Acquiring customers via word-of-mouth referrals

Managers are increasingly using word-of-mouth (WOM) acquisition strategies, such as seeded WOM or referral programs, to acquire new customers. These strategies have proven successful in recruiting customers with higher margin and lower churn probability compared to customers acquired otherwise. However, the question remains to what extent referred customers pass on the referral they received to others—what is their referral value—and what drives this behavior. Constant Pieters and Aurélie Lemmens collect large-scale survey data among U.S. movie viewers and find that exposure to WOM referrals is non-random. Customers who were exposed to WOM referrals are systematically different than customers who were not exposed to WOM referrals. Ignoring this self-selection mechanism leads to an overestimation of the effect of referral exposure on referral value. On average, customer exposure to WOM referrals did not have a significant effect on a customer’s referral value. Furthermore, a moderated mediation analysis was performed to study the process that leads to these effects; in particular the mediation of satisfaction and the moderating effect of the referral match, which captures the extent to which the movie recommendations a customer receives in general fit her tastes. The results reveal that referred customers who receive referrals that do not fit their tastes well (badly matched referrals) end up less satisfied than non-referred customers, leading them to refer less in turn. It is posited that WOM referrals create unrealistic expectations about a movie which are likely to be disconfirmed, leading to lower satisfaction. The mediation of satisfaction explains almost 80 percent of the total effect of WOM referral exposure on referral value. The results suggest that managers should use WOM acquisition strategies cautiously as they may be not as successful in attracting customers with a high referral value as they are in recruiting profitable customers. Moreover, managers should not expect long chains or cascades of referrals as a result of WOM acquisition strategies. Finally, companies should make sure their prospective customers have realistic expectations prior to consumption (for instance by means of information tools), and push referrers not to refer to anyone but to take the recipient’s tastes into account when referring (for instance by means of matching tools)

Consumer confidence in Europe: United in diversity?

The ongoing unification taking place in the European political scene, along with recent advances in consumer mobility and communication technology, raises the question of whether the European Union can be treated as a single market to exploit potential synergy effects from pan-European marketing strategies. Previous research, which mostly used domain-specific segmentation bases, has resulted in mixed conclusions.status: publishe

Batch Mode Active Learning for Individual Treatment Effect Estimation

Field experimentation has become a well-established practice to estimate individual treatment effects. In recent years, the Active Learning (AL) literature has developed methods to optimize the design of field experiments and reduce their cost. In this paper, we propose a novel AL algorithm for individual treatment effect estimation that works in batch mode for cases where the outcomes of an intervention are not immediate. It uniquely combines Expected Model Change Maximization and Bayesian Additive Regression Trees. Our approach (B-EMCMITE) uses the predictive uncertainty around the individual treatment effects to actively sample new units for experimentation and decide which treatment they will receive. We perform extensive simulations and test our approach on semi-synthetic, real-life data. B-EMCMITE outperforms alternative approaches and substantially reduces the number of observations needed to estimate individual treatment effects compared to A/B tests

Six Methods for Latent Moderation Analysis in Marketing Research: A Comparison and Guidelines

It is common in moderation analysis that at least one of the target moderation variables is latent and measured with measurement error. This article compares six methods for latent moderation analysis: multigroup, means, corrected means, factor scores, product indicators, and latent product. It reviews their use in marketing research, describes their assumptions, and compares their performance with Monte Carlo simulations. Several recommendations follow from the results. First, although the means method is the most frequently used method in the review (95% of articles), it should only be used when reliabilities of the moderation variables are close to 1, which is rare. Then, all methods except the multigroup method perform similarly well. Second, the results support using the factor scores method and latent product method when reliabilities are smaller than 1. These methods perform best with parameter and standard error bias less than or equal to 5% under most investigated conditions. Third, specific settings can warrant using the multigroup method (if the moderator is discrete), the corrected means method (if moderation variables are single indicators), and the product indicators method (if indicators are nonnormally distributed). Practical guidelines and sample code for four statistical platforms (SPSS, Stata, R, and Mplus) are provided

Six latent moderation methods

Supplemental material for the article "Six Methods for Latent Moderation Analysis in Marketing Research: A Comparison and Guidelines