With the rapid development of IT technology and fierce competition of market, the customer relationship management(CRM) has gained its importance in the market. Companies have attached importance to acquiring and retaining the most profitable customers. So calculating customer’s value is a significant segment for every effective CRM. Many researches have been performed to calculate customer’s value based on customer lifetime value (LTV). But, these calculations can’t effectively include the whole customer value, especially for the word-of-mouth marketing value. This paper proposes a new LTV model which considers the customer’s past profit contribution, potential value and word-of-mouth marketing value, and gives a more reasonable LTV value in CRM for the company to make a decision

Liu, Yongmei

Lu, Hongjian

Zhang, Yanlong

Zhao, Zhenjun

English

AIS Electronic Library (AISeL)

Association for Information Systems AIS Electronic Library (AISeL) ICEB 2004 Proceedings International Conference on Electronic Business (ICEB) Winter 12-5-2004 A Model of Customer Lifetime Value Consider with Word-of-mouth Marketing Value Yongmei Liu Zhenjun Zhao Yanlong Zhang Hongjian Lu Follow this and additional works at: https://aisel.aisnet.org/iceb2004 This material is brought to you by the International Conference on Electronic Business (ICEB) at AIS Electronic Library (AISeL). It has been accepted for inclusion in ICEB 2004 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact elibrary@aisnet.org. The Fourth International Conference on Electronic Business (ICEB2004) / Beijing  245Customer LTV model Current value Potential value Customer loyalty Communicative value Customer profit Customer data Customer trade Customer valueCustomer segmentation  Marketing strategies A Model of Customer Lifetime Value Consider with Word-of-mouth Marketing Value  Yongmei Liu, Zhenjun Zhao, Yanlong Zhang, Hongjian Lu Business School, Central South University, Changsha, Hunan 410083, China ymliu@mail.csu.edu.cn  ABSTRACT With the rapid development of IT technology and fierce competition of market, the customer relationship management(CRM) has gained its importance in the market. Companies have attached importance to acquiring and retaining the most profitable customers. So calculating customer’s value is a significant segment for every effective CRM. Many researches have been performed to calculate customer’s value based on customer lifetime value (LTV). But, these calculations can’t effectively include the whole customer value, especially for the word-of-mouth marketing value. This paper proposes a new LTV model which considers the customer’s past profit contribution, potential value and word-of-mouth marketing value, and gives a more reasonable LTV value in CRM for the company to make a decision.  Keywords: Customer Relationship Management, Customer Lifetime Value, Word-of-Mouth Marketing Value  1. INTRODUCTION  Customer Relation Management（CRM）is a strategy used to comprehend and influence customers’ behaviors through effective communication in order to improve the customers’ acquisition, retention and loyalty and achieve profitable growth ultimately [1].One hot topic in CRM research is how to measure customer value, as the accurately calculating of customer value is the groundwork of identifying and classifying target customers effectively, and it is also the key factors of success of CRM. Customer value is studied through customer equity, customer profitability and lifetime value. In this paper we consider the customer values as the total benefit for corporation during customers’ lifetime, namely, LTV—Customer Lifetime Value [2]. There are already several definitions of LTV, we take Frederick Newell’s [3] here and amend it as: LTV is, which follows along with the time continuing, the revenues gained from the customers (individual, family or middleman) after the deduction of the total cost of attracting, selling, and servicing customers, and discounted to the present.  It was generally accepted that customer value comprises customers’ present value, potential value and loyalty, but we do think the value of world-of-mouth communication is an important component of customer value. Corporations can be benefited as customers recommend productions or services to their friends or family members through world-of-mouth communication way, considering that customers depend more on informal information sources such as acquaintances during decision-making of purchasing [4-5]. According to a tailed survey to retailing conducted by a Japanese investigating company, 80% saleroom is from present customers, and 60% new customers are from commends of present customers [3]. Google is a good example: it holds a great deal of users with few commercial advertisements [6], showing the effectiveness of world-of-mouth communication. Therefore, considers the customer value from four aspects includes current value, potential value, customer loyalty and communication value would be better to indicate the true connotation of LTV.  So, based on the Hyunseok Hwang’s conceptual framework of CRM [7], a CRM conceptual framework based on LTV is shown in Fig.1.              Fig.1. CRM framework based on LTV Fourth International Conference on Electronic Business (ICEB2004) / Beijing  246Fig. 1 reflects main flow of CRM and the important roles of customer value composing and measure in CRM. Although the measure of LTV is a key factor of CRM, but most corporations haven’t got LTV data so far. One important reason of this situation is the complexity of factors, which can influence calculating of LTV. This paper reviewed past LTV models and analyzed factors which influence LTV, proposed a new LTV model considers the customer’s past profit contribution, potential value and word-of-mouth marketing. This model can gives a more reasonable LTV value in CRM for the company to make a decision, and provide an academic foundation for corporations to manage customer relation better.   2. RELATED RESEARCHES ABOUT LTV  There are a lot of researches on calculating customer value, some researches, such as Huang Yixiao, try to establish an evaluation system by using neural network [8], but most researchers follow Frederick FR.’s net present value (NPV) evaluation system [3]. However most LTV models based on NPV evaluation system exist various limitations, it’s difficult for them to reflect the true customer value. The basic LTV model [9] formed based on revenues of loyal customers as follows: LTV=( )rrIn n ⎪⎭⎪⎬⎫⎪⎩⎪⎨⎧ ⎥⎦⎤⎢⎣⎡+− 111            (1) Where In  are revenues that the corporation gain from the loyal customers, r  is interest rate, n  is the number of years that the customer is loyalty for the corporation. The virtue of the model is its simple calculation, and the disadvantage is that it based on the future revenues’ calculation. The measure didn’t consider the cost and tend to lead LTV to crossroad.  Considering this situation, Berger and Nasr（1998）have proposed LTV calculation model [7], which reflect the fluctuation of sales and costs. LTV= ( )ini rt +×∑= 11)π(0           (2) Where i  is the period of cash flow customer transactions, n  is total number of periods of projected life of the customer under consideration, and r  is interest rate, )π(t is the function of customer profits according to time t . Formulating precise )π(t  is the most important factor in calculating LTV precisely. LTV models evaluate the long-term value of customers focused on the entire lifetime of customers, however, the long-term value does not fit for the industry having stiff competitions and rapid changes of market environments, the wireless communication industry is good example, which are very sensitive to the external environments and the customer defections. Customer defection is also a critical issue of LTV model because it affects the length of service period and the future profit generation. Therefore, Hyunseok Hwang suggest a LTV model considering churn rate [7]: LTVi=( ) ( )( )∑∑ +++=−=−++++1E1f0pii1)B()(π1)(πiNNtNtiiNttNiiiiiiiirttrt  (3) it : service period index of customer i  iN : total service period of customer i  r : interest rate  )(iE : expected service period of customer i  )(p itπ : past profit contribution of customer i         at period it  )(f itπ : future profit contribution of customer i   at period it  )( itB : potential benefit from customer i  at     period it    This model ignored the importance word-of -mouth marketing value from customers, especially in cybereconomy era, so it is also limitation for calculating the customer’s value.  3. A MODEL OF LTV CONSIDERED THE WORD-OF-MOUTH MARKETING VALUE  3.1 A New LTV Model  The word-of-mouth communication has been concerned by people, for the word-of-mouth communication can increase corporations’ profits by attracting the new customer for corporations. Suppose the customer i  has a little profit for a corporation, but he (she) can attract many more profitable customers for the corporation by word-of-mouth communication. So the customer i  should have high customer value. However the models above can’t consider this situation, and therefore we propose a model as follows: LTVi= ( ) ( )∑∑=−=− +++ i iiiiiNttNiNttNi rtrt0p0pii 1)(S1)(π   ( )( )( )( )∑∑ +++=−+++=− +++++1E1f1E1f1)(S1)B()(π iNNtNtiiNNtNtiiiiiiiiiiii rtrtt  (4) Compare with Eq.(3), Eq.(4) adds two parts ( )∑=−+iiiiNttNi rt0p 1)(S  and ( )( )∑ +++=−+1E1f1)(SiNNtNtiiiiiirt,  they denote the net present value(NPV) of customer’s past word-of-mouth communication and the future word-of-mouth communication respectively, where )(Sp it  is past word-of-mouth communication value of customer at period it , and )(Sf it  is future word-of-mouth communication value of customer at period it .  3.2 Calculating )(S p it  And )(S f it  The Fourth International Conference on Electronic Business (ICEB2004) / Beijing  247In order to calculate the customer’s word-of-mouth marketing value, we divide it into direct and indirect word-of-mouth marketing value. Suppose there is a customer i , then we define direct word-of-mouth marketing value 0S  as a profit contributed by customer i  attracting new customer for corporation directly; indirect word-of-mouth marketing value 1S  means a portion profit contributed through attracting new customers for corporation by the customers those who directly attracted by customer i . Then we have:        )(S)(S)(S p1p0p iii ttt +=           (5)                                     )(S)(S)(S f1f0f iii ttt +=            (6) Where )(S 0p it and )(S 1p it  mean direct and indirect word-of-mouth marketing value of customer i  at the past period it  respectively. )(S 0f it and )(S 1f it  mean direct and indirect word-of-mouth marketing value of customer i  at the future period it  respectively. We can calculate 0S  and 1S  as follows: ( ) ( ) ctmtS ii ×=0                  (7) ( ) ( ) ( )( ) ( ) ( ){ } λ××−×−= − ctmtpXEXEtS ii1tti ii1   (8) Where ( )itm  is the number of customers that directly attracted by customer i  at period it , ( )itXE  is the expect number of customers from period 0  to period it . And c  is the cost that the corporation attracts a new customer by itself. λ  is a parameter represents profit sharing ratio of indirect word-of-mouth marketing value ( λ  can fix on by consulting percentage take from sell profit and relational experience). ( )itp  is ratio that the new customers attracted by customers in the whole new customer at period it , namely the ratio of attracting new customers by word-of-mouth communication，generally speaking, for different commodities, the word-of-mouth communication have different effects [10]. To calculate ( )i1S t , we simplified ( )itp  by p , which is a certain constant. Then ( )i1S t  can be calculate by  ( ) ( ) ( )( ) ( ){ } λctmpXEXEtS i1tti ii ××−×−= −1  (9) Word-of-mouth communication has the same characters as virus spread, as a result, word-of-mouth communication marketing has been called virus marketing [11]. So we use the epidemic model [12-13] to calculate ( )itXE , and also discrete mathematics [14] model is used to build the word-of-mouth communication model. Now we consider a group of consumers with N people in a certain region, then N means the maximum consumers can be attracted as new customers for a corporation (attracted by either customers or corporation itself). We number the group consumer with 1a ， 2a ，……， Na ,and define set A ={ 1a ， 2a ，……， Na }. The word-of-mouth communication among consumer is very complex, we simplified it here and define it as a binary relation on A , i.e. AR ={< ia , ja >∣ ia , ja ∈ A ,i≠j, ia transmits information to ja }, we call ia  transmitter and ja  receiver in the binary relation. Then we define set C ={ ia ∣ ia ∈ A , ia  has been the customer of corporation}. Research is start from the beginning of the production(or service) come into group consumer A ,at this time， there is a people ia  among the group consumer who buy the production(or service) and become the first customer of the corporation. Then the condition that another people ja  among the group consumers is attracted by the first people ia  can be described as:   （< ia , ja >∈ AR ）∩（ ia ∈ C ）∩（ ja ∉ C ）; Let itX  be the number of customers of the corporation in A  at period it , then { itX ， it ≥1}forms a discrete stochastic process. And ( )itXE  is the number of the corporation’s customer at period it , ( )( )itXEN − is the number of potential customers of the corporation. Then during the M times transmissions between period it  and period it +1, ( )itXENM ×  is the expected number of people who are not only the transmitter but also the customer of the corporation, ( )itXENMM ×−  is the expected number of people who are receivers but not belong to set C . According to the hypothesis and stochastic process of transmission, after the series of transmissions, ( )itXENM × × ( )⎟⎟⎠⎞⎜⎜⎝⎛ −NXEit1 is the expect number of new customer of corporation that attracted by the customer. Then we have: ( ) p)E(X)E(Xii t1t×−+  )E(XNMit×= × ⎟⎟⎠⎞⎜⎜⎝⎛ −N)E(Xit1  = )E(XNMit×2 ( ))( itXEN −×                (10) Let pNMk 2=                   (11) Then ( ) ( )ii ttXEXE −+1 = ( ))E(XN)E(Xk ii tt −××     (12) So Fourth International Conference on Electronic Business (ICEB2004) / Beijing  248( ) ( )( ) =−+−+iit1tttXEXEii1( ))E(XN)E(Xkii tt−××    (13) Regard ( )itXE  as a discrete value of a continuous function f(x)  at x = it , according to Eq.(13), the differential equation of f(x)  is: [ ]f(x)Nf(x)kxf(x) −××=dd and 10 =)f(     (14) Solve the equation, we can get ( )itXE =NktieNN××−−+ )1(1        (15)  4. CONCLUSION  This paper analyzes factors which influence LTV and their methods of influencing at first, points out that, among all researches on calculating LTV of a customer, no one of them considered about the word-of-mouth marketing value. And then the paper suggests a new LTV model considering the past profit contribution, potential profit, customer loyalty and word-of-mouth marketing value. The model gives a more complete calculation of customer LTV and can be used for customer segmentation especially in the market which word-of-mouth marketing can make good effects.  REFERENCES  [1] Ronald S. Swift, Accelerating Customer Relationships, translated by Yang Donglong, Beijing, China Economy Press，pp15-16, 2001. [2] Zhang Guofang, Jin Guodong, ”Survey of Application and Theory Research of CRM”, Science & Technology Progress and Policy, Vol.3, pp176-179, 2003. [3] Wang Sufen, Tang Bingyong, “Analysis Of Customer Lifetime Value”, Journal Of Donghua University (Social Sciences), Vol.2, No.2, pp32-34, 2002. [4] Huang Xiaojun, Xu Weiqing, “Reflections On The Word-of –Mouth Communication Research”, Journal of Zhejiang University (Social Sciences), Vol.34, No.1, pp125-132, 2004. [5] Christian Derbaix, Joelle Vanhamme, “Inducing Word-of-mouth By Eliciting Surprise —  A Pilot Investigation”, Journal of Economic psychology, Vol.24, pp99-116, 2003. [6] Li Zhengquan, “Word-of-mouth Marketing of Google”, Foreign Investment In China, Vol.3, pp48-50, 2003. [7] Hyunseok Hwang, Taesoo Jung, Euiho Suh, “An LTV Model And Customer Segmentation Based On Customer Value：A Case Study On The Wireless Telecommunication Industry”, Expert Systems with Applications, Vol. 26,pp181-188, 2004. [8] Huang yi-xiao, Shao pei-ji, Li jing-jing. “The Research About Customer Segmentation Method Based On Customer Value”, FORECASTING, Vol.23 No.3, pp31-35, 2004. [9] Wang Su-fen, Tang Bing-yong, “The Study Of The Model Of Customer Life-time Value”, Journal of Natural Science Heilongjiang University, Vol.19, No.2, pp24-28, 2002. [10] Wei Guihua, “The Power of Word-of-mouth”, Finance And Accounting Monthly, Vol.06X, pp5-7, 2002. [11] Li Guangdou, “Noah's Ark and New Marketing Tools”, China Marketing, Vol.7, pp80-82, 2003. [12] Lu Li-hua, Luo Peng-fei, Li Xing, Sun Ai-ming, “Summary Of Propagation Model For Network Malicious Mobile Code”, Computer Applications, Vol.23,No.6, pp17-22, 2003. [13] Pan Jian-ping, Ding Wei, Gu Guan-qun, “A Mathematical Description On The Propagation Of Computer Virus”, Journal Of Southeast University, Vol.26, No.6A, pp138-144, 1996. [14] George F. Simmons, Introduction To Topology And Modern Analysis, New York，MvGaaw-Hill Book Company， pp3-49, 1963.  

A Model of Customer Lifetime Value Consider with Word-of-mouth Marketing Value

https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1046&amp;context=iceb2004

A Model of Customer Lifetime Value Consider with Word-of-mouth Marketing Value

Abstract

Similar works

Full text

Available Versions

AIS Electronic Library (AISeL)