ASSOCIATION FOR CONSUMER RESEARCH

Labovitz School of Business & Economics, University of Minnesota Duluth, 11 E. Superior Street, Suite 210, Duluth, MN 55802 Are Free Trial Customers Worth Less Than Regular Customers?

Bram Foubert, Associate Professor, Maastricht University, the Netherlands Hannes Datta, PhD student, Maastricht University, the Netherlands Harald van Heerde, Professor, University of Waikato, New Zealand

There is surprisingly little research on whether a customer acquired via a free trial is worth less to a firm than a regular customer. To

address this question, we conceptualize and test how the acquisition mode drives service usage behavior and consumer retention

decisions, and, in turn, customer lifetime value.

[to cite]:

Bram Foubert, Hannes Datta, and Harald van Heerde (2012) ,"Are Free Trial Customers Worth Less Than Regular Customers?",

in AP - Asia-Pacific Advances in Consumer Research Volume 10, eds. , Duluth, MN : Association for Consumer Research,

Pages: 163-168.

[url]:

http://www.acrwebsite.org/volumes/1011148/volumes/ap11/AP-10

[copyright notice]:

This work is copyrighted by The Association for Consumer Research. For permission to copy or use this work in whole or in

part, please contact the Copyright Clearance Center at http://www.copyright.com/.

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$UH�)UHH�7ULDO�&XVWRPHUV�:RUWK�/HVV�7KDQ�5HJXODU�&XVWRPHUV" Hannes Datta , PhD student, Maastricht University,

the Netherlands Bram Foubert, Associate Professor, Maastricht University, the Netherlands

Harald van Heerde, Professor, University of Waikato, New Zealand

ABSTRACT There is surprisingly little research on whether a customer acquired via a free trial is worth less to a À�UP�WKDQ�D�UHJXODU�FXVWRPHU��7R�DGGUHVV�WKLV�TXHVWLRQ��we conceptualize and test how the acquisition mode drives service usage behavior and consumer retention decisions, and, in turn, customer lifetime value. Content Code: Adoption and Innovation / Product Trial; Loyalty Method Code: Econometric Analysis; Multivariate Data Analysis

PURPOSE OF THE RESEARCH0DQ\� VHUYLFH� À�UPV� DFTXLUH� FXVWRPHUV� E\� RIIHULQJ�free trials for a limited amount of time. Well-known examples include mobile telephone providers (e.g., AT&T in the US) and digital TV (e.g., Sky television in Australia and New Zealand). A key question to a service provider reviewing its customer base is whether the Customer Lifetime Value (CLV) of customers acquired by a free trial promotion is different from the CLV of regular customers. A factor that complicates this analysis is that many VXEVFULSWLRQ� VHUYLFHV� LQFOXGH� WZR� FRPSRQHQWV�� Á�DW�rate services (e.g., regular TV programs) and pay-per-use services (e.g., videos-on-demand). While the usage of both services may determine the likelihood that the customer is retained (e.g., Bolton and Lemon 1999; Bolton, Lemon, and Verhoef 2004), usage of SD\�SHU�XVH� VHUYLFHV�� XQOLNH�Á�DW�UDWH� XVDJH�� GLUHFWO\�generates revenues. Therefore, this research seeks to understand the effects of free trials on the service usage behaviors that underlie customer retention and CLV. 2XU�REMHFWLYH�LV�WR�KHOS�PDQDJHUV�XQGHUVWDQG�����WKH�implications of free-trial acquisition for customer value, and (2) how marketing actions could be used to increase CLV. To answer these questions, we develop models for consumers’ usage and retention decisions and calibrate them on consumer panel data from a ODUJH�(XURSHDQ�GLJLWDO�79�SURYLGHU��:H�À�QG�WKDW�WKH�lifetime value of free-trial customers is, on average, 34% lower than that of regular customers. However, free-trial customers are more responsive to marketing activities.

CONTRIBUTION TO EXTANT LITERATURE

The CLV literature has grown strongly in the past decade (e.g., Fader and Hardie 2010; Gupta et al. 2006; Rust, Lemon, and Zeithaml 2004). A recent line of research in this area documents how acquisition mode affects customer value. For instance, research has investigated acquisition tools such as price discounts (e.g., Lewis 2006), contact channels (e.g., Reinartz, Thomas, and Kumar 2005), and word-of-mouth (e.g., Schmitt, Skiera, and Van den Bulte 2011). However, precious little research has looked at how acquisition through free trials� LQÁ�XHQFHV�customer value. Research that studies the effect of trial on customer behavior (e.g., Bawa and Shoemaker 2004; Gedenk and Neslin 1999; Scott 1976), does not compare the customer lifetime value of free-trial customers and regular customers. Another novel aspect is that we study the impact of free trials on customers’ usage behavior. Studying usage behavior is essential for a good understanding of the relation between acquisition mode and CLV, as we explain next.

CONCEPTUAL FRAMEWORK

The conceptual framework in Figure 1 represents the consumer’s decision process for usage levels and UHWHQWLRQ��ZKLFK�MRLQWO\�GULYH�&/9��

Extent of service usageEvery period, consumers decide on their usage levels. We distinguish between two types of service usage RIWHQ�IRXQG�LQ�D�VHUYLFH�VHWWLQJ������XVDJH�RI�WKH�Á�DW�rate service, which is included in the subscription charges, and (2) usage of the pay-per-use service, for which consumers pay per unit of consumption. In

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Table 1Estimation Results (Excerpt)

Estimate (standard error)a

Estimate (standard error)a

 Population Mean

Standard Deviation

Population Mean

Standard Deviation

RetentionFlat-rate service (log zaps)

Trial -.298 .019 Trial -.140 (.008) .116Flat-rate usage .163 .002

Lagged usage of "at-rate .277 (.001) .005

× Trial .020 .006 × Trial -.024 (.002) .029Pay-per-use usage .054 .018

× Trial .015 .016Pay-per-use service (VODs)

Direct marketing .073 .017 Trial .082 (.009) .036

× Trial .032 .017Lagged usage of pay-per-use .026 (.000) .003

Advertising .031 .047 × Trial -.005 (.000) .003× Trial .249 .029a Numbers in bold are signi$cant at the p < .05 level.Notes: 2 × log-likelihood: -624.064. Full estimation results are available upon request.

line with extant literature (Lemon, White, and Winer 2002), we expect consumers to show habit persistence in their preferences for service usage.

Service retentionAt the end of every period, a consumer decides whether to retain the service or not. Consistent with past research, we postulate that the retention decision is driven by previous usage intensity, representing the personal value of the service (e.g., Bolton and Lemon 1999), and by marketing activities (Blattberg, Malthouse, and Neslin 2009; Narayanan and Manchanda 2009; Prins and Verhoef 2007).

Differences between free-trial and regular customersWe expect that the retention decision process for consumers acquired with free trials differs from the decision process of regular customers. One difference is that the baseline usage and retention levels may diverge for the two customer groups. Free trials may attract consumers that a priori have lower valuations for the service (e.g., Anderson and Simester 2004; Neslin and Shoemaker 1989), and who may therefore use the service less intensively and show lower retention rates. Furthermore, we expect that previous usage intensity may be more informative for the retention decision to free-trial customers than to regular customers, as the former customers are likely to evaluate the personal relevance of the service more carefully. We also expect that free-trial customers may respond differently to marketing actions such as direct marketing and advertising. Consumers acquired with free trials may especially seek ways to reduce their uncertainty about the service. Marketing activities, in that respect, remind customers of the EHQHÀWV�RI�XVLQJ�WKH�VHUYLFH�DQG�PD\�WKHUHE\�UHGXFH�perceived risk.

DATA AND METHODOur data comprises marketing efforts and customers’ usage and retention decisions across 24 months for 22,832 customers of a large European interactive TV (iTV) provider. iTV is a technology that enables customers to interact with the TV, e.g., by browsing an electronic program guide or watching video-on-demand (VOD). Our data includes two types of usage: ���� ÁDW�UDWH� XVDJH�� ZKLFK� LV� PHDVXUHG� E\� PRQWKO\�channel zaps, and (2) usage of pay-per-use services, which is measured by the monthly amount of VODs a customer has watched. Further, our dataset contains

information on (1) direct marketing, measured as the number of monthly direct-marketing contacts, and (2) advertising spending, measured as the focal company’s share of voice. The company’s acquisition strategy offers a unique setting to study the impact of free-trial acquisition in a quasi-experimental setting. In an effort to accelerate customer base growth, the company offered free trials for a period of 10 months during the 24-month observational period. At the same time,

the company continued to offer regular subscriptions. Our data set comprises 10,609 free-trial customers and 12,223 regular customers. We model the three dependent variables �L�H��� UHWHQWLRQ�� XVDJH�RI�ÁDW�UDWH� VHUYLFH�� DQG�XVDJH�of pay-per-use service) with a system of equations. Furthermore, we account for heterogeneity by DOORZLQJ� WKH� PRGHO� FRHIÀFLHQWV� WR� YDU\� DFURVV�consumers.

MAIN RESULTS2XU�UHVXOWV�DUH�LQ�7DEOH����:H�ÀQG�WKDW��ZKLOH�IUHH�WULDO�FXVWRPHUV�XVH�WKH�ÁDW�UDWH�FRPSRQHQW�less intensively than customers acquired with regular subscriptions,1 they use the pay-per-use service more intensively. Free-trial customers have a lower retention rate than regular customers, even after controlling for higher defection during the free-trial period. Flat-rate usage and usage of the pay-per-use service are positive drivers of retention. However, relative to regular customers, free-trial customers rely more on these usage components when deciding whether to retain the service or not. Finally, the impact of both direct marketing and advertising on retention is positive,

1 We use p < .05 throughout.

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but free-trial customers are more responsive to both marketing actions. We next illustrate the impact of acquiring customers with free trials on CLV across a 24-month time period. Free-trial customers, on average, are worth 34% less than regular customers (¤321 vs. ¤212, see Figure 2a). The substantial difference in CLV can be explained by free-trial customers’ lower XVDJH�RI�WKH�Á�DW�UDWH�VHUYLFH�DQG��KLJKHU�GLVDGRSWLRQ�rates. Higher usage of the pay-per-use service cannot make up for these negative effects. The company, however, can increase CLV for free-trial customers via its marketing activities. The elasticity of advertising with respect to CLV is substantially higher for free-trial customers than for regular customers: 0.204% versus 0.010% (see Figure 2b). Similarly, customers acquired with free trials are more responsive to direct marketing than regular customers (see Figure 2c).

IMPLICATIONSIn this research, we examine the value implications RI�DFTXLULQJ�FXVWRPHUV�ZLWK�IUHH�WULDOV��2XU�À�QGLQJV�have three important implications. First, customers attracted with free trials tend to generate lower revenues than regular customers. Second, we show that the acquisition mode correlates with a customer’s usage behavior. Utilizing this link may lead to improved decisions. For instance, since past usage levels are stronger drivers of retention decisions for free-trial customers, a drop in usage for a free-trial customer calls for immediate action to avoid the customer to churn. Third, managers need to consider that marketing-mix instruments may differentially impact customers, depending on how they were DFTXLUHG��:H�À�QG�WKDW�D�PDQDJHU�FRXOG�LQFUHDVH�WKH�return on marketing investments by targeting more of the direct-marketing and advertising budget to free-trial customers.

REFERENCES Anderson, E. T. and D. I. Simester (2004), “Long-

Run Effects of Promotion Depth on New Versus Established Customers: Three Field Studies,” Marketing Science, 23 (1), 4-20.

Bawa, K. and R. Shoemaker (2004), “The Effects of Free Sample Promotions on Incremental Brand Sales,” Marketing Science, 23 (3), 345-63.

Blattberg, R. C., E. C. Malthouse, and S. A. Neslin (2009), “Customer Lifetime Value: Empirical Generalizations and Some Conceptual Questions,” Journal of Interactive Marketing, 23 (2), 157-68.

Bolton, R. N. and K. N. Lemon (1999), “A Dynamic Model of Customers’ Usage of Services: Usage as an Antecedent and Consequence of Satisfaction,” Journal of Marketing Research, 36 (2), 171-86.

Bolton, R. N., K. N. Lemon, and P. C. Verhoef (2004), “The Theoretical Underpinnings of Customer Asset Management: A Framework and Propositions for Future Research,” Journal of the Academy of Marketing Science, 32 (3), 271-92.

Fader, P. S. and B. G. S. Hardie (2010), “Customer-Base Valuation in a Contractual Setting: The Perils of Ignoring Heterogeneity,” Marketing Science, 29 (1), 85-93.

Gedenk, K. and S. A. Neslin (1999), “The Role of Retail Promotion in Determining Future Brand Loyalty: Its Effect on Purchase Event Feedback,” Journal of Retailing, 75 (4), 433-59.

Gupta, S., D. Hanssens, B. Hardie, W. Kahn, V. Kumar, N. Lin, N. Ravishanker, and S. Sriram (2006), “Modeling Customer Lifetime Value,” Journal of Service Research, 9 (2), 139-55.

Lemon, Katherine N., Tiffany Barnett White, and Russell S. Winer (2002), “Dynamic Customer Relationship Management: Incorporating Future Considerations into the Service Retention

5

Free-trial customers have a lower retention rate than regular customers, even after controlling for higher defection during the free-trial period. Flat-rate usage and usage of the pay-per-use service are positive drivers of retention. However, relative to regular customers, free-trial customers rely more on these usage components when deciding whether to retain the service or not. Finally, the impact of both direct marketing and advertising on retention is positive, but free-trial customers are more responsive to both marketing actions.

We next illustrate the impact of acquiring customers with free trials on CLV across a 24-month time period. Free-trial customers, on average, are worth 34% less than regular customers (�321 vs. �212, see Figure 2a). The substantial difference in CLV can be explained by free-trial customers’ lower usage of the flat-rate service and higher disadoption rates. Higher usage of the pay-per-use service cannot make up for these negative effects.

The company, however, can increase CLV for free-trial customers via its marketing activities. The elasticity of advertising with respect to CLV is substantially higher for free-trial customers than for regular customers: 0.204% versus 0.010% (see Figure 2b). Similarly, customers acquired with free trials are more responsive to direct marketing than regular customers (see Figure 2c).

Figure 2 CUSTOMER LIFETIME VALUE & RESPONSIVENESS TO MARKETING ACTIVITIES

6. Implications

In this research, we examine the value implications of acquiring customers with free trials. Our findings have three important implications. First, customers attracted with free trials tend to generate lower revenues than regular customers. Second, we show that the acquisition mode correlates with a customer’s usage behavior. Utilizing this link may lead to improved decisions. For instance, since past usage levels are stronger drivers of retention decisions for free-trial customers, a drop in usage for a free-trial customer calls for immediate action to avoid the customer to churn. Third, managers need to consider that marketing-mix instruments may differentially impact customers, depending on how they were acquired. We find that a manager could increase the return on marketing investments by targeting more of the direct-marketing and advertising budget to free-trial customers.

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Decision,” Journal of Marketing, 66 (1), 1-14.Lewis, M. (2006), “Customer Acquisition

Promotions and Customer Asset Value,” Journal of Marketing Research, 43 (2), 195-203.

Narayanan, S. and P. Manchanda (2009), “Heterogeneous Learning and the Targeting of Marketing Communication for New Products,” Marketing Science, 28 (3), 424-41.

Neslin, S. A. and R. W. Shoemaker (1989), “An Alternative Explanation for Lower Repeat Rates after Promotion Purchases,” Journal of Marketing Research, 26 (2), 205-13.

Prins, R. and P. C. Verhoef (2007), “Marketing Communication Drivers of Adoption Timing of a New E-Service among Existing Customers,” Journal of Marketing, 71 (2), 169-83.

Reinartz, W., J. S. Thomas, and V. Kumar (2005), “Balancing Acquisition and Retention Resources WR�0D[LPL]H�&XVWRPHU�3URÀWDELOLW\�µ�Journal of Marketing, 69 (1), 63-79.

Rust, R. T., K. N. Lemon, and V. A. Zeithaml (2004), “Return on Marketing: Using Customer Equity to Focus Marketing Strategy,” Journal of Marketing, 68 (1), 109-27.

Schmitt, Philipp, Bernd Skiera, and Christophe Van den Bulte (2011), “Referral Programs and Customer Value,” Journal of Marketing, 75 (1), 46-59.

Scott, Carol A. (1976), “The Effects of Trial and Incentives on Repeat Purchase Behavior,” Journal of Marketing Research, 13 (3), 263-69.

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Figure 1: Conceptual framework

Self-face concerns H4Visibility of the situation (1=covert, 0 =overt)H2

H5H3

Unethical beliefs:

1. Actively bene!tting2. Passively bene!tting3. No harm/no foul H1

Unethical behavioral intentions

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Miao (Vivian) Li, Masters Student, University of Waikato, New Zealand Harald van Heerde, Professor of Marketing, University of Waikato, New Zealand

ABSTRACTThis study attempts to alleviate important gaps in the literature by examining how self-face concerns affect unethical consumer behavior in a Chinese cultural context. Surprisingly, consumers who rate highly on self-face concerns are found to show more unethical behavioral intentions when their actions are not monitored. .H\ZRUGV�� Consumer unethical beliefs, Self-face concern, Consumer unethical behavioral intentions, Chinese cultural context, Unethical behavior model.

INTRODUCTIONThe concept of ‘face’ is a crucial aspect of Chinese identity. It represents the reputation and credibility one has earned in a social network (Chang and Holt, 1994). Although the concept of ‘face’ was originally developed to explain social interactions and human relationships, it is believed that self-face FRQFHUQV� DUH� SDUWLFXODUO\� VLJQLÀFDQW� LQ� LQÁXHQFLQJ�how the Chinese behave as consumers, especially when they are in situations with ethical issues (Cupach and Metts, 1994; Liu and Su, 2007). In the marketing exchange progress, Chinese consumers might maintain and protect their ‘face’ by avoiding unethical behaviors. In addition, they might maintain separate ethical standards for their public and private lives since ‘face’ is a social construction issue (Ting-Toomey, 1988). Overall, this unique and interesting cultural phenomenon of preserving face provides an important theoretical opportunity to broaden our understanding of consumer ethics and behaviors in a Chinese context.

CONTRIBUTION TO EXTANT LITERATURE

The current literature on unethical consumer behavior has looked at how beliefs about unethical consumer actions affect unethical behavioral intentions. We expand on this literature by examining how self-face concern affects unethical consumer behaviors in a Chinese cultural context. By doing this, we gain insights into consumer ethics as well as consumer behaviors in a market environment which is culturally different from Western countries. In addition, by exploring the role of self-face concern in unethical consumer behaviors, this study broadens the knowledge of how Chinese consumers react and behave with respect to this important cultural element.

CONCEPTUAL FRAMEWORK AND HYPOTHESIS

consumers perceive certain activities to be in the marketing exchange process (Muncy and Vitell). Adopting the theory of reasoned action, unethical beliefs are considered as the moral standard that LQÁXHQFHV� FRQVXPHUV·� LQWHUQDO� EHKDYLRUDO� LQWHQWLRQ�EHIRUH�� GXULQJ� DQG� DIWHU� D� SXUFKDVH� �$M]HQ� DQG�)LVKEHLQ��������$M]HQ��������'H�0RRLM���������7KXV��consumers may avoid performing certain unethical behaviors when these behaviors are evaluated as being wrong in their internal beliefs. Building upon Muncy and Vitell (1992), we posit there are three distinct areas of consumer unethical beliefs: Actively EHQHÀWWLQJ� �L�H��� FRQVXPHUV� SURDFWLYHO\� JDLQLQJ� D�EHQHÀW� DW� WKH� H[SHQVH� RI� WKH� VHOOHU�� H�J�� WDNLQJ� WKH�merchandise away without paying for it), Passively EHQHÀWWLQJ� �L�H��� FRQVXPHUV� SDVVLYHO\� JHWWLQJ� WKH�EHQHÀW�DW�WKH�H[SHQVH�RI�WKH�VHOOHU��H�J���UHFHLYLQJ�WRR�much change without saying anything), and No harm / no foul (i.e. consumers believing certain behaviors

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GR�QRW�KXUW�WKH�SURÀW�RI�VHOOHU��H�J��VSHQGLQJ�RYHU�DQ�hour on different dresses without purchasing any). We posit that: +�� Consumer unethical beliefs are positively related to unethical consumer behavioral intentions.In addition, given the collectivism construction of Chinese culture, individuals with a high level of self-face concern have the intense desire to be respected and admired by others, which may make them more willing to accept ethical principles, further restraining their unethical beliefs (Liao and Wang, 2009). Thus, it is assumed: +�� Self-face concerns reduce consumer unethical beliefs. When confronted with ethical issues in the purchase and consumption process, consumers do not always take the most ethical behaviors (Marks and Mayo 1991). However, in the Chinese cultural context, the concern for self-image perceived by the public can work as a pervasive social sanction, generating social and moral pressures to avoid unethical consumer behaviors (Ho, 1994). Under this pressure, the Chinese consumers are not likely to behave unethically in order to maintain and construct a positive “self” for a particular situation (Cupach and Metts, 1994). Thus, we hypothesize: +��� Self-face concerns reduce unethical consumer behavioral intentions.Further, considering the social and interpersonal aspects of self-face concern, the visibility of the situation (covert vs. overt situation) may moderate the relationship between self-face concern and unethical behavioral intention. That is, if a consumer does not feel monitored, self-face concerns are play a less strong role in reducing unethical behavioral intentions. Therefore, we propose: +���Compared to overt situations, a covert�situation will reduce the negative effect of self-face concerns on unethical behavioral intentions.Moreover, we expect that consumers, in covert situations, are more likely to conduct unethical behaviors while, in overt situations, the intentions of doing behaving unethically may reduce. It is proposed: +�� The more visible the situation, the lower a consumer’s unethical behavioral intentions. Data and method� 7R� PHHW� WKH� REMHFWLYHV� RI� WKLV� UHVHDUFK��we developed a questionnaire, including unethical belief items, self-face concern items and unethical EHKDYLRUDO� LQWHQWLRQ� LWHPV�� 0RUH� VSHFLÀFDOO\�� ZH�

used the items from Muncy and Vitell (1992) for the three domains of consumer unethical beliefs: (1) ´DFWLYHO\�EHQHÀWWLQJ�DW� WKH�H[SHQVH�RI�WKH�VHOOHUµ����LWHPV��� ���� ´SDVVLYHO\� EHQHÀWLQJ� DW� WKH� H[SHQVH� RI�the seller” (2 items); and (3) “no harm/no foul” (2 items). For self-face concerns, we used items from Zane and Yeh (2002). For unethical behavioral intentions, we particularly designed 7 situations. (7 items; Cronbach’s alpha = 0.835). For the visibility of the situation, we used a EHWZHHQ�VXEMHFW�GHVLJQ�E\�PDQLSXODWLQJ�WZR�YHUVLRQV��covert situations (the consumer was not monitored) and overt situations (the consumer was monitored). For example, in the covert situation, one Likert item for unethical belief read like “Breaking a bottle of wine by accident when doing grocery shopping, but nobody notice what happened. Would you walk away and pretend nothing happened?” In the overt situation, it was reformulated as “Breaking a bottle of wine by accident when doing grocery shopping, but some customers notice what happened. Would you walk away and pretend nothing happened?”.� 7ZR� H[SHUWV� LQ� ERWK� ODQJXDJHV� MRLQWO\�translated the questionnaire from English to Chinese. 7KH� UHVHDUFK�ZDV�FRQGXFWHG� LQ�7LDQMLQ�� D� ODUJH�FLW\�in the east of China, over a period of three weeks. Questionnaires were handed out at airports, bus stations and resting areas of the shopping malls where people have more free time to participate in the research. To make sure all information obtained reliable and credible, consumers were asked to complete the questionnaire anonymously and voluntarily. Totally, 300 questionnaires were handed out, with 150 on each version. 219 complete questionnaires were collected: 113 of the overt version and 106 of the covert version.

MAIN RESULTSTo test our hypothesis, four regression models were run: one for each of the three domains of unethical beliefs, and one for unethical intentions. Each of the four models include, besides the hypothesized effects, a dummy for female, because gender was found to KDYH�D�VLJQLÀFDQW�HIIHFW�RQ�FRQVXPHU�XQHWKLFDO�EHOLHIV�in past research (Muncy and Vitell, 1992; Babakus, Cornwell, Mitchell and Schlegelmilch, 2004). Our results support Hypothesis 1, which involves the effect of unethical beliefs on consumer unethical behavioral intentions (ActivBenef, B=.165, p =.071*; PassivBenef, B=.244, p =.002**; NoHarm, B=.117, p =.073*). Thus, consumers with higher level of unethical beliefs are more likely to conduct