Incorporating word-of-mouth effects in estimating customer lifetime value Received (in revised form): 13th September, 2006

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1 Incorporating word-of-mouth effect in etimating cutomer lifetime value Received (in revied form): 13th September, 2006 Jonathan Lee i Aociate Profeor of Marketing in College of Buine Adminitration at California State Univerity, Long Beach. He ha alo been a faculty at Indiana Univerity and the Univerity of Southern California. He teache in the area of marketing reearch, deciion model in marketing, and cutomer relationhip management and hi reearch interet i in the area of conumer choice model, B2B marketing, and cutomer equity management. He ha publihed article in the Management Science, Marketing Science, and Journal of Conumer Reearch. Janghyuk Lee i an aitant profeor of Marketing at Korea Univerity, Buine School. Hi reearch interet are in cutomer lifetime value management and internet marketing. He ha publihed article in Marketing Science, Journal of Service Marketing and Seoul Journal of Buine. Before joining Korea Univerity, he worked for Samung Electronic Europe a a enior marketing analyt from 1999 to 2001 and taught at HEC School of Management, Pari from 2001 to Lawrence Feick i Profeor of Buine Adminitration at the Joeph M. Katz Graduate School of Buine at the Univerity of Pittburgh where he ha been a faculty member ince He ha publihed article in the Journal of Conumer Reearch, International Journal of Reearch in Marketing, Journal of Marketing, Journal of Marketing Reearch, Journal of Retailing, Pychological Bulletin, and Public Opinion Quarterly. He ha erved a a conultant to a number of profit and non-profit firm, including Eatman Kodak, General Motor, and Newweek. He ha been a viiting profeor at the Univerity of Augburg (Germany), Czech Management Center (Czech Republic), International Management Center (Hungary), Comeniu Univerity (Slovak Republic), and Univeridad Santa Maria (Ecuador). Keyword cutomer lifetime value, word of mouth, acquiition cot, cutomer valuation, cutomer loyalty Abtract The benefit of managing cutomer relationhip by input (acquiition and retention cot) and output (revenue) for each cutomer i that marketing manager can better prioritie their effort by examining the return on marketing invetment and thu better differentiate cutomer by their relative benefit and cot. Valuing cutomer and meauring marketing effect uing only direct financial contribution, however, carrie a potential rik of mileading marketing manager ince much of the relationhip-baed indicator are latent uch a word of mouth (WOM) but till contribute ubtantially to cutomer lifetime value (CLV). In thi paper, baed on the company data and imulation, we empirically invetigate the effect of WOM in etimating CLV. Managerial implication and future reearch direction are dicued. Journal of Databae Marketing & Cutomer Strategy Management (2006) 14, doi: /palgrave.dbm Jonathan Lee College of Buine Adminitration California State Univerity Long Beach 1250 Bellflower Blvd. Long Beach, CA , USA Tel: ; Fax: ; jlee6@culb.edu INTRODUCTION The cutomer lifetime value (CLV) approach examine cutomer value over time by comparing acquiition and retention cot to revenue contribution, thu changing the marketing focu from a tatic, ingle-tranaction model to one that i dynamic, incorporating a erie of tranaction. In addition, the level of analyi often i refocued from the ma market or egment to the individual cutomer, typically allowing for cutomer heterogeneity. By introducing the idea of a cutomer lifetime and it dicounted value, the CLV 2006 Palgrave Macmillan Ltd $30.00 Vol. 14, 1, Databae Marketing & Cutomer Strategy Management 29

2 Lee, Lee and Feick framework hift attention from a ingle tranaction perpective to one of continuou relationhip and encourage reearch on the link between cutomer atifaction and loyalty 1 4 and profitability. 4 6 The baic driver of CLV i largely baed on the economic of loyalty. Reichheld 7 identifie four major ource of loyalty that affect the incremental value of cutomer: (1) revenue growth over time through increae in cutomer pending, often a a conequence of cro and up-elling, (2) cot reduction by improving the efficiency of erving exiting cutomer and of recruiting new one, (3) price premium extracted for value-added ervice, and (4) revenue increae originating from new cutomer acquired from referral a exiting cutomer pread poitive word of mouth (WOM) to their family, friend, and acquaintance. Exiting CLV model focu mainly on the firt three element of the economic of loyalty: direct revenue and cot generating one that appear tangibly in the profit and lo tatement of the firm Although widely recognied for it practice under variou form, referral effect are not ytematically integrated into CLV management, which may milead manager to underetimate the lifetime value of cutomer with low direct financial contribution but with high poitive WOM behaviour. In thi paper, we analye the impact of WOM contribution to CLV by focuing on the aving in cutomer acquiition cot through WOM. In modelling cutomer acquiition, we take into account two type of acquiition: the one generated by market growth, which reult in the net increae of new cutomer, and the other through the replacement of exiting cutomer due to cutomer churn. Both effect are integrated into a ingle framework to ae the WOM contribution to CLV, not explicitly examined in previou reearch. We find a ubtantial increae in the low revenue / high WOM egment, where it contribution i raied from 11 to 18 per cent, a lift of 102 per cent per cutomer. Later we alo preent an extended imulation augmenting the actual data and the reult highlight the importance of managing WOM epecially in market with high growth rate and intene competition. THEORETICAL FRAMEWORK WOM and acquiition cot aving Dating back to the work of Katz and Lazarfeld, 11 interperonal influence in purchae ha been acknowledged a critically important in conumer deciion making and choice. The literature note that WOM affect brand awarene, attitude, preference, conideration et compoition, and choice Previou reearch tell u that the impact of WOM i ubtantial, but varie among product categorie. 15 For example, nearly half of conumer rely on WOM in chooing a new doctor, while fewer than 20 per cent rely on WOM for a peronal loan. 16 Some previou finding report the impact of WOM in variou context: a urvey in Sweden (with 92,273 obervation) and the US (with 37,340 obervation) indicate that thoe who tranmitted WOM told on average 9.49 and 7.88 other, repectively. The US Office of Conumer Affair uggeted that atified cutomer for conumer ervice are likely to tell five other. 17 Bowman and Narayanda 18 report that cutomer-initiated contact uch a product inquirie, requet for refund, and product complaint have a ignificant impact on WOM frequency. Baed on the data from even US conumer good manufacturer, they found that 57 per cent of the ample told omeone about their experience and the median number of people told wa three. The contribution of WOM to CLV can be partially aeed by meauring company pending on cutomer acquiition. Companie incur large upfront expenditure to attract cutomer. Thee cot include advertiing directed at potential cutomer and aleforce cot: commiion on ale to new cutomer and aleforce overhead Databae Marketing & Cutomer Strategy Management Vol. 14, 1, Palgrave Macmillan Ltd $30.00

3 Incorporating word-of-mouth effect Since many of thoe targeted do not become cutomer, the cot per acquired cutomer i higher. Bolton 1 noted that on average $ 600 per cutomer wa pent to acquire new mobile phone ubcriber in Reichheld 7 report average acquiition cot (including the cot of direct mail, credit evaluation, card iuance, and modification of databae) per new credit card cutomer of between $ 50 and $ 100. Incorporating WOM effect into CLV The impact of WOM on CLV i incorporated in two way: (i) through the variation of acquiition cot to ubtitute the cutomer churn, and (ii) through the aving of acquiition cot of cutomer accrued by the market growth. To account for the heterogeneity in WOM intention, we adopt a egment-level analyi. We aume that each egment need to replace defected cutomer to make the impact comparative acro egment. We aume that the WOM aving i accrued to new cutomer who are the recipient of the WOM from exiting cutomer. WOM aving per egment i baed on the proportion of WOM intent and a egment-level effect ratio given a WOM Saving = (Total Acq_Cot Average WOM intent) t aggregate aving exp(wom intent ) S exp(wom intent ) = 1 egment - level effect ratio We ue the logit ratio of WOM intent for each egment to reflect the reult of Anderon, 19 which howed a U-haped relationhip between atifaction and WOM tranmiion having a harp increae on the right extreme of the cale. Given S number of egment and the duration T, we have a modified equation for CLV incorporating the WOM effect at the egment-level a = S T Revenue t Cot t CLV, t =1 t=1 (1+ d) (2) (1) where Revenue t = Cutomer t Average Revenue = (Retained_Cutomer t + New_Cutomer t ) Average Revenue (3) and the total cot of acquiition and retention adjuted uing the Equation (1), Cot t = Acq_Cot t + Ret_Cot t WOM Saving t (4) Equation (2) (3) i a combination of the baic tructural model of Berger and Nar 8 and the cutomer migration model of Dwyer 20 with ome minor modification. Thi model retain the baic tructure having revenue and cot tream dicounted for the time value of money and include the dynamic of a migration model inide thee tream. The number of cutomer in each egment, Cutomer t, remain the ame at a given period but increae over time baed on the market growth rate o that Cutomer t = Cutomer t (1 + growth rate). A the number of retained cutomer varie from one egment to another according to it egment-level defection rate, the number of new cutomer that need to be acquired alo varie and hence the cot of acquiition differ acro egment. The cot tream for each egment take into account the WOM effect hown in Equation (4). DATA AND EMPIRICAL STUDY In thi ection, we empirically examine the link between referral intent and cutomer retention of a French mobile telephone ervice provider in order to ae the impact of WOM on CLV through retention. Mobile telephone ervice ha a number of advantage in analying CLV. Firt, the mobile phone ervice i characteried a a continuou ale tranaction in which cutomer with long duration are conidered a loyalty. In other indutrie uch a retailer, hotel, and airline where ale 2006 Palgrave Macmillan Ltd $30.00 Vol. 14, 1, Databae Marketing & Cutomer Strategy Management 31

4 Lee, Lee and Feick tranaction are dicrete, cutomer duration with the ame ervice provider doe not necearily explain cutomer loyalty. Second, cutomer are typically ingle brand uer. That i, except for ome profeion-baed cae, mot cutomer do not carry multiple mobile phone at a time. The ingle brand uage implifie the analyi ince it then i not neceary to account for category hare a it i in indutrie (uch a banking and inurance) where conumer could be multi-brand uer. Third, the market i volatile and competitive: according to Gartner, 21 about 23 per cent of French mobile phone ervice uer witched provider in 1999 a did about 26 per cent in The average revenue per unit wa down to $ 563 in 1999 from $ 1,395 in Finally, the relationhip with the ervice provider tart with the initial contract and we meaure the cutomer retention after the period. In our data, we elected individual who igned up for a 12-month binding contract. Data include both urvey and tranaction meaure of 1,493 cutomer oberved from the tarting date of ubcription varying from May 1999 to December Survey reult were obtained from a ubcriber quetionnaire adminitered by phone from January 2000 to March All individual were urveyed between two and nine month after the tart of their ubcription. Cutomer who had igned up for one-year contract were randomly elected. No information about the refual rate i communicated. In thi period, the mobile phone ervice uage wa being generalied. The proportion of male cutomer from our data ample explain thi phenomenon a male proportion evolved from 80 per cent among urvey repondent of January 1998 to 53 per cent of March 2001, which make our ample dominated by male uer (71 per cent). The average duration at the time of the urvey wa 5.8 month. The urvey included a meaure of atifaction (overall atifaction) and the magnitude of WOM intent (willingne to recommend). Thee item were meaured on a 10-point cale. Similar to reult from other urvey, the atifaction and loyalty meaure are left kewed with a mean greater than 7 out of 10-point cale 3,22,23 In our data et, there are very few defection prior to the 12th month ince the data et include only cutomer who igned up for a one-year contract that bind them to pay the baic fee for 12 month, even if they drop the ervice before 12 month. Etimation of egment-level CLV A mentioned earlier, direct financial element of revenue and cot hould not be the only component that determine CLV. It i important to include the indirect effect of WOM in etimating CLV and we examine it impact in thi ection. In Table 2, we report four egment baed on the level of WOM intent and that of monthly revenue in order to compare the lifetime value between high and low WOM intent egment aligned by the level of direct financial contribution. A for the WOM intent, we ue a median plit for high and low intent. For monthly revenue, we plit cutomer baed on their plan type of varying minute. Cutomer having ubcribed to a plan type le than 4 hour per month are aigned to low revenue egment, and cutomer with a longer plan type are aigned to high revenue egment. Among high revenue cutomer, the defection rate i lower in high WOM intent egment compared to low WOM intent egment by 4 per cent acro period, 20 to 16 per cent at 12th month, and 72 to 68 per cent at 24th month. In term of financial impact, the 4 per cent difference of defection rate will be ubtantial a reported in Reichheld, 7 which howed huge financial gain reulted from the 5 per cent improved defection rate. Next, baed on the current egment tructure, we extend our model uing imulation to how the long-run impact of WOM in calculating CLV. 32 Databae Marketing & Cutomer Strategy Management Vol. 14, 1, Palgrave Macmillan Ltd $30.00

5 Incorporating word-of-mouth effect Augmenting imulation to actual data In thi ection, we contruct a imulation model that etimate CLV by extending our model over time. We complement the actual data with other etimate obtained from econdary ource to compare the difference of lifetime value with and without integration of WOM effect. The following parameter are ued to compute CLV of egment of high / low revenue and high / low WOM intent. In the imulation, the total duration ( T ) i fixed to five year. Four egment baed on WOM intent and revenue contribution are applied. To capture the dynamic, the average annual growth rate of 37 per cent between 1999 and 2003, reported by Table 1 : Sample characteritic Variable Mean.d. Total duration (month) (5.97) Duration at the moment of 5.81 (1.81) urvey (month) Defection rate at 12th month 0.19 (0.39) Defection rate at 24th month 0.70 (0.46) Monthly pending (FF) (248.13) Overall atifaction 7.34 (1.58) WOM intent 7.45 (2.37) Gartner 21 i ued. A for the level of operating cot, we take 80 per cent of operation revenue that correpond to one of global operator, Vodafone level of operating cot (2001 annual report). And two different amount of acquiition cot i applied, high revenue egment (1,200 French Franc (FF)) and low revenue egment (600 FF) under an aumption to recover thi initial invetment within a year of the contract term. One-fifth of acquiition cot i imputed a remarketing cot following 5:1 ratio of acquiition and remarketing cot. 24 For the direct impact of WOM on acquiition cot aving ( WOM Saving ), we apply 16.4 per cent, which i baed on the average of Forreter Reearch Report. 25 To include the effect of WOM on the retention of cutomer, we include the defection rate at the 12th month of each egment baed on the actual company data reported in Table 2. To dicount the future revenue and cot, 5 per cent of market average interet rate i ued. Table 3 report imulation reult that compare the etimate of CLV with and Table 2 : Claification by revenue and WOM intent Mean.d. Low revenue Low WOM intent ( N =347) Defection rate at 12th month Defection rate at 24th month Monthly pending (FF) Overall atifaction WOM intent High WOM intent ( N =448) Defection rate at 12th month Defection rate at 24th month Monthly pending (FF) Overall atifaction WOM intent High revenue Low WOM intent ( N =283) Defection rate at 12th month Defection rate at 24th month Monthly pending (FF) Overall atifaction WOM intent High WOM intent ( N =415) Defection rate at 12th month Defection rate at 24th month Monthly pending (FF) Overall atifaction WOM intent Palgrave Macmillan Ltd $30.00 Vol. 14, 1, Databae Marketing & Cutomer Strategy Management 33

6 Lee, Lee and Feick Table 3 : Calculating CLV with/without WOM effect With WOM Without WOM Low revenue/low WOM Y1 Y2 Y3 Y4 Y5 Y1 Y2 Y3 Y4 Y5 Total cutomer Retained cutomer New cutomer End term defector Revenue cot 252, , , , , , , , , ,734 Operation 201, , , , , , , , , ,187 Acquiition 60,000 35,400 48,498 66,442 91,026 60,000 33,600 46,032 63,064 86,397 Retention 12,000 16,440 22,523 30,856 42,273 12,000 16,440 22,523 30,856 42,273 Acquiition cot aving Profit by period 21,600 17,561 24,058 32,959 45,154 21,600 19,008 26,041 35,676 48,876 Segment LTV (Contribution) 78,634 (8%) 86,811 (11%) LTV difference 9.4% Low revenue/high WOM Total cutomer Retained cutomer New cutomer End term defector Revenue cot 253, , , ,067 8,91, , , , , ,961 Operation 202, , , , , , , , , ,569 Acquiition 60,000 33,000 45,210 61,938 84,855 60,000 33,600 46,032 63,064 86,397 Retention 12,000 16,440 22,523 30,856 42,273 12,000 16,440 22,523 30,856 42,273 Acquiition cot aving 15,447 21,163 28,993 39, Profit by period 21,360 35,384 48,476 66,412 90,985 21,360 19,337 26,491 36,293 49,722 Segment LTV (contribution) 179,554 (18%) 88,897 (11%) LTV difference 102.0% High revenue/low WOM Total cutomer Retained cutomer New cutomer End term defector Revenue cot 584, ,628 1,096,860 1,502,699 2,058, , ,628 1,096,860 1,502,699 2,058,697 Operation 467, , ,488 1,202,159 1,646, , , ,488 1,202,159 1,646,958 Acquiition 120,000 68,400 93, , , ,000 67,200 92, , ,795 Retention 24,000 32,880 45,046 61,712 84,546 24,000 32,880 45,046 61,712 84,546 Acquiition cot aving Profit by period 27,120 59,178 81, , ,167 27,120 60,046 82, , , Databae Marketing & Cutomer Strategy Management Vol. 14, 1, Palgrave Macmillan Ltd $30.00

7 Incorporating word-of-mouth effect Table 3 : Continued With WOM Without WOM Y1 Y2 Y3 Y4 Y5 Y1 Y2 Y3 Y4 Y5 308,486 (32%) 313,389 (40%) Sum Of egment LTV contribution LTV difference 1.6% High revenue/high WOM Total cutomer Retained cutomer New cutomer End term defector Revenue cot 574, ,476 1,078,842 1,478,014 2,024, , ,476 1,078,842 1,478,014 2,024,879 Operation 459, , ,074 1,182,411 1,619, , , ,074 1,182,411 1,619,903 Acquiition 120,000 63,600 87, , , ,000 67,200 92, , ,795 Retention 24,000 32,880 45,046 61,712 84,546 24,000 32,880 45,046 61,712 84,546 Acquiition cot aving 16,734 22,925 31,408 43, , , , ,920 29,040 57,415 78, , ,635 Profit by period 29,040 Segment LTV (contribution) 411,573 (42%) 296,701 (38%) LTV difference 38.7% Sum of egment LTV 978,247 (100%) 785,799 (100%) Average difference 24.5% without referral and compare the percentage difference acro four egment of revenue / referral intent. We aume no impact of WOM for period 1 a there i no cutomer bae in the firt period. In addition, we aume that there i no change in egment hare over five-year pan and that egment ize change are due only to the market growth rate of 37 per cent per year. Therefore, a higher defection rate implie that the firm need to recruit more new cutomer to maintain the hare equal to the bae ize plu market growth. The impact of WOM on CLV i introduced in two way: direct aving in acquiition cot (hown in acquiition cot aving of each egment in Table 3 ) and increaed cutomer retention (the difference of acquiition cot between with / without WOM in Table 3 ). While the former i alway poitive for all egment depending on the level of WOM intent of each egment, the latter could be poitive or negative a it i normalied to the average defection rate at the aggregate level. In other word, only egment having lower defection rate than the average receive a poitive WOM impact on CLV. For the direct aving in acquiition cot, 224 new cutomer are acquired in total (59, 55, 57, and 53 for each egment) in Year 2 and it cot 600 FF for low revenue egment cutomer and 1,200 FF for high revenue egment one which reult in 200,800 FF in total (35,400, 33,000, 68,400, and 63,600 for each egment). A thi direct aving addree to all new cutomer regardle of their revenue ize, the ervice provider can ave 16.4 per cent of thi pending (32,866 FF) on acquiition if it take WOM aving into account. On the other hand, the direct aving of acquiition cot varie by egment with their WOM intent tranformed by logit ratio in Equation (1): 1.1 per cent for low REV / low WOM egment, 47.0 per cent for low REV / high WOM, 1.0 per cent for high REV / low WOM, and 50.9 per cent for high REV / high WOM Palgrave Macmillan Ltd $30.00 Vol. 14, 1, Databae Marketing & Cutomer Strategy Management 35

8 Lee, Lee and Feick Out of total direct aving of 32,866 FF generated by WOM, 353 FF, 15,447 FF, 332 FF, and 16,734 FF are contributed by each egment: Under the current imulation etting, which aume the ame growth rate of cutomer in each egment, more new cutomer need to be acquired if a egment had higher defection rate than other, and that i why the number of total cutomer i ame for all egment acro all period. For example, a low REV / high WOM egment ha to acquire only 55 compared to 59 for low REV / low WOM intent egment. It pend le money on cutomer acquiition, 33,000 FF, for low REV / high WOM egment compared to that, 35,400 FF, for low REV / low WOM intent egment. By integrating WOM impact, the lifetime value of high WOM intent egment increae ubtantially. For low REV / high WOM intent egment, the increae of CLV i by per cent from 88,897 to 179,554. For high REV / high WOM intent egment, the increae i by 38.7 per cent from 296,701 to 411,573. On the other hand, CLV i reduced in low WOM intent egment becaue there i little increae through WOM and their defection rate i higher than high intent egment. Note that, epecially for the egment with low REV / high WOM, a ubtantial ize of CLV can be gained. The greatet impact of WOM appear in the low REV / high WOM egment. Thi egment probably would not be profitable enough to attract the attention of manager if they rely on the traditional view of CLV. A hown in the without WOM column in Table 3, it lifetime value contribution account for only 11 per cent of total lifetime value. With the incorporation of WOM effect, it account for about 18 per cent of the total lifetime value due to it high level of WOM intent that reduce acquiition cot directly and indirectly through the reduced defection rate. The ubtantial difference of CLV in low REV / high WOM egment hould undercore the importance for manager to incorporate latent element uch a WOM into CLV management. The current CLV framework, which only take into account direct financial contribution, bear a potential rik of underetimating the true value of cutomer who can contribute indirectly by generating poitive WOM. WOM impact under varying market condition In the previou ection, our analyi focued on egment level impact of WOM baed on actual company data, but limited in term of market growth rate and the level of competition. We extend our finding howing how the impact of WOM varie a the firm face different market ituation of growth rate and competition level. Firm typically pay more attention to cutomer acquiition than to cutomer retention while the market i growing rapidly. It i important for firm to be able to attract new cutomer a a mean of generating growth. 26 A market aturate, however, firm grow by attracting competitor cutomer while keeping it own. Under thee condition, competition focue on gaining market penetration and cutomer retention. It i thu ueful to think about the extent of market growth and competition a important market contingencie that influence the impact of WOM. Uing imulation, we compare the mixed impact of WOM on lifetime value under varying market growth rate and level of competition. Figure 1 illutrate imulation reult in which we how the impact of varying the level of competition with annual churn rate and market growth rate with the percentage difference on CLV with and without WOM effect. It how that WOM ha a bigger impact on the CLV etimate a the defection rate goe up and a the market grow fat, all ele being equal. Thu, it become critical to manage WOM in a market facing high competition and high 36 Databae Marketing & Cutomer Strategy Management Vol. 14, 1, Palgrave Macmillan Ltd $30.00

9 Incorporating word-of-mouth effect Mobile Phone Operator 80.0% 70.0% Cutomer Equity Difference 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 30% 0% 5% 10% 15% 20% 25% Growth Rate 30% 35% 40% 45% 50% 20% Churn Rate 10% 0% Figure 1 : Impact of churn rate and market growth on CLV with / without WOM growth. Note that thee reult follow the conventional widom that cutomer retention become the dominant tak in the aturated market. Greater competition could come from a variety of ource: the exitence of powerful firm in the marketplace becaue of concentration, increaing homogeneity of tate or need on the part of conumer, greater homogeneity of product produced by provider, or lower witching cot. 2 Under any of thee market condition, we would expect to have increaed churn rate and therefore, more notable effect of WOM. The market growth rate can be een a a reflection of the product lifecycle tage; for example, a higher growth rate early in the lifecycle. Our reult howing the importance of WOM i thu conitent with the finding of the diffuion literature which emphaie the role of interperonal influence in product diffuion early in the lifecycle. 27,28 The reult i alo conitent with the ocial contagion theory that emphaie the role of WOM in the early tage of the lifecycle in helping the product take off. 26 DISCUSSION In thi paper, we have examined the importance of incorporating the WOM effect in etimating CLV. The lifetime value of cutomer increae more through direct aving of acquiition cot and extended duration a cutomer generate more poitive WOM. By aeing the contribution of WOM to CLV, we have further confirmed the economic of cutomer loyalty 7 and extended the perpective of previou reearch on WOM. 18,19,29 Our approach to integrate WOM effect into CLV complement other approache to CLV management a prioritiing invetment in cutomer Palgrave Macmillan Ltd $30.00 Vol. 14, 1, Databae Marketing & Cutomer Strategy Management 37

10 Lee, Lee and Feick Reward programme, for example, are routinely affected by lifetime value etimate. 30 We argue that, if only direct financial contribution are incorporated in CLV etimation, companie run the rik of miing the latent apect of cutomer value and thu mijudge cutomer true lifetime value. To maximie the lifetime value of cutomer, it i neceary to evaluate cutomer incorporating the indirect effect of WOM, and then invet accordingly to enhance cutomer willingne to generate poitive WOM. Our finding are particularly germane for the indutry in which WOM i a major information ource for purchae. Oftentime, ervice provider are more ubject to intangible than producer 15 and potential cutomer ue the experience of exiting cutomer a the information ource for their own. 13 In the current tudy, we have focued on etimating the impact of WOM on CLV through it effect on aving of acquiition cot. Becaue of the limited data availability, we ued an attitudinal rather than behavioural meaure of WOM. Our finding are limited in the ene that (i) we could not examine the negative WOM, (ii) if extremely poitive WOM create unreaonable expectation, it might lead to cognitive dionance, reulting in higher defection rate. We believe the underlying principle of the propoed approach i intact when we further incorporate actual tracking-baed referral intead of urvey meaurement. Alo, ince we only had acce to limited information on company cot and market condition, we had to ue a number of aumption that may not be neceary in applying the propoed model. Finally, it would be ueful to know if cutomer who are acquired by referral are more or le profitable compared to thoe acquired by traditional marketing effort uch a advertiing ince it will affect the CLV of cutomer acquired by different ource. Reference 1 Bolton, R. N. ( 1998 ) A dynamic model of the duration of the cutomer relationhip with a continuou ervice provider, Marketing Science, Vol. 17, No. 1, pp Jone, T. O. and Earl Saer Jr., W. ( 1995 ) Why atified cutomer defect, Harvard Buine Review (March April), pp Anderon, E. W. and Sullivan, M. W. ( 1993 ) The antecedent and conequence of cutomer atifaction for firm, Marketing Science, Vol. 12, No. 2, pp Anderon, E. W. and Vika, M. ( 2000 ) Strengthening the atifaction-profit chain, Journal of Service Reearch, Vol. 3, No. 2, pp Reinartz, W. J. and Kumar, V. ( 2000 ) On the profitability of long-life cutomer in a noncontractual etting: An empirical invetigation and implication for marketing, Journal of Marketing, Vol. 15 (October), pp Rut, R. T., Zahorik, A. and Keiningham, T. L. ( 1995 ) Return on quality (ROQ): Making ervice quality financially accountable, Journal of Marketing, Vol. 59 (April), pp Reichheld, F. F. ( 1996 ) The loyalty effect, Harvard Buine School Pre, Boton. 8 Berger, P. D. and Nar, N. I. ( 1998 ) Cutomer lifetime value: Marketing model and application, Journal of Interactive Marketing, Vol. 12, No. 1, pp Blattberg, R. C. and Deighton, J. ( 1996 ) Manage marketing by the cutomer lifetime value tet, Harvard Buine Review (July Augut), pp Jain, D. and Singh, S. S. ( 2002 ) Cutomer lifetime value reearch in marketing: A review and future direction, Journal of Interactive Marketing, Vol. 16, No. 2, pp Katz, E. and Lazarfeld, P. ( 1955 ) Peronal influence, The Free Pre, New York, NY. 12 Zeithaml, V. A. and Bitner, M. J. ( 2003 ) Service marketing, 3rd Edition, McGraw Hill, New York, NY. 13 Banal, H. S. and Voyer, P. A. ( 2000 ) Word-of-mouth procee within a ervice purchae deciion context, Journal of Service Reearch, Vol. 3 (November), pp Brown, J. J. and Reingen, P. H. ( 1987 ) Social tie and word-of-mouth referral behavior, Journal of Conumer Reearch, Vol. 14 (December), pp Murray, K. B. and Schlacter, J. L. ( 1990 ) The impact of ervice veru god on conumer aement of perceived rik and variability, Journal of the Academy of Marketing Science, Vol. 18, No. 1, pp Walker, J. L. ( 1995 ) Service encounter atifaction: Conceptualized, Journal of Service Marketing, Vol. 9, No. 1, pp Hekett, J. L., Earl Saer Jr, W. and Schleinger, L. A. ( 1997 ) The ervice profit chain, The Free Pre, New York. 18 Bowman, D. and Narayanda, D. ( 2001 ) Managing cutomer-initiated contact with manufacturer: The impact on hare of category requirement and wordof-mouth behavior, Journal of Marketing Reearch, Vol. 38 (Augut), pp Databae Marketing & Cutomer Strategy Management Vol. 14, 1, Palgrave Macmillan Ltd $30.00

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