Customer Retention Management Research Articles

Subscriber retention management: SRM framework and future research agenda

Purpose This article aims to systematically review the fragmented but increasingly relevant research field of customer retention management in subscription scenarios, proposing the subscriber retention management framework for retention management in subscription settings and directing future research. Design/methodology/approach The authors systematically reviewed 1,295 articles to offer a comprehensive, unbiased overview on customer retention management in subscription-based services. From 122 selected articles, the authors conducted a descriptive analysis, conceptualized key insights into a novel framework and recognized research gaps. Findings Among 122 articles, 111 focus on private customers across various service sectors, while lacking reflection on new digital industries, reactive retention and the corporate context. The conceptualization of results merges existing insights into seven dimensions, encompassing strategic and tactical aspects and the interplay of controllable and uncontrollable factors. The authors also outline 12 emerging research directions. Research limitations/implications Given the extensive body of literature, the authors were unable to delve into detailed explanations. The findings are limited to management science research sourced from Scopus, adhering to a rigorous filtering process. Sub-domains, such as reactive or business-to-business retention management, remain somewhat provisional due to little results. Practical implications A sustainable subscriber retention strategy hinges on: (1) integrating retention into business strategy, (2) adjusting acquisition and retention tactics to specific markets and (3) using a consistent retention marketing mix considering affective, calculative and habitual commitment factors. Originality/value The authors contribute with a first systematic review of subscription-specific retention management in a rapidly growing area. This results in a novel framework that broadens the understanding of subscriber retention and identifies research gaps.

Hybrid black-box classification for customer churn prediction with segmented interpretability analysis

Customer retention management relies on advanced analytics for decision making. Decision makers in this area require methods that are capable of accurately predicting which customers are likely to churn and that allow to discover drivers of customer churn. As a result, customer churn prediction models are frequently evaluated based on both their predictive performance and their capacity to extract meaningful insights from the models. In this paper, we extend hybrid segmented models for customer churn prediction by incorporating powerful models that can capture non-linearities. To ensure the interpretability of such segmented hybrid models, we introduce a novel model-agnostic approach that extends SHAP. We extensively benchmark the proposed methods on 14 customer churn datasets on their predictive performance. The interpretability aspect of the new model-agnostic approach for interpreting hybrid segmented models is illustrated using a case study. Our contributions to decision making literature are threefold. First, we introduce new hybrid segmented models as powerful tools for decision makers to boost predictive performance. Second, we provide insights in the relative predictive performance by an extensive benchmarking study that compares the new hybrid segmented methods with their base models and existing hybrid models. Third, we propose a model-agnostic tool for segmented hybrid models that provide decision makers with a tool to gain insights for any hybrid segmented model and illustrate it on a case study. Although we focus on customer retention management in this study, this paper is also relevant for decision makers that rely on predictive modeling for other tasks.

Ramping up the food park business: a case study of Urban Chowk

Learning outcomes After completion of the case study, students will be able to critically appraise the strategic growth opportunities of an “eating out” industry brand; evaluate the role of customer relationship and retention management in sustaining a brand; and analyse the importance of developing core competencies for long-term business viability. Case overview/synopsis Since its inception in 2017, Urban Chowk was one of the pioneer places that successfully managed to create a hospitality format called “food and lifestyle park” which combined multiple food brands with good ambience and entertainment, all under the same roof. Although Urban Chowk was one of the popular brands in the given format, its USP was not difficult to challenge or copy. The owner, Mr Kartikey Rajput, anticipated a deluge of similar establishments in the near future. Urban Chowk led the pack in terms of footfalls compared to similar establishments, with Rajput looking to expand his brand beyond Ahmedabad. The case took 2022 as the timeline and brought forth the challenge that Urban Chowk faced. The primary challenge was retaining the existing customer base along with adding newer ones. Second, with the industry being extremely dynamic, it was important to determine how marketing analytics could be used to collect customer data and convert them into repeat customers, thus building a loyal customer base. Complexity academic level This case is applicable for teaching at undergraduate and graduate levels for understanding the concepts of brand development and strategic growth as well as creating and maintaining customer relationships, also helping the students in assimilating these concepts as a part of brand building. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

Proactive customer retention management in a non-contractual B2B setting based on churn prediction with random forests

Customer churn prediction enables companies to target customers at risk with proactive retention measures. We develop a churn prediction model for a non-contractual business-to-business (B2B) wholesale setting and apply it in a field study. Our experiment shows that compared to random targeting, contacting the customers with the highest predicted churn probabilities reduces churn in the population significantly. We demonstrate that this also entails a positive financial impact in terms of revenue development.In addition to validating B2B churn prediction and retention in the field, we contribute to the literature by identifying the most important features. On top of the common recency, frequency and monetary value features, we show that features specific to customer relationship management such as the recency of the last contact with a field representative are important. We provide a concept on how to integrate proactive churn management into operations by leveraging existing customer care processes.

Uplift modeling and its implications for B2B customer churn prediction: A segmentation-based modeling approach

Business-to-business (B2B) customer retention relies heavily on analytics and predictive modeling to support decision making. Given this, we introduce uplift modeling as a relevant prescriptive analytics tool. In particular, the uplift logit leaf model offers a segmentation-based algorithm that combines predictive performance with interpretability. Applied to a real-world data set of 6432 customers of a European software provider, the uplift logit leaf model achieves superior performance relative to three other popular uplift models in our study. The accessibility of output gained from the uplift logit leaf model also is showcased with a case study, which reveals relevant managerial insights. This new tool thus delivers novel insights in the form of customized, global, and segment-level visualizations that are especially pertinent to industrial marketing settings. Overall, the findings affirm the viability of uplift modeling for improving decisions related to B2B customer retention management.

Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling

An important business domain that relies heavily on advanced statistical- and machine learning algorithms to support operational decision-making is customer retention management. Customer churn prediction is a crucial tool to support customer retention. It allows an early identification of customers who are at risk to abandon the company and provides the ability to gain insights into why customers are at risk. Hence, customer churn prediction models should complement predictive performance with model insights. Inspired by their ability to reconcile strong predictive performance and interpretability, this study introduces rule ensembles and their extension, spline-rule ensembles, as a promising family of classification algorithms to the customer churn prediction domain. Spline-rule ensembles combine the flexibility of a tree-based ensemble classifier with the simplicity of regression analysis. They do, however, neglect the relatedness between potentially conflicting model components which can introduce unnecessary complexity in the models and compromises model interpretability. To tackle this issue, a novel algorithmic extension, spline-rule ensembles with sparse group lasso regularization (SRE-SGL) is proposed to enhance interpretability through structured regularization. Experiments on fourteen real-world customer churn data sets in different industries (i) demonstrate the superior predictive performance of spline-rule ensembles with sparse group lasso over a set well yet powerful benchmark methods in terms of AUC and top decile lift; (ii) show that spline-rule ensembles with sparse group lasso regularization significantly outperform conventional rule ensembles whilst performing at least as well as conventional spline-rule ensembles; and (iii) illustrate the interpretable nature of a spline-rule ensemble model and the advantage of structured regularization in SRE-SGL by means of a case study on customer churn prediction for a telecommunications company.

Customer retention management: A complementary use of data mining and soft systems methodology

BACKGROUND: Customer retention and management of customer churn are deemed as among the most significant issues for businesses. Given the fact that customer churn is not typically predictable easily, identifying and analyzing customer churn is necessary for businesses. OBJECTIVE: Therefore, the current research was conducted to employ a complementary approach to identify the reasons influencing customer churn. METHODS: To do so, initially, customers’ data were clustered by recruiting the K-means method. Each cluster represented customers who held similar values and the probability of churn behavior. In the next step, stakeholder groups are identified based on the K-means algorithm. Then, Soft Systems Methodology (SSM) was employed to encapsulate each of the identified interested groups’ world-view to better understand logical reasons for churned customers. Purposeful activity modeling (human activity system) was adopted for each interested group utilizing SSM techniques. RESULTS: Using SSM techniques, purposeful activity modeling (human activity system) for each interested group adopted. Utilizing human activity systems for structuring debate sessions about change actions, short-term and long-term plans have been proposed to maintain and improve customer retention programs. CONCLUSIONS: SSM can be considered as an overarching approach that can afford a better understanding of the processes derived from data mining.

In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions

In today’s turbulent business environment, customer retention presents a significant challenge for many service companies. Academics have generated a large body of research that addresses part of that challenge—with a particular focus on predicting customer churn. However, several other equally important aspects of managing retention have not received similar level of attention, leaving many managerial problems not completely solved, and a program of academic research not completely aligned with managerial needs. Therefore, our goal is to draw on previous research and current practice to provide insights on managing retention and identify areas for future research. This examination leads us to advocate a broad perspective on customer retention. We propose a definition that extends the concept beyond the traditional binary retain/not retain view of retention. We discuss a variety of metrics to measure and monitor retention. We present an integrated framework for managing retention that leverages emerging opportunities offered by new data sources and new methodologies such as machine learning. We highlight the importance of distinguishing between which customers are at risk and which should be targeted—as they are not necessarily the same customers. We identify trade-offs between reactive and proactive retention programs, between short- and long-term remedies, and between discrete campaigns and continuous processes for managing retention. We identify several areas of research where further investigation will significantly enhance retention management.

In Pursuit of Enhanced Customer Retention Management

In today’s turbulent business environment, customer retention presents a significant challenge for many companies. Academics have generated a large body of research that addresses part of that challenge – with a particular focus on predicting customer churn. However, several other equally important aspects of managing retention have not received similar level of attention. This suggests a managerial problem not completely solved, and a program of academic research not completely aligned with managerial needs. Therefore, our goal is to draw on previous research and current practice to provide insights on managing retention and identify areas for future research. This examination leads us to advocate a broad perspective on customer retention. We propose a definition that extends the concept beyond the traditional binary retain/not retain view of retention. We discuss a variety of metrics to measure and monitor retention. We develop a framework for managing retention that leverages emerging opportunities offered by new data sources and new methodologies such as machine learning. We highlight the importance of distinguishing between which customers are at risk and which should be targeted – as they aren’t necessarily the same customers. We identify trade offs between reactive and proactive retention programs, between short-term and long-term remedies, and between discrete campaigns and continuous processes for managing retention. We identify several areas of research where further investigation will significantly enhance retention management.

Customer Retention Management Strategies in Freight Transport Service Industry in Ghana.

This paper reviewed both theoretical and empirical literature on why customers defect in businesses, and roles played by Freight Transport firms to adopt customer retention management strategies to achieve their corporate goals. This paper seeks to explore variables, which may be considered in any Customer Retention Management Strategy and Customer Retention by drawing together strands from various literature on: Strategies and analytical approaches for managing customer defections; Customer defection groups, reasons for their defections, and the role of Life Time Value of Customer (LTVC); Models that can be adopted to manage customer defections and to promote retention; and Factors that constitute actual switching barriers. It is found that freight transport businesses like other services can and should put in place proven marketing strategies to monitor and lessen customer defections. Specifically, freight transport firms in Ghana need to modify their business philosophies by focusing on those customers who leave instead of spending huge costs to attract prospects purposely to replace existing ones.

Dynamic Customer Acquisition and Retention Management

In consulting, finance, and other service industries, customers represent a revenue stream, and must be acquired and retained over time. In this paper, we study the resource allocation problem of a profit maximizing service firm that dynamically allocates its resources toward acquiring new clients and retaining unsatisfied existing ones. The interaction between acquisition and retention in our model is reflected in the cash constraint on total expected spending on acquisition and retention in each period. We formulate this problem as a dynamic program in which the firm makes decisions in both acquisition and retention after observing the current size of its customer base and receiving information about customers in danger of attrition, and we characterize the structure of the optimal acquisition and retention strategy. We show that when the firm's customer base size is relatively low, the firm should spend heavily on acquisition and try to retain every unhappy customer. However, as its customer base grows, the firm should gradually shift its emphasis from acquisition to retention, and it should also aim to strike a balance between acquisition and retention while spending its available resources. Finally, when the customer base is large enough, it may be optimal for the firm to begin spending less in both acquisition and retention. We also extend our analysis to situations where acquisition or retention success rate, as a function of resources allocation, is uncertain and show that the optimal acquisition and retention policy can be surprisingly complex. However, we develop an effective heuristic for that case. This paper aims to provide service managers some analytical principles and effective guidelines on resource allocation between these two significant activities based on their firm's customer base size.

Customer relationship management and customer retention: evidence from a bank in Ghana

This study investigates the relationship between customer retention and customer relationship management CRM constructs represented by five dimensions namely bonding, empathy, reciprocity, trust and responsiveness using data from five branches of a bank in Ghana. Structured questionnaires were used in collecting data from 282 respondents who were customers of these branches. The results indicate the existence of a positive relationship between customer retention and four of the dimensions of customer relationship construct namely bonding, reciprocity, trust and responsiveness. Empathy was removed from the analysis owing to its low reliability value. The results also provide new insights into CRM and customer retention in the banking sector of Ghana. The study also indicates a strong relationship between reciprocity and bonding as well as responsiveness and trust. It is, therefore, important for banks to develop strategies to reciprocate and to be responsive to increase the switching costs of their customers.

Improving customer retention management through cost-sensitive learning

Purpose – Retailers realize that customer churn detection is a critical success factor. However, no research study has taken into consideration that misclassifying a customer as a non-churner (i.e. predicting that (s)he will not leave the company, while in reality (s)he does) results in higher costs than predicting that a staying customer will churn. The aim of this paper is to examine the prediction performance of various cost-sensitive methodologies (direct minimum expected cost (DMECC), metacost, thresholding and weighting) that incorporate these different costs of misclassifying customers in predicting churn. Design/methodology/approach – Cost-sensitive methodologies are benchmarked on six real-life churn datasets from the retail industry. Findings – This article argues that total misclassification cost, as a churn prediction evaluation measure, is crucial as input for optimizing consumer decision making. The practical classification threshold of 0.5 for churn probabilities (i.e. when the churn probability is greater than 0.5, the customer is predicted as a churner, and otherwise as a non-churner) offers the worst performance. The provided managerial guidelines suggest when to use each cost-sensitive method, depending on churn levels and the cost level discrepancy between misclassifying churners versus non-churners. Practical implications – This research emphasizes the importance of cost-sensitive learning to improve customer retention management in the retail context. Originality/value – This article is the first to use the concept of misclassification costs in a churn prediction setting, and to offer recommendations about the circumstances in which marketing managers should use specific cost-sensitive methodologies.

A study on the effect of knowledge management on customer orientation in governmental organization

Article history: Received April 17, 2012 Accepted 11 June 2012 Available online June 12 2012 Knowledge management (KM) plays an important role on increasing organizational performance especially governmental agencies where there are different skills and knowledge within the organization. In this study, we present a study to investigate the effect of KM on having more customer oriented organizations. We use a standard questionnaire among 146 randomly selected employees out of 170 people who work for four governmental organizations in city of Chahbahar, Iran. The collected information is analyzed using structural equation modeling and by validating the results using Cronbach alphas of more than the minimum desired level of 0.70, we determine the positive relationships between KM and three other variables including customer absorption, customer retention and customer relationship management. The results of this survey reveal that an increase of one unit in KM yields almost the same increase in three mentioned variables accordingly. In addition, an increase of one percent in KM will increase customer orientation by almost one percent. © 2012 Growing Science Ltd. All rights reserved.

Insurance Dynamics – A Data Mining Approach for Customer Retention in Health Care Insurance Industry

Abstract Extraction of customer behavioral patterns is a complex task and widely studied for various industrial applications under different heading viz., customer retention management, business intelligence and data mining. In this paper, authors experimented to extract the behavioral patterns for customer retention in Health care insurance. Initially, the customers are classified into three general categories - stable, unstable and oscillatory. To extract the patterns the concept of Novel index tree (a variant of K-d tree) clubbed with K-Nearest Neighbor algorithm is proposed for efficient classification of data, as well as outliers and the concept of insurance dynamics is proposed for analyzing customer behavioral patterns

CUSTOMER EXPERIENCE MANAGEMENT IN HORTICULTURAL VALUE CHAINS

ISHS XXVIII International Horticultural Congress on Science and Horticulture for People (IHC2010): International Symposium on Integrating Consumers and Economic Systems CUSTOMER EXPERIENCE MANAGEMENT IN HORTICULTURAL VALUE CHAINS

Customer retention management in the recession

The purpose of this article is to explore customer retention strategies and tactics implemented by firms in recession. Our investigations show just how big a challenge many organizations face in their ability to manage customer retention effectively. While leading organizations have embedded real-time customer life cycle management, developed accurate early warning systems, price elasticity models and ‘deal calculators’, the organizations we spoke to have only gone as far as calculating the value at risk and building simple predictive models.

Customer retention: A theoretical perspective of service failure and service recovery in the hotel industry

Purpose: The purpose of this paper is to provide a theoretical perspective on customer retention activities associated with service failure management and service recovery as a means to retain customers in the hotel industry - and to improve the competitiveness of a hotel. The research is pursued with the following objectives in mind: To determine the customer retention activities involved in the service failure management needed by hotels to retain customers. To determine the customer retention activities associated with the service recovery that hotels might perform in order to retain customers. Design/methodology/approach: Secondary sources such as textbooks, scientific journals, and previous studies on the subject were used to obtain relevant background information. A literature search of secondary sources was conducted to elucidate questions and areas of enquiry that relate to the research. Findings: The theoretical perspectives on service failure management and service recovery provided in this paper suggest several customer retention activities that might be used by hotels in order to manage service failure and service recovery - and thus become more competitive. Implications: This paper suggests a number of approaches that hotel managers might take to improve service failure management and service recovery. Such activities could assist a hotel in determining where it is lacking and where to improve its performance in these areas. These activities could provide guidance to managers who are dealing with the day-to-day operations of a hotel. They might help to avoid service failures in the first place, and, secondly, to give ideas about service recovery if failures do occur. On a more strategic level, these activities could also assist managers to develop a general customer retention strategy that incorporates service failure management and service recovery. Originality/value: Service failure management and service recovery are viewed as ways to retain customers and to improve the competitiveness of hotels. Service failure and service recovery activities are proposed; these might form part of a framework for customer retention management for the hotel industry in Gauteng, South Africa. Such a framework has not been attempted before. The paper discusses the environment in which hotels find themselves, provides a theoretical background to service failure and service recovery, and suggests strategies that could be put in place by hotels in order to retain customers. Managers of private or owner-managed - or of group or branded hotels - could perform these activities and thus improve their effectiveness.

Customer retention management processes

PurposeCustomer retention has been a significant topic since the mid‐1990s, but little research has been conducted into management processes that are associated with excellent customer retention performance. This research investigates the associations between customer retention outcomes and a number of management processes including customer retention planning, budgeting and accountability and the presence of a documented complaints‐handling process.Design/methodology/approachThis is carried out using a quantitative survey of 170 companies in Australia. Participants represented all major standard industrial classification (SIC) codes.FindingsIt was found that excellence at customer retention is positively and significantly associated with the presence of documented complaints‐handling processes. None of the other variables is significantly associated with the dependent variable.Research limitations/implicationsThis research has limited generalisability to other regions and the self‐report nature of the data is not independently corroborated.Practical implicationsThe research emphasises the importance of developing and implementing documented complaints‐handling processes. Future research should examine whether standardised processes such as those embodied in ISO 10002 are more effective than ad hoc processes.Originality/valueThe major contribution of this paper is the clear link that it establishes between customer retention performance and the presence of a documented complaints‐handling process.

Customer Retention Management in the B2C Electronic Business

Businesses competing in the internet economy are turning their attention and resources towards increasing the retention of their customers and users. This paper examines how customer retention can be achieved and which strategy best fits which business model. The authors based their analysis on theoretical reasoning and interviews with B2C executives. Their findings differentiate the most appropriate strategies for particular business models. For example, building customer trust and convenience are the most appropriate for commerce-based businesses while the offer of free services are better suited to content, context and connection business models.