Estimating cellphone providers' customer equity

This paper provides insights about how customer equity estimates can help businesses monitor the competition as well as aid managers in making their marketing investment decisions, and how companies can employ their marketing investments to maximize current and future yield/returns. The article concerns itself with the current offer of cellphone providers and their main products. The research includes survey data through interviews with 302 cellphone users of Sao Paulo, Brazil. The study uses this data combined with a number of economic assumptions and a financial marketing model to create an insight in customer equity values of cellphone providers in the region. The scenario dated October 2005 is that the estimated customer equity of the service provider Vivo is, respectively, 93 and 91% larger than those of competing providers Claro and TIM. The research underlines that on average the customer equity flowing from the post-paid segment is 3.5 times larger than that of the pre-paid. In addition to these results the study provides the customer lifetime value (CLV) estimates for Claro's, TIM's and Vivo's pre- and post-paid customers and analyzes the retention and loss figures of CLV. Also a discussion follows of the implications that these values will likely have for the companies' marketing strategy.     

Measuring customer equity and managing the customer asset: an explanatory study in the Australian hotels

Although there are a range of conceptual and theoretical studies about customer equity (CE) measurement and management in the literature, there are few empirical studies where these two concepts are examined together. This paper focuses on the need for a more synthesised approach and addresses calls for more research into this specific area of marketing management. A conceptual framework was developed that synthesises both customer lifetime value and customer relationship management from a customer equity management (CEM) perspective. The framework was used to empirically examine how four CE strategies, together with a firm’s use of the two types of customer data (aggregated and disaggregated), impact on CEM outcomes. Data were collected from 114 hotel managers in Australia and analysed through a decision tree methodology. The results show that hotels are utilising their CE strategies for acquisition purposes more than retention purposes (customer asset management), which overall appears to satisfice more than maximise their hotel’s capacity in achieving effective CE outcomes. The study’s contribution to theory is through empirically testing a framework that combines two discrete theoretical concepts in one study to provide insights to guide future research.     

The role of channel quality in customer equity management

Maximization of customer equity is a core objective of customer–company relationship management. We present an extended model of customer equity for determining the optimal allocation of marketing resources across acquisition and retention activities. Focusing on the negative relationship between acquisition and retention, we motivate channel quality as a relevant decision variable, explicate its role in the model, and demonstrate the existence of an optimal value. In addition, rather than making concavity assumptions about acquisition and retention rate response curves, we use the flexible ADBUDG model (Little, JDC, Models and Managers: the Concept of a Decision Calculus. Manag Sci 1970; 16(8): 466–484.), which allows for both S-shaped and strictly-concave relationships, and parameterize it using decision calculus. We show how to estimate and apply the model and then provide sensitivity analyses with respect to changes in the true values of model parameters as well as inaccuracy in managerial inputs. We conclude by comparing our model with extant models and discussing the implications of our research.     

A generalized framework for estimating customer lifetime value when customer lifetimes are not observed

Measuring customer lifetime value (CLV) in contexts where customer defections are not observed, i.e. noncontractual contexts, has been very challenging for firms. This paper proposes a flexible Markov Chain Monte Carlo (MCMC) based data augmentation framework for forecasting lifetimes and estimating customer lifetime value (CLV) in such contexts. The framework can be used to estimate many different types of CLV models—both existing and new. Models proposed so far for estimating CLV in noncontractual contexts have built-in stringent assumptions with respect to the underlying customer lifetime and purchase behavior. For example, two existing state-of-the-art models for lifetime value estimation in a noncontractual context are the Pareto/NBD and the BG/NBD models. Both of these models are based on fixed underlying assumptions about drivers of CLV that cannot be changed even in situations where the firm believes that these assumptions are violated. The proposed simulation framework—not being a model but an estimation framework—allows the user to use any of the commonly available statistical distributions for the drivers of CLV, and thus the multitude of models that can be estimated using the proposed framework (the Pareto/NBD and the BG/NBD models included) is limited only by the availability of statistical distributions. In addition, the proposed framework allows users to incorporate covariates and correlations across all the drivers of CLV in estimating lifetime values of customers.

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Using customer lifetime value to plan optimal promotions

The purpose of this study is to develop a methodology to guide managers in determining the optimal promotion campaigns to be directed towards different market segments in order to maximize the value of customers. For the purposes of this study, a two-step methodology is used, based on stochastic dynamic programming and the classification and regression tree. This methodology groups the customers according to their value. Within this framework, an experiment is conducted in which each of the different promotion campaigns is assigned to different randomly selected groups. The impact of each type of promotion on each type of market segment is analysed in order to find the optimal promotion campaigns appropriate for each. In contrast to previous research, this study takes into account a firm that provides more than one specific type of product or service. In addition, it analyses the impact of widely used types of promotion campaigns compared with the narrow scope of those investigated in previous studies. Therefore, this research presents important insights into managing relations with the customers in a more interactive and profitable way.     

An empirical study of dynamic customer relationship management

We apply the Gönül and Shi (1998) approach to the analysis of the optimal messaging and pricing policy mix by studying the past transaction patterns between a local supermarket and its consumers. We develop a dynamic customer relationship management model and investigate the relationship between customer utility and purchasing frequency by modifying the return function of the model discussed in Gönül and Shi (1998). In particular, we extend the analysis to consider a messaging and pricing policy mix, and we use a genetic algorithm in our empirical estimation. When applied to some non-seasonal products in a local supermarket, we find that our model is suitable and far superior to the one-stage model commonly used. Our dynamic model gives the optimal marketing mix strategies in different customer states and the results show that the firm could enjoy a 22% increase in profit.     

Brand equity in hospital marketing

Health care marketers face unique challenges around the world, due in part to the role the health care field plays in contributing to public welfare. Hospital marketing in Korea is particularly challenging since Korean law prohibits hospitals from running any advertising. As a result, Korean hospitals depend heavily on customer relationship management (CRM). This study identifies five factors that influence the creation of brand equity through successful customer relationships: trust, customer satisfaction, relationship commitment, brand loyalty, and brand awareness. An empirical test of the relationships among these factors suggests that hospitals can be successful in creating image and positive brand equity if they can manage their customer relationships well.     

Customer Selection and Prioritization

Abstract

That a firm benefits most when customers stick around for the long haul is a central premise of relationship marketing. This conviction has manifested itself over the past decade in the form of numerous formal loyalty programs that aim to seek and foster customer relationships. Scant evidence for the success of such programs has accrued since. This has lead firms to re-evaluate their investments in customer relationship management. Optimal resource allocation models address issues of relative investments in customer acquisition versus retention, and on managing relationships with profitable customers so that the value of a firm's customer base is maximized.     

Customer equity management as formative second-order construct

Despite intensive discussions about customer equity, little research addresses how to manage customer equity from a firm's perspective. Recent literature proposes various concepts of customer equity management but does not feature an empirical study that identifies and quantifies activities that aim explicitly to maximize customer equity. In the current study, the authors develop a formative measurement instrument for customer equity management as a second-order construct that indicates how intensively firms orient their customer management toward customer value and equity. The study presents a complete process for conceptualizing and operationalizing a formative second-order construct, including a thorough literature review, intensive qualitative research, and a quantitative study with 92 customer equity managers. On the basis of this process, the authors model customer equity management as a function of three formative dimensions - customer equity analysis, customer equity strategy, and customer equity actions - measured by several formative indicators. The resulting formative operationalization satisfies the criteria for evaluating formative indexes.     

Managing retailer profitability—one customer at a time!

This study examines how customer lifetime value (CLV) can be computed at individual customer level in a retail setting to maximize profitability. The study finds that maximum positive impact to CLV occurs when the customer cross-purchases, shows multi-channel shopping behavior, stays longer with the firm, buys specific product categories and purchases more frequently with the firm. Interestingly, the CLV follows an inverted U relationship with increase in return of prior purchases. Other interesting findings include a surprisingly low correlation between customer loyalty and future profitability and low correlation between stores’ historic revenues and future profitability. Several implications are suggested for retailers to manage and maximize customer profitability as well as store profitability.     

Making customer relationship decisions: Analytics v rules of thumb

This article explores how customer management decisions are made in the leading Nordic retail banks and whether these decisions are driven by mainstream analytical approaches to customer lifetime value available through the banks' CRM systems, or by rule of thumb heuristics. The results indicate that the use of managerial heuristics is surprisingly widespread and, counter-intuitively, that rule of thumb heuristic-based decision making frequently outweighs measures such as customer lifetime value with regard to customer management decisions. The implications are considerable because if successful banks are making widespread use of heuristics, managers and academics would benefit from understanding the conditions under which heuristic decision making can be more successful than an analytic approach. This understanding, in turn, may highlight a limitation of CRM systems and point to a more flexible approach to customer management decisions in which experience-based managerial heuristics modify data from formal CRM systems.     

Modeling CLV: A test of competing models in the insurance industry

Customer Lifetime Value (CLV) is one of the key metrics in marketing and is considered an important segmentation base. This paper studies the capabilities of a range of models to predict CLV in the insurance industry. The simplest models can be constructed at the customer relationship level, i.e. aggregated across all services. The more complex models focus on the individual services, paying explicit attention to cross buying, but also retention. The models build on a plethora of approaches used in the existing literature and include a status quo model, a Tobit II model, univariate and multivariate choice models, and duration models. For all models, CLV for each customer is computed for a four-year time horizon. We find that the simple models perform well. The more complex models are expected to better capture the richness of relationship development. Surprisingly, this does not lead to substantially better CLV predictions.

The role of lifetime activity cues in customer base analysis

This paper develops the notion of lifetime activity cues in customer base analysis. The authors first discuss the impact of lifetime indicators, such as customers' conceptual response to marketing activities, and then demonstrate how such lifetime cues can be embedded into the Pareto/NBD model. The authors theoretically analyze the implication of this additional behavioral indication on the model's predictions. In an illustrative example, they aim to establish an intuitive understanding of the effects of such information. Evidence from the cellular phone industry supports the relevance of this concept: The empirical study finds a substantial improvement in predictive accuracy in two independent holdout samples. The study concludes with a discussion of the managerial relevance of the proposed approach and opportunities for further research.     

From perceptions to propositions: Profiling customer value across retail contexts

The core claim made in the paper is that retailers wishing to identify and manage competitive customer value propositions succeed by measuring and modeling customer value perceptions with reference to specific contexts relevant for their competitive advantage. Hence, the purpose of the paper is to present development and validation of a scale for measuring and modeling customer value and to illustrate how contextual perspective contributes to the evaluation of customer value propositions. The findings, based on empirical data from Finland, Japan, and the U.S., validate a framework wherein customer value reflects economic, functional, emotional, and symbolic dimensions of value, associating with satisfaction and word of mouth effects. The customer value profiles generated on this basis provide analytical insight for evaluating how country, channel, product category, and competitive situation influence the criteria for contextual evaluation of customer value propositions.     

客户关系管理的研究现状、不足和未来展望

本文认为,企业实施客户关系管理的动因主要有顾客角色的变化与交换双方权力的转移,超强竞争环境,营销管理重心的转变以及互联网等通讯基础设施与技术的蓬勃发展等四个方面。目前客户关系管理研究中的主要问题是狭隘地理解客户关系管理,仅仅将其看作是一种管理软件;顾客权益的内涵尚不明确;尚未找到一种能够平衡顾客价值与企业价值的绩效评价方法;对顾客知识的研究基本空白等。文章还指出了未来客户关系管理研究的四大主要领域,即顾客权益合理内涵、评价方法与驱动因素及其在CRM中的应用;顾客价值的内涵多维动态评价体系、创造机制与驱动因素;顾客知识管理模型与数据挖掘技术的运用以及价值导向型客户关系管理的实施流程与竞争能力等。     

论商业银行的顾客获取战略——关系营销视角的思考

顾客获取与顾客保持对于商业银行的客户关系管理同样重要。在关系营销的框架下,从战略层面为商业银行寻求顾客获取的基本流程,突出顾客获取与顾客保持的衔接,并确定潜在目标范围,选择顾客获取战略,保持与客户的长期关系。     

Will the frog change into a prince? Predicting future customer profitability

More and more companies have customer databases that enable them to analyze customer profitability over time. These companies often seek to determine the most important customers as indicated by their current or historical profitability and focus attention on them. Focusing on profitable customers can result in more efficient use of marketing resources, but this approach neglects the fact that customers can evolve over time. Some customers begin as low-profit customers but eventually develop into high-profit customers. Others may start out as high-profit customers but become unprofitable over time. Previous efforts to predict future profitability have been relatively unsuccessful, with relatively simple, naïve models often performing just as well as or better than more sophisticated ones. Our paper presents a new approach to predicting customer profitability in future periods that performs significantly better than naïve models. We estimate the models on data from a high-tech company in a business-to-business context and validate the models' predictive ability on a holdout sample.We show that a model based on simulation of customer futures provides large improvements over naïve extrapolation of average profits. By using the simulation model to select customers, ROI from marketing efforts is projected to increase by 58%.     

Choosing the Right Metrics to Maximize Profitability and Shareholder Value

There is an ever-present need for managers to justify marketing expenditures to the firm. This can only be done when we can establish a direct link between marketing metrics and future customer value and firm performance. In this article, we assess the marketing literature with regard to marketing metrics. Subsequently, we develop a framework that identifies key metrics that firms should focus on that can give a firm a better picture of how they got to where they are now and insights towards how they can continue to grow into the future. We then identify several organizational challenges that need to be addressed in order for firms to build the capabilities of collecting the right data, measuring the right metrics, and linking those metrics to customer value and firm performance. Finally, we offer guidelines for future research with regard to marketing metrics to help firms establish successful marketing strategies, measure marketing effectiveness, and justify marketing expenditures to top management.     

The antecedents of customer loyalty: A moderated mediation model of customer relationship management quality and brand image

This study examines the mediating role of customer relationship management (CRM) quality to better explain the effects of service evaluation variables (service quality, customer satisfaction and customer value) on customer loyalty. The study also investigates the moderating effect of brand image on these mediated relationships. The mediating role of CRM quality on the relationship between the service evaluation variables and customer loyalty is supported. Further, it is found that the indirect effect of customer satisfaction on customer loyalty via CRM quality is stronger when perceived brand image is high than when it is low. The results have implications for relationship managers, brand managers and scholars who use service evaluation and relational metrics to predict customer loyalty.     

Perceived value and customer loyalty in financial services

Taking into account how expensive it is to maintain customer loyalty programmes, it is necessary to know which aspects of the firm’s service are the ones that really deliver value to the customer. The aim of this paper is to study the relationships between perceived value, satisfaction and consumer loyalty in the financial services market, in order to know which factors are the most important for gaining the customers’ loyalty. A personal survey was made of 200 customers of financial entities, and structural equations models were used to test the relationships posited.