Consequences of customer loyalty to the loyalty program and to the company

Gaining customer loyalty is an important goal of marketing, and loyalty programs are intended to help in reaching it. Research on loyalty programs suggests that customers differentiate between loyalty to a company and loyalty to a loyalty program, yet little is known about the consequences of these two types of loyalty. Therefore, our study intends to make two main contributions: (1) improving our understanding of the constructs "program loyalty" and "company loyalty", (2) investigating the relative impact of the two types of loyalty on preference, intention, and purchase behavior for the case of a multi-firm loyalty program. Results indicate that company loyalty influences a customer's choice to visit a particular provider and to prefer it over competitors, but it is not a strong predictor of purchase behavior. Conversely, program loyalty is a far more important driver of purchase behavior. This implies that company loyalty primarily attracts customers to a particular provider and program loyalty ensures that once inside the store, more money is spent. © 2011 Academy of Marketing Science.

The role of the sales employee in securing customer satisfaction

Purpose: Previous research has emphasized the pivotal role that salespeople play in customer satisfaction. In this regard, the relationship between salespeople's attitudes, skills, and characteristics, and customer satisfaction remains an area of interest. The paper aims to make three contributions: first, it seeks to examine the impact of salespeople's satisfaction, adaptive selling, and dominance on customer satisfaction. Second, this research aims to use dyadic data, which is a better test of the relationships between constructs since it avoids common method variance. Finally, in contrast to previous research, it aims to test all of the customers of salespeople rather than customers selected by salespeople. Design/methodology/approach: The study employs multilevel analysis to examine the relationship between salespeople's satisfaction with the firm on customer satisfaction, using a dyadic, matched business-to-business sample of a large European financial service provider that comprises 188 customers and 18 employees. Findings: The paper finds that customers' evaluation of service quality, product quality, and value influence customer satisfaction. The analysis at the selling firm's employee level shows that adaptive selling and employee satisfaction positively impact customer satisfaction, while dominance is negatively related to customer satisfaction. Practical implications: Research shows that customer-focus is a key driver in the success of service companies. Customer satisfaction is regarded as a prerequisite for establishing long-term, profitable relations between company and customer, and customer contact employees are key to nurturing this relationship. The role of salespeople's attitudes, skills, and characteristics in the customer satisfaction process are highlighted in this paper. Originality/value: The use of dyadic, multilevel studies to assess the nature of the relationship between employees and customers is, to date, surprisingly limited. The paper examines the link between employee attitudes, skills, and characteristics, and customer satisfaction in a business-to-business setting in the financial service sector, differentiating between customer- and employee-level drivers of business customer satisfaction.

Identification and analysis of moderator variables:investigating the customer satisfaction-loyalty link

Purpose – Research on the relationship between customer satisfaction and customer loyalty has advanced to a stage that requires a more thorough examination of moderator variables. Limited research shows how moderators influence the relationship between customer satisfaction and customer loyalty in a service context; this article aims to present empirical evidence of the conditions in which the satisfaction-loyalty relationship becomes stronger or weaker. Design/methodology/approach – Using a sample of more than 700 customers of DIY retailers and multi-group structural equation modelling, the authors examine moderating effects of several firm-related variables, variables that result from firm/employee-customer interactions and individual-level variables (i.e. loyalty cards, critical incidents, customer age, gender, income, expertise). Findings – The empirical results suggest that not all of the moderators considered influence the satisfaction-loyalty link. Specifically, critical incidents and income are important moderators of the relationship between customer satisfaction and customer loyalty. Practical implications – Several of the moderator variables considered in this study are manageable variables. Originality/value – This study should prove valuable to academic researchers as well as service and retailing managers. It systematically analyses the moderating effect of firm-related and individual-level variables on the relationship between customer satisfaction and loyalty. It shows the differential effect of different types of moderator variables on the satisfaction-loyalty link.

How habits, social ties, and economic switching barriers affect customer loyalty in contractual service settings

While existing literature acknowledges positive effects of satisfaction and economic switching barriers for building customer loyalty, studies analyzing interactions of these antecedents reveal mixed findings. Prior research does not consider, as antecedents of switching barriers, either habits or social ties that result from shared service-usage within a family or community. This paper contributes to the literature, first, by replicating the effects of satisfaction, economic switching barriers, and their interaction with customer loyalty and word-of-mouth of subscribers to a contractual service. Second, the study empirically tests the role of social ties as a social switching barrier. Third, the study introduces and tests the effects of habits as a precursor of economic and social switching barriers. Results reveal significant positive effects of satisfaction, economic switching barriers, and social ties on customer loyalty and word-of-mouth. Additionally, economic switching barriers and social ties interact significantly with satisfaction and habits act as a precursor of economic switching barriers and social ties.

How employer and employee satisfaction affect customer satisfaction:an application to franchise services

In small-service settings, how do owner satisfaction, front-line employee satisfaction, and customer satisfaction relate to one another? The authors use generalized exchange theory (GET) to examine how satisfaction levels of these three constituents are reciprocated. The authors examine a European franchise system comprising 50 outlets, 933 employees, and 20,742 customers. Their results show two important findings. First, the effect of owner-franchisee's satisfaction on customer satisfaction is fully mediated by front-line employee satisfaction. Thus, managers of a service outlet can strongly impact the satisfaction and behavioral intentions of their customer base, even without direct contact with them. Second, the link between customer satisfaction and purchase intention is moderated by employee satisfaction at an outlet. The link between customer satisfaction and customer purchase intentions is almost twice as strong when employees are satisfied than when they are not. Thus, there is a "doublepositive effect:" not only does higher employee satisfaction at an outlet directly lead to higher customer satisfaction but it also indirectly strengthens the association between customer satisfaction and their repurchase intentions.

Customer equity drivers and future sales

Research on linking operational marketing inputs to customer attitudes and customer behavior has been gaining significance concomitant with the growing recognition that customers are market-based assets. In response to this, researchers and practitioners have proposed several conceptual models. Despite recent advances in research, the results are still inconclusive as to the relationship between customer attitude and future sales. A reason for this could be due to the paucity of studies combining survey-based data with behavioral data to understand better the drivers of customer behavior. With that in mind, the authors investigate the effects of customer perceptions of key marketing actions on customer attitudes and actual customer behavior as reflected by future sales. The authors propose that customer perceptions of value, brand, and relationship—“customer equity drivers”—affect loyalty intentions and future sales. The results of the study, which is based on a sample of 5694 customers of a large European do-it-yourself retailer, suggest that customer equity drivers can significantly predict future sales, even after the authors control for the current sales level.

Customer value based entry decision in International markets the concept of International Added Customer Equity

Market entry decisions are some of a firm's most important long-term strategic choices. Still, the international marketing literature has not yet fully incorporated the idea of relationship marketing in general, and the customer value concept in particular, as a basis for market entry decisions. This article presents some conceptual ideas about a customer value based market selection model. The metric International Added Customer Equity (IACE), a straightforward decision criterion derived from the customer equity concept is presented as an additional decision criterion for export market selection and ultimately market entry.

The relationship between transformational leadership and customer-orientated boundary-spanning behaviours:examining the role of compassion

Customer-oriented boundary-spanning behaviours (COBSBs) are critical to the success of service organisations. Transformational leadership, with its emphasis on the social elements of the leader-subordinate dyad, is a likely antecedent to COBSBs. Similarly, the interpersonal nature of services suggests leader compassion could have a significant effect on the saliency of the relationship between transformational leadership and COBSBs. This paper reports on a study of the moderating effect of leader compassion on the relationship between transformational leadership and COBSBs (service delivery behaviours, internal influence and external representation). Transformational leadership and compassion both have significant and positive influences on COBSBs. However, compassion plays no moderating role. These findings are discussed and avenues for further research are proposed.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Self-training from labeled features for sentiment analysis

Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents.

Incorporating sentiment prior knowledge for weakly supervised sentiment analysis

This article presents two novel approaches for incorporating sentiment prior knowledge into the topic model for weakly supervised sentiment analysis where sentiment labels are considered as topics. One is by modifying the Dirichlet prior for topic-word distribution (LDA-DP), the other is by augmenting the model objective function through adding terms that express preferences on expectations of sentiment labels of the lexicon words using generalized expectation criteria (LDA-GE). We conducted extensive experiments on English movie review data and multi-domain sentiment dataset as well as Chinese product reviews about mobile phones, digital cameras, MP3 players, and monitors. The results show that while both LDA-DP and LDAGE perform comparably to existing weakly supervised sentiment classification algorithms, they are much simpler and computationally efficient, rendering themmore suitable for online and real-time sentiment classification on the Web. We observed that LDA-GE is more effective than LDA-DP, suggesting that it should be preferred when considering employing the topic model for sentiment analysis. Moreover, both models are able to extract highly domain-salient polarity words from text.