Referral Programs and Customer Value
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The authors thank the management of a company that wants to remain anonymous for making the data available and Christian Barrot, Jonah Berger, Xavier Drèze, Peter Fader, Jeanette Heiligenthal, Gary Lilien, Renana Peres, Jochen Reiner, Christian Schulze, Russell Winer, Ezra Zuckerman, and the anonymous JM reviewers for providing com- ments on previous drafts of this article. Word of mouth (WOM) has reemerged as an impor- tant marketing phenomenon, and its use as a cus- tomer acquisition method has begun to attract renewed interest (e.g., Godes and Mayzlin 2009; Iyengar, Van den Bulte, and Valente 2011). Traditionally, WOM’s appeal has been in the belief that it is cheaper than other acquisition methods. A few recent studies have documented that customers acquired through WOM also tend to churn less than customers acquired through traditional channels and that they tend to bring in additional customers through their own WOM (Choi 2009; Trusov, Bucklin, and Pauwels 2009; Villanueva, Yoo, and Hanssens 2008). Villanueva, Yoo, and Hanssens (2008) further suggest that customers acquired through WOM can generate more revenue for the firm than customers acquired through traditional marketing efforts. From a managerial point of view, these findings are encouraging and suggest purposely stimulating WOM to acquire more customers. However, there are concerns that firm-stimulated WOM may be substantially less effective than organic WOM in generating valuable customers (Trusov, Bucklin, and Pauwels 2009; Van den Bulte 2010) because (1) targeted prospects may be suspicious of stimu- lated WOM efforts; (2) such efforts often involve a mone- tary reward for the referrer, who, as a result, may seem less trustworthy; (3) programs providing economic benefits tend not to be sustainable (Lewis 2006); (4) unlike organic WOM, stimulated WOM is not free, raising questions about cost effectiveness; and (5) stimulated WOM is prone to abuse by opportunistic referrers. “referral programs in which the firm rewards existing customers for bringing in new customers.” through its referral program (referred customers) between January 2006 and December 2006 and a random sample of 4633 customers the same bank acquired through other methods (nonreferred customers) over the same period. For both groups of customers, we track profitability (measured as contribution margin) and loyalty (measured as retention) at the individual level from the date of acquisition until Sep- tember 2008. The total observation period spans 33 months. We use two metrics of customer value: (1) the present value of the actually observed contribution margins realized within the data window and (2) the expected present value over a period of six years from the day of acquisition. Although our study is limited to a single research site, as is common for studies that require rich and confidential data, the methodology and findings are of broad interest. Cus- tomer referral programs are gaining popularity in many industries, including financial services, hotels, automobiles, newspapers, and contact lenses (Ryu and Feick 2007). We make the following contributions: First, we provide empirical evidence that a referral program, a form of stimu- lated WOM, is an attractive way to acquire customers. Referred customers exhibit higher contribution margins, retention, and customer value. Second, building on our find- ing that differences in contribution margin erode over time whereas those in retention do not, we document that referred customers are more valuable in both the short and the long run. Third, we show that the referral effect need not be present in every customer segment. Finally, we illustrate how the type of analysis we conduct enables firms to calcu- late the return on investment and the upper bound for the reward in their customer referral programs. We proceed by describing referral programs and develop- ing our hypotheses. A description of the research setting, the data, and the model specifications follows. Then, we report the results.
“The average value of a referred customer is at least 16% higher than that of a nonreferred customer with similar demographics and time of acquisition.” The authors thank the management of a company that wants to remain anonymous for making the data available and Christian Barrot, Jonah Berger, Xavier Drèze, Peter Fader, Jeanette Heiligenthal, Gary Lilien, Renana Peres, Jochen Reiner, Christian Schulze, Russell Winer, Ezra Zuckerman, and the anonymous JM reviewers for providing com- ments on previous drafts of this article. Word of mouth (WOM) has reemerged as an impor- tant marketing phenomenon, and its use as a cus- tomer acquisition method has begun to attract renewed interest (e.g., Godes and Mayzlin 2009; Iyengar, Van den Bulte, and Valente 2011). Traditionally, WOM’s appeal has been in the belief that it is cheaper than other acquisition methods. A few recent studies have documented that customers acquired through WOM also tend to churn less than customers acquired through traditional channels and that they tend to bring in additional customers through their own WOM (Choi 2009; Trusov, Bucklin, and Pauwels 2009; Villanueva, Yoo, and Hanssens 2008). Villanueva, Yoo, and Hanssens (2008) further suggest that customers acquired through WOM can generate more revenue for the firm than customers acquired through traditional marketing efforts. From a managerial point of view, these findings are encouraging and suggest purposely stimulating WOM to acquire more customers. However, there are concerns that firm-stimulated WOM may be substantially less effective than organic WOM in generating valuable customers (Trusov, Bucklin, and Pauwels 2009; Van den Bulte 2010) because (1) targeted prospects may be suspicious of stimu- lated WOM efforts; (2) such efforts often involve a mone- tary reward for the referrer, who, as a result, may seem less trustworthy; (3) programs providing economic benefits tend not to be sustainable (Lewis 2006); (4) unlike organic WOM, stimulated WOM is not free, raising questions about cost effectiveness; and (5) stimulated WOM is prone to abuse by opportunistic referrers.
“have a higher retention rate, and this difference persists over time;” The authors thank the management of a company that wants to remain anonymous for making the data available and Christian Barrot, Jonah Berger, Xavier Drèze, Peter Fader, Jeanette Heiligenthal, Gary Lilien, Renana Peres, Jochen Reiner, Christian Schulze, Russell Winer, Ezra Zuckerman, and the anonymous JM reviewers for providing com- ments on previous drafts of this article. Word of mouth (WOM) has reemerged as an impor- tant marketing phenomenon, and its use as a cus- tomer acquisition method has begun to attract renewed interest (e.g., Godes and Mayzlin 2009; Iyengar, Van den Bulte, and Valente 2011). Traditionally, WOM’s appeal has been in the belief that it is cheaper than other acquisition methods. A few recent studies have documented that customers acquired through WOM also tend to churn less than customers acquired through traditional channels and that they tend to bring in additional customers through their own WOM (Choi 2009; Trusov, Bucklin, and Pauwels 2009; Villanueva, Yoo, and Hanssens 2008). Villanueva, Yoo, and Hanssens (2008) further suggest that customers acquired through WOM can generate more revenue for the firm than customers acquired through traditional marketing efforts. From a managerial point of view, these findings are encouraging and suggest purposely stimulating WOM to acquire more customers. However, there are concerns that firm-stimulated WOM may be substantially less effective than organic WOM in generating valuable customers (Trusov, Bucklin, and Pauwels 2009; Van den Bulte 2010) because (1) targeted prospects may be suspicious of stimu- lated WOM efforts; (2) such efforts often involve a mone- tary reward for the referrer, who, as a result, may seem less trustworthy; (3) programs providing economic benefits tend not to be sustainable (Lewis 2006); (4) unlike organic WOM, stimulated WOM is not free, raising questions about cost effectiveness; and (5) stimulated WOM is prone to abuse by opportunistic referrers.
“have a higher contribution margin, though this difference erodes over time;” The authors thank the management of a company that wants to remain anonymous for making the data available and Christian Barrot, Jonah Berger, Xavier Drèze, Peter Fader, Jeanette Heiligenthal, Gary Lilien, Renana Peres, Jochen Reiner, Christian Schulze, Russell Winer, Ezra Zuckerman, and the anonymous JM reviewers for providing com- ments on previous drafts of this article. Word of mouth (WOM) has reemerged as an impor- tant marketing phenomenon, and its use as a cus- tomer acquisition method has begun to attract renewed interest (e.g., Godes and Mayzlin 2009; Iyengar, Van den Bulte, and Valente 2011). Traditionally, WOM’s appeal has been in the belief that it is cheaper than other acquisition methods. A few recent studies have documented that customers acquired through WOM also tend to churn less than customers acquired through traditional channels and that they tend to bring in additional customers through their own WOM (Choi 2009; Trusov, Bucklin, and Pauwels 2009; Villanueva, Yoo, and Hanssens 2008). Villanueva, Yoo, and Hanssens (2008) further suggest that customers acquired through WOM can generate more revenue for the firm than customers acquired through traditional marketing efforts. From a managerial point of view, these findings are encouraging and suggest purposely stimulating WOM to acquire more customers. However, there are concerns that firm-stimulated WOM may be substantially less effective than organic WOM in generating valuable customers (Trusov, Bucklin, and Pauwels 2009; Van den Bulte 2010) because (1) targeted prospects may be suspicious of stimu- lated WOM efforts; (2) such efforts often involve a mone- tary reward for the referrer, who, as a result, may seem less trustworthy; (3) programs providing economic benefits tend not to be sustainable (Lewis 2006); (4) unlike organic WOM, stimulated WOM is not free, raising questions about cost effectiveness; and (5) stimulated WOM is prone to abuse by opportunistic referrers.
“firms should use a selective approach for their referral programs.” The authors thank the management of a company that wants to remain anonymous for making the data available and Christian Barrot, Jonah Berger, Xavier Drèze, Peter Fader, Jeanette Heiligenthal, Gary Lilien, Renana Peres, Jochen Reiner, Christian Schulze, Russell Winer, Ezra Zuckerman, and the anonymous JM reviewers for providing com- ments on previous drafts of this article. Word of mouth (WOM) has reemerged as an impor- tant marketing phenomenon, and its use as a cus- tomer acquisition method has begun to attract renewed interest (e.g., Godes and Mayzlin 2009; Iyengar, Van den Bulte, and Valente 2011). Traditionally, WOM’s appeal has been in the belief that it is cheaper than other acquisition methods. A few recent studies have documented that customers acquired through WOM also tend to churn less than customers acquired through traditional channels and that they tend to bring in additional customers through their own WOM (Choi 2009; Trusov, Bucklin, and Pauwels 2009; Villanueva, Yoo, and Hanssens 2008). Villanueva, Yoo, and Hanssens (2008) further suggest that customers acquired through WOM can generate more revenue for the firm than customers acquired through traditional marketing efforts. From a managerial point of view, these findings are encouraging and suggest purposely stimulating WOM to acquire more customers. However, there are concerns that firm-stimulated WOM may be substantially less effective than organic WOM in generating valuable customers (Trusov, Bucklin, and Pauwels 2009; Van den Bulte 2010) because (1) targeted prospects may be suspicious of stimu- lated WOM efforts; (2) such efforts often involve a mone- tary reward for the referrer, who, as a result, may seem less trustworthy; (3) programs providing economic benefits tend not to be sustainable (Lewis 2006); (4) unlike organic WOM, stimulated WOM is not free, raising questions about cost effectiveness; and (5) stimulated WOM is prone to abuse by opportunistic referrers.
46 Journal of Marketing Vol. 75 (January 2011), 46–59 © 2011, American Marketing Association ISSN: 0022-2429 (print), 1547-7185 (electronic) Philipp Schmitt, Bernd Skiera, & Christophe Van den Bulte Referral Programs and Customer Value Referral programs have become a popular way to acquire customers. Yet there is no evidence to date that customers acquired through such programs are more valuable than other customers. The authors address this gap and investigate the extent to which referred customers are more profitable and more loyal. Tracking approximately 10,000 customers of a leading German bank for almost three years, the authors find that referred customers (1) have a higher contribution margin, though this difference erodes over time; (2) have a higher retention rate, and this difference persists over time; and (3) are more valuable in both the short and the long run. The average value of a referred customer is at least 16% higher than that of a nonreferred customer with similar demographics and time of acquisition. However, the size of the value differential varies across customer segments; therefore, firms should use a selective approach for their referral programs. Keywords: customer referral programs, customer loyalty, customer value, customer management, word of mouth, social networks Philipp Schmitt is a doctoral student (e-mail: [email protected]. de), and Bernd Skiera is Professor of Marketing and Member of the Board of the E-Finance Lab at the House of Finance (e-mail: [email protected]), School of Business and Economics, Goethe University Frankfurt. Christophe Van den Bulte is Associate Professor of Marketing, the Whar- ton School, University of Pennsylvania (e-mail: [email protected]. edu). “customers acquired through WOM also tend to churn less than customers acquired through traditional channels” The uncertainty about the benefits of stimulated WOM in customer acquisition is frustrating for managers facing demands to increase their marketing return on investment and considering whether to use this method. Our study addresses this managerial issue by investigating the value of customers acquired through stimulated WOM and compar- ing it with the value of customers acquired through other methods. We do so in the context of a specific WOM mar- keting practice that is gaining prominence, namely, referral programs in which the firm rewards existing customers for bringing in new customers. Although these programs are typically viewed as an attractive way to acquire customers, their benefits are often viewed to be their targetability and cost effectiveness (Mummert 2000). We broaden this view by assessing the value of customers acquired through these types of programs. Specifically, we answer four questions: (1) Are cus- tomers acquired through a referral program more valuable than other customers? (2) Is the difference in customer value large enough to cover the costs of such stimulated WOM customer acquisition efforts? (3) Are customers acquired through a referral program more valuable because they generate higher margins, exhibit higher retention, or both? and (4) Do differences in margins and retention remain stable, or do they erode? The answers to the last two questions provide deeper insight into what might be driving the value differential. We answer these four questions using panel data on all 5181 customers that a leading German bank acquired
The uncertainty about the benefits of stimulated WOM in customer acquisition is frustrating for managers facing demands to increase their marketing return on investment and considering whether to use this method. Our study addresses this managerial issue by investigating the value of customers acquired through stimulated WOM and compar- ing it with the value of customers acquired through other methods. We do so in the context of a specific WOM mar- keting practice that is gaining prominence, namely, referral programs in which the firm rewards existing customers for bringing in new customers. Although these programs are typically viewed as an attractive way to acquire customers, their benefits are often viewed to be their targetability and cost effectiveness (Mummert 2000). We broaden this view by assessing the value of customers acquired through these types of programs. Specifically, we answer four questions: (1) Are cus- tomers acquired through a referral program more valuable than other customers? (2) Is the difference in customer value large enough to cover the costs of such stimulated WOM customer acquisition efforts? (3) Are customers acquired through a referral program more valuable because they generate higher margins, exhibit higher retention, or both? and (4) Do differences in margins and retention remain stable, or do they erode? The answers to the last two questions provide deeper insight into what might be driving the value differential. We answer these four questions using panel data on all 5181 customers that a leading German bank acquired “enables firms to calcu- late the return on investment and the upper bound for the reward in their customer referral programs.” Finally, we discuss implications for prac- tice, the limitations, and opportunities for further research. Customer Referral Programs Customer referral programs are a form of stimulated WOM that provides incentives to existing customers to bring in new customers. An important requirement for such pro- grams is that individual purchase or service histories are available so the firm can ascertain whether a referred cus- tomer is indeed a new rather than an existing or a former customer. Referral programs have three distinctive characteristics. First, they are deliberately initiated, actively managed, and continuously controlled by the firm, which is impossible or very difficult with organic WOM activities such as sponta- neous customer conversations and blogs. Second, the key idea is to use the social connections of existing customers with noncustomers to convert the latter. Third, to make this conversion happen, the firm offers the existing customer a reward for bringing in new customers. Although leveraging the social ties of customers with noncustomers to acquire the latter is not unique to customer referral programs, the three distinctive characteristics of these programs set them apart from other forms of network- based marketing (Van den Bulte and Wuyts 2007). Unlike Referral Programs and Customer Value / 47 organic WOM, the firm actively manages and monitors referral programs. Unlike most forms of buzz and viral mar- keting, the source of social influence is limited to existing customers rather than anyone who knows about the brand or event. Unlike multilevel marketing, existing customers are rewarded only for bringing in new customers. They do not perform any other sales function (e.g., hosting parties) and do not generate any income as a function of subsequent sales. Consequently, referral programs do not carry the stigma of exploiting social ties for mercantile purposes, as multilevel marketing does (Biggart 1989).
through its referral program (referred customers) between January 2006 and December 2006 and a random sample of 4633 customers the same bank acquired through other methods (nonreferred customers) over the same period. For both groups of customers, we track profitability (measured as contribution margin) and loyalty (measured as retention) at the individual level from the date of acquisition until Sep- tember 2008. The total observation period spans 33 months. We use two metrics of customer value: (1) the present value of the actually observed contribution margins realized within the data window and (2) the expected present value over a period of six years from the day of acquisition. Although our study is limited to a single research site, as is common for studies that require rich and confidential data, the methodology and findings are of broad interest. Cus- tomer referral programs are gaining popularity in many industries, including financial services, hotels, automobiles, newspapers, and contact lenses (Ryu and Feick 2007). We make the following contributions: First, we provide empirical evidence that a referral program, a form of stimu- lated WOM, is an attractive way to acquire customers. Referred customers exhibit higher contribution margins, retention, and customer value. Second, building on our find- ing that differences in contribution margin erode over time whereas those in retention do not, we document that referred customers are more valuable in both the short and the long run. Third, we show that the referral effect need not be present in every customer segment. Finally, we illustrate how the type of analysis we conduct enables firms to calcu- late the return on investment and the upper bound for the reward in their customer referral programs. We proceed by describing referral programs and develop- ing our hypotheses. A description of the research setting, the data, and the model specifications follows. Then, we report the results. “the key idea is to use the social connections of existing customers with noncustomers to convert the latter.” In most referral programs, the reward is given regardless of how long the new referred customers stay with the firm. Such programs are prone to abuse by customers. Although the firm does not commit to accept every referral, the incen- tive structure combined with imperfect screening by the firm creates the potential for abuse in which existing cus- tomers are rewarded for referring low-quality customers. This kind of moral hazard is less likely to occur with WOM campaigns that do not involve monetary rewards condi- tional on customer recruitment. Existing studies of customer referral programs have provided guidance about when rewards should be offered (Biyalogorsky, Gerstner, and Libai 2001; Kornish and Li 2010), have quantified the impact of rewards and tie strength on referral likelihood (Ryu and Feick 2007; Wirtz and Chew 2002), and have quantified the monetary value of making a referral (Helm 2003; Kumar, Petersen, and Leone 2007, 2010). The key managerial question of the (differen- tial) value of customers acquired through referral programs has not yet been addressed. Hypotheses Because referral programs are a customer acquisition method, an important metric to assess their effectiveness is the value of the customers they acquire. Additional insights come from investigating differences between referred and nonreferred customers in contribution margins and retention rates, the two main components of customer value (e.g., Gupta and Zeithaml 2006; Wiesel, Skiera, and Villanueva 2008). Our hypotheses regarding these customer metrics of managerial interest are informed by prior work in economics and sociology on employee referral (e.g., Coverdill 1998; Rees 1966), especially the work of Fernandez, Castilla, and Moore (2000), Neckerman and Fernandez (2003), and Castilla (2005) on the quality of employee referral programs.
through its referral program (referred customers) between January 2006 and December 2006 and a random sample of 4633 customers the same bank acquired through other methods (nonreferred customers) over the same period. For both groups of customers, we track profitability (measured as contribution margin) and loyalty (measured as retention) at the individual level from the date of acquisition until Sep- tember 2008. The total observation period spans 33 months. We use two metrics of customer value: (1) the present value of the actually observed contribution margins realized within the data window and (2) the expected present value over a period of six years from the day of acquisition. Although our study is limited to a single research site, as is common for studies that require rich and confidential data, the methodology and findings are of broad interest. Cus- tomer referral programs are gaining popularity in many industries, including financial services, hotels, automobiles, newspapers, and contact lenses (Ryu and Feick 2007). We make the following contributions: First, we provide empirical evidence that a referral program, a form of stimu- lated WOM, is an attractive way to acquire customers. Referred customers exhibit higher contribution margins, retention, and customer value. Second, building on our find- ing that differences in contribution margin erode over time whereas those in retention do not, we document that referred customers are more valuable in both the short and the long run. Third, we show that the referral effect need not be present in every customer segment. Finally, we illustrate how the type of analysis we conduct enables firms to calcu- late the return on investment and the upper bound for the reward in their customer referral programs. We proceed by describing referral programs and develop- ing our hypotheses. A description of the research setting, the data, and the model specifications follows. Then, we report the results. “they are deliberately initiated, actively managed, and continuously controlled by the firm” In most referral programs, the reward is given regardless of how long the new referred customers stay with the firm. Such programs are prone to abuse by customers. Although the firm does not commit to accept every referral, the incen- tive structure combined with imperfect screening by the firm creates the potential for abuse in which existing cus- tomers are rewarded for referring low-quality customers. This kind of moral hazard is less likely to occur with WOM campaigns that do not involve monetary rewards condi- tional on customer recruitment. Existing studies of customer referral programs have provided guidance about when rewards should be offered (Biyalogorsky, Gerstner, and Libai 2001; Kornish and Li 2010), have quantified the impact of rewards and tie strength on referral likelihood (Ryu and Feick 2007; Wirtz and Chew 2002), and have quantified the monetary value of making a referral (Helm 2003; Kumar, Petersen, and Leone 2007, 2010). The key managerial question of the (differen- tial) value of customers acquired through referral programs has not yet been addressed. Hypotheses Because referral programs are a customer acquisition method, an important metric to assess their effectiveness is the value of the customers they acquire. Additional insights come from investigating differences between referred and nonreferred customers in contribution margins and retention rates, the two main components of customer value (e.g., Gupta and Zeithaml 2006; Wiesel, Skiera, and Villanueva 2008). Our hypotheses regarding these customer metrics of managerial interest are informed by prior work in economics and sociology on employee referral (e.g., Coverdill 1998; Rees 1966), especially the work of Fernandez, Castilla, and Moore (2000), Neckerman and Fernandez (2003), and Castilla (2005) on the quality of employee referral programs.
Finally, we discuss implications for prac- tice, the limitations, and opportunities for further research. Customer Referral Programs Customer referral programs are a form of stimulated WOM that provides incentives to existing customers to bring in new customers. An important requirement for such pro- grams is that individual purchase or service histories are available so the firm can ascertain whether a referred cus- tomer is indeed a new rather than an existing or a former customer. Referral programs have three distinctive characteristics. First, they are deliberately initiated, actively managed, and continuously controlled by the firm, which is impossible or very difficult with organic WOM activities such as sponta- neous customer conversations and blogs. Second, the key idea is to use the social connections of existing customers with noncustomers to convert the latter. Third, to make this conversion happen, the firm offers the existing customer a reward for bringing in new customers. Although leveraging the social ties of customers with noncustomers to acquire the latter is not unique to customer referral programs, the three distinctive characteristics of these programs set them apart from other forms of network- based marketing (Van den Bulte and Wuyts 2007). Unlike Referral Programs and Customer Value / 47 organic WOM, the firm actively manages and monitors referral programs. Unlike most forms of buzz and viral mar- keting, the source of social influence is limited to existing customers rather than anyone who knows about the brand or event. Unlike multilevel marketing, existing customers are rewarded only for bringing in new customers. They do not perform any other sales function (e.g., hosting parties) and do not generate any income as a function of subsequent sales. Consequently, referral programs do not carry the stigma of exploiting social ties for mercantile purposes, as multilevel marketing does (Biggart 1989). “Such programs are prone to abuse by customers.” These studies show that the benefits of such programs are realized through distinct mechanisms, of which better matching and social enrichment appear particularly relevant to marketers. Better matching is the phenomenon that refer- rals fit with the firm better than nonreferrals, and social enrichment is the phenomenon that the relationship of the referral to the firm is enriched by the presence of a common third party (i.e., the referrer). Customer and employees referral programs are likely to be subject to similar mechanisms because they share the three distinctive characteristics of having active manage- ment, using the social connections of existing contacts, and
The uncertainty about the benefits of stimulated WOM in customer acquisition is frustrating for managers facing demands to increase their marketing return on investment and considering whether to use this method. Our study addresses this managerial issue by investigating the value of customers acquired through stimulated WOM and compar- ing it with the value of customers acquired through other methods. We do so in the context of a specific WOM mar- keting practice that is gaining prominence, namely, referral programs in which the firm rewards existing customers for bringing in new customers. Although these programs are typically viewed as an attractive way to acquire customers, their benefits are often viewed to be their targetability and cost effectiveness (Mummert 2000). We broaden this view by assessing the value of customers acquired through these types of programs. Specifically, we answer four questions: (1) Are cus- tomers acquired through a referral program more valuable than other customers? (2) Is the difference in customer value large enough to cover the costs of such stimulated WOM customer acquisition efforts? (3) Are customers acquired through a referral program more valuable because they generate higher margins, exhibit higher retention, or both? and (4) Do differences in margins and retention remain stable, or do they erode? The answers to the last two questions provide deeper insight into what might be driving the value differential. We answer these four questions using panel data on all 5181 customers that a leading German bank acquired “enables firms to calcu- late the return on investment and the upper bound for the reward in their customer referral programs.” Finally, we discuss implications for prac- tice, the limitations, and opportunities for further research. Customer Referral Programs Customer referral programs are a form of stimulated WOM that provides incentives to existing customers to bring in new customers. An important requirement for such pro- grams is that individual purchase or service histories are available so the firm can ascertain whether a referred cus- tomer is indeed a new rather than an existing or a former customer. Referral programs have three distinctive characteristics. First, they are deliberately initiated, actively managed, and continuously controlled by the firm, which is impossible or very difficult with organic WOM activities such as sponta- neous customer conversations and blogs. Second, the key idea is to use the social connections of existing customers with noncustomers to convert the latter. Third, to make this conversion happen, the firm offers the existing customer a reward for bringing in new customers. Although leveraging the social ties of customers with noncustomers to acquire the latter is not unique to customer referral programs, the three distinctive characteristics of these programs set them apart from other forms of network- based marketing (Van den Bulte and Wuyts 2007). Unlike Referral Programs and Customer Value / 47 organic WOM, the firm actively manages and monitors referral programs. Unlike most forms of buzz and viral mar- keting, the source of social influence is limited to existing customers rather than anyone who knows about the brand or event. Unlike multilevel marketing, existing customers are rewarded only for bringing in new customers. They do not perform any other sales function (e.g., hosting parties) and do not generate any income as a function of subsequent sales. Consequently, referral programs do not carry the stigma of exploiting social ties for mercantile purposes, as multilevel marketing does (Biggart 1989).
tomer is approximately 25% more valuable to the bank than a comparable nonreferred customer, within the observation period. If we take into account the difference in acquisition costs of approximately 20 euros, the difference in customer value is nearly 35%. These results strongly support H3. Because the margin differential of referred customers erodes over time even though the loyalty differential does not, the question arises whether referred customers remain more valuable beyond the observation period. Repeating the analysis for the six-year customer lifetime value, we show that referred customers indeed remain more valuable. The average customer lifetime value of referred customers is approximately 6 euros higher than that of other customers (Mann–Whitney test, p < .001). After we control for differ- ences in customer demographics and time of acquisition, the value differential is approximately 40 euros (Column 6 of Table 2; p < .001). Because the average customer lifetime value of a nonreferred customer is 253 euros, a referred cus- tomer is approximately 16% more valuable to the bank than a comparable nonreferred customer over a horizon of six years. If we take into account the difference in acquisition costs of approximately 20 euros, the difference in customer lifetime value is approximately 25%. This value differential is quite considerable. We also assess the extent to which the differences in customer value are robust across various subsets of cus- tomers. Table 3 reports the regression coefficients for the referral program in models of customer value, with the same controls as in the previous analysis in Columns 5 and 6 of Table 2. Row 1 of Table 3 shows that the results for the retail customer segment are nearly identical to those for the entire sample. This is not surprising, because retail cus- tomers make up approximately 90% of our overall sample. “Overall, the acquisition through a referral program is associated with higher customer value for the majority of customer types, but not for all.” The dif- ference in daily contribution margin increased from 7.6 cents to 16 cents per day, the margin erosion increased from 23.1 cents to 45.4 cents per thousand days, the churn hazard reduction remained at 20%, and the difference in customer lifetime value increased from 40 euros to 66 euros. These 8Low-margin customers and high-margin customers are found in both the retail and the nonretail segments. 9Because some readers may be interested in how the effect of referral program is moderated by covariates in the time-varying contribution model, we estimate the latter using a random coeffi- cients specification rather than the fixed-effects specification used in Table 2. TAble 3 Results for Difference in Customer Value Within Various Segments Observed Customer Customer lifetime Value Value (Robust (Robust N (N of Standard Standard Referred errors) errors) Customers) Retail customers 48.620*** 39.082*** 8384 (6.574) (6.633) (4473) Nonretail customers 77.309** 69.803* 1111 (29.855) (30.023) (536) High margin customers 80.421** 69.669* 950 (27.768) (28.004) (533) Low margin customers –1.146 –13.212*** 962 (1.581) (2.087) (247) Male customers 51.679*** 42.305*** 4371 (10.600) (10.669) (2150) Female customers 47.437*** 38.274*** 5124 (9.604) (9.690) (2859) <25 years of age 35.662** 17.701 1808 (12.914) (12.945) (1242) 26–35 years of age 101.975*** 85.280*** 2170 (14.908) (14.822) (1298) 36–45 years of age 66.148*** 57.401** 1621 (17.534) (17.707) (835) 46–55 years of age 62.763** 56.834** 1437 (19.671) (19.827) (617) 56–65 years of age 9.433 5.122 1153 (21.189) (21.195) (481) >65 years of age –1.577 –8.589 1306 (21.421) (21.409) (536) *p < .05. **p < .01. ***p < .001. Notes: Each row displays the coefficient of referral program in models with the same control variables as in Table 2 but estimated for specific segments.
Doubts about the benefits of stimulated WOM have long frustrated managers facing demands to increase their marketing return on investment. Our findings are important news for practitioners considering deploying customer referral programs in their own firm. Assuaging prior skepti- cism, we document a positive value differential, in both the short run and the long run, between customers acquired through a referral program and other customers. Impor- tantly, this value differential is larger than the referral fee. Thus, referral programs can indeed pay off. The positive differential indicates that abuse by oppor- tunistic customers and other harmful side effects of referral programs are much less important than their benefits. The referral program we analyzed was especially prone to exploitation because no conditions, such as minimum stay or assets, applied to the newly acquired customer. Finding a positive value differential of referred customers in this set- ting is especially compelling. Moving beyond referral pro- grams specifically, our study indicates that a stronger focus on stimulated rather than organic WOM is worth consider- ing (Godes and Mayzlin 2009). However, our results also suggest that firms should think carefully about what prospects to target with referral programs and how big of a referral fee to provide. For the program we analyzed, we found that the customer value dif- ferential is much larger in some segments than in others. People under the age of 55 and high-margin customers are more attractive to acquire through a referral program. It is not necessarily a coincidence that these customers also tend to be more profitable for banks (and many other consumer marketers). “Thus, instead of the currently practiced “all-in” approach, firms should design and target referral programs such that attractive customers are more likely to be enticed.” ties and face-to-face interactions. Managers must also make it convenient for prospects to actually become a customer. A possible application is to partner with online communities and make it easy for people to start a relationship with the firm online, immediately after they receive a referral from an existing customer in the same community. Our results suggest that such awareness and facilitation efforts should be targeted selectively to customers who offer the highest value differential. The referral fee is another issue that requires attention when designing a referral program. Many programs offer the same reward to each referrer (Kumar, Petersen, and Leone 2010). Yet, as we show, the value of referred cus- tomers can vary widely even for one company. Thus, firms may benefit from offering rewards based on the value of the referred customer. However, the question then becomes how to do this without adding too much complexity to the program. There may be a simple answer: A standard homophily argument suggests that valuable referrers are more likely to generate valuable referrals. Thus, firms may want to make the referral fee a function of the value of the referrer. A different approach to take advantage of the referral effect would be to try to generate conditions in which non- referred customers become subject to the same mechanisms that make referred customers more valuable. To the extent that the differences we have documented stem from better matching, from social enrichment, or from other mecha- nisms that firms can actively foster among all customers, firms may be able to dramatically “scale up” the beneficial referral effect beyond dyads of referring and referred cus- tomers. For example, pharmaceutical companies increas- ingly involve local opinion leaders in their speaker pro- grams and other medical education efforts. They do so to capitalize on these physicians’ relevance and credibility with practicing physicians.
ties and face-to-face interactions. Managers must also make it convenient for prospects to actually become a customer. A possible application is to partner with online communities and make it easy for people to start a relationship with the firm online, immediately after they receive a referral from an existing customer in the same community. Our results suggest that such awareness and facilitation efforts should be targeted selectively to customers who offer the highest value differential. The referral fee is another issue that requires attention when designing a referral program. Many programs offer the same reward to each referrer (Kumar, Petersen, and Leone 2010). Yet, as we show, the value of referred cus- tomers can vary widely even for one company. Thus, firms may benefit from offering rewards based on the value of the referred customer. However, the question then becomes how to do this without adding too much complexity to the program. There may be a simple answer: A standard homophily argument suggests that valuable referrers are more likely to generate valuable referrals. Thus, firms may want to make the referral fee a function of the value of the referrer. A different approach to take advantage of the referral effect would be to try to generate conditions in which non- referred customers become subject to the same mechanisms that make referred customers more valuable. To the extent that the differences we have documented stem from better matching, from social enrichment, or from other mecha- nisms that firms can actively foster among all customers, firms may be able to dramatically “scale up” the beneficial referral effect beyond dyads of referring and referred cus- tomers. For example, pharmaceutical companies increas- ingly involve local opinion leaders in their speaker pro- grams and other medical education efforts. They do so to capitalize on these physicians’ relevance and credibility with practicing physicians. “Firms should calculate the reward considering their specific program and the customers it attracts instead of merely following their competitors.” It may also be useful to know if the motivation of the referrer changes depending on the reward and whether the size of the reward affects the quality of the referred customer. Several of the implications for practice point to the benefits of better understanding the drivers of the value dif- ferential we documented. Although our results are consis- tent with the better matching and social enrichment mecha- nisms we used to develop our hypotheses, our analysis focused on the consequences for contribution margin, reten- tion, and customer value rather than on the intervening mechanisms. Research aimed at more directly parsing out the mechanisms is likely to require information about actual dyads of referring and referred customers. This would enable researchers to test, for example, the social enrich- ment argument by matching the referred customer with the respective referrer and analyzing the dependence of their retention. Additional survey data may help document differ- ences in product knowledge over time and shed light on the existence of an informational advantage eroding over time. Having matched dyad-level data on both referring and referred customers would also make it possible to check whether referral dyads exhibit homophily and whether the value of referred customers varies systematically with that of their referrer (Haenlein 2010; Nitzan and Libai 2010). This would yield valuable insights for the design of individ- ual rewards instead of the currently practiced “one-size-fits- all” approach. Conclusion This study provides the first assessment of economically rele- vant differences between customers acquired through a refer- ral program and customers acquired through other methods. It documents sizable differences in contribution margin, retention, and customer value; analyzes whether these differ- ences erode or persist over time; and investigates differences across customer segments.
ties and face-to-face interactions. Managers must also make it convenient for prospects to actually become a customer. A possible application is to partner with online communities and make it easy for people to start a relationship with the firm online, immediately after they receive a referral from an existing customer in the same community. Our results suggest that such awareness and facilitation efforts should be targeted selectively to customers who offer the highest value differential. The referral fee is another issue that requires attention when designing a referral program. Many programs offer the same reward to each referrer (Kumar, Petersen, and Leone 2010). Yet, as we show, the value of referred cus- tomers can vary widely even for one company. Thus, firms may benefit from offering rewards based on the value of the referred customer. However, the question then becomes how to do this without adding too much complexity to the program. There may be a simple answer: A standard homophily argument suggests that valuable referrers are more likely to generate valuable referrals. Thus, firms may want to make the referral fee a function of the value of the referrer. A different approach to take advantage of the referral effect would be to try to generate conditions in which non- referred customers become subject to the same mechanisms that make referred customers more valuable. To the extent that the differences we have documented stem from better matching, from social enrichment, or from other mecha- nisms that firms can actively foster among all customers, firms may be able to dramatically “scale up” the beneficial referral effect beyond dyads of referring and referred cus- tomers. For example, pharmaceutical companies increas- ingly involve local opinion leaders in their speaker pro- grams and other medical education efforts. They do so to capitalize on these physicians’ relevance and credibility with practicing physicians. “Firms should calculate the reward considering their specific program and the customers it attracts instead of merely following their competitors.” It may also be useful to know if the motivation of the referrer changes depending on the reward and whether the size of the reward affects the quality of the referred customer. Several of the implications for practice point to the benefits of better understanding the drivers of the value dif- ferential we documented. Although our results are consis- tent with the better matching and social enrichment mecha- nisms we used to develop our hypotheses, our analysis focused on the consequences for contribution margin, reten- tion, and customer value rather than on the intervening mechanisms. Research aimed at more directly parsing out the mechanisms is likely to require information about actual dyads of referring and referred customers. This would enable researchers to test, for example, the social enrich- ment argument by matching the referred customer with the respective referrer and analyzing the dependence of their retention. Additional survey data may help document differ- ences in product knowledge over time and shed light on the existence of an informational advantage eroding over time. Having matched dyad-level data on both referring and referred customers would also make it possible to check whether referral dyads exhibit homophily and whether the value of referred customers varies systematically with that of their referrer (Haenlein 2010; Nitzan and Libai 2010). This would yield valuable insights for the design of individ- ual rewards instead of the currently practiced “one-size-fits- all” approach. Conclusion This study provides the first assessment of economically rele- vant differences between customers acquired through a refer- ral program and customers acquired through other methods. It documents sizable differences in contribution margin, retention, and customer value; analyzes whether these differ- ences erode or persist over time; and investigates differences across customer segments.