The increasing emphasis on understanding the antecedents and consequences of customer-to-customer (C2C) interactions is one of the essential developments of customer management in recent years. This interest is driven much by new online environments that enable customers to be connected in numerous new ways and also supply researchers' access to rich C2C data. These developments present an opportunity and a challenge for firms and researchers who need to identify the aspects of C2C research on which to focus, as well as develop research methods that take advantage of these new data. The aim here is to take a broad view of C2C interactions and their effects and to highlight areas of significant research interest in this domain. The authors look at four main areas: the different dimensions of C2C interactions; social system issues related to individuals and to online communities; C2C context issues including product, channel, relational and market characteristics; and the identification, modeling, and assessment of business outcomes of C2C interactions.
Many firms capitalize on their customers' social networks to improve the success rate of their new products. In this article, the authors analyze the dynamic effects of social influence and direct marketing on the adoption of a new high-technology product. Social influence is likely to play a role because the decision to adopt a high-involvement product requires extensive information gathering from various sources. The authors use call detail records to construct ego networks for a large sample of customers of a Dutch mobile telecommunications operator. Using a fractional polynomial hazard approach to model adoption timing and multiple social influence variables, they provide a fine-grained analysis of social influence. They show that the effect of social influence from cumulative adoptions in a customer's network decreases from the product introduction onward, whereas the influence of recent adoptions remains constant. The effect of direct marketing is also positive and decreases from the product introduction onward. This study provides new insights into the adoption of high-technology products by analyzing dynamic effects of social influence and direct marketing simultaneously.
In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging procedure. Bagging consists of averaging the results of multiple models that have each been estimated on a bootstrap sample from the original sample. We test the models using customer data of two firms from different industries, namely the internet service provider and insurance markets. The results show that the classification tree in combination with a bagging procedure outperforms the other three methods. It is shown that the ability to identify high risk customers of this model is similar for the in-period and one-period-ahead forecasts. However, for all methods the staying power is rather low, as the predictive performance deteriorates considerably within a few periods after the estimation period. This is due to the fact that both the parameter estimates change over time and the fact that the variables that are significant differ between periods. Our findings indicate that churn models should be adapted regularly. We provide a framework for database analysts to reconsider their methods used for churn modeling and to assess for how long they can use an estimated model.
Purpose This study aims to study to what extent the helpfulness votes others attach to a review affect a consumer’s perceived helpfulness of that review. In addition, the purpose of this study is to investigate whether this social influence moderates the relationships among several content presentation factors and perceived helpfulness. Design/methodology/approach A choice-based conjoint experiment was carried out in which 201 respondents evaluated different reviews and chose the review they perceive as most helpful. Findings Consumers perceive reviews as more (less) helpful in the presence of clearly valenced positive (negative) helpfulness votes. In addition, helpfulness votes of others diminish the positive impact of structure and the negative impact of spelling errors. Research limitations/implications The experimental setup may limit the external validity of the study. Practical implications Providing a helpfulness button gives firms an instrument to offer content that consumers perceive as more useful and to exert some influence on the effects of content presentation factors on the review’s helpfulness. Social implications Consumers tend to follow other consumers’ opinions without forming their own opinion. Firms could misuse this tendency by hiring people to vote on reviews that are not necessarily helpful for consumers, but are helpful for the firm. Originality/value This study is the first to assess the extent to which social influence affects consumers’ evaluation of reviews. Given that consumers use helpfulness votes to distinguish reviews, it is important to understand to what extent these votes reflect the actual helpfulness of the information in the review and to what extent they reflect previous helpfulness votes.
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