘積的累加和,便作為該客戶的支付意愿。并收集消費者歷史購物數(shù)據(jù),采用本文算法測量其支付意愿,與直接詢問值作對比分析。 第三,基于消費者支付意愿,建立了關于產(chǎn)品成本、庫存、消費者決策系數(shù)的個性化定價模型,并引入遺傳算法。研究了遺傳算法的原理,闡述了本文選擇遺傳算法的原因,構造了個性化定價的優(yōu)化方法。根據(jù)實驗所得支付意愿,運用遺傳算法對所建模型進行優(yōu)化搜索,通過大量實驗確定遺傳算法的參數(shù),并對模型搜索結果進行了分析。 第四,研究了實施個性化定價的具體方案,指出設置“門檻”為本文所建模型的最優(yōu)選擇,指出在對客戶實施個性化定價時,先標出商品的價格,分析完通過“門檻”的客戶信息,在客戶到達或主動發(fā)放相應的折扣券給客戶。
[Abstract]:Under the background of the date of demand of different customers, we have entered the era of personalized, through the analysis of consumers' personal information, to provide more products and services meet consumer preferences, especially in the e-commerce environment, the personalized service is more convenient, more favored by consumers. On the one hand, personalized products, electronic the business of personalized service, personalized recommendation and rapid development, but the current research on marketing of e-commerce personalized, tend to study personalized products and services recommended, and rarely involve individual price and promotion, make personalized pricing is not attractive. Over the same period of development, on the other hand, in recent years, with the the pursuit of individual people, the development of e-commerce personalized service, personalized pricing as an important part of personalized service, More and more attention has been paid to it. Coupled with the maturity of data mining technology, the industry is optimistic about the application prospect of personalized pricing in e-commerce environment.
This article from the consumer behavior, e-commerce product pricing environment and other aspects, analyzes the main steps of personalized pricing. For personalized pricing three steps: measuring willingness to pay, the search target customers, the implementation of personalized pricing, the mainstream method of measuring willingness to pay based on the combination of customer survey and analysis correlation coefficient method level the idea of using clustering method to obtain the willingness to pay for each customer; then based on the willingness to pay, cost, inventory, factors and consumer decision coefficient set up a personalized pricing model, and the introduction of genetic algorithm, constructed the enterprise profit maximization goal optimization search algorithm, this algorithm for determining the pricing scheme of every customer; the final analysis of the reasonable plan implementation of personalized pricing, the implementation of personalized pricing to determine the target customers. This research The main contents are as follows:
First, we analyze the concept and objectives of pricing, summarize the traditional pricing methods, analyze the origin, types and social welfare of personalized pricing, and point out the challenges and opportunities of personalized pricing under e-commerce environment.
Second, the general steps of personalized pricing, according to the flow, measuring method of consumer willingness to pay and the technology, and combined with the analysis of the correlation coefficient and the level of thought of customer survey proposed the willingness to pay of measurement for the content of this paper. The clustering of consumers using fuzzy C means clustering algorithm, with the highest price the group of consumers to buy similar products in the history as the willingness to pay for the group, with the clustering results of membership as individuals belonging to each group. This degree as consumers association coefficient in various groups of willingness to pay, and calculate the degree of willingness to pay for the product of each group of the accumulation and as the customer's willingness to pay and collect consumer shopping history data, measure the willingness to pay by this algorithm, and direct inquiry The value is compared and analyzed.
Third, based on consumer willingness to pay, based on the product cost, inventory, personalized pricing model of consumer decision-making coefficient, and the genetic algorithm is introduced. The principle of genetic algorithm, this paper expounds the reasons for choosing genetic algorithm, structural optimization method of personalized pricing. The willingness to pay according to the experimental result, using genetic algorithm to optimize the search the model parameters, the genetic algorithm is determined by experiments, and the model of search results were analyzed.
Fourth, study the specific implementation programs of personalized pricing, points out that establishing "threshold" to select the best model built in this paper, points out that in the implementation of personalized pricing to customers, marked the first commodity prices, after analyzing through the "threshold" of the customer information, arrival or active payment of the appropriate discount coupons to customers in the customer.

【學位授予單位】：華中師范大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F724.6;F274

【參考文獻】

相關期刊論文 前9條

1 井浩涌;差別定價方法分析[J];商業(yè)研究;2002年08期

2 郭哲;吳俊新;汪定偉;;電子商務中的耐用品定價[J];東北大學學報;2006年02期

3 任平;遺傳算法(綜述)[J];工程數(shù)學學報;1999年01期

4 姜友雪;王登良;;消費者對安全茶葉的支付意愿——基于廣州市消費者的實證研究[J];廣東農(nóng)業(yè)科學;2009年06期

5 韓飛;于洪彥;;消費者價格敏感影響因素的實證研究[J];價格理論與實踐;2011年11期

6 劉偉江,王廣惠 ,張朝輝;電子商務中的價格歧視現(xiàn)象[J];經(jīng)濟與管理研究;2004年02期

7 鐘峗;王文明;;商家的價格歧視策略及其社會福利分析[J];今日南國(理論創(chuàng)新版);2008年05期

8 徐翔斌;王佳強;涂歡;穆明;;基于改進RFM模型的電子商務客戶細分[J];計算機應用;2012年05期

9 曾小青;徐秦;張丹;林大瀚;;基于消費數(shù)據(jù)挖掘的多指標客戶細分新方法[J];計算機應用研究;2013年10期

相關博士學位論文 前1條

1 劉朝華;基于客戶價值的客戶分類模型研究[D];華中科技大學;2008年

，

本文編號：1768222