Should I stay or should I go: Key determinants for efficiently retaining a subscribed customer who decided to leave

Subscription-based businesses have exponentially outperformed product-based businesses since 2012, leading to a revenue growth rate seven times higher than S&P 500 companies’ growth in 2020 (Zuora, 2021). Also formerly product-based businesses such as the New York Times have successfully managed the shift towards subscriptions with an increase of 690 percent in digital subscriptions from 2015 till 2020 (New York Times, 2021). At the same time, however, churn rose considerably, too. Digital service subscriptions have reached churn rates of up to 41 percent in 2021 from 29 percent two years before (Zuora, 2021). This recent development increases the meaning of retention management for subscriptions as a central construct in marketing theory (Schweidel, Bradlow and Fader, 2011; MSI, 2020) and among top executives (Rioux, 2020), which has been underrepresented for years (Homburg, 2017).info:eu-repo/semantics/acceptedVersio

New Insights in the Quality-Satisfaction Link : Identifying Asymmetric and Dynamic Effects

This study explores the relationship between service quality and customer satisfaction. Building on existing literature, the link is proposed to be asymmetric in nature. Drawing on customer delight theory and opponent-process theory, we also study the dynamics of the relationship and develop an integrative perspective. Results are obtained by applying dummy variable regression and time-based cohort analysis in two different e-service settings. The findings show that functional-utilitarian quality attributes (efficiency, fulfillment, system availability, and privacy) display habituation effects over time, so that they tend to lose their capability to delight customers. In contrast, hedonistic attributes (website design, enjoyment, and image) seem to be increasingly enjoyed after initial experience with an e-service and develop customer delight capabilities in a later relationship stage. These insights are vital for e-service managers as they help to improve the efficiency of quality investments on the Internet

A model to improve management of banking customers

Purpose – The purpose of this study is to provide a model to assess and classify banking customers based on the concept of Customer Lifetime Value (CLV) in order to determine which kind of customers creates more value to the bank. Design/methodology/approach – The proposed model comprises two sub-models: (sub-model 1) modelling and prediction of CLV in a multiproduct context using Hierarchical Bayesian models as input to (sub-model 2) a value-based segmentation specially designed to manage customers and products using the Latent Class regression. The model is tested using real transaction data of 1,357 randomly-selected customers of a bank. Findings – This research demonstrates which drivers of customer value better predict the contribution margin and product usage for each of the products considered in order to get the CLV measure. Using this measure, the model implements a value-based segmentation, which helps banks to facilitate the process of customer management. Originality/value – Previous CLV models are mostly conceptual, generalization is one of their main concerns, are usually focused on single product categories, and they are not design with a special emphasis on their application as support for managerial decisions. In response to these drawbacks, the proposed model will enable decision-makers to improve the understanding of the value of each customer and their behaviour towards different financial products

A Double Loop Learning Model For Integrated and Proactive Customer Relationship Management

The rapid development of information technology has changed how firms interact with their customers. On one hand, firms are better capable of collecting customer data, and equip themselves with more powerful analytical tools. On the other hand, customers are becoming more sophisticated in their purchase decision making and other non-purchase interactions, which create higher demand uncertainty for the firm. To survive in this complex and dynamic environment, firms need to manage their customer relationships with an integrated and proactive approach. Recent studies in adaptive learning helped the firm to answer the question of How to learn about customers so they can be proactive in their CRM practice. In this study, we introduce the concept of Double Loop Learning, where we added a strategic learning loop to the adaptive learning loop. With this double loop structure, we also answer the questions of Why to learn and What to learn and Who should be learn simultaneously in an integrated framework. We use a Partially Observable Markov Decision Process (POMDP) approach to 1). Generate optimal marketing contact policy which balances exploration (learning how various modes of marketing contacts affect the transition of customer relationship state) and exploitation (maximizing short-term profit), and 2). Assess the Value of Learning (VOL) at individual customer level to give a feedback to the strategic learning loop where we can answer the questions of Why, What to learn at individual customer level. Theoretically, we introduced the concept of Double Loop Learning to marketing literature which is fundamental in that it achieves both effectiveness and efficiency in the marketing strategy development. Methodologically, we adopted a POMDP approach which enables us to access the value of information for connecting two loops in an integrated framework. In the first essay, we did extensive review on the CRM and Adaptive Learning literature, based on which we developed the conceptual framework for Double Loop Learning model. We also developed an analytical model to demonstrate the relationship between the VOL and Dynamic Customer Value (DCV) of the customers. In the second essay, we apply the proposed framework to an IT B2B firm. We show that the firm can achieve value gains by managing VOL and DCV simultaneously

Next-Purchase Prediction Using Projections of Discounted Purchasing Sequences

A primary task of customer relationship management (CRM) is the transformation of customer data into business value related to customer binding and development, for instance, by offering additional products that meet customers’ needs. A customer’s purchasing history (or sequence) is a promising feature to better anticipate customer needs, such as the next purchase intention. To operationalize this feature, sequences need to be aggregated before applying supervised prediction. That is because numerous sequences might exist with little support (number of observations) per unique sequence, discouraging inferences from past observations at the individual sequence level. In this paper the authors propose mechanisms to aggregate sequences to generalized purchasing types. The mechanisms group sequences according to their similarity but allow for giving higher weights to more recent purchases. The observed conversion rate per purchasing type can then be used to predict a customer’s probability of a next purchase and target the customers most prone to purchasing a particular product. The bias– variance trade-off when applying the models to target customers with respect to the lift criterion are discussed. The mechanisms are tested on empirical data in the realm of cross-selling campaigns. Results show that the expected bias–variance behavior well predicts the lift achieved with the mechanisms. Results also show a superior performance of the proposed methods compared to commonly used segmentation-based approaches, different similarity measures, and popular class predictors. While the authors tested the approaches for CRM campaigns, their parameterization can be adjusted to operationalize sequential features of high cardinality also in other domains or business functions

The Strategies of Local Utilities After the Liberalization of the European Energy Sector: Which Is the Emerging Business Model? The Case Study of Italy

The European energy sector has gone through a period of significant change since the 1980s. External factors such as regulation, market competition, and technological innovation, as well as internal factors such as ownership, corporate governance, and culture, have affected the strategic positioning of local utilities providers. In this context, the paper aims at applying a new construct to the public utility sector – the business model meant as a new unit of strategic analysis – in order to better understand the strategic behaviour and competitive positioning of local utilities after the liberalization of the European energy market. As result of a multiple case study analysis, three main business models of local utilities have been outlined: traditional local utility, multiutility company, and global specialist company.Public Service; Energy Sector; Local Utility; Strategic Management; Business Model

The Theoretical Underpinnings of Customer Asset Management

Most research in customer asset management has focused on specific aspects of the value of the customer to the company. The purpose of this article is to propose an integrated framework ? called CUSAMS -- that enables service organizations to comprehensively assess the value of their "customer assets" and to understand the influence of marketing instruments on them. The foundation of the CUSAMS framework is a careful specification of key customer behaviors that reflect the length, depth and breadth of the customer-service provider relationship: duration, usage, and cross-buying. This framework is the starting point for a set of theoretically based propositions regarding how marketing instruments influence customer behavior within the relationship, thereby influencing customer value. Then, building on prior research, we provide two empirical examples of how the CUSAMS framework can be used to conduct financial analyses of the return on investment from marketing expenditures designed to influence behavior and increase the value of the customer base. The framework and propositions provide the impetus for a research agenda that identifies critical issues in customer asset managemen

The Impact of Technovation and Collaboration on Strategic Service Classification in the Digital Economy

Service organizations increasingly organize themselves and operate on a value chain level. This creates important challenges and opportunities, which call for a realignment of strategic focuses, in particular with respect to the impact of technovation on service creation and services modus operandi, their resulting service classification, and the restructuring amongst different service value chain industries. This research builds on a recently developed classification scheme, referred to as the Services Cubicle, that transcends current industry boundaries and includes upcoming service business trends in technovation. The paper subsequently illustrates a variety of service industry examples in order to clarify the resulting service classifications, taking into account deployment of varying degrees of technovation in that industry