Linking transaction-specific satisfaction and customer loyalty – The case of casino resorts

This study investigates the relationship between customer satisfaction, delight, place attachment, and loyalty in a casino context. Customer satisfaction in this study refers to customers’ transactional experience with shopping, food and beverage, and gaming within a casino resort. The study examines how each transactional, satisfying, experience affects customer delight and place attachment, which subsequently influences customer loyalty. Customer delight and place attachment are modelled as mediators between satisfaction and loyalty. The results show that each transaction-specific satisfaction is significantly related to customer delight and place attachment with the exception that a positive experience with casino gaming is not related to place attachment. Whilst place attachment has a significant influence on customer loyalty, customer delight has little effect on the loyalty indicators. The study provides a fresh perspective on the customer satisfaction-loyalty relationship. The implications of these findings are highlighted for researchers and practitioners.     

A deep hybrid learning model for customer repurchase behavior

Smartphones have become an integral part of our daily lives, which has led to the rapid growth of the smartphone market. As the global smartphone market tends to remain stable, retaining existing customers has become a challenge for smartphone manufacturers. This study investigates whether a deep hybrid learning approach with various customer-oriented types of data can be useful in exploring customer repurchase behavior of same-brand smartphones. Considering data from more than 74,000 customers, the proposed deep learning approach showed a prediction accuracy higher than 90%. Based on the results of deep hybrid learning models, we aim to provide better understanding on customer behavior, such that it could be used as valuable assets for innovating future marketing strategies.     

Making customer relationship decisions: Analytics v rules of thumb

This article explores how customer management decisions are made in the leading Nordic retail banks and whether these decisions are driven by mainstream analytical approaches to customer lifetime value available through the banks' CRM systems, or by rule of thumb heuristics. The results indicate that the use of managerial heuristics is surprisingly widespread and, counter-intuitively, that rule of thumb heuristic-based decision making frequently outweighs measures such as customer lifetime value with regard to customer management decisions. The implications are considerable because if successful banks are making widespread use of heuristics, managers and academics would benefit from understanding the conditions under which heuristic decision making can be more successful than an analytic approach. This understanding, in turn, may highlight a limitation of CRM systems and point to a more flexible approach to customer management decisions in which experience-based managerial heuristics modify data from formal CRM systems.     

A multi-category customer base analysis

Customer base analysis is an essential tool to measure and develop relationships with customers. While various models have been proposed in a noncontractual setting, they focus primarily on analyzing transactional patterns associated with a single product category or a firm-level activity, such as the times at which purchases are made at a particular retailer. This research proposes a modeling framework for customer base analysis in a multi-category context. Specifically, we model the time between a customer's purchases at the firm and the product categories that comprise her shopping basket arising from multi-category choice decisions. The proposed model uses a latent space approach that parsimoniously captures the dynamics of multi-category shopping behavior due to the interplay between purchase timing and shopping basket composition. We also account for interdependence among multiple categories, temporal dependence across category choices, and latent customer attrition. Using category-level transaction data, we show that the proposed model offers excellent fit and performance in predicting customer purchase patterns across multiple categories. The forecasts and inferences afforded by our model can assist managers in tailoring marketing efforts across categories.     

Employee versus supervisor ratings of performance in the retail customer service sector: Differences in predictive validity for customer outcomes

Based on data from Maxham and Netemeyer [Maxham, J. G. and R. G. Netemeyer (2003). “Firms Reap What They Sow: The Effects of Employee Shared Values and Perceived Organizational Justice on Customer Evaluations of Complaint Handling,” Journal of Marketing, 67, 46-62], the authors present two field samples to examine predictive validity differences of service employee ratings of their performance versus supervisor ratings of employee performance with respect to customer satisfaction and customer likelihood of spreading positive word-of-mouth (WOM) after a service recovery attempt. The results generally show that supervisor ratings are more strongly positively related to customer satisfaction and WOM than are employee ratings of their own performances. The results also show that both supervisor ratings and employee ratings are related to customer satisfaction and WOM in a curvilinear fashion (as well as linear fashion). Employee extra-role performances (toward customers and the firm) show increasing returns at the higher levels of performance, and employee in-role customer performance generally shows a decreasing return at the higher level of customer in-role performance.These results suggest two managerial implications. First, supervisor ratings of customer service employee performances may be the preferred form of measurement for predicting customer outcomes. Second, maximizing in-role performance inputs may have decreasing returns for customer evaluations in the service recovery context; but maximizing extra-role performance inputs may actually “delight” customers, i.e., increasing returns for customer evaluations.     

The effects of loyalty programs on customer lifetime duration and share of wallet

In the retailing sector, consumers typically patronize multiple outlets, which confronts these outlets with an important issue: determining how to gain a greater part of consumer expenditures. One potential avenue is to increase consumer lifetime duration and repeat purchases through loyalty cards. This research, using BehaviorScan single-source panel data, examines the impact of loyalty programs on customer lifetime duration in grocery stores. The findings suggest that loyalty schemes have positive effects on customer lifetimes and share of consumer expenditures. However, multiple loyalty card memberships of geographically close retailers reduce lifetime duration. Furthermore, the higher the share of consumer expenditures in a store, the longer the lifetime duration will be.     

Trustmarks: Strategies for exploiting their full potential in e-commerce

Internet-based commerce has undergone explosive growth over the past decade and is expected to keep growing. With the increasing popularity of online marketplaces, trust is seen as a key foundation for consumers’ willingness to purchase, in particular, from unknown sellers. While trust has been examined in various contexts, limited focus has been placed on the importance of displaying institutional trust assurances such as trustmarks on retailers’ websites. We conducted two studies into how the use of trustmarks impacts consumer trust, consumer risk perceptions, and, consequently, influences consumer purchase intentions. The results of the two studies suggest that the use of trustmarks increases consumer online trust and purchase intentions, as well as reducing their perceived risk. We use these results to inform managerial decision making in e-commerce, particularly for marketers and e-retailers. We provide three important managerial lessons to be learned from our insights. Our implications are important not only for mature e-commerce markets to create a competitive advantage but also for growing and emerging e-commerce markets, where new retailers are trying to establish trust among their consumers to increase market share.     

Understanding success and failure in customer relationship management

Customer Relationship Management (CRM) systems can help organizations manage customer interactions more effectively. Like many new technologies, CRM has been accompanied by vendor hype and stories of implementation failure. Work on critical success factors (CSFs) should encourage more appropriate implementation practice; however many CSF studies conclude with a list of factors but provide little further guidance. In particular, there is a need for stronger theoretical models of the entire CRM innovation process which can be used by managers to understand better the underlying causes of success and failure. This paper adopts a novel approach to this problem by firstly developing a conceptual model of CRM innovation and then converting this model into a dynamic simulation model. Some early simulation results illustrating changes in CRM benefits and organizational support over time are presented together with a discussion of the underlying causes and suggestions for how managers can counteract potential innovation failure.     

Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design

The aesthetic qualities of products are critical factors in achieving higher customer satisfaction. This study presents a robust design approach incorporating the Kano model to obtain the optimal combination of design form elements. This can effectively enhance customer satisfaction and aesthetic product qualities with multiple-criteria characteristics. The Kano model is used to better understand the relationship between performance criteria and customer satisfaction, and to resolve trade-off dilemma in multiple-criteria optimization by identifying the key criteria in customer satisfaction. The robust design approach combines grey relational analysis with the Taguchi method to optimize subjective quality with multiple-criteria characteristics. This simultaneously yields the optimal aesthetic performance and reduces the variations in customer evaluations. Based on Kano model analysis, a weight adjustment process determines the weight of each product criterion for achieving the desired customer satisfaction performance. This process guides the prioritizing of multiple criteria, leading to higher customer satisfaction. A mobile phone design experiment was conducted to verify the benefits of using the proposed integrative approach. Results show that the generated optimal mobile phone design can effectively enhance overall aesthetic performance and customer satisfaction. Although mobile phone designs are the examples of this study, the proposed method may be further used as a universal robust design approach for enhancing customer satisfaction and product quality with multiple-criteria characteristics.     

Perceptions of Chinese restaurants in the U.S.: What affects customer satisfaction and behavioral intentions?

This study investigates American customers’ perceptions of Chinese restaurants in the U.S., using the Importance–Performance Analysis (IPA) approach. This study also examines which attributes of Chinese restaurants influence American customers’ satisfaction and behavioral intentions. The IPA shows that environmental cleanliness and attentive service are two important areas where Chinese restaurateurs can make improvements. In addition, food taste and service reliability appear to be key attributes for Chinese restaurants’ success. Overall, this study indicates that food quality, service reliability and environmental cleanliness are three pivotal attributes to create satisfied customers and positive post-dining behavioral intentions. Chinese restaurant managers should reasonably allocate their limited resources to restaurant attributes based on their contributions to customer satisfaction and behavioral intentions.