Abstract
Firms can substantially profit from customer referrals, but they must understand the different stages of the referral process to determine what drives the number of referrals (first stage), conversion (second stage), and average contribution margin per referral (third stage). Applying a framework that integrates perceptual and behavioral drivers, this study uses a financial services company’s customer survey and transaction data to investigate how the effect of contribution margins of referring customers at all three stages depends on their perceived extraversion and opinion leadership. Extreme extraversion and opinion leadership diminish the positive effect of the contribution margins of referring customers on the number of referrals; their effect on the number of successful referrals is insignificant. In terms of the contribution margin of successful referrals, extraversion has a negative and opinion leadership a positive moderating effect.
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References
Ajzen, I. (1987). Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology. Advances in Experimental Social Psychology, 20, 1–63.
Anderson, E. W. (1998). Customer satisfaction and word of mouth. Journal of Service Research, 1(1), 5–17.
Anderson, C., John, O. P., Keltner, D., & Kring, A. M. (2001). Who attains social status? Effects of personality and physical attractiveness in social groups. Journal of Personality and Social Psychology, 81(1), 116–132.
Armelini, G., Barrot, C., & Becker, J. U. (2015). Referral programs, customer value, and the relevance of dyadic characteristics. International Journal of Research in Marketing, 32(4), 449–452.
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402.
Bansal, H. S., & Voyer, P. A. (2000). Word-of-mouth processes within a services purchase decision context. Journal of Service Research, 3(2), 166–177.
Berry, L., & Mirabito, A. M. (2005). Recapturing marketing's mission. In J. N. Sheth & R. S. Sisodia (Eds.), Does marketing need reform (2nd ed., pp. 295–299). New York: Routledge.
Biyalogorsky, E., Gerstner, E., & Libai, B. (2001). Customer referral management: Optimal reward programs. Marketing Science, 20(1), 82–95.
Bos, J. W., & Koetter, M. (2011). Handling losses in translog profit models. Applied Economics, 43(3), 307–312.
Bowman, D., & Narayandas, D. (2001). Managing customer-initiated contacts with manufacturers: The impact on share of category requirements and word-of-mouth behavior. Journal of Marketing Research, 38(3), 281–297.
Brown, J. J., & Reingen, P. H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 14(3), 350–362.
Brown, T. J., Barry, T. E., Dacin, P. A., & Gunst, R. F. (2005). Spreading the word: Investigating antecedents of consumers’ positive word-of-mouth intentions and behaviors in a retailing context. Journal of the Academy of Marketing Science, 33(2), 123–138.
Chandon, P., Wansink, B., & Laurent, G. (2000). A benefit congruency framework of sales promotion effectiveness. Journal of Marketing, 64(4), 65–81.
Costa, P. T., & MacCrae, R. R. (1992). Revised neo personality inventory and neo five factor inventory. FL: Odessa.
De Bruyn, A., & Lilien, G. L. (2008). A multi-stage model of word-of-mouth influence through viral marketing. International Journal of Research in Marketing, 25(3), 151–163.
de Matos, C. A., & Rossi, C. A. V. (2008). Word-of-mouth communications in marketing: A meta-analytic review of the antecedents and moderators. Journal of the Academy of Marketing Science, 36(4), 578–596.
Dichter, E. (1966). How word-of-mouth advertising works. Harvard Business Review, 44(6), 147–160.
Du, R. Y., Kamakura, W. A., & Mela, C. F. (2007). Size and share of customer wallet. Journal of Marketing, 71(2), 94–113.
Feiler, D. C., & Kleinbaum, A. M. (2015). Popularity, similarity, and the network extraversion bias. Psychological Science, 26(5), 593–603.
Ferguson, R. J., Paulin, M., & Bergeron, J. (2010). Customer sociability and the total service experience: Antecedents of positive word-of-mouth intentions. Journal of Service Management, 21(1), 25–44.
Flynn, L. R., Goldsmith, R. E., & Eastman, J. K. (1996). Opinion leaders and opinion seekers: Two new measurement scales. Journal of the Academy of Marketing Science, 24(2), 137–147.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Ganesh, J., Arnold, M. J., & Reynolds, K. E. (2000). Understanding the customer base of service providers: An examination of the differences between switchers and stayers. Journal of Marketing, 64(3), 65–87.
Gilly, M. C., Graham, J. L., Wolfinbarger, M. F., & Yale, L. J. (1998). A dyadic study of interpersonal information search. Journal of the Academy of Marketing Science, 26(2), 83–100.
Gnambs, T., & Batinic, B. (2012). A personality-competence model of opinion leadership. Psychology & Marketing, 29(8), 606–621.
Godes, D., & Mayzlin, D. (2009). Firm-created word-of-mouth communication: Evidence from a field test. Marketing Science, 28(4), 721–739.
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380.
Haenlein, M. (2011). A social network analysis of customer-level revenue distribution. Marketing Letters, 22(1), 15–29.
Haenlein, M., & Libai, B. (2013). Targeting revenue leaders for a new product. Journal of Marketing, 77(3), 65–80.
Haenlein, M., & Libai, B. (2017). Seeding, referral, and recommendation: Creating profitable word-of-mouth programs. California Management Review, 59(2), 68–91.
Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153–161.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38–52.
Hogan, J. E., Lemon, K. N., & Libai, B. (2004). Quantifying the ripple: Word-of-mouth and advertising effectiveness. Journal of Advertising Research, 44(3), 271–280.
Jacoby, J., & Hoyer, W. D. (1981). What if opinion leaders didn't know more? A question of nomological validity. Advances in Consumer Research, 8(1), 299–303.
Jin, L., & Huang, Y. (2014). When giving money does not work: The differential effects of monetary versus in-kind rewards in referral reward programs. International Journal of Research in Marketing, 31(1), 107–116.
John, O. P., Naumann, L. P., & Soto, C. J. (2008). Paradigm shift to the integrative big five trait taxonomy. In Handbook of personality: Theory and research (Vol. 3, 3rd ed., pp. 114–158). New York: Guilford Press.
Karlis, D., & Ntzoufras, I. (2005). Bivariate poisson and diagonal inflated bivariate poisson regression models in r. Journal of Statistical Software, 14(10), 1–36.
Keaveney, S. M., & Parthasarathy, M. (2001). Customer switching behavior in online services: An exploratory study of the role of selected attitudinal, behavioral, and demographic factors. Journal of the Academy of Marketing Science, 29(4), 374–390.
Kossinets, G., & Watts, D. J. (2006). Empirical analysis of an evolving social network. Science, 311(5757), 88–90.
Kumar, V., Petersen, J. A., & Leone, R. P. (2007). How valuable is word of mouth? Harvard Business Review, 85(10), 139–146.
Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmanns, S. (2010a). Undervalued or overvalued customers: Capturing total customer engagement value. Journal of Service Research, 13(3), 297–310.
Kumar, V., Petersen, J. A., & Leone, R. P. (2010b). Driving profitability by encouraging customer referrals: Who, when, and how. Journal of Marketing, 74(5), 1–17.
Kumar, V., Petersen, J. A., & Leone, R. P. (2013). Defining, measuring, and managing business reference value. Journal of Marketing, 77(1), 68–86.
Lichtenstein, D. R., Netemeyer, R. G., & Burton, S. (1995). Assessing the domain specificity of deal proneness: A field study. Journal of Consumer Research, 22(3), 314–326.
Mangold, W. G., Miller, F., & Brockway, G. R. (1999). Word-of-mouth communication in the service marketplace. Journal of Services Marketing, 13(1), 73–89.
Mano, H., & Oliver, R. L. (1993). Assessing the dimensionality and structure of the consumption experience: Evaluation, feeling, and satisfaction. Journal of Consumer Research, 20(3), 451–466.
McCrae, R. R. (1989). The structure of interpersonal traits: Wiggins's circumplex and the five-factor model. Journal of Personality and Social Psychology, 56(4), 586–595.
McCrae, R. R., & Costa, P. T. (2003). Personality in adulthood: A five-factor theory perspective. Guilford Press.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.
Mittal, B. (1990). The Relative Roles of Brand Beliefs and Attitude towards the Ad as Mediators of Brand Attitude: A Second Look. Journal of Marketing Research, 27(2), 209–219.
Mooradian, T. A., & Olver, J. M. (1997). “I can't get no satisfaction:” the impact of personality and emotion on postpurchase processes. Psychology and Marketing, 14(4), 379–393.
Mooradian, T. A., & Swan, K. S. (2006). Personality-and-culture: The case of national extraversion and word-of-mouth. Journal of Business Research, 59(6), 778–785.
Mosteller, F., & Tukey, J. W. (1977). Data analysis and regression: A second course in statistics. In Addison-Wesley Series in Behavioral Science. Quantitative Methods.
Newman, M. E. (2003). Mixing patterns in networks. Physical Review E, 67(2), 1–14.
Paunonen, S. V., & Ashton, M. C. (2001). Big five factors and facets and the prediction of behavior. Journal of Personality and Social Psychology, 81(3), 524–539.
Richins, M. L., & Root-Shaffer, T. (1988). The role of involvement and opinion leadership in consumer word-of-mouth: An implicit model made explicit. Advances in Consumer Research, 15(1), 32–36.
Rogers, E. M., & Cartano, D. G. (1962). Methods of measuring opinion leadership. The Public Opinion Quarterly, 26(3), 435–441.
Ryu, G., & Feick, L. (2007). A penny for your thoughts: Referral reward programs and referral likelihood. Journal of Marketing, 71(1), 84–94.
Schmitt, P., Skiera, B., & Van den Bulte, C. (2011). Referral programs and customer value. Journal of Marketing, 75(1), 46–59.
Sun, B., & Morwitz, V. G. (2010). Stated intentions and purchase behavior: A unified model. International Journal of Research in Marketing, 27(4), 356–366.
Sun, T., Youn, S., Wu, G., & Kuntaraporn, M. (2006). Online word-of-mouth (or mouse): An exploration of its antecedents and consequences. Journal of Computer-Mediated Communication, 11(4), 1104–1127.
Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. Journal of Marketing, 73(5), 90–102.
Van den Bulte, C., Bayer, E., Skiera, B., & Schmitt, P. (2018). How customer referral programs turn social capital into economic capital. Journal of Marketing Research, 55(1), 132–146.
Van Hoye, G., & Lievens, F. (2009). Tapping the grapevine: A closer look at word-of-mouth as a recruitment source. Journal of Applied Psychology, 94(2), 341–352.
Verhoef, P. C., Franses, P. H., & Hoekstra, J. C. (2002). The effect of relational constructs on customer referrals and number of services purchased from a multiservice provider: Does age of relationship matter? Journal of the Academy of Marketing Science, 30(3), 202–216.
Verlegh, P. J., Ryu, G., Tuk, M., & Feick, L. (2013). Receiver responses to rewarded referrals: The motive inferences framework. Journal of the Academy of Marketing Science, 41(6), 669–682.
Villanueva, J., Yoo, S., & Hanssens, D. M. (2008). The impact of marketing-induced versus word-of-mouth customer acquisition on customer equity growth. Journal of Marketing Research, 45(1), 48–59.
Viswanathan, V., Sese, F. J., & Krafft, M. (2017). Social influence in the adoption of a B2B loyalty program: The role of elite status members. International Journal of Research in Marketing, 34(4), 901–918.
von Wangenheim, F., & Bayón, T. (2007). The chain from customer satisfaction via word-of-mouth referrals to new customer acquisition. Journal of the Academy of Marketing Science, 35(2), 233–249.
Wentzel, D., Tomczak, T., & Henkel, S. (2014). Can friends also become customers? The impact of employee referral programs on referral likelihood. Journal of Service Research, 17(2), 119–133.
Westbrook, R. A., & Oliver, R. L. (1981). Developing better measures of consumer satisfaction: Some preliminary results. Advances in Consumer Research, 8(1), 94–99.
Wirtz, J., Orsingher, C., Chew, P., & Tambyah, S. K. (2013). The role of metaperception on the effectiveness of referral reward programs. Journal of Service Research, 16(1), 82–98.
Acknowledgements
The first three authors contributed equally to the paper. We gratefully acknowledge inputs by Sebastian Feld, Kay Peters and V. Kumar who were co-authors of a materially different, earlier version of this paper.
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Appendices
Appendix 1
Measurement items
Constructs and Items (1 = “I do not agree at all” to 7 = “I fully agree”; r = reverse coded) | Loadings | Cronbach’s α |
|---|---|---|
Extraversion (Costa and MacCrae 1992): | ||
I am rather communicative. I am enthusiastic. I often take center stage. I am rather reserved. (r) I am gregarious. I am self-confident. | .87 .82 .77 .77 .75 .69 | .87 |
Opinion Leadership (Flynn et al. 1996): | ||
People that I know pick their direct bank based on what I have told them. I often persuade other people to do business with a direct bank that I like. I often influence people’s opinion about direct banks. When they choose a direct bank other people often turn to me for advice. | .87 .81 .83 .79 | .89 |
Involvement (Ganesh et al. 2000; Keaveney and Parthasarathy 2001): | ||
I check multiple direct banks before I open an account with a direct bank. I compare the prices and rates of several direct banks before I select a direct bank. I am particularly engaged in the choice of a direct bank. In general, I am very interested in direct banks. | .85 .79 .69 .65 | .77 |
Attitude Towards Referral Programs (Chandon et al. 2000; Lichtenstein et al. 1995) | ||
I wish there were more firms offering customer referral programs. With customer referral programs, I do not feel like referring the firm. (r) I like customer referral programs a lot. Compared to other people, I am very likely to participate in customer referral programs. | .89 .85 .80 .76 | .87 |
Customer Satisfaction (Mano and Oliver 1993; Westbrook and Oliver 1981): | ||
XYZ-bank offers exactly what I need. My choice to build up a relationship with XYZ-bank was a wise one. I am very satisfied with the service and the individual customer support of XYZ-bank. The products offered by XYZ-bank worked out as well as I thought they would. | .86 .85 .80 .73 | .83 |
Appendix 2
Latent Class Regression
We first checked for the presence of unobserved heterogeneity by estimating three latent class models, one for each stage of the referral process. Therefore, we compared the information criterion scores for each model to evaluate if the additional parameters estimated for each additional unobserved cluster significantly improved model fit. Specifically, for each stage of the referral process, we estimated models with different latent segments (from 1 to 5 segments) and then compared the Bayesian Information Criterion (BIC) and Consistent Akaike’s Information Criterion (CAIC) values to determine the model that has the best balance of model fit and parsimony (Kumar et al. 2013). Overall, we found that estimating a model with more than one cluster did little to improve model fit. Below we report the information criterion values obtained from the latent class estimation for each stage and explain our rationale for estimating a model with only one cluster in our study.
Number of Referrals | Number of Successful Referrals | Average Contribution Margins of Successful Referrals | |||||||
|---|---|---|---|---|---|---|---|---|---|
Segments | LL | BIC(LL) | CAIC(LL) | LL | BIC(LL) | CAIC(LL) | LL | BIC(LL) | CAIC(LL) |
1 | −914 | 1935 | 1951 | −546 | 1200 | 1216 | −305 | 702 | 720 |
2 | −794 | 1808 | 1841 | −504 | 1228 | 1261 | −262 | 711 | 748 |
3 | −773 | 1880 | 1930 | −487 | 1308 | 1358 | −228 | 739 | 795 |
4 | −765 | 1977 | 2044 | −475 | 1398 | 1465 | −184 | 746 | 821 |
5 | −758 | 2077 | 2161 | −472 | 1505 | 1589 | −159 | 792 | 886 |
For the number of successful referrals and the average contribution margins of successful referrals, both the BIC and CAIC values suggest that a one-segment solution provides the best balance of model fit and parsimony. In other words, there is no benefit gained from segmenting customers into multiple latent clusters.
For the number of referrals the BIC and CAIC values suggest the presence of two segments. However, a comparison of the coefficients (using a Wald test) estimated from a two-segment model vis-à-vis a one-segment model reveals that the coefficient of only one variable significantly differs for the two clusters solution. Specifically, only the interaction effect of opinion leadership and contribution margin differs significantly in the two clusters model (p = .028, see Table below). The additional costs incurred from explicitly considering unobserved heterogeneity (namely, complexity in the estimation methodology as well as theoretical development) far exceed any benefit gained from estimating additional parameters for two or more clusters. We therefore consider it reasonable to estimate a model that considers a one-segment solution for all outcomes of the referral process.
Cluster 1 | Cluster 2 | Paired Comparison | ||||||
|---|---|---|---|---|---|---|---|---|
Estimate | SE | z stat | Estimate | SE | z stat | Wald | p value | |
Intercept | −4.846 | 2.849 | −1.701 | −1.154 | 1.612 | −.716 | 1.279 | .260 |
Acquired by referral | −.506 | .305 | −1.659 | .097 | .159 | .611 | 3.494 | .062 |
Relationship duration | −.005 | .040 | −.116 | −.094 | .029 | −3.245 | 3.040 | .081 |
Cross buying behavior | .365 | .114 | 3.205 | .270 | .070 | 3.846 | .530 | .470 |
Number of calls to service center | −.005 | .010 | −.498 | .004 | .003 | 1.059 | .685 | .410 |
Number of web logins | .225 | .077 | 2.924 | .233 | .102 | 2.283 | .004 | .950 |
Number of past referrals | .061 | .032 | 1.895 | .038 | .031 | 1.215 | .291 | .590 |
Customer satisfaction | .109 | .946 | .116 | −.394 | .616 | −.640 | .203 | .650 |
Customer satisfaction2 | .014 | .086 | .159 | .043 | .059 | .722 | .080 | .780 |
Involvement | .230 | .150 | 1.534 | .077 | .070 | 1.099 | .955 | .330 |
Attitude to referral program | .089 | .084 | 1.058 | .185 | .061 | 3.021 | .867 | .350 |
Contribution margin | .363 | .282 | 1.289 | −1.829 | 1.283 | −1.425 | 2.792 | .095 |
Opinion leadership | −.016 | .106 | −.152 | .039 | .068 | .574 | .189 | .660 |
Extraversion | .094 | .130 | .723 | .169 | .067 | 2.544 | .285 | .590 |
Extraversion x Contrib. margin | .064 | .046 | .401 | .016 | .123 | .131 | .135 | .710 |
Opinion leadership x Contribution margin | −.139 | .051 | −2.731 | .305 | .203 | 1.504 | 4.809 | .028 |
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Viswanathan, V., Tillmanns, S., Krafft, M. et al. Understanding the quality–quantity conundrum of customer referral programs: effects of contribution margin, extraversion, and opinion leadership. J. of the Acad. Mark. Sci. 46, 1108–1132 (2018). https://doi.org/10.1007/s11747-018-0603-8
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DOI: https://doi.org/10.1007/s11747-018-0603-8