Customer Loyalty Programs and Privacy Concerns

In recent years, loyalty programs have been established allowing the creation of detailed consumer profiles by collecting and processing purchase information. Collecting this information, however, raises privacy concerns of customers. In this work, we provide the results of an empirical study which reveal that privacy concerns have an impact on the probability of participating in loyalty programs. We identify a privacy-sensitive segment of customers using demographic and psychographic data that, in principle, would participate in a loyalty program, however, refrains from doing so because of privacy concerns. Moreover, we found that people participating in customer loyalty programs are more concerned about their privacy than non-participants, which is an interesting though counterintuitive result

InShopnito: an advanced yet privacy-friendly mobile shopping application

Mobile Shopping Applications (MSAs) are rapidly gaining popularity. They enhance the shopping experience, by offering customized recommendations or incorporating customer loyalty programs. Although MSAs are quite effective at attracting new customers and binding existing ones to a retailer's services, existing MSAs have several shortcomings. The data collection practices involved in MSAs and the lack of transparency thereof are important concerns for many customers. This paper presents inShopnito, a privacy-preserving mobile shopping application. All transactions made in inShopnito are unlinkable and anonymous. However, the system still offers the expected features from a modern MSA. Customers can take part in loyalty programs and earn or spend loyalty points and electronic vouchers. Furthermore, the MSA can suggest personalized recommendations even though the retailer cannot construct rich customer profiles. These profiles are managed on the smartphone and can be partially disclosed in order to get better, customized recommendations. Finally, we present an implementation called inShopnito, of which the security and performance is analyzed. In doing so, we show that it is possible to have a privacy-preserving MSA without having to sacrifice practicality

Can consumer privacy concern be a thorn for loyalty programs?

Every day, large amounts of personal information are collected by private companies from consumers through multiple sources. Loyalty programs are one of the most popular tools, used to gather such information. Information that is used to offer more personalised options and to target more effectively their promotions. However, many consumers are still attracted to such programs because of the rewards and other benefits offered. Privacy concerns over loyalty programs seem to take their toll. According to a Colloquy (2015) report the numbers of active members is dropping and one of the main reasons cited in the report is privacy concerns. Declining numbers and increased privacy concerns raise the question of how concerned consumers appreciate the benefits offered by loyalty programs and how their satisfaction and loyalty are affected. Apparently, loyalty programs cannot always guarantee loyalty (Nielsen, 2013) as a large portion of consumers demand better protection of their privacy (Madden, 2014) and decline to subscribe to such programs over privacy concerns (Maritz, 2013). The objectives of this study are firstly to examine the underlying reasons behind consumers’ privacy perceptions and secondly to investigate how such perceptions alter consumers’ appraisal of the benefits offered by the loyalty program as well as satisfaction with the program and consumer loyalty. Based on a review of the relevant literature a set of testable hypotheses was developed. To test the hypothesised relationships, survey data were collected from a sample of 984 consumers through an online panel in US. Structural Equation modelling and mediation analysis were the main statistical techniques used. Analysis revealed a strong effect of privacy perceptions on both the perceived value of the program’s benefits and satisfaction with the program. Results suggested that retailers should place more emphasis on the perceived control of information rather than trying to soften consumers perceptions of the risks related to privacy. Additionally the total effects of perceived privacy to both satisfaction with the program and loyalty are substantial and cannot be ignored by practitioners. One implication is that companies should provide clearer privacy policies, more transparency and more power to their customers. The loyalty program benefit perceptions that are affected more by information control and perceived risk are the symbolic and hedonic benefits. Utilitarian benefits appear to be affected to a lesser extent. In general it was revealed that the total impact of hedonic and symbolic benefits on customer loyalty is significantly higher than that of the utilitarian benefits. Hence, practitioners should look carefully on the structure of the hedonic and symbolic benefits in conjunction to their consumer privacy policy

The effects of loyalty programs on customer satisfaction, trust, and loyalty toward high- and low-end fashion retailers

This study examines the differential effects of the benefits customers receive from a loyalty program (LP) on satisfaction with the LP, trust in the LP, and store loyalty for high- and low-end fashion retailers. With survey data from U.S. LP subscribers, the study tests the relationships using multiple regressions and analysis of covariance. The results show that symbolic benefits are more important for high-end fashion store consumers' satisfaction with the LP; conversely, utilitarian benefits increase consumers' satisfaction with the LP more in low-end fashion retailing, whereas hedonic benefits increase consumers' satisfaction with the LP in both types of retailers. All benefits in both types of retailers affect trust in the LP. Finally, satisfaction with and trust in the LP are important drivers of loyalty to the retailer. The findings have important implications on how managers of high- and low-end fashion retailing can effectively design their LP rewards to maximize loyalty

Do Loyalty Programs Really Enhance Behavioral Loyalty? An Empirical Analysis Accounting for Self-Selecting Members

One of the pressing issues in marketing is whether loyalty programs really enhance behavioral loyalty. Loyalty program members may have a much higher share-of-wallet at the firm with the loyalty program than non-members have, but this does not necessarily imply that loyalty programs are effective. Loyal customers may select themselves to become members in order to benefit from the program. Since this implies that program membership is endogenous, we estimate models for both the membership decision (using instrumental variables) and for the effect of membership on share-of-wallet, our measure of behavioral loyalty. We use panel data from a representative sample of Dutch households who report their loyalty program memberships for all seven loyalty programs in grocery retailing as well as their expenditures at each of the 20 major supermarket chains. We find a small positive yet significant effect of loyalty program membership on share-of-wallet. This effect is seven times smaller than is suggested by a naïve model that ignores the endogeneity of program membership. The predictive validity of the proposed model is much better than for the naïve model. Our results show that creating loyalty program membership is a crucial step to enhance share-of-wallet, and we provide guidelines how to achieve this.Attraction models;Endogeneity;Grocery retailing;Loyalty programs;Tobit-II model

A hybrid strategy for privacy-preserving recommendations for mobile shopping

To calculate recommendations, recommender systems col-lect and store huge amounts of users ’ personal data such as preferences, interaction behavior, or demographic infor-mation. If these data are used for other purposes or get into the wrong hands, the privacy of the users can be com-promised. Thus, service providers are confronted with the challenge of o↵ering accurate recommendations without the risk of dissemination of sensitive information. This paper presents a hybrid strategy combining collaborative filtering and content-based techniques for mobile shopping with the primary aim of preserving the customer’s privacy. Detailed information about the customer, such as the shopping his-tory, is securely stored on the customer’s smartphone and locally processed by a content-based recommender. Data of individual shopping sessions, which are sent to the store backend for product association and comparison with simi-lar customers, are unlinkable and anonymous. No uniquely identifying information of the customer is revealed, making it impossible to associate successive shopping sessions at the store backend. Optionally, the customer can disclose demo-graphic data and a rudimentary explicit profile for further personalization

GROCERY STORE BUYING BEHAVIOR: EVIDENCE FROM LOYALTY PROGRAM DATA

Consumer/Household Economics,

CSI Las Vegas: Privacy, Policing, and Profiteering in Casino Structured Intelligence

This Article argues that the intricate, vast amounts of consumer information compiled through casino structured intelligence require greater protection and oversight in the contexts of both bankruptcy and law enforcement. Section II examines the various types of casino technology and information gathering that casinos perform. Section III considers the available protections of private information in terms of security breaches, law enforcement sharing, and sales in the context of a bankruptcy. Section IV discusses additional safeguards and ethical concerns that should be considered as casinos continue to increase their data mining efforts. Finally, Section V concludes that, minimally, consumers are entitled to more candid disclosures and a meaningful opportunity to protect their own privacy

Effects of customer trust and online experiences in building hospitality brands

Customer trust embodies customer beliefs of actually receiving a promised service and manifestations of consumer’s confidences in an exchange parties reliability and integrity. The study is based on the fact as to how trusts criteria affect online purchase especially in regard to booking and buying the accommodations and also that accommodation providers assume that are very essential for consumers to make the online purchase. In total 150 consumers and 80 hotels owners/operators in India were examined. There are enormous discrepancies between consumers and accommodation providers were searched. Like formal guarantee of providers, security concern, refund of price paid delivery time and information about confirmation and they will switch from one brand to other due to promise breakage, less service quality, high price charged. However, these trust criteria were viewed inconsequential by the accommodation providers. It concluded with vast number of suggestions and recommendations for the accommodation providers need to include in their websites and build reputation and strong brands in the hospitality market

An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection

The biological immune system (BIS) is characterized by networks of cells, tissues, and organs communicating and working in synchronization. It also has the ability to learn, recognize, and remember, thus providing the solid foundation for the development of Artificial Immune System (AIS). Since the emergence of AIS, it has proved itself as an area of computational intelligence. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. The V-Detectors algorithm depends greatly on the random detectors generated in monitoring the status of a system. These randomly generated detectors suffer from not been able to adequately cover the non-self space, which diminishes the detection performance of the V-Detectors algorithm. This research therefore proposed CSDE-V-Detectors which entail the use of the hybridization of Cuckoo Search (CS) and Differential Evolution (DE) in optimizing the random detectors of the V-Detectors. The DE is integrated with CS at the population initialization by distributing the population linearly. This linear distribution gives the population a unique, stable, and progressive distribution process. Thus, each individual detector is characteristically different from the other detectors. CSDE capabilities of global search, and use of L´evy flight facilitates the effectiveness of the detector set in the search space. In comparison with V-Detectors, cuckoo search, differential evolution, support vector machine, artificial neural network, na¨ıve bayes, and k-NN, experimental results demonstrates that CSDE-V-Detectors outperforms other algorithms with an average detection rate of 95:30% on all the datasets. This signifies that CSDE-V-Detectors can efficiently attain highest detection rates and lowest false alarm rates for anomaly detection. Thus, the optimization of the randomly detectors of V-Detectors algorithm with CSDE is proficient and suitable for anomaly detection tasks