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Purpose The main purpose of this conceptual paper is to identify and explain the technique of branding “Halal” as a marketing strategy to reach and capture a certain market segment (niche), particularly Muslims. Design/methodology/approach The approach of this paper is descriptive based on literature survey as well as the author’s own opinion, experience and judgment. Findings In many cases, particularly in food market, the term “Halal” is being used to attract the Muslim customers’ religious feelings rather than providing them cost-effective value products. Research limitations/implications The paper is expected to contribute to the theoretical base, as it focuses on the use of religious sentiments in marketing that is worth further research conduction. Practical implications The arguments of this paper can be used by the producers of “Halal” products in including certain features that might satisfy the demands of religious Muslims regarding food products. Social implications This paper can be contributive for the greater needs regarding societal and Islamic values. Originality/value This paper is one of the initial attempts to highlight the religious feelings to attract a specific market segment.

After deregulation of the airline industry, revenue management has evolved from flight leg and flight segment-based inventory controls to origin and destination inventory controls. Advances in revenue management have always focused on more granular controls to maximize revenues. Revenue management of individual seats is the most granular level of inventory control by customer segment or individual customer. This paper discusses the evolution of revenue management leading up to the inventory control of individual seats.

Firms need to deliver high quality customer service to retain clients and improve performance. For online companies, a key customer segment is the highly technologically skilled millennials. This s...

On the basis of the pattern of organization business-model the author puts forward business-models of the organization of the quadro-sector of economy of knowledge. Mainly, university is an organization of this sector and it carries out education and academic activity. Therefore, a typical business-model was designed for it and it was called 'mirror'. University activity as a business organization has always had certain distinguishing features. These features imply, first of all, that future specialist during the whole period of training acts both as a key resource and a customer and, finally as a value offer. The next important feature is connected with the fact that knowledge is not only transferred in the university but it is developed there. In economy of knowledge the university is assigned a part of a researcher and an impetus to development. At the same time, the university, which can train personnel for other industries and develop knowledge, can also in cooperation with other organizations develop knowledgeintensive products. In this case another business-model is necessary, which was called 'refraction'. Its own business-model was developed for the research organization. The article reveals the content and specific features of each block of advanced business-models. And finally, the author formulated the problem that should be resolved by economic science due to shaping of economy of knowledge.

The paper reviews an assessment regime for its capacity to engage university learners, enabling them to design radically new business offerings. We explore the effect of the briefing approach on defining the customer/offering match. This study is framed by participatory action research, where data draws on two distinctive module deliveries: one where the design brief asks learners to generate the offering first and then shape the customer segment. The second one supplies an archetype and asks learners to define customer first and then develop the offering. Our analysis reveals that learners’ engagement with the design brief prompts an emergence of five patterns of learners’ responses, leading to conclusions that the nature of design brief elements has an impact in shaping the overall learning. Moreover, going from customer to offering appears to generate better iterations between the two, overall leading to learners’ engagement with the process not simply seeking an outcome.

To attract and retain consumers born in the era of social networks and grown up with the smartphones requires marketing and customer relationship management to be reshaped and adapted to their needs, habits, and aspirations. Digital natives expect digital customer experience through all the stages of the customer journey. Besides the challenges common for all industries, for mobile operators this additionally means to satisfy the customer segment which has the most demanding requirements in terms of mobile network and services quality but perceive it as a commodity. This paper addresses the topic of relationship between Generation Z and mobile operators, leveraging on the survey made among post-Millennials in Serbia. It presents their preferences and expectations and outlines the most important implications this might have on mobile operator’s business.

The customer number of Mobile Virtual Network Operators (MVNOs) is increasing in Japan, though the mobile phone market in Japan was mainly shared by three Major Mobile-Carriers (3MMC): docomo, au and SoftBank. The purpose of this study is to understand the preference for 3MMC vs. MVNOs considering the current mobile-phone market. In order to clarify the difference of the characteristics between 3MMC customers and MVNO customers, we classified these two customer segments into two customer segments: stable customers and unstable customers, respectively. Stable customers mean customers who would like to continue to use the same mobile carrier. We analyze the differences between two segments: stable 3MMC customers and stable MVNO customers on the basis of a new original survey conducted in January 2018. And we analyze the differences between iPhone users and Android phone users in each customer segment. Supervised learning models to create differential descriptions of these customer segments are constructed in order to clarify the differences between these segments

In light of the complex customer behaviour and customer journeys in omnichannel retailing, this study aims to investigate customer co-creation behaviour, customer response and customer experiential values as critical aspects of the customer journey, and examine their impact on the level of customer journey satisfaction. The results confirmed the significance of all factors, except information seeking and arousal, in all phases and exploration in the post-purchase phase, on customer journey satisfaction. For a further examination, this study classified the participants into three different segments according to their journey pattern and scrutinised whether the impact of the three predictors of customer journey satisfaction varied across customer segments. The analysis showed that customer segments were fragmented. The impact of customer co-creation behaviour, customer response and customer experiential values on customer journey satisfaction varied across the three customer segments. A heatmap highlighted the most influential factors of each customer segment. The study yielded several theoretical contributions and insights into omnichannel customers for managers.

In this study a firm in a monopoly environment is considered. The firm sells two types of perishable products using a commodity bundling practice. This study aims to find the optimal strategy of selling these products. We determine whether they should be offered separately or in a bundle, define their optimal prices and determine the initial amount of the bundle which should be made from components with limited stocks. A benefit-lost cost in a case that the customer segment does not find their desirable product is considered along with a shortage cost in a case that the customer segment selects a product or the bundle but there is not enough of it to satisfy all of the customer segment demands. In this study several different customer segments with different behaviors and reservation prices are assumed. The problem is solved using the Mixed-Integer Non-Linear Programming solver in LINGO software and a Genetic Algorithm. Finally, the superiority of obtained model and results are presented. This is the first study to locate optimal strategy of selling two types of perishable products using a commodity bundling practice. This study introduces a new mathematical optimization model based on previous gaps. Moreover, it covers the significant gaps of previous studies with respect to both practical and theoretical aspects.

Customers, increasingly, seem concerned about social and environmental issues that often affect their buying behavior and attitudes toward the quality of goods and services purchased. Despite this trend, there exists little empirical research regarding how socially responsible consumers evaluate services and service quality. In this study, we investigate the role that consumer attitudes toward social responsibility play in evaluating service quality. The results of factor analysis show that social responsibility is a salient dimension of service quality and that high socially responsible customers use the concept of social responsibility more pronouncedly than others when evaluating service quality. Our study contributes to the current literature on the evaluation of service quality by a growing customer segment and outlines implications for managers and for future research.

Today, brick-and-mortar retailers with integrated online shops have many alternatives to increase their customers’ shopping satisfaction: They could invest in (for the retailers) new physical store technologies (e.g., beacons or magical mirrors if not implemented up to now), new online shop technologies (e.g., visual tagging or webcams), or in new less technology-based improvement options (e.g. events or product testing). We propose an adapted Kano based stage-gate approach to support multichannel retailers confronted with such selection problems. The approach is applied to a major European sporting goods retailer. 37 improvement options are pre-selected and evaluated by current and potential customers. It can be shown that instore returns, instore services, reserve&collect, click&collect, customization, product testing, and magical mirrors are drivers of satisfaction for the current customers and should be implemented in contrast to, e.g., geofencing or beacons. The potential customers are more technology-savvy, but show similar categorizations as a current customer segment and so confirm the selection.

In recent years, firms launch interactive technologies to engage and communicate with their customers. Branded mobile applications (apps) are one example. In light of this trend, understanding mobile adopters' cross-platform and engagement behavior is essential in managing customer relationships. This research proposes the context complementarity hypothesis and shows that a branded app is complementary to a firm's other engagement platforms such as PC and mobile websites. PC and app users are also more likely to respond to e-mail promotional offers and have higher spending than non-users. These influences differ by customer segment and brand relationship. Specifically, low-tier customers show a more significant increase in cross-platform engagement than customers who are already loyal. On the other hand, high-spending customers that are relatively new to the firm show a higher increase in spending and promotional responses than low-tier customers. In addition to substantive insights, we conclude with managerial implications and future directions regarding branded apps, mobile and digital platforms, and customer relationship management.

The data collected from a firm’s online account enables enterprises to understand their consumers better and accordingly adjust their business processes. The development of effective customer relationship management strategies and the enhancement of consumers’ experience with customer service centers require accurate prediction of future customer telephone queries. In this paper, we leverage the collected big data from customers’ activities at a firm’s online account to estimate the likelihood that an individual customer will phone the firm’s contact centers within the next thirty days. Our predictive modeling approach has two distinguished characteristics: (i) predicting calls at an individual customer level, and (ii) incorporating the big data from online account activities, in addition to the customer’s past telephone queries. The individual-level data used in this study is from contact centers of a major U. S. insurance firm. Various classes of features specifying the customer segment, recency and frequency of customer interactions are considered. Different neural network architectures are investigated to achieve the best prediction accuracy. Out-of-sample performance analyses evince the capability of the developed model to accurately predict future policyholders’ calls at both the individual and aggregate levels.

The availability of big data collected by multichannel contact centers creates opportunities for businesses to more accurately predict future interactions with their customers. This paper presents a data-driven modeling approach to forecast the likelihood of a call arrival by an individual customer within the next thirty days, based on the multichannel data from contact centers. This model incorporates information related to the past Web activities of an individual customer to predict his future telephone queries. Our study relies on big datasets from contact centers of one of the largest U.S. insurance companies. Various characteristics related to the customer segment, recency and frequency of customer interactions, and cross-class features are considered. We find evidence that some of the recent web activities of a policyholder significantly increases the probability that the policyholder would make a telephone call in the next 30 days. In addition, recency and frequency of contacts impact the probability of the policyholder's call, for a specific set of reasons for past contacts.

Improving service quality may increase passengers' satisfaction and enhance the competitiveness of an airline in the market. Importance-performance analysis (IPA) is a simple and effective technique that can assist practitioners in prioritizing passenger attributes when taking steps to enhance service quality and customer satisfaction. However, the importance and performance attributes are not independent of each other when using self-stated importance. A revised IPA was used to measure the importance of the service attribute in this paper, which can prevent customer satisfaction from affecting the importance evaluation. Based on customer segment theory, passengers' evaluations of airline service quality are collected by a survey among business passengers, leisure passengers, high frequency flyers and low frequency flyers. The direction of an effective and appropriate action to improve service quality is proposed and is believed to be conducive for airline managers to achieve a competitive advantage.

Little is known about the effectiveness of casino free-play campaigns, despite hundreds of millions of dollars in annual redemptions. These costly play incentives are awarded to individual players, based largely on management’s evaluation of their historical play. Extant campaign-level research suggests these incentives may not be effective in driving spend per visit, but there has been no attempt to examine efficacy across player tiers (e.g., light, medium, and heavy users). Analysis of 365 days of performance data from a Las Vegas Strip casino produced varied results across tiers, but all tier-level findings indicated a failure to recover the face value of the free-play incentives. While no support was garnered for the house money effect, the results were consistent with the notion of loss aversion. The methodological approach outlined herein provides the means to critically evaluate free-play offers at the tier level, fast-tracking campaign optimization via more targeted revisions.

In marketing analytics, customer segmentation (clustering) divides a customer base into groups of similar individuals, while buyer targeting (classification) identifies promising customers. Both customer segmentation and buyer targeting help the business to improve marketing performances by allocating resources to the most profitable customers. Due to the heterogeneity across the customer groups, some studies have been made on combining the tasks of customer segmentation and buyer targeting for tailored marketing strategies. However, these efforts usually combine these two tasks in a simple step-by-step approach. It is still unclear how to implement these two tasks in a more integrated and optimized way, which is the research objective of this paper. Specifically, we formulate customer segmentation and buyer targeting as a unified optimization problem. Then, the customer segments are adaptively realized during the targeting optimization process. In this way, the integrated approach not only improves the buyer targeting performances but also provides a new perspective of segmentation based on the buying decision preferences of the customers. The unified customer segmentation and buyer targeting method not only quantifies the purchase tendency of a specific customer but also characterizes the buying decision behaviors at the segment level. We also develop an efficient K-Classifiers Segmentation algorithm to solve the unified optimization problem. Moreover, we show that the customer segmentation based on the buying decision preferences can also be consistent with the features on customer profiles. Finally, we have performed the extensive experiments on several real-world Business to Business (B2B) marketing data sets. The results show that our approach offers not only more accurate targeting of promising customers but also meaningful customer segmentation solutions with interpretable buying decision preferences for each customer segment.

Customer targeting for mobile advertisements is a high end exercise in big data. The universe of users is on the order of 300M while active advertising campaigns are typically several hundred in number. Matching users with the campaigns they are most likely to engage with in extreme real-time environments requires adaptive model management, advanced parallel processing hardware and software, and the integration of multiple very large databases. A key component in this overall process is the generation of a mobile device-driven customer targeting profile. We present a dynamic customer profiling technique using latticing and device-histories which maps mobile devices to specific lattices (geographic locations) and tracks user behavior via device-histories. This targeting methodology provides a distinct advantage over existing approaches by profiling on an individual, rather than a customer segment, basis which enables a much finer spatial and temporal parsing of the user marketplace.

Panzani’s appreciation of Muslims’ preferences and tastes in France illustrates the importanttask of accounting for the needs of different ethnic groups, which increasingly form a crucial customer segment. As the marketplace becomes ever more pluralistic, accommodating various minorities and cultural subgroups, marketers increasingly need to be aware of these differences in order to remain viable and competitive players.

Understanding the differences in various types of consumers groups to the complaint resolution process in a banking services industry was the objective of this research. The three groups compared were promoters, passively satisfied and detractors based on Reicheld (2001) framework. The multi-group comparison results indicated that all the three groups were different in their response to the complaint resolution process. The detractors emerged as the most important customer segment in complaint handling process with highest impact on recommendation of bank to the peer group. Implications for complaint management in services industries are discussed as well as addition to the body of knowledge in consumer complaining behaviour.