Explore the vast range of titles available.

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.

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 r

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.

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.

PurposeMany firms are investing in digital services to improve customer experiences. Virtual service agents, or “e-service agents” (“e-agents”) such as chatbots, are examples of these efforts. Chatbots are types of virtual-assistant software programs that interact with users through speech or text. This paper aims to investigate whether the perceived hedonic and utilitarian attributes of chatbots can influence customer satisfaction and, consequently, their relationships with brands.Design/methodology/approachData were collected through a questionnaire-based survey among a sample of Italian consumers. A convenience sampling technique was used. Data were then analyzed through Partial Least Squares Structural Equation Modeling to provide a prediction-oriented model assessment. The findings were then complemented with an importance–performance map analysis (IPMA) to gain more detailed insights and actionable guidelines for managers.FindingsThe findings highlighted that the perceived hedonic and utilitarian attributes of chatbots positively influenced customer satisfaction and improved customer relationships with the brands. However, the IMPA highlighted that the performance levels of two most important attributes – system quality and experience with chatbot – could be improved resulting in additional improvements of customer satisfaction.Practical implicationsThis study suggests the importance of firms’ investments in and adoption of e-agents to strengthen consumer–brand relationships and of considering both the hedonic and utilitarian attributes of their e-agents.Originality/valueThis article attempts to enrich and consolidate the growing body of literature concerning the impacts of new technologies – and, specifically, chatbots – in service marketing.

We propose a novel two-stage data-analytic modeling approach to gamer matching for multiplayer video games. In the first stage, we build a hidden Markov model to capture how gamers' latent engagement state evolves as a function of their game-play experience and outcome and the relationship between their engagement state and game-play behavior. We estimate the model using a data set containing detailed information on 1,309 randomly sampled gamers' playing histories over 29 months. We find that high-, medium-, and low-engagement-state gamers respond differently to motivations, such as feelings of achievement and need for challenge. For example, a higher per-period total score (achievement) increases the engagement of gamers in a low or high engagement state but not those in a medium engagement state; gamers in a low or medium engagement state enjoy within-period score variation (challenge), but those in a high engagement state do not. In the second stage, we develop a matching algorithm that learns (predicts) the gamer's current engagement state on the fly and exploits that learning to match the gamer to a round to maximize game-play. Our algorithm increases gamer game-play volume and frequency by 4%–8% conservatively, leading to economically significant revenue gains for the company. We propose a novel two-stage data-analytic modeling approach combining theories, statistical analysis, and optimization techniques to model player engagement as a function of motivation to maximize customer game-play via matching in the large and growing online video game industry. In the first stage, we build a hidden Markov model (HMM) based on theories of customer engagement and gamer motivation to capture the evolution of gamers’ latent engagement state and state-dependent participation behavior. We then calibrate the HMM using a longitudinal data set, obtained from a major international video gaming company, that contains detailed information on 1,309 randomly sampled gamers’ playing histories over a period of 29 months comprising more than 700,000 unique game rounds. We find that high-, medium-, and low-engagement-state gamers respond differently to motivations, such as feelings of effectance and need for challenge. In the second stage, we use the results from the first stage to develop a matching algorithm that learns (infers) the gamer’s current engagement state “on the fly” and exploits that learning to match the gamer to a round to maximize game-play. Our algorithm increases gamer game-play volume and frequency by 4%–8% conservatively, leading to economically significant revenue gains for the company.

PurposeThis study investigates business model innovation in small- and medium-sized manufacturing enterprises (SMEs) and its impact on firm growth.Design/methodology/approachThe study was based on analyzing data collected through a questionnaire survey. Structural equation modeling was applied to test the hypotheses.FindingsBusiness model innovation has a positive effect on SME growth in the manufacturing sectors. Moreover, growth is also achieved through the indirect effect of business model innovation on customer trust and commitment.Practical implicationsManagers will benefit from understanding how business model innovation can help their companies to overcome resource constraints and achieve sustained growth. When manufacturing SMEs engage in modular or structural changes to their business model, they may find it worthwhile to focus on maintaining a relationship of trust and commitment with their customers.Originality/valueThis study highlights business model innovation as a unique and important, yet underexplored, factor in manufacturing SME growth. The findings also untangle the complex processes of customer relationship management by which business model innovation improves manufacturing competitive advantage for SMEs.

We study the value of dynamic pricing to maximize revenues in queueing systems with price- and delay-sensitive customers. The system queue length is visible so that upon arrival, customers decide to join the system based on the congestion and the price at that time. We analyze this problem in the asymptotic regime of large customer market size and capacity. We find that dynamic pricing performs significantly better than static pricing at mitigating the effect of uncertainty. Asymptotically, the revenue in such systems consists of a positive deterministic component and a negative stochastic component, the latter representing the impact of variability. Static pricing leads to the n 1/2 -scale effect of variability, i.e., the expected steady-state queue length is Kn 1/2 for some K > 0, where n represents the system size. However, dynamic pricing can lower this effect of variability to the n 1/3 -scale. We further show that a simple policy of using only two prices can achieve most of the benefits of dynamic pricing. We also discuss how our results can apply to other dynamic control problems in queueing systems. The e-companion is available at https://doi.org/10.1287/opre.2017.1668 .

Three fundamental questions when operating a service system are (1) how many employees to staff, and (2) how to route work to them, and (iii) how to pay them. These questions have often been studied separately; that is, the queueing and network-design literature that considers staffing and workload routing generally ignores payment, and the literature on employee payment generally ignores issues surrounding staffing and routing. In “Staffing, Routing, and Payment to Trade Off Speed and Quality in Large Service Systems,” D. Zhan and A.R. Ward study how the aforementioned three decisions jointly affect system throughput and the quality of the service delivered when the employers maximize their own payment. They find that the system manager should first solve a joint optimization problem to determine the staffing level, the routing policy, and the service speed, and second, design a payment contract under which the employees work at the desired service speed. Most common queueing models used for service-system design assume that the servers work at fixed (possibly heterogeneous) rates. However, real-life service systems are staffed by people, and people may change their service speed in response to incentives. The delicacy is that the resulting service speed is jointly affected by staffing, routing, and payment decisions. Our objective in this paper is to find a joint staffing, routing, and payment policy that induces optimal service-system performance. We do this under the assumption that there is a trade-off between service speed and quality and that employees are paid based on both. The employees selfishly choose their own service speed to maximize their own expected utility (which depends on the staffing through their busy time). The endogenous service-rate assumption leads to a centralized control problem in which the system manager jointly optimizes over the staffing, routing, and service rate. By solving the centralized control problem under fluid scaling, we find four different economically optimal operating regimes: critically loaded, efficiency driven, quality driven, and intentional idling (in which there is simultaneous customer abandonment and server idling). Then we show that a simple piece-rate payment scheme can be used to solve the associated decentralized control problem under fluid scaling.