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PurposeThe purpose of this paper is to present a comprehensive picture of the innovative efforts undertaken over time to develop the digital technologies for managing the interface between supply chain management and marketing processes and the role they play in sustaining supply chain management-marketing (SCM-M) integration from an information processing point of view.Design/methodology/approachPatent analysis and actual examples are used to carry out this study. In detail, first, the authors identify the subset of enabling technologies pertaining to the fourth industrial revolution (Industry 4.0) that can be considered the most relevant for effective SCM-M integration (i.e. Industrial Internet of Things, Cloud computing, Big Data analytics and customer profiling, Cyber security). Second, the authors carry out a patent analysis aimed at providing a comprehensive overview of the patenting activity trends characterizing the set of digital technologies under investigation, hence highlighting their innovation dynamics and applications.FindingsThis research provides insightful information about which digital technologies may enable the SCM-M integration. Specifically, the authors highlight the role those solutions play in terms of information acquisition, storage and elaboration for SCM-M integration by relying on illustrative actual examples. Moreover, the authors present the organisations more involved in the development of digital technologies for SCM-M integration over time and offer an examination of their technological impact in terms of influence on subsequent technological developments.Originality/valueSo far, much has been said about why marketing and supply chain management functions should be integrated. However, a clear picture of the digital technologies that might be adopted to achieve this objective has yet to be revealed. Thus, the paper contributes to the literature on SCM-M integration and Industry 4.0 by highlighting the enabling technologies for the Industry 4.0 that may particularly serve for managing the SCM-M interface from an information processing perspective.

The media and consumer research groups have been keeping the Millennials in spotlight for many years now; perhaps it is time to turn some of the attention on Gen Z, which began its foray into mainstream consumption. This exploratory study examines the shopping orientation of Gen Z online shoppers using the generational cohort theory (GCT) as a framework and provides insights to e-retailers to understand how this generation approaches the online shopping. The penetration of Internet and accelerated growth of online shopping have enthused the e-retailers to offer a wide range of goods at greater efficiency than the traditional players. By cluster analysis (K-means) of nine online shopping orientation factors (two were eliminated prior due to low factor loading scores), four segments were identified: (a) ‘Economic-quality seekers’, (b) ‘Convenience shoppers’, (c) ‘Deal hunting-convenience seekers’ and (d) ‘Brand and quality conscious shoppers’, and the study profiled each segment based on the demographic data through chi-square analysis. Finally, implications for online retailers and marketing practitioners are enumerated towards the end of the article.

We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers' decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical analysis that was conducted on data regarding purchases of insurance micro-policies. Two aspects are investigated: the propensity to buy an insurance policy and the risk of churn of an existing customer. The results from the analysis reveal that customers can be effectively and quickly grouped according to a similar set of characteristics, which can predict their buying or churn behaviour well.

Customer profiling in e-commerce is a powerful tool that enables organizations to create personalized offers through direct marketing. One crucial objective of customer profiling is to predict whether a website visitor will make a purchase, thereby generating revenue. Machine learning models are the most accurate means to achieve this objective. However, the opaque nature of these models may deter companies from adopting them. Instead, they may prefer simpler models that allow for a clear understanding of the customer attributes that contribute to a purchase. In this study, we show that companies need not compromise on prediction accuracy to understand their online customers. By leveraging website data from a multinational communications service provider, we establish that the most pertinent customer attributes can be readily extracted from a black box model. Specifically, we show that the features that measure customer activity within the e-commerce platform are the most reliable predictors of conversions. Moreover, we uncover significant nonlinear relationships between customer features and the likelihood of conversion.

The efforts of regulators and food industry actors to achieve ambitious European sustainability objectives should not only be based on, but also supported by, consumers’ behavior, since customers’ demand has the ability to determine changes in the whole food system. This paper’s systemic approach to customers’ sustainable food-related habits and opinions during purchase, consumption and waste management offers a comprehensive view of their decision criteria, their motivations and their preferred incentives. Researching the Romanian consumer’s sustainable habits yields some results which confirm findings of previous studies, including customers’ distrust of sustainable labels and ecological products being considered too expensive. Meanwhile, other results offer novel insights on the matter, such as distrust in the European Union food policy and the high importance of proximity both for retailers and for recycling facilities. Four customer profiles with different interests and behaviors were identified: the Principled, adopting many sustainable behaviors out of principle, despite their low level of food expenditures; the Wannabes, adopting some fashionable sustainable habits; and the Privileged and the Sceptics, adopting very few sustainable habits, the first to ensure their social and economic status and the second to save some money.

Customer segmentation and profiling has become an important marketing strategy in most businesses as a preparation for better customer services as well as enhancing customer relationship management. This study presents the segmentation and classification technique for insurance industry via data mining approaches: K-Modes Clustering and Decision Tree Classifier. Data from an insurance company were gathered. Decision Tree Algorithm was applied for customer profile classification comparing two methods which are Entropy and Gini. K-Modes Clustering segmentized the customers into three prominent groups which are “Potential High-Value Customers”, “Low Value Customers” and “Disinterested Customers”. Decision Tree with Gini model with 10-fold cross validation was found as the best fit model with average accuracy of 81.30%. This segmentation would help marketing team of insurance company to strategize their marketing plans based on different group of customers by formulating different approaches to maximize customer values. Customers can receive customization of insurance plans which satisfy their necessity as well as better assistance or services from insurance companies.

We propose a method for customer profiling based on Binary Decision Tree: BDT and k-means clustering with customer related big data for sales prediction; valuable customer findings as well as customer relation improvements. Through the customer related big data, not only sales prediction but also categorization of customers as well as Corporate Social Responsibility (CSR) can be done. This paper describes a method for these purposes. Examples of the analyzed data relating to the sales prediction, valuable customer findings and customer relation improvements are shown here. It is found that the proposed method allows sales prediction, valuable customer findings with some acceptable errors.

Purpose – This purpose of this paper is to investigate the status of Islamic banking in Lebanon, through addressing the perceptions of existing and potential clients. The study has two objectives: one is to identify and measure the factors that clients perceive as important in deciding to patronize an Islamic bank, and the other is to draw a client profile for Islamic banks operating in Lebanon. Design/methodology/approach – The literature review provided the theoretical framework this study builds on. A survey instrument was developed and the data were analyzed using SPSS (19.0). To draw the client profile, the researcher conducted cluster analysis followed by discriminant analysis. To identify and measure the Islamic bank selection criteria, the researcher used factor analysis followed by regression analysis. Findings – Findings show that clients consider five variables in deciding whether or not to patronize Islamic banks. These variables are trust in Islamic banks and their true compliance with Sharia, customers’ familiarity with Islamic modes of finance, cost of financing and other transactions, accessibility of Islamic banks, and the quality of service offered by those banks. The study was also able to delineate the significant attributes of IB clients, raising the issue of changing the target market segment. Research limitations/implications – This study employed a usable sample size of 199 questionnaires collected from one MENA region nation, Lebanon. It may be useful to probe the research questions of this study using a larger sample size collected from several MENA region nations, in order to reach a more validated conclusion. In addition, it may be equally useful to assess other demographic and psychographic variables as distinguishing factors among client clusters, for the purpose of reaching a deeper understanding of Islamic bank clientele in this region. Practical implications – It is suggested that Islamic banks consider the five factors identified in this study, while preparing their marketing strategy, for the purpose of increasing their market share in the non-GCC MENA region. It is also suggested that Islamic banks approach the so far neglected market segments, rather than sticking to their traditional clients. Originality/value – This paper is the first to investigate the status of Islamic banks in Lebanon. The findings of this study will help refocus the marketing strategies of Islamic banks in Lebanon. They may also apply to other developing non-GCC countries in the MENA region.

Malaysia's online retail industry is growing sophisticated for the past years and is not expected to stop growing in the following years. Meanwhile, customers are becoming smarter about buying. Online Retailers have to identify and understand their customer needs to provide appropriate services/products to the demanding customer and attracting new customers. Customer profiling is a method that helps retailers to understand their customers. This study examines the usefulness of the LRFMP model (Length, Recency, Frequency, Monetary, and Periodicity), the models that comprised part of its variables, and its predecessor RFM model using the Silhouette Index test. Furthermore, an automated Elbow Method was employed and its usefulness was compared against the conventional visual analytics. As result, the RM model was selected as the finest model in performing K-Means Clustering in the given context. Despite the unusefulness of the LRFMP model in K-Means Clustering, some of its variables remained useful in the customer profiling process by providing extra information on cluster characteristics. Moreover, the effect of sample size on cluster validity was investigated. Lastly, the limitations and future research recommendations are discussed alongside the discussion to bridge for future works.

Purpose - The paper aims at a multi-faceted review of scholarly work, analyzing the current state of empirical studies dealing with privacy and online social networking (OSN) as well as the theoretical \"puzzle\" of privacy approaches related to OSN usage from the background of diverse disciplines. Drawing on a more pragmatic and practical level, aspects of privacy management are presented as well.Design methodology approach - Based on individual privacy concerns and also publicly communicated threats, information privacy has become an important topic of public and scholarly discussion. Beside diverse positive aspects of OSN sites for users, their information is for example also being used for data mining and profiling, pre-recruiting information as well as economic espionage. This review highlights information privacy mainly from an individual point-of-view, focusing on the usage of OSN sites (OSNs).Findings - This analysis of scholarly work shows the following findings: first, adults seem to be more concerned about potential privacy threats than younger users; second, policy makers should be alarmed by a large part of users who underestimate risks of their information privacy on OSNs; third, in the case of using OSNs and its services, traditional one-dimensional privacy approaches fall short. Hence, findings of this paper further highlight the necessity to focus on multidimensional and multidisciplinary frameworks of privacy, for example considering a so-called \"privacy calculus paradigm\" and rethinking \"fair information practices\" from a more and more ubiquitous environment of OSNs.Originality value - The results of the work presented in this paper give new opportunities for research as well as suggestions for privacy management issues for OSN providers and users.