Multi-tier Loyalty Programs to Stimulate Customer Engagement

Customers differ in their purchase behavior, profitability, attitude toward the firm, and so on. These differences between customers have led to numerous firms introducing multi-tier loyalty programs. A multi-tier loyalty program explicitly distinguishes between customers by means of hierarchical tiers (e.g. Silver, Gold, Platinum) and assigns customers to different tiers based on their past purchase behavior. Next, customers in different tiers are provided varying levels of tangible rewards and intangible benefits, which are potentially powerful instruments to stimulate customer engagement. In this chapter, we focus on the design and effectiveness of such multi-tier loyalty programs. Building on loyalty program and customer prioritization research, we discuss whether, why, and how multi-tier loyalty programs are effective (or not) in influencing customer behavior, thereby enhancing customer engagement and financial performance

Linking relationship marketing and technology acceptance variables to predict customer retention of smart services

Increasingly, manufacturers integrate information and communication technologies into the tangible goods they produce that allow them to provide “smart services”. In contrast to transactional sales for tangible products, manufacturers are now confronted with the challenge to understand customer usage and retention behavior for those smart services. The present study synthesizes an information system (IS) and a marketing perspective on smart services. In a field study conducted jointly with a large European car manufacturer, the authors show that the usage of smart services is best understood when integrating knowledge generated in both disciplines. The authors show that although customer retention can be predicted by behavioral or perceptual variables only, combining attitudinal and behavioral variables increases the prediction accuracy of forecasting models of customer retention and drop-out. Furthermore, they demonstrate the degree to which behavioral and perceptual data is helpful for understanding usage of and retention for smart services and what type of data should be tracked for predicting customer usage and retention of smart services

Behavioral consequences of overbooking service capacity

As a consequence of implementing revenue management systems, many service firms (e.g., airlines, hotels, car rentals) systematically overbook capacity, thus striving to maximize the revenue at one particular point in time (i.e., one flight, one night, and one day). The academic literature has not addressed how customers behaviorally respond to overbooking experiences, such as downgrading, denied service, or upgrading. In this article, the authors use the econometric technique of conditional difference-in-differences analysis to study the effect of such incidences on customer usage patterns in an airline context. They find that customers who experience negative consequences of revenue management significantly reduce the amount of their transactions with the airline, whereas upgraded customers exhibit only weak positive responses. The effects of the negative events are stronger for high-value customer groups, whereas significant effects of positive events can be found only for a low-value customer group. The results suggest the need for a stronger focus on customer reactions to revenue management practices. On a more general level, the study contributes to a more interdisciplinary view of service management by demonstrating the need for a closer interaction between management functions (e.g., marketing and operations) in developing and managing concepts of companywide importance

Lifetime value prediction at early customer relationship stages

Predicting a customer’s future behavior could provide many opportunities for a firm to manage its customer relationships. It could decide whether to concentrate on those customers who have a low predicted lifetime value or on those who are already high-value customers. It could establish direct channels and direct communication with customers who indicate a willingness to conduct more business with the firm. It could also determine what special offers cause a customer to use the firm’s service or product more frequently, and how many customers will be active in subsequent time periods. Customer lifetime value (CLV) is one way to measure the relative attractiveness of a customer or a customer segment. Predicting CLV at very early stages of the customer relationship is of particular importance. Firms could identify customers with a high CLV, even before they have proven to be high-value customers. In this study, author Wangenheim develops a model that aims at predicting number of transactions per period, upgrading behavior, and, subsequently, CLV, based on information that is available to a firm early on in the customer relationship. The model uses data from customer communications, channel choices, the availability of choices from competition, and exhibited transaction behavior. The model tests data from a major European airline (disguised). The results show that it is possible to predict customers’ future behavior, early in the relationship. Regular updating of the CLV estimates improves the accuracy of the prediction. By using share of wallet (SOW) and customer satisfaction data for a subset of the customers analyzed, Wangenheim also shows that customers for which CLV is overestimated conduct a smaller proportion of their business with the firm and are less satisfied than customers for which CLV was underestimated. Hence, the model forecasts can be used to determine which customers are worth focusing o

A Telemonitoring and Hybrid Virtual Coaching Solution “CAir” for Patients with Chronic Obstructive Pulmonary Disease: Protocol for a Randomized Controlled Trial

Background: Chronic obstructive pulmonary disease (COPD) is one of the most common disorders in the world. COPD is characterized by airflow obstruction, which is not fully reversible. Patients usually experience breathing-related symptoms with periods of acute worsening and a substantial decrease in the health-related quality-of-life. Active and comprehensive disease management can slow down the progressive course of the disease and improve patients’ disabilities. Technological progress and digitalization of medicine have the potential to make elaborate interventions easily accessible and applicable to a broad spectrum of patients with COPD without increasing the costs of the intervention. Objective: This study aims to develop a comprehensive telemonitoring and hybrid virtual coaching solution and to investigate its effects on the health-related quality of life of patients with COPD. Methods: A monocentric, assessor-blind, two-arm (intervention/control) randomized controlled trial will be performed. Participants randomized to the control group will receive usual care and a CAir Desk (custom-built home disease-monitoring device to telemonitor disease-relevant parameters) for 12 weeks, without feedback or scores of the telemonitoring efforts and virtual coaching. Participants randomized to the intervention group will receive a CAir Desk and a hybrid digital coaching intervention for 12 weeks. As a primary outcome, we will measure the delta in the health-related quality of life, which we will assess with the St. George Respiratory Questionnaire, from baseline to week 12 (the end of the intervention). Results: The development of the CAir Desk and virtual coach has been completed. Recruitment to the trial started in September 2020. We expect to start data collection by December 2020 and expect it to last for approximately 18 months, as we follow a multiwave approach. We expect to complete data collection by mid-2022 and plan the dissemination of the results subsequently. Conclusions: To our knowledge, this is the first study investigating a combination of telemonitoring and hybrid virtual coaching in patients with COPD. We will investigate the effectiveness, efficacy, and usability of the proposed intervention and provide evidence to further develop app-based and chatbot-based disease monitoring and interventions in COPD

Monte-Carlo based Reduction of Motion in Contrast Agent enhanced MR-Mammographies

Introduction We present an algorithm for fast reduction of motion artifacts in MR images (MRI). The application field in the center of interest is the automatic detection of malignant tissue in contrast agent enhanced MR-Mammographies. Here, a series of up to ten MRI-volumes recorded at discrete time points is taken, describing the signal increase in tissue voxels (volume elements) by contrast agent enhancement (Gd-DTPA). Due to the patient's breathing, motion is induced and by this a dislocation of tissue areas over time. However, critical for a proper tissue classification is that the investigated tissue areas lie as accurately as possible at the same voxel positions of every akquired MRI-volume. Only by this, the signal behaviour of the tissue can be traced properly. Although signal averaging by windowing filters like 2DGaussian filters can reduce classification errors induced by tissue dislocation, the applied window size is limited by the given minimum size of tissue t

Personalization of conversational agent-patient interaction styles for chronic disease management: Two consecutive cross-sectional questionnaire studies

10.2196/26643Journal of Medical Internet Research235e2664