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The power of word of mouth (WOM) communication and its influence on consumer decision making is well established in academic literature. The recent adoption of online communication by many consumers has facilitated a fundamental change to the structure of many WOM interactions by exposing consumers to electronic WOM (eWOM) from virtual strangers. The current study seeks to uncover whether traditional findings on social ties and WOM communication hold for eWOM information.

Data were collected from 482 college students with varying levels of expertise with eWOM forums, specifically RateMyProfessors.com in the USA. Participants completed a 20‐question survey related to university professor and class choice.

The study finds that students seeking information on which professor to take weight the information they obtain from eWOM forums to be equally influential in their decision as their own primary experience with the professor. Furthermore, the information gained from the eWOM forum is more influential in their decision than speaking with friends in person (WOM). While existing research suggests that strong tie referral sources are more influential than weak tie information sources on decision making, this research finds that some weak tie information sources are rated as more influential.

A limitation of the study is the focus on one eWOM forum, RateMyProfessors.com. Future research would benefit from expanding the number and type of eWOM forums.

While the emergence of the Internet and social networking has spawned an interest in the overall study of eWOM, this study is the first to evaluate eWOM in the context of tie strength, homophily and decision making. The study also investigates whether existing theories of interpersonal communication hold in an online context.

The current approach to valuing customers is based on the notion of discounted profit generated by the customers over the lifetime of the relationship, also known as customer lifetime value (CLV). However, in the financial services industry, the customers who contribute the most to the profitability of a firm are also the riskiest customers. If the riskiness of a customer is not considered, firms will overestimate the true value of that customer. This paper proposes a methodology to adjust CLV for different types of risk factors and creates a comprehensive measure of risk-adjusted lifetime value (RALTV).

Using data from a major credit card company, we develop a measure of risk adjusted lifetime value (RALTV) that accounts for diverse types of customer risks. The model is estimated using Stochastic Frontier Analysis (SFA).

Major findings indicate that rewards cardholders and affinity cardholders tend to score higher within the RALTV framework than non-rewards cardholders and non-affinity cardholders, respectively. Among the four different modes of acquisition, the Internet generates the highest RALTV, followed by direct mail.

This paper not only controls for different types of consumer risks in the financial industry and creates a comprehensive risk-adjusted lifetime value (RALTV) model but also shows empirically the value of using RALTV over CLV for predicting future performance of a set of customers. Further, we investigate the impact of a firm’s acquisition and retention strategies on RALTV. The measure of risk-adjusted lifetime value is invaluable for managers in financial services.

This study examines the effect of credit card teaser rates on consumer indebtedness and the revenue generated by new customers.

A unique dataset from a national bank in the United States of America is utilized to employ a relatively new method called the covariate balancing propensity score matching, which measures the causal effects of teaser rates.

The results indicate that offering teaser rates improves the revenue generated by customers by indirectly increasing indebtedness. Such offers increase customers' willingness to borrow at regular interest rates that are significantly higher than the teaser rate – the “spillover effects.” Interestingly, customers who pay off their promotional balances before the termination of the promotional period borrow even more at regular rates than customers who do not pay off their balances timely.

The results can assist managers of credit card companies in measuring the value of teaser rates more accurately. Furthermore, the results have implications for public policy aimed at reducing credit card debt by enhancing the understanding of credit card customers' borrowing behavior.

To the authors' knowledge, this is the first study that documents the direct and indirect impacts of teaser rates on credit card customers' borrowing behavior and the resulting bank revenue.

The authors offer a new approach to segment credit card customers by classifying customers into two unobserved (latent) segments: opportunistic and needy.

The authors develop a finite mixture model to estimate customers’ tendency to borrow using the three alternatives available to them—promotional cash advances, regular cash advances and retail balances.

The results support the presence of at least two segments among credit card customers. The authors find that relative to opportunistic individuals, needy customers are typically more sensitive to interest rates. Additionally, the results indicate that offering promotional cash advances to current credit card customers increases their sensitivity to regular interest rates. Furthermore, the findings indicate that needy customers tend to have a higher stickiness in their debt. In the post-estimation analyses, the authors observe that needy customers generate more revenue than opportunistic customers. Interestingly, the bank does not perform well in targeting needy individuals and targets both groups with the same probability.

The authors argue that teaser rates attract at least two segments of borrowers—the “needy” segment, which is more likely to be cash-strapped, and the “opportunistic” segment, which looks at these teaser rates as an opportunity. However, banks do not observe segment membership. Hence, the authors offer a new approach to identifying these segments and show that understanding the behavior of these latent segments could help a bank target profitable customers more effectively.