6,277 research outputs found

    Machine Learning and AI in Business Intelligence: Trends and Opportunities

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    The integration of machine learning and artificial intelligence (AI) in business intelligence has brought forth a plethora of trends and opportunities. These cutting-edge technologies have revolutionized how businesses analyze data, gain insights, and make informed decisions. One prominent trend is the rise of predictive analytics. Machine learning algorithms can sift through vast amounts of historical data to identify patterns and trends, enabling businesses to make accurate predictions about future outcomes. This empowers organizations to optimize operations, anticipate customer needs, and mitigate risks.  By leveraging business intelligence, companies can uncover hidden patterns, identify opportunities for growth and improvement, optimize business processes, and ultimately make informed decisions that drive their success. Another trend is the adoption of AI-powered chatbots and virtual assistants. The opportunities presented by machine learning and AI in business intelligence are extensive. From automated data analysis and anomaly detection to demand forecasting and dynamic pricing, these technologies empower businesses to optimize processes, reduce costs, and identify new revenue streams. In conclusion, the integration of machine learning and AI in business intelligence offers promising trends and abundant opportunities. By leveraging these technologies, businesses can gain a competitive edge, drive innovation, and unlock new levels of success in the digital era

    How Supervisors Influence Performance: A Multilevel Study of Coaching and Group Management in Technology-Mediated Services

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    This multilevel study examines the role of supervisors in improving employee performance through the use of coaching and group management practices. It examines the individual and synergistic effects of these management practices. The research subjects are call center agents in highly standardized jobs, and the organizational context is one in which calls, or task assignments, are randomly distributed via automated technology, providing a quasi-experimental approach in a real-world context. Results show that the amount of coaching that an employee received each month predicted objective performance improvements over time. Moreover, workers exhibited higher performance where their supervisor emphasized group assignments and group incentives and where technology was more automated. Finally, the positive relationship between coaching and performance was stronger where supervisors made greater use of group incentives, where technology was less automated, and where technological changes were less frequent. Implications and potential limitations of the present study are discussed

    Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks

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    We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns. To econometrically identify the effectiveness of different viral features in creating social contagion, we designed and conducted a randomized field experiment involving the 1.4 million friends of 9,687 experimental users on Facebook.com. We find that viral features generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral features generate a 246% increase in peer influence and social contagion, whereas adding active-personalized viral features generate only an additional 98% increase. Although active-personalized viral messages are more effective in encouraging adoption per message and are correlated with more user engagement and sustained product use, passive-broadcast messaging is used more often, generating more total peer adoption in the network. Our work provides a model for how randomized trials can identify peer influence in social networks

    The Role of the Mangement Sciences in Research on Personalization

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    We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,

    Refocusing Loyalty Programs in the Era of Big Data: A Societal Lens Paradigm

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    Big data and technological change have enabled loyalty programs to become more prevalent and complex. How these developments influence society has been overlooked, both in academic research and in practice. We argue why this issue is important and propose a framework to refocus loyalty programs in the era of big data through a societal lens. We focus on three aspects of the societal lens-inequality, privacy, and sustainability. We discuss how loyalty programs in the big data era impact each of these societal factors, and then illustrate how, by adopting this societal lens paradigm, researchers and practitioners can generate insights and ideas that address the challenges and opportunities that arise from the interaction between loyalty programs and society. Our goal is to broaden the perspectives of researchers and managers so they can enhance loyalty programs to address evolving societal needs

    Strategic Segmentation in Frontline Services: Matching Customers, Employees, and Human Resource Systems

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    This paper examines variation in the use of high involvement work practices in service and sales operations. I argue that the relationship between the customer and frontline service provider is a central feature that distinguishes production-level service activities from manufacturing. In particular, through strategic segmentation, firms are able to segment customers by their demand characteristics and to match the complexity and potential revenue stream of the customer to the skills of employees and the human resource system that shapes the customer-employee interface. Unlike manufacturing, where high involvement systems have emerged in a wide variety of product markets, therefore, service organizations are likely to use high involvement systems only to serve higher value-added customers because of the high costs of these systems and the labor-intensive nature of services. Data from a nationally random sample of 354 call centers in U.S. telecommunications documents this pattern: from classic mass production approaches for back office workers and increasingly for front office residential service agents, to greater involvement for small business service providers, and high involvement practices for middle market service agents
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