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One of the most important tasks in marketing is to create and communicate value to customers to drive their satisfaction, loyalty, and profitability. In this study, the authors assume that customer value is a dual concept. First, in order to be successful, firms (and the marketing function) have to create perceived value for customers. Toward that end, marketers have to measure customer perceived value and have to provide customer perceptions of value through marketing-mix elements. Second, customers in return give value through multiple forms of engagement (customer lifetime value, in the widest sense) for the organization. Therefore, marketers need to measure and manage this value of the customer(s) to the firm and have to incorporate this aspect into real-time marketing decisions. The authors integrate and synthesize existing findings, show the best practices of implementation, and highlight future research avenues.

Understanding customer experience and the customer journey over time is critical for firms. Customers now interact with firms through myriad touch points in multiple channels and media, and customer experiences are more social in nature. These changes require firms to integrate multiple business functions, and even external partners, in creating and delivering positive customer experiences. In this article, the authors aim to develop a stronger understanding of customer experience and the customer journey in this era of increasingly complex customer behavior. To achieve this goal, they examine existing definitions and conceptualizations of customer experience as a construct and provide a historical perspective of the roots of customer experience within marketing. Next, they attempt to bring together what is currently known about customer experience, customer journeys, and customer experience management. Finally, they identify critical areas for future research on this important topic.

Banks are engaged in their core business of accepting deposits and lending loans, their activities are regulated and restricted by RBI. Bank's core activities are also not so profitable; hence, their bottom-line is shrinking. Thus, they have to think of other activities and sources of income. Bancassurance is the buzzword in the current financial service market, where the banks are forming joint ventures with insurance companies, to encash the wide customer database and the everlasting trust. The insurance companies get sufficient business and premiums through the collaboration, enhancing their presence and market share. On the other hand, the banks are earning handsome commission income. Today, banks are having more than 50% other income in their balance sheet, by selling insurance, mutual funds, pension plans and unit-linked products. Bancassurance has gained so much momentum that almost all the banks have joined hands with one or the other life and non-life insurance company. Some banks have even adopted organic growth mode by starting their own insurance business like SBI life. Insurance is a service industry; it is there to serve the changing needs of the customers. Insurance is a matter of solicitation and needs a push strategy by the seller to convince the customers. The bank customers are not fully aware of their insurance cover requirements. To make both ends meet and for the mutual co-existence, bancassurance is the answer, to provide tailor-made and customized products for the mutual benefits of customers, insurer and the bank. It has also created a huge wave of recruitment not only for management professionals but also at every level.

Managerial responses to online customer reviews not only affect customers who receive the responses but may also influence subsequent customers who observe the responses. This externality arises because of the public nature of online interactions. However, prior studies were mainly in offline settings where such externality rarely exists. In this study, we assess the magnitude of such externality. Using a difference-in-difference-in-differences framework and matched hotels across two large travel agencies, we find that managerial responses indeed have a significant and positive impact on the volume of subsequent customer reviews. The impact on the review valence is not evident, which can be attributed to the unique design of identity disclosure in our research context. Furthermore, our results suggest nuances that were not known in the prior literature. For example, responding to positive and negative reviews may have different effects on future reviews, and managers should provide detailed responses to negative reviews but brief ones to positive reviews. Our results offer managerial implications to service providers on how to improve customer engagement in the interconnected online environment. The online appendix is available at https://doi.org/10.1287/isre.2018.0781 .

We examine whether customer-base concentration has a differential impact on profitability for firms contracting with major government customers versus firms contracting with major corporate customers. We document that firm profitability increases with the concentration of major government customers, but decreases with the concentration of major corporate customers. We attribute the contrasting results to the differential impact of major government and corporate customers on demand uncertainty. Specifically, firms contracting with major government customers face lower demand uncertainty that enables them to realize more efficiency gains from customer-specific investments, whereas firms contracting with major corporate customers are exposed to higher demand uncertainty that reduces the efficiency of customer-specific investments. Overall, our study suggests that major government customers are unique and important in the composition of customer base, and they impact firm outcomes in a significantly different way than major corporate customers.

Statistical Methods in Customer Relationship Management focuses on the quantitative and modeling aspects of customer management strategies that lead to future firm profitability, with emphasis on developing an understanding of Customer Relationship Management (CRM) models as the guiding concept for profitable customer management. To understand and explore the functioning of CRM models, this book traces the management strategies throughout a customer's tenure with a firm. Furthermore, the book explores in detail CRM models for customer acquisition, customer retention, customer acquisition and retention, customer churn, and customer win back. Statistical Methods in Customer Relationship Management: * Provides an overview of a CRM system, introducing key concepts and metrics needed to understand and implement these models. * Focuses on five CRM models: customer acquisition, customer retention, customer churn, and customer win back with supporting case studies. * Explores each model in detail, from investigating the need for CRM models to looking at the future of the models. * Presents models and concepts that span across the introductory, advanced, and specialist levels. Academics and practitioners involved in the area of CRM as well as instructors of applied statistics and quantitative marketing courses will benefit from this book.

In this study, we highlight the need and develop a framework for customer engagement (CE) by reviewing the marketing literature and analyzing popular press articles. By understanding the evolution of customer management, we develop the theory of engagement, arguing that when a relationship is satisfying and has emotional connectedness, the partners become engaged in their concern for each other. As a result, the components of customer engagement include both the direct and the indirect contributions of CE. Based on the theoretical support, our proposed framework elaborates on the components of CE as well as the antecedents (satisfaction and emotion) and consequences (tangible and intangible outcomes) of CE. We also discuss how convenience, nature of the firm (B2B vs. B2C), type of industry (service vs. product), value of the brand (high vs. low), and level of involvement (high vs. low) moderate the link between satisfaction and direct contribution, and between emotions and indirect contribution of CE, respectively. Further, we show how customer engagement can be gained and how firm performance can be maximized by discussing relevant strategies.

Based on the literature, we present an artificial intelligence marketing (AIM) framework that enables autonomous machines to receive big data and information, use artificial intelligence (AI) to create knowledge, and then disseminate and apply the knowledge to enhance customer relationships in a knowledge-based environment. To develop the AIM framework, we bring together and curate a wide range of relevant literatures including real-life examples and cases, and then understand how these literatures contribute to the framework in this research topic. We explain the AIM framework from the interdisciplinary perspective, which is an important role of both the artificial intelligence and marketing academia. The AIM framework includes three main components, including the pre-processor, the main processor, and the memory storage. The main processor, which is the key component, uses AI to process structured data processed by pre-processor in order to make real-time decisions and reasonings. The AI approach is characterized by its hypothetical abilities, learning paradigms, and operation modes with human. The strategic use of the developed AIM framework based on the literature to enhance customer relationships, including customer trust, satisfaction, commitment, engagement, and loyalty, is presented. Finally, future potential investigations are presented to drive forward this interdisciplinary research topic.