Referral Programs and Customer Value

Schmitt, Philipp; Skiera, Bernd; Bulte, Christophe Van den

doi:10.1509/jmkg.75.1.46

Cited by 199 publications

(180 citation statements)

References 28 publications

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“…Villanueva et al [37] show that customers who joined the firm as a result of WOM recommendations of social connections add almost twice as much long-term value to the firm than customers who did not join as a result of WOM. The same was found by Schmitt et al [33]. They concluded that the difference in customer lifetime value between referred (attracted as a result of WOM recommendations) and non-referred customers is at least 16%.…”

Section: Introductionsupporting

confidence: 77%

“…This is driven by the growing acceptance of the fact that people are highly influenced by information received from others [17] and that word-of-mouth (WOM) is the most influential source of information to a customer [21]. Empirical research confirmed that consumers rely heavily on the advice of others in their personal network when making purchase decisions [19,36,32,20,33] and that positive WOM has a positive effect on business outcomes, i.e. sales [31,3].…”

Section: Introductionmentioning

confidence: 99%

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Identifying influencers in a social network: The value of real referral data

Roelens

Baecke

Benoit

2016

Decision Support Systems

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Individuals influence each other through social interactions and marketers aim to leverage this interpersonal influence to attract new customers. It still remains a challenge to identify those customers in a social network that have the most influence on their social connections. A common approach to the influence maximization problem is to simulate influence cascades through the network based on the existence of links in the network using diffusion models. Our study contributes to the literature by evaluating these principles using real-life referral behaviour data. A new ranking metric, called Referral Rank, is introduced that builds on the game theoretic concept of the Shapley value for assigning each individual in the network a value that reflects the likelihood of referring new customers. We also explore whether these methods can be further improved by looking beyond the one-hop neighbourhood of the influencers. Experiments on a large telecommunication data set and referral data set demonstrate that using traditional simulation based methods to identify influencers in a social network can lead to suboptimal decisions as the results overestimate actual referral cascades. We also find that looking at the influence of the two-hop neighbours of the customers improves the influence spread and product adoption. Our findings suggest that companies can take two actions to improve their decision support system for identifying influential customers : (1) improve the data by incorporating data that reflects the actual referral behaviour of the customers or (2) extend the method by looking at the influence of the connections in the two-hop neighbourhood of the customers.

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Section: Introductionsupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Identifying influencers in a social network: The value of real referral data

Roelens

Baecke

Benoit

2016

Decision Support Systems

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“…For example, Verhoef and Donkers (2005) find that acquisition through the Internet leads to higher retention rates than acquisition through direct mail or direct-response commercials. A second set of papers addresses the impact of customer referral (Chan, Wu, and Xie 2011;Schmitt, Skiera, and Van den Bulte 2011). Villanueva, Yoo, and Hanssens (2008), for instance, show that customers acquired through word-of-mouth referral have longer lifetimes.…”

mentioning

confidence: 99%

The Challenge of Retaining Customers Acquired with Free Trials

Datta

Foubert

Heerde

2015

Journal of Marketing Research

114

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Many service firms acquire customers by offering free-trial promotions. However, a crucial challenge is to retain the customers acquired with these free trials. To address this challenge, firms need to understand how free-trial customers differ from regular customers in terms of their decisions to retain the service. This article conceptualizes how marketing communication and usage behavior drive customers’ retention decisions and develops hypotheses about the impact of free-trial acquisition on this process. To test the hypotheses, the authors model a customer's retention and usage decisions, distinguishing usage of a flat-rate service and usage of a pay-per-use service. The model allows for unobserved heterogeneity and corrects for selection effects and endogeneity. Using household panel data from a digital television service, the authors find systematic behavioral differences that cause the average customer lifetime value of free-trial customers to be 59% lower than that of regular customers. However, free-trial customers are more responsive to marketing communication and usage rates, which offers opportunities to target marketing efforts and enhance retention rates, customer lifetime value, and customer equity.

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“…Engaging options proposed in Section 5 show similar characteristics as referral programs studied in [29]. By helping the Cloud provider to better utilize its virtualized hardware, the energy consumption accounted to the users accounts can be reduced.…”

Section: Related Workmentioning

confidence: 94%

“…By helping the Cloud provider to better utilize its virtualized hardware, the energy consumption accounted to the users accounts can be reduced. Research shows that such programs can achieve up to 16% better results [29]. Referrals are often more personal than normal advertisements or SLAs and, therefore, show a high impact on user behavior as showed in [30].…”

Section: Related Workmentioning

confidence: 98%

An architecture to stimulate behavioral development of academic cloud users

Kecskeméti

Ostermann

Prodan

2014

Sustainable Computing: Informatics and Systems

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Academic cloud infrastructures are constructed and maintained so they minimally constrain their users. Since they are free and do not limit usage patterns, academics developed such behavior that jeopardizes fair and flexible resource provisioning. For efficiency, related work either explicitly limits user access to resources, or introduce automatic rationing techniques. Surprisingly, the root cause (i.e., the user behavior) is disregarded by these approaches. This article compares academic cloud user behavior to its commercial equivalent. We deduce, that academics should behave like commercial cloud users to relieve resource provisioning. To encourage commercial like behavior, we propose an architectural extension to existing academic infrastructure clouds. First, every user's energy consumption and efficiency is monitored. Then, energy efficiency based leader boards are used to ignite competition between academics and reveal their worst practices. Leader boards are not sufficient to completely change user behavior. Thus, we introduce engaging options that encourage academics to delay resource requests and prefer resources more suitable for the infrastructure's internal provisioning. Finally, we evaluate our extensions via a simulation using real life academic resource request traces. We show a potential resource utilization reduction (by the factor of at most 2.6) while maintaining the unlimited nature of academic clouds.

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Referral Programs and Customer Value

Cited by 199 publications

References 28 publications

Identifying influencers in a social network: The value of real referral data

Identifying influencers in a social network: The value of real referral data

The Challenge of Retaining Customers Acquired with Free Trials

An architecture to stimulate behavioral development of academic cloud users

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