87,996 research outputs found

    Purposeful empiricism: how stochastic modeling informs industrial marketing research

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    It is increasingly recognized that progress can be made in the development of integrated theory for understanding, explaining and better predicting key aspects of buyer–seller relationships and industrial networks by drawing upon non-traditional research perspectives and domains. One such non-traditional research perspective is stochastic modeling which has shown that large scale regularities emerge from the individual interactions between idiosyncratic actors. When these macroscopic patterns repeat across a wide range of firms, industries and business types this commonality suggests directions for further research which we pursue through a differentiated replication of the Dirichlet stochastic model. We demonstrate predictable behavioral patterns of purchase and loyalty in two distinct industrial markets for components used in critical surgical procedures. This differentiated replication supports the argument for the use of stochastic modeling techniques in industrial marketing management, not only as a management tool but also as a lens to inform and focus research towards integrated theories of the evolution of market structure and network relationships

    Case studies/Strategic group analyses report of high value organic products

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    This report concerns the analysis of successful high-value products as case studies in each of the three meat categories: chicken, beef and pork. The main objective of our study was to identify strategic groups within the meat market, in which the new, high-value organic meat products were going to perform. The strategic group analysis also serves as a benchmark for determining the competitive situation on the Danish meat market. Hence, we analyse the market performance of the strategic groups and we describe the competition on the market, in order to identify the optimal market position for the new products

    Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

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    Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

    Adding Bricks to Clicks: The Contingencies Driving Cannibalization and Complementarity in Multichannel Retailing

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    This paper empirically explores the contingencies that drive cannibalizing and complementary effects across channels to provide sales forecasting, promotion planning, and customer relationship management guidance to multichannel managers. We investigate three contingencies in a sales analysis of a leading U.S. retailer who adds a new retail store channel to existing catalog and online channels. We show that the emergence and strength of cannibalizing and complementary effects varies over time, across type of channel, and by type of customer, and provide insight into when and where managers can expect these effects to dominate and how to counter cannibalization and promote complementarity across channels. We find that opening retail stores cannibalizes sales in the catalog and online channels in the short term, but produces complementary effects in both channels in the long term; cannibalization is magnified in the catalog channel, while complementarity is magnified in the online channel. Customer analysis suggests that opening retail stores paves the way for higher rates of customer acquisition and higher rates of repeat purchasing among existing customers in the direct channels in the long term.Multichannel Retailing, Channels of Distribution, Direct Marketing, E-commerce, Channel Management

    Perceived congruence and online loyalty as segmentation variables in multichannel retailing: a comparison between appparel and electronics

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    As the interest of the literature on congruity between offline and online stores is relatively recent, empirical evidence is required to help marketing managers choose the most effective ways of contributing to the formation of consistent offerings as well as their contribution to generate customer loyalty. This study examines whether congruity can help to identify segments of heterogeneous consumers that differ significantly regarding these variables as well as other constructs related to the customer relationship with the retailer. The study attempts to identify which congruity attribute(s) are most relevant for differentiating customers by their loyalty towards the online store, so that retailers can design strategies for improving congruity between physical and online stores, and ultimately, increase online store loyalty

    Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

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    Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

    Loyalty Programmes: Practices, Avenues and Challenges

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    <div align=justify>Complexity of modern business requires managers to strive for innovative strategies to acquire and retain customers in any product market field. As acquiring new customers is getting costlier day by day, business organizations have offered continuity/loyalty programmes to retain/reward existing customers and maintain relationships. The premise of CRM is that once a customer is locked in, it will be advantageous to both the organization as well as customer to maintain relationships and would be a win-win situation for both. Consumers find it beneficial to join such programmes to earn rewards for staying loyal. Through loyalty programmes, firms can potentially gain more repeat business, get opportunity to cross-sell and obtain rich customer data for future CRM efforts (Yuping Liu, 2007). This paper, exploratory in nature, attempts to provide a conceptual overview of Loyalty in organized retail sector, outlines practices of grocery retail outlets in Ahmedabad, the largest city in the state of Gujarat and the seventh-largest urban agglomeration in India, with a population of 56 lakhs (5.6 million). It also throws light on consumer expectations, perceptions and problems faced through indepth exploration. Based on literature review and environment in India, an emerging economy, it attempts to predict future of such programmes specifically in Indian organised retail sector and discusses managerial challenges of managing loyalty programmes and provides agenda for future research directions.</div>
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