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Customer-Based Corporate Valuation for Publicly Traded Noncontractual Firms
Abstract
There is growing interest in “customer-based corporate valuation”—that is, explicitly tying the value of a firm’s customer base to its overall financial valuation using publicly disclosed data. While much progress has been made in building a well-validated customer-based valuation model for contractual (or subscription-based) firms, there has been little progress for noncontractual firms. Noncontractual businesses have more complex transactional patterns because customer churn is not observed, and customer purchase timing and spend amounts are more irregular. Furthermore, publicly disclosed data are aggregated over time and across customers, are often censored, and may vary from firm to firm, making it harder to estimate models for customer acquisition, ordering, and spend per order. The authors develop a general customer-based valuation methodology for noncontractual firms that accounts for these issues. They apply this methodology to publicly disclosed data from e-commerce retailers Overstock.com and Wayfair, provide valuation point estimates and valuation intervals for the firms, and compare the unit economics of newly acquired customers.
... To address these questions and to assist managers in designing their marketing programs accordingly, the marketing discipline has produced a rich stream of literature. These contributions include predictive models and techniques for customer targeting and reactivation timing (Gönül & ter Hofstede, 2006;Simester, Sun, & Tsitsiklis, 2006;Holtrop & Wieringa, 2020), market response models for firm-and/or customer-initiated marketing actions (e.g., Hanssens, Parsons, & Schultz (2003), Blattberg, Kim, & Neslin (2008), Sarkar & De Bruyn (2021)), methods for churn prediction and prevention (e.g., Ascarza (2018), Ascarza, Iyengar, & Schleicher (2016), Lemmens & Gupta (2020)), as well as a growing literature on customer valuation (e.g., McCarthy, Fader, & Hardie (2017), McCarthy & Fader (2018)) and customer prioritizing (Homburg, Droll, & Totzek, 2008). However, none of these qualify as a (Swiss Army knife-like) general-purpose problem solver that generalizes across the described decision tasks of managing customer relationships. ...
... Consider, for example, the situation for a few prototypical customer transaction histories depicted in Fig. 1, which are from a customer cohort of a large U.S. charity organization we will study in our empirical evaluation section in more detail. From a managerial perspective, accurately spotting the future activity patterns of such customers is of vital importance because of their value to the company (Blattberg & Deighton, 1996;McCarthy & Fader, 2018). They were all high frequency donors in the past; however, as we will further demonstrate in more detail in the empirical evaluation section, the evaluation of their future with the charity institution will lead us to different conclusions. ...
... As demonstrated already by Ebbes, Papies, andvan Heerde (2011, p. 1116), holdout sample validation and superior predictive performance favors regression estimates that are not corrected for endogeneity. Similarly, Schweidel and Knox (2013, p. 479) and more recently McCarthy and Fader (2018) and Bachmann et al. (2021) also find that controlling for endogeneity is of minor importance when high predictive accuracy of future forecasts is the primary objective. However, as opposed to forecasts made by BTYD models that are only conditioned on observed transaction data, we acknowledge that when it comes to planning conditional future marketing actions a more sophisticated approach is necessary than our autoregressive simulation setup. ...
One of the primary goals that researchers look to achieve through customer base analysis is to leverage historical records of individual customer transactions and related context factors to forecast future behavior, and to link these forecasts with actionable characteristics of individuals, managerially significant customer sub-groups, and entire cohorts. This paper presents a new approach that helps firms leverage the automatic feature extraction capabilities of a specific type of deep learning models when applied to customer transaction histories in non-contractual business settings (i.e., when the time at which a customer becomes inactive is unobserved by the firm). We show how the proposed deep learning model improves on established models both in terms of individual-level accuracy and overall cohort-level bias. It also helps managers in capturing seasonal trends and other forms of purchase dynamics that are important to detect in a timely manner for the purpose of proactive customer-base management. We demonstrate the model performance in eight empirical real-life settings which vary broadly in transaction frequency, purchase (ir)regularity, customer attrition, availability of contextual information, seasonal variance, and cohort size. We showcase the flexibility of the approach and how the model further benefits from taking into account static (e.g., socio-economic variables, demographics) and dynamic context factors (e.g., weather, holiday seasons, marketing appeals). We make an open-source reference implementation of the newly developed method available.
... Focusing on platforms, this research explores the role network e↵ects play in creating long term customer value. Current models of customer lifetime value (CLV), however, omit network e↵ects (Gupta et al., 2004;Pfeifer and Farris, 2004;Zhang et al., 2012;McCarthy and Fader, 2018), missing the positive externalities they create for each other. Standard models of network e↵ects are static (Rochet and Tirole, 2003;Parker and Van Alstyne, 2005;Armstrong, 2006), missing the chain of value creation over time. ...
... An important stream of research that is closely related to firm value is the concept of CLV in marketing (Gupta et al., 2004;McCarthy and Fader, 2018). This line of research emphasizes user stickiness and explicitly incorporates user retention in CLV calculation (Gupta et al., 2004;Pfeifer and Farris, 2004;Gupta, 2005). ...
... We first look at the scenario when the platform uses advertising marketing as the user-growth strategy. The consumer acquisition cost (CAC) in this case is calculated as the total marketing expense divided by the total number of acquired consumers (Gupta et al., 2004;McCarthy and Fader, 2018). In its 2012 annual report, Groupon reported a total marketing expense of $336.85 million with 7.3 million consumers acquired worldwide, yielding an acquisition cost of $46 per new consumer globally. ...
... La CLV du client, définition et déterminants 1.1.a. Définition de la valeur à vie du client (CLV)La CLV s'est imposée comme une mesure incontournable en marketing et même au-delà puisqu'elle a pénétré la sphère financière(Bauer et al., 2003 ;Bauer et Hammerschmidt, 2005 ; McCarthy etFader, 2018). En effet, la CLV est la valeur de chaque client en fonction de ses flux de revenus passés, présents et futurs(Castéran et al., 2017 ;Gupta et al., 2004). ...
... En effet, la CLV est la valeur de chaque client en fonction de ses flux de revenus passés, présents et futurs(Castéran et al., 2017 ;Gupta et al., 2004). Pour les organisations, le client constitue l'actif le plus important au sens financier du terme(McCarthy et Fader, 2018). Comme tout actif, celui-ci peut fluctuer à la hausse grâce à des actions de fidélisation ou d'amélioration des liens existants, ou à la baisse à cause de l'attrition ou la diminution des montants dépensés(Bolton et al., 2004).Depuis la perspective des organisations, la valeur à vie du client(Bauer et Hammerschmidt, 2005) se définit comme la somme actualisée des revenus générés par un client sur toute sa période de consommation(Berger et Nasr, 1998 ;Castéran, 2010 ;Dwyer, 1989).La modélisation de la CLV estime la valeur du client pour l'entreprise d'un point de vueChapitre III -Mise en perspective de la valeur à vie du client avec le contexte expérientiel et proposition d'un modèle de recherche ...
Cette étude s’articule autour de trois enjeux que présente le courant expérientiel en marketing : une asymétrie de la recherche puisque la perspective du consommateur est plus développée que celle des organisations (Kranzbühler et al., 2018) ; des confusions régulières entre l’expérience et le contexte expérientiel ; la difficulté de mesurer financièrement les stratégies expérientielles (Ferraro et al., 2017 ; Roederer et Filser, 2015). Le contexte expérientiel physique commercial (CEPC) est conceptualisé sous le prisme de la théorie de l’agencement (Deleuze et Guattari, 1980). Une méthodologie multiméthodes permet de collecter les données qualitatives autour d’entretiens semi-directifs, d’un corpus photographique et d’une observation non-participante. Dans un second temps, le concept de la valeur à vie du client (CLV) est mobilisé pour la première fois, à notre connaissance, pour capturer les effets de la modification d’un CEPC de façon longitudinale. Deux terrains sont investigués dont l’un à caractère hédonique et l’autre utilitaire. Une méthodologie quasi expérimentale est employée afin de comparer les effets entre un groupe traité et de contrôle. Les résultats font émerger une structuration du CEPC autour d’une intention d’expression et de six dispositifs. Le CEPC est rythmé par un cycle de vie, mais aussi par un réseau rhizomique dans lequel il est ancré. La valeur à vie permet de mettre en évidence les effets d’un remodelage d’un CEPC dans le temps selon que le contexte soit utilitaire ou hédonique.
... Perlu juga diketahui bahwa alasan mendasar bagi perusahaan yang ingin membangun hubungan dengan pelanggan adalah alasan ekonomi. Perusahaan menghasilkan hasil yang lebih baik ketika mereka mengelola basis pelanggan mereka untuk mengidentifikasi, memperoleh, memuaskan, dan mempertahankan pelanggan professional (McCarthy, 2018). Ini adalah tujuan utama dari banyak strategi CRM. ...
... CLV digunakan untuk memperkirakan nilai finansial suatu perusahaan (Gupta, 2008;Oblander et al., 2020) (lihat Gambar 12.2). (McCarthy and Fader, 2018) memperluas ini untuk memasukkan konsep akuntansi dan keuangan lebih lanjut, memungkinkan pengukuran yang lebih tepat dari peran akuisisi pelanggan dan retensi pada penilaian perusahaan. memperluas pendekatan penilaian berbasis CLV untuk memperhitungkan pengurangan laten dalam pengaturan non-kontraktual. ...
Buku ini dirancang untuk menyoroti masalah utama yang memengaruhi bisnis yang telah mengadopsi internet sebagai alat perdagangan atau meningkatkan proses internal. Bisnis elektronik (e-business) adalah penggunaan internet untuk tujuan ini. Akibatnya, bisnis elektronik memiliki implikasi untuk berbagai masalah yang memengaruhi organisasi, termasuk adopsi teknologi, pilihan model bisnis, ekonomi, pemasaran, masalah hukum dan keamanan, manajemen dan strategi untuk mendapatkan keunggulan kompetitif. Buku ini menyoroti dan menjelaskan sifat dan karakteristik e-business dalam konteks masing-masing masalah utama ini.
Struktur dan isi buku ini telah disusun untuk membantu mahasiswa sarjana dan pascasarjana yang baru mengenal subjek e-business memahami isu-isu utama baik dari perspektif teoritis dan praktis. Buku ini juga merupakan sumber panduan dan informasi yang berharga bagi praktisi yang mencari wawasan tentang e-business pada beberapa bab berikut ini :
Bab 1 Konsep dan Definisi E-Business
Bab 2 Komponen Dalam Model E-Bisnis
Bab 3 Kontribusi Internet Pada E-Bisnis
Bab 4 Aspek Legal Dalam E-Business
Bab 5 Peranan Website dalam E-Business
Bab 6 Model-Model E-Business
Bab 7 Strategi Pemasaran Dalam E-Business
Bab 8 Model-Model Transaksi Secara Online
Bab 9 Kompetisi dalam E-Business
Bab 10 Sistem Keamanan dalam E-Business
Bab 11 Keuntungan Menggunakan E-Commerce Dalam Bisnis
Bab 12 Customer Relationship Management (CRM)
Bab 13 Supply Chain Management (SCM)
Bab 14 Enterprise Resource Planning (ERP)
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... An adjacent body of literature focuses on customer valuation (see exhaustive reviews in Ascarza et al. 2017 andKumar 2018), building on a wide range of CLV modeling that reflects a variety of business models (broadly categorized in terms of contractual or noncontractual settings), markets (business-to-business versus business-to-consumer), customer data, and demographic typologies (e.g., Bonacchi et al. 2015;McCarthy, Fader, and Hardie 2017;McCarthy and Fader 2018). McCarthy and Pereda (2020) emphasize a lack of agreement about CLV and CE definition and operationalization; this hinders their implementation by financial analysts and company executives. ...
SYNOPSIS
This study examines how firms deploy customer analytics in their performance measurement and reporting systems. Firstly, we synthesize insights from the literature on customer analytics in accounting and marketing and conduct interviews with experts in the field. We then present the results of an online survey conducted among a sample of subscription-based firms known for their early adoption of customer analytics. Our findings reveal that the use of customer analytics varies significantly by metric type, with traditional indicators (e.g., number of customers) showing higher levels of integration compared with more advanced metrics, such as customer lifetime value and customer equity. The extent of adoption in performance measurement and reporting systems appears to depend on the ability of a firm to fit customer analytics into its organizational architecture. We conclude by identifying research avenues reflecting current trends that will likely shape the emerging literature on customer analytics.
... Marketing perfor-mance is one of the issues of firm financial performance, often associated with marketing effectiveness, efficiency, productivity and metrics [4]. Customer-based corporate valuation (CBCV) is the process of valuing a firm by forecasting current and future customer behaviours, using customer data in conjunction with traditional financial data [5]. Digital technologies enable firms and other institutions to enhance their business processes and improve their predictions and planning [6]. ...
The aim of this study is to analyse the research gap regarding the relationship between environmental, social and governance dimensions (ESG) of corporate sustainability initiatives and customer lifetime value (CLV). We divide an entire data sample (547 U.S. listed firms from the Refinitiv Thomson Reuters Eikon database) of both industrial and technological industries into three segments, using prediction-oriented modelling segmentation to test the hypotheses and evaluate the predictive validity of a partial least squares (PLS) model. As a result, we show that environmental, social and governance dimensions (ESG) encompass ten sustainability initiatives that, in turn, are the precursors of future financial firm performance, represented by CLV. Moreover, we found different poor-to-medium effects of each ESG dimension on CLV in segment 1. However, a stronger effect of the social dimension on CLV in segment 3 is completed with a poor effect, both positive by governance and negative by environmental dimensions, on CLV, while only the environmental dimension had greater effects on CLV in segment 2. The contribution of this research to the body of literature is twofold. First, it deepens the impact of each ESG dimension instead of considering sustainability initiatives as a whole. Second, it evaluates sustainability initiatives with a customer-based corporate firm valuation approach.
... Our main goal was to design a framework that addressed all aspects of CLV estimation, including data acquisition from company repositories, the creation of a unified customer record, the classification of customers into ranked groups, the interpolation of missing parameters, the calculation of CLV values, and the validation of models. It is noted that customer retention is not only a crucial element for CLV models, but also differentiates both contractual and non-contractual settings (McCarthy and Fader 2018). Models like Pareto/NBD have been widely used in businesses such as retail. ...
Customer lifetime value is a core measure that allows companies to predict the potential net profit from future relationships with their customers. It is a metric that is computed by recording customer behavior over a long term and helps to build customized business strategies. However, existing research focuses either on a conceptual model of customer \({\text {CLV}}_{\text {s}}\) or assumes that all variables required for the computation of CLV are readily available. In this research, we employ a real customer dataset of insurance policies to construct a holistic framework that covers all aspects of CLV computation. In addition, we develop an extensive validation process, aiming to verify our results and obtain an understanding as to which CLV models perform best in the insurance context. In this research, we deliver a framework which comprises all aspects of CLV estimation using a real insurance policy dataset provided by a large business partner. The framework addresses the creation of a unified customer record, classification of customers into ranked groups, interpolation of missing parameters, through to the calculation and validation of individual CLV values. Our method also includes a robust validation with both subjective and objective evaluations of our findings.
... This implies that one of the biggest challenges faced by organizations relying on this business model face is to determine a meaningful time period to use for defining a customer as lost (e.g., if no purchases are made in three consecutive months, then a customer is considered a churner), as this definition will affect all further modeling efforts and classification results [6]. It is also one of the main reasons for the disproportion that can be observed when the number of studies focusing on contractual business settings is compared to the number of those exploring cases where formal contracts between a company and their customers do not exist (i.e., non-contractual business settings) [7]. ...
Customer churn is a problem virtually all companies face, and the ability to predict it reliably can be a cornerstone for successful retention campaigns. In this study, we propose an approach to customer churn prediction in non-contractual B2B settings that relies exclusively on invoice-level data for feature engineering and uses multi-slicing to maximally utilize available data. We cast churn as a binary classification problem and assess the ability of three established classifiers to predict it when using different churn definitions. We also compare classifier performance when different amounts of historical data are used for feature engineering. The results indicate that robust models for different churn definitions can be derived by using invoice-level data alone and that using more historical data for creating some of the features tends to lead to better performing models for some classifiers. We also confirm that the multi-slicing approach to dataset creation yields better performing models compared to the traditionally used single-slicing approach.
... On an operational level, the CLV can additionally substantiate marketing decisions like individual service offers. On the general management level, the cumulated CLV helps to determine the financial value of a company (McCarthy and Fader 2018). ...
The Pareto/NBD model is one of the best-known models in customer base analysis. Extant literature has brought up three different Markov Chain Monte Carlo (MCMC) procedures for parameter estimation of this model. Nevertheless, three main research gaps remain. Firstly, the issue of hyper parameter sensitivity for these procedures has been disregarded even though this is crucial when dealing with small sample sizes. Secondly, present research lacks a performance comparison between the different MCMC procedures as well as with Maximum Likelihood Estimates (MLE). Thirdly, existing minimal data set requirements for this model neglect MCMC estimation procedures as they only refer to MLE. To tackle these gaps, we perform two extensive simulation studies. We demonstrate that the algorithms differ in their sensitivity towards the hyper distributions and identify one algorithm that outperforms the other procedures in all respects. In addition, we provide deeper insights into individual level forecasts when using MCMC and enhance extant data set limitation guidelines by considering not only the cohort size but also the length of the calibration period.
... With the importance of CE being recognized, it has been applied using publicly available data on companies such as American subscription-based enterprises (Bonacchi, Kolev, & Lev, 2015): DISH Network and Sirius XM Holdings (McCarthy, Fader, & Hardie, 2017), Netflix (Schulze, Skiera, & Wiesel, 2012;Wiesel et al., 2008;Zhang, 2016), Overstock.com andWayfair (McCarthy &Fader, 2018), and Sky Deutschl AG and DIRECTV (Rhouma & Zaccour, 2018). Furthermore, several studies estimated CE using internal data, which enable elaborated analyses. ...
This study proposes a framework for variance analysis of customer equity (CE) based on a Markov chain model called the Leslie matrix, developed in mathematical biology. Because customer lifetime value represents the net present value of customer derived cashflow, CE can be characterized as a forward-looking measure. Numerous studies have addressed the use of CE for planning. However, few studies have utilized CE for control. When CE is used as a basis for control, variance analysis is indispensable. In the proposed framework, CE variance is divided into three parts: customer payoff variance, customer lifecycle variance, and customer state variance. Thereafter, customer lifecycle variance is further broken down into customer acquisition, retention, and expansion. The aforementioned framework was applied to membership customers of a Japanese resort hotel chain. The results revealed that customer retention was a major cause of CE variance, whereas customer acquisition and expansion had a smaller impact.
... CBCV describes the process of valuing a firm by forecasting current and future customer behavior using customer data in conjunction with traditional financial metrics. For many firms, customer equity represents a major share in shareholder value enabling the link between user behavior and enterprise valuation [20]. A vast number of scholars have published articles that further analyze this link [3,2,14,21]. ...
Motivated by recent market developments, this study presents a stochastic user-based corporate valuation model, that is able to forecast user base development, estimate customer equity values for any contractual digital business and link it to the firm value of such companies. As no theory is as good as hard facts, we further venture to provide evidence for the accuracy of our model, by applying it to three real-world business cases.As a consequence of the increasing importance and exponential growth of digital technologies, a new class of digital business models has emerged. These powerful new business models and their implementation in the modern economy have substantially different characteristics from traditional business models, which is why traditional company valuation techniques often fall short in explaining the high market valuation of these companies. The purpose of this study is to develop a quantitative framework to value such companies based on user data rather than traditional financial performance measures.The study shows that the suggested customer equity estimates track the market capitalization of the investigated companies remarkably well. The study further provides some strategic insights for value-based management for these firms derived from comprehensive sensitivity analyses for the most crucial input parameters of the suggested model.
... Customer equity is an essential metric to measure firm performance and it provides a good proxy for firm value. Many researchers believe customer equity is a new tool for firm valuation [17,18]. This customer-based corporate valuation method can make better estimates of what a firm is really worth than the traditional financial-valuation methods. ...
Customers are important intangible assets of firms. Customer equity (CE) and customer equity sustainability ratio (CESR) cannot only provide a crucial basis for measuring the growth potential of firms but also provide managers a reference standard to allocate the marketing resource. This empirical study discussed the CE measurement of a mobile payments aggregator. With the rapid development of mobile payment in China, it is very meaningful to calculate the CE of these aggregators as an emerging business pattern because calculating CE cannot only help the mobile payments aggregator evaluate its future business development but also help it to provide value-added services and generate service fee from its clients, i.e., the retailers. The main purpose of this paper is to calculate CE of a mobile payments aggregator generated from a specific retailer from the perspective of technology diffusion. Based on the Bass model and Rogers’ theory of innovation diffusion, we calculated CE and CESR for five segments, namely innovators, early adopters, early majorities, late majorities, and laggards. The results show that it is the early adopters and the early majorities who generate most of the profit and it is also these two segments that have the greatest growth potential in the future.
... They, too, report a higher-than-unity elasticity of customer equity on shareholder value. More recently, McCarthy and Hardie (2017) demonstrated that publicly disclosed customer data are sufficient to derive customer-based corporate valuations and that this can be done even for non-contractual firms (McCarthy & Fader, 2018). Overall, the establishment of a strong link between customer equity and firm value (meta-analytic average elasticity of 0.72 in Edeling & Fischer, 2016) is perhaps the most important indicator of the strategic importance of good marketing. ...
The marketing–finance interface is an important research field in marketing, helping demonstrate the accountability of marketing within companies and building a necessary interdisciplinary bridge to finance and accounting research. Since the first comprehensive review article by Srinivasan and Hanssens (2009), the marketing–finance field has broadened considerably, as has research in finance and accounting. This updated systematic review of extant and new research integrates research in marketing, finance, and accounting into an overarching marketing–finance research framework. We discuss new methodological developments and offer solutions to recent technical debates on the event-study method and Tobin's q. Motivated in part by a survey of marketing–finance researchers, the article identifies and synthesizes four key emerging research areas: digital marketing and firm value, tradeoffs between “doing good” and “doing well,” the mechanisms of firm-value effects, and feedback effects. The article closes with a future research agenda for this dynamic research field and offers key conclusions.
... When information about a firm's intangible assets is available, investors clearly use this in their valuations. For example, studies have shown that when concrete information about firms customer base is available (e.g., customer churn and acquisition rates in subscription-based businesses and some contractual businesses such as telephone and cable companies), it can be used to compute the firm's customer equity (the total value of the firms current and expected customers lifetime value) which closely tracks valuations of the firm's stock (e.g., Bonacchi et al. 2015;Gupta et al. 2004;McCarthy and Fader 2018). A firm's managers may have a great deal of data about its customers which provides the opportunity to develop valuations of individual customer relationships and assess the quality and value of the firm's overall customer base (e.g., Kumar and Shah 2009). ...
A significant proportion of many firms’ customers are unprofitable. The question of how unprofitable customers should be managed has recently received increasing research attention from the customer and manager angles, but the effects of unprofitable customer management (UCM) strategies on shareholder value is unknown. Using an event study methodology, we examine stock market reactions to disclosures of firms’ UCM strategy decisions. Results from a sample of UCM strategy disclosure events reveal an average short-term abnormal stock return of −0.53%. Drawing on signaling theory logic, we explore a number of signal (UCM strategy), signaler (firm engaging in UCM), and signaling environment characteristics that may affect the shareholder value effects of firms’ UCM approaches. Our analyses show that investors respond more favorably to indirect UCM strategies than to direct customer divestment strategies. We also find that particular types of indirect UCM strategy approaches and strategic intent in UCM strategy adoption, stronger firm marketing capabilities and, and positive publicity can help mitigate the generally negative abnormal stock returns observed. Overall, our findings have important implications for marketing theory and provide actionable new insights for managers into how to approach the management of unprofitable customers.
This report summarizes the finalists of the ISMS Gary Lilien Marketing Science Practice Prize Competition.
This article tries to deepen our knowledge on the nature of value creation and management in times of turbulent environments both for business management and academic research. The current uncertainties in our economy—coming from exogenous shocks (e.g., financial crises, political standoffs [US–China], pandemic and Ukrainian war) or from demand for more sustainable activities—affect existing companies and provide opportunities for new ventures. Both type of firms need to think of innovative and out-of-the-box ideas to keep generating value for all stakeholders, and particularly to their customers. The nuanced understanding of the factors describing the current economic situation and their effect on value creation and management affects management by, for example, efficient use of resources, and assists in strategy formulation and decision-making. Customer value creation is a dual concept: creating value for customers (value-to-customer [V2C]) as well as extracting some of that customer value at the same time to create value for the firm (value-to-firm [V2F]). While this might be known, it is rarely implemented. In order to deliver V2C, firms should look at the customer journey from a customers’ point of view rather than a firms’ point of view. Only then, the real revenue potential can be realized when thinking about monetizing the customer interface and, hence, developing the customer relationship options beyond the current business. Even if it exists for a long time already, customer lifetime value (CLV) und customer equity (CE) are the right techniques to measure and manage value in such times. Besides, in today’s environment, the gap between CLV and CE and shareholder value needs to be closed. Existing customer-based corporate valuation studies do exactly so and, more importantly, will become even more important in the future due to new business models.
Predicting future purchase levels is an important and constant challenge for marketing professionals, as purchase patterns often vary over time and across customers. Moreover, purchases often follow individual and cross-sectional seasonal patterns, which affect forecasts of purchase propensity and customer dropout. The authors develop the hierarchical Bayesian seasonal model with dropout (HSMDO), which captures the interrelation between individual and cross-sectional seasonality, purchase, and dropout rates, with the aim of improving forecast accuracy at specific points in time. They perform (1) a parameter recovery analysis with synthetic data; (2) an empirical validation on three noncontractual retail data sets; (3) an analysis of different model variants to isolate the effects of dropout, seasonality, and hierarchical seasonality; and (4) a comparison with several probabilistic models from the marketing literature. The results demonstrate that the HSMDO provides increased forecast accuracy and that tracking errors decrease further with data exhibiting strong seasonality and high customer retention. The HSMDO yields a measure of individual seasonality that has high discriminative power even with sparse data sets and is useful to customer relationship management analysts for customer segmentation, portfolio management, and improvement in the timing of marketing actions.
Multinational corporations (MNCs) are continuing to invest more in expanding into new markets around the world. These firms are faced with determining the optimal go-to-market strategy in these heterogeneous new markets to attract and retain profitable customers. This paper provides an organizing framework to help firms develop profitable customer-level strategies across countries in the digital environment. We start by providing a summary of the marketing literature on a customer-based execution strategy. Next, we discuss how the evolving digital landscape is affecting firms’ relationships with customers and describe some of the current digital product and process innovations in the marketplace. We discuss boundary conditions for how these digital product and process innovations might affect profitable customer strategies in a global context. In addition, we discuss implementation challenges that MNCs will likely face in deploying these customer-level strategies and other stakeholders (outside of customers) that will likely play a role in the execution of these customer-level strategies. Finally, we summarize set of research questions to guide future research on customer-level strategies in a global digital context.
This paper examines how the Customer Success (CS) function is a misunderstood area of opportunity for B2B companies. It explains how to avoid the opportunity cost of lost revenue and update the CS model to achieve exponential growth and greater customer impact.
A computationally scalable, statistically efficient aggregate-disaggregate data fusion method that corrects for selection bias is applied to model customer relationship dynamics at a subscription-based firm.
Customer base analysis of noncontractual businesses builds on modeling purchases and latent attrition. With the Pareto/NBD model, this has become a straightforward exercise. However, this simplicity comes at a price. Customer-level predictions often lack precision. This issue can be addressed by acknowledging the importance of contextual factors for customer behavior. Considering contextual factors might contribute in two ways: (1) by increasing predictive accuracy and (2) by identifying the impact of these determinants on the purchase and attrition process. However, there is no generalization of the Pareto/NBD model that incorporates time-varying contextual factors. Preserving a closed-form maximum likelihood solution, this study proposes an extension that facilitates modeling time-invariant and time-varying contextual factors in continuous noncontractual settings. These contextual factors can influence the purchase process, the attrition process, or both. The authors further illustrate how to control for endogenous contextual factors. Benchmarking with three data sets from the retailing industry shows that explicitly modeling time-varying contextual factors significantly improves the accuracy of out-of-sample predictions for future purchases and latent attrition.
For decades, researchers in customer management have demonstrated the business importance of firm-customer relationships, developed models for understanding customer behavior and response to marketing, established the link between customer behavior and firm performance, and proposed policies to optimize customer management activities. This paper overviews customer management research, from its historical origins to its recent developments. We identify promising directions for future research, with an emphasis on novel data sources, online marketplaces, and new technologies; additionally, we highlight the privacy and fairness considerations that come with these developments.
Although scholars have established that customer satisfaction affects different dimensions of firm financial performance, a managerially important but overlooked aspect is its effect on a firm’s future cost of selling (COS), that is, expenditures associated with persuading customers and providing convenience to them. Accordingly, this study presents the first empirical and theoretical examination of the impact of customer satisfaction on future COS. The authors propose that while higher customer satisfaction can lower future COS, the degree to which a firm realizes this benefit depends on its strategy and operating environment. Analyzing almost two decades of data from 128 firms, the authors find that customer satisfaction has a statistically and economically significant negative effect on future COS. While the negative effect of customer satisfaction on future COS is weaker for firms with higher capital intensity and financial leverage, this effect is stronger for more diversified firms and for firms operating in industries with higher growth and labor intensity. The authors also find that these effects may vary across two components of COS, cost of persuasion and convenience.
The availability of customer data has been enhanced in the digital economy, which can be used to measure the performance of marketing activities. Accounting techniques using customer data are collectively called customer accounting (CA). To seek the interface between management accounting and marketing, this study first discusses the three accounting weaknesses (the lack of revenue milepost information, revenue sustainability measurements, and intangibles capitalization) and relate them to marketing concepts (customer journey, customer acquisition and retention, and customer assets). Next, this study reviews literature on CA. Revenue accounting (RA), which focuses on revenue in an accounting period, and aims at the planning and control of marketing. Customer profitability analysis (CPA) has developed in the stream of strategic management accounting. The central focus of CPA is the allocation of selling, general and administrative expenses to individual customers through activity-based costing. Customer lifetime value and customer equity (CLV and CE) focus on the present value of cash flow from customer acquisition and retention. While RA and CPA respectively focus on revenues and costs in an accounting period, CLV and CE take cash flows in all future periods into consideration. Finally, links of each CA tool to the three marketing concepts are discussed to seek further development of CA.
Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But how can marketers best use such data? This article provides an overview of automated textual analysis and details how it can be used to generate marketing insights. The authors discuss how text reflects qualities of the text producer (and the context in which the text was produced) and impacts the audience or text recipient. Next, they discuss how text can be a powerful tool both for prediction and for understanding (i.e., insights). Then, the authors overview methodologies and metrics used in text analysis, providing a set of guidelines and procedures. Finally, they further highlight some common metrics and challenges and discuss how researchers can address issues of internal and external validity. They conclude with a discussion of potential areas for future work. Along the way, the authors note how textual analysis can unite the tribes of marketing. While most marketing problems are interdisciplinary, the field is often fragmented. By involving skills and ideas from each of the subareas of marketing, text analysis has the potential to help unite the field with a common set of tools and approaches.
The growth of subscription-based commerce has changed the types of data that firms report to external shareholders. More than ever, companies are discussing and disclosing information on the number of customers acquired and lost, customer lifetime value, and other data. This has fueled an increasing interest in linking the value of a firm’s customers to the overall value of the firm, with the term “customer-based corporate valuation” being used to describe such efforts. Although several researchers in the fields of marketing and accounting have explored this idea, their underlying models of customer acquisition and retention do not adequately reflect the empirical realities associated with these behaviors, and the associated valuation models do not meet the standards of finance professionals. The authors develop a framework for valuing subscription-based firms that addresses both issues, and they apply it to data from DISH Network and Sirius XM Holdings.
Investors, analysts, and regulators frequently advocate greater disclosure of nonfinancial information, such as customer metrics. Managers, however, argue that such metrics are costly to report, reveal sensitive information to competitors, and therefore will lower future cash flows. To examine these counterarguments, this study presents the first empirical examination of the prevalence and consequences of backward- and forward-looking disclosures of customer metrics by manually coding 511 annual reports of firms in two industries, telecommunications (365 reports) and airlines (146 reports). The results reveal significant heterogeneity in the disclosure of customer metrics across firms and between industries. On average, in both industries, firms make more backward-looking than forward-looking disclosures. Notably, forward-looking disclosures of customer metrics are negatively associated with investors' uncertainty in both industries and with analysts' uncertainty in the telecommunications industry. Importantly, the results do not support the managerial thesis that such disclosures have a negative impact on future cash flows.
Accurate predictions of a customer’s activity status and future purchase propensities are crucial for managing customer relationships. This article extends the recency–frequency paradigm of customer-base analysis by integrating regularity in interpurchase timing in a modeling framework. By definition, regularity implies less variation in timing patterns and thus better predictability. Whereas most stochastic customer behavior models assume a Poisson process of “random” purchase occurrence, allowing for regularity in the purchase timings is beneficial in noncontractual settings because it improves inferences about customers’ latent activity status. This especially applies to those valuable customers who were previously very frequently active but have recently exhibited a longer purchase hiatus. A newly developed generalization of the well-known Pareto/NBD model accounts for varying degrees of regularity across customers by replacing the NBD component with a mixture of gamma distributions (labeled Pareto/GGG). The authors demonstrate the impact of incorporating regularity on forecasting accuracy using an extensive simulation study and a range of empirical applications. Even for mildly regular timing patterns, it is possible to improve customer-level predictions; the stronger the regularity, the greater the gain. Furthermore, the cost in terms of data requirements is marginal because only one additional summary statistic, in addition to recency and frequency, is needed that captures historical transaction timing.
Full text available via; http://www.reutterer.com/papers/platzer&reutterer_pareto-ggg_2016.pdf
Data clustering is an important data exploration technique with many applications in data mining. K-means is one of the most well known methods of data mining that partitions a dataset into groups of patterns, many methods have been proposed to improve the performance of the K-means algorithm. Standardization is the central preprocessing step in data mining, to standardize values of features or attributes from different dynamic range into a specific range. In this paper, we have analyzed the performances of the three standardization methods on conventional K-means algorithm. By comparing the results on infectious diseases datasets, it was found that the result obtained by the z-score standardization method is more effective and efficientthan min-max and decimal scaling standardization methods.
We address a critical question that many firms are facing today: Can customer data be stored and analyzed in an easy-to-manage and scalable manner without significantly compromising the inferences that can be made about the customers' transaction activity? We address this question in the context of customer-base analysis. A number of researchers have developed customer-base analysis models that perform very well given detailed individual-level data. We explore the possibility of estimating these models using aggregated data summaries alone, namely repeated cross-sectional summaries (RCSS) of the transaction data. Such summaries are easy to create, visualize, and distribute, irrespective of the size of the customer base. An added advantage of the RCSS data structure is that individual customers cannot be identified, which makes it desirable from a data privacy and security viewpoint as well. We focus on the widely used Pareto/NBD model and carry out a comprehensive simulation study covering a vast spectrum of market scenarios. We find that the RCSS format of four quarterly histograms serves as a suitable substitute for individual-level data. We confirm the results of the simulations on a real dataset of purchasing from an online fashion retailer. © 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) with in the International Federation of Operational Research Societies(IFORS). All rights reserved.
Dynamic customer targeting is a common task for marketers actively managing customer relationships. Such efforts can be guided by insight into the return on investment from marketing interventions, which can be derived as the increase in the present value of a customer’s expected future transactions. Using the popular latent attrition framework, one could estimate this value by manipulating the levels of a set of nonstationary covariates. We propose such a model that incorporates transaction-specific attributes and maintains standard assumptions of unobserved heterogeneity. We demonstrate how firms can approximate an upper bound on the appropriate amount to invest in retaining a customer and demonstrate that this amount depends on customers’ past purchase activity, namely the recency and frequency of past customer purchases. Using data from a B2B service provider as our empirical application, we apply our model to estimate the revenue lost by the service provider when it fails to deliver a customer’s requested level of service. We also show that the lost revenue is larger than the corresponding expected gain from exceeding a customer’s requested level of service. We discuss the implications of our findings for marketers in terms of managing customer relationships.
Many service firms acquire customers by offering free-trial promotions. However, a crucial challenge is to retain the customers acquired with these free trials. To address this challenge, firms need to understand how free-trial customers differ from regular customers in terms of their decisions to retain the service. This article conceptualizes how marketing communication and usage behavior drive customers' retention decisions and develops hypotheses about the impact of free-trial acquisition on this process. To test the hypotheses, the authors model a customer's retention and usage decisions, distinguishing usage of a flat-rate service and usage of a pay-per-use service. The model allows for unobserved heterogeneity and corrects for selection effects and endogeneity. Using household panel data from a digital television service, the authors find systematic behavioral differences that cause the average customer lifetime value of free-trial customers to be 59% lower than that of regular customers. However, free-trial customers are more responsive to marketing communication and usage rates, which offers opportunities to target marketing efforts and enhance retention rates, customer lifetime value, and customer equity.
The authors present a unified strategic framework that enables competing marketing strategy options to be traded off on the basis of projected financial return, which is operationalized as the change in a firm's customer equity rel- ative to the incremental expenditure necessary to produce the change. The change in the firm's customer equity is the change in its current and future customers' lifetime values, summed across all customers in the industry. Each customer's lifetime value results from the frequency of category purchases, average quantity of purchase, and brand-switching patterns combined with the firm's contribution margin. The brand-switching matrix can be esti- mated from either longitudinal panel data or cross-sectional survey data, using a logit choice model. Firms can ana- lyze drivers that have the greatest impact, compare the drivers' performance with that of competitors' drivers, and project return on investment from improvements in the drivers. To demonstrate how the approach can be imple- mented in a specific corporate setting and to show the methods used to test and validate the model, the authors illustrate a detailed application of the approach by using data from the airline industry. Their framework enables what-if evaluation of marketing return on investment, which can include such criteria as return on quality, return on advertising, return on loyalty programs, and even return on corporate citizenship, given a particular shift in cus- tomer perceptions. This enables the firm to focus marketing efforts on strategic initiatives that generate the great- est return.
Can a marketer drive the stock price of the firm? Yes, it should be possible. Toward this endeavor, the authors develop a framework to link customer equity (CE) (as determined by the customer lifetime value metric) to market capitalization (MC) (as determined by the stock price of the firm). The authors test the framework in an empirical field experiment with two Fortune 1000 firms in the business-to-business and business-to-consumer contexts, respectively. The findings show that (1) a CE-based framework can reliably predict the MC of the firm and (2) marketing strategies directed at increasing the CE not only increase the stock price of the firm but also beat market expectations. Furthermore, the results indicate that the relationship between CE and MC is moderated by risk factors in the form of volatility and vulnerability of cash flows from customers. By accounting for these factors, the authors improve the association between CE and MC. The findings broaden the scope and role of marketing while reinforcing the importance of the marketer to any organization.
Customers are the most important assets of most companies, such that customer equity has been used as a proxy for shareholder value. However, linking customer metrics to shareholder value without considering debt and nonoperating assets ignores their effects on relative changes in customer equity and leads to biased estimates. In developing a new theoretical framework for customer-based valuation, grounded in valuation theory, this article links the value of all customers to shareholder value and introduces a new leverage effect that can translate percentage changes in customer equity into shareholder value. The average leverage effect in more than 2000 companies across ten years is 1.55, which indicates that a 10% increase in customer equity is amplified to a 15.5% increase in shareholder value. This research also compares the influence of customer and financial metrics on shareholder value. The findings challenge previous notions about the dominant effect of the retention rate and underline the importance of predicting the number of future acquired customers for a company.
Market response models based on field-generated data need to address potential endogeneity in the regressors to obtain consistent parameter estimates. Another requirement is that market response models predict well in a holdout sample. With both requirements combined, it may seem reasonable to subject an endogeneity-corrected model to a holdout prediction task, and this is quite common in the academic marketing literature. One may be inclined to expect that the consistent parameter estimates obtained via instrumental variables (IV) estimation predict better than the biased ordinary least squares (OLS) estimates. This paper shows that this expectation is incorrect. That is, if the holdout sample is similar to the estimation sample so that the regressors are endogenous in both samples, holdout sample validation favors regression estimates that are not corrected for endogeneity (i.e., OLS) over estimates that are corrected for endogeneity (i.e., IV estimation). We also dis-cuss ways in which holdout samples may be used sensibly in the presence of endogeneity. A key takeaway is that if consistent parameter estimates are the primary model objective, the model should be validated with an exogenous (rather than endogenous) holdout sample.
Service churn and retention rates remain central as constructs in marketing activities, such as valuation of service subscribers and resource allocation. Although extant approaches have been proposed to relate service churn to external factors, such as reported satisfaction, marketing-mix activities, and so on, managers often face situations in which the only information available is the duration for which subscribers have had service. In such cases, can they forecast service churn and understand the contributing factors, which may allow for subsequent intervention? The authors propose a framework to examine factors that may underlie service retention in a contractual setting. Specifically, they use a model of retention that accounts for (1) duration dependence, (2) promotional effects, (3) subscriber heterogeneity, (4) cross-cohort effects, and (5) calendar-time effects (e.g., seasonality). Then, they apply the framework to subscription databases of seven services offered by a telecommunications provider, mirroring the format commonly used to forecast future service churn (and to make managerial decisions). Across all seven services, the inclusion of promotional effects always improves the forecast accuracy of retention behavior, whereas including cross-cohort effects does not significantly improve it. In five of the services, customer heterogeneity, calendar-time effects, and duration dependence also contribute to improved forecasts. The authors use these results to understand how the expected value of a subscription differs across model specifications. They find considerable variation across model specifications, indicating that model misspecification can affect resource allocation decisions and other marketing efforts that are important to a firm., The Wharton School, University of Pennsylvania. The authors express their appreciation to the anonymous firm that gra-ciously provided the data used herein.
The authors present a new model that links the well-known RFM (recency, frequency, and monetary value) paradigm with customer life-time value (CLV). Although previous researchers have made a concep-tual link, none has presented a formal model with a well-grounded behavioral "story." Key to this analysis is the notion of "iso-value" curves, which enable the grouping of individual customers who have different purchasing histories but similar future valuations. Iso-value curves make it easy to visualize the interactions and trade-offs among the RFM meas-ures and CLV. The stochastic model is based on the Pareto/NBD frame-work to capture the flow of transactions over time and a gamma-gamma submodel for spend per transaction. The authors conduct several holdout tests to demonstrate the validity of the model's underlying components and then use it to estimate the total CLV for a cohort of new customers of the online music site CDNOW. Finally, the authors discuss broader issues and opportunities in the application of this model in actual practice.
Purpose
Synthesis of the customer lifetime value and the shareholder value (SHV) approach in order to develop an integrated, marketing‐based method for corporate valuation.
Design/methodology/approach
Discusses the limitations and assumptions of existing methods to estimate customer value components and examines the limitations of the SHV concept. By linking the customer equity (CE) and the SHV approach, a formal model to calculate corporate value is developed. The discounted cash flow method is used for modelling the profit streams.
Findings
Provides formulas for the estimation of both the individual lifetime value of a customer and CE. Provides a comprehensive model to estimate corporate value based on customer‐related cash flows and traditional financial metrics. Introduces typical cases, in which the use of a customer‐based valuation seems beneficial. Illustrates how our approach can be applied by using a simple case study on M&A in the telecommunication industry. Gives suggestions on how to obtain the necessary data, partially even from publicly available sources.
Research limitations/implications
Advancement of the quantitative techniques for modelling the customer value components would allow for relaxing some restrictive assumptions. The explicit modelling of the future growth of the customer base (the acquisition rate) would increase the applicability of the model. Additionally, taking into account heterogeneity within the customer cohorts is a task for future research. Finally, our model needs to be applied more extensively using real data for the input variables.
Practical implications
A CE‐based valuation approach can guide marketing investments and helps to avoid misallocation of resources. Based on an example in the field of M&A, we demonstrate the usefulness of the approach for obtaining a realistic indicator of firm value. It helps to assess whether an acquisition is economically sensible. We provide evidence for the superiority of a customer‐based approach over traditional financial methods.
Originality/value
While the traditional SHV method considers cash flows at a highly aggregated level, our approach employs disaggregated cash flows on the level of individual customers. Thereby we do incorporate the lifetime values of future customers by considering different cohorts. We do capture customer defection by incorporating retention rates. Our model enables a more detailed and valid estimation of corporate value by accounting for the single customer activities that drive marketing actions. This enables a better forecasting of the free cash flow. Incorporating customer‐related drivers into financial valuation models makes easier to assess the return on marketing investments.
Discrete choice models of demand have typically been estimated assuming that prices are exogenous. Since unobservable (to the researcher) product attributes, such as coupon availability, may impact consumer utility as well as price setting by firms, we treat prices as endogenous. Specifically, prices are assumed to be the equilibrium outcomes of Nash competition among manufacturers and retailers. To empirically validate the assumptions, we estimate logit demand systems jointly with equilibrium pricing equations for two product categories using retail scanner data and cost data on factor prices. In each category, we find statistical evidence of price endogeneity. We also find that the estimates of the price response parameter and the brand-specific constants are generally biased downward when the endogeneity of prices is ignored. Our framework provides explicit estimates of the value created by a brand, i.e., the difference between consumers' willingness to pay for a brand and its cost of production. We develop theoretical propositions about the relationship between value creation and competitive advantage for logit demand systems and use our empirical results to illustrate how firms use alternative value creation strategies to accomplish competitive advantage.
An econometric market response model for measuring the effect of coupon promotions upon market share is developed and estimated. In addition to the brand's own couponing efforts, the model takes into account retailer promotions for the brand as well as competitive couponing activity. The model is multi-equation, simultaneous, and is estimated using scanner panel data. Results indicate that coupons have a pronounced effect upon market share, although the effect varies from brand to brand and may not be strong enough for some brands for couponing to be profitable in the short term.
Marketers have long struggled with developing forecasts for new products before their launch. We focus on one data source—advance purchase orders—that has been available to retailers for many years but has rarely been tied together with postlaunch sales data. We put forth a duration model that incorporates the basic concepts of new product diffusion, using a mixture of two distributions: one representing the behavior of innovators (i.e., those who place advance orders) and one representing the behavior of followers (i.e., those who wait for the mass market to emerge). The resulting mixed-Weibull model specification can accommodate a wide variety of possible sales patterns. This flexibility is what makes the model well-suited for an experiential product category (e.g., movies, music, etc.) in which we frequently observe very different sales diffusion patterns, ranging from a rapid exponential decline (which is most typical) to a gradual buildup characteristic of “sleeper” products. We incorporate product-specific covariates and use hierarchical Bayes methods to link the two customer segments together while accommodating heterogeneity across products. We find that this model fits a variety of sales patterns far better than do a pair of benchmark models. More importantly, we demonstrate the ability to forecast new album sales before the actual launch of the album, based only on the pattern of advance orders.
This study is emphasized on different types of normalization. Each of which was testedagainst the ID3 methodology using the HSV data set. Number of leaf nodes, accuracy and tree growingtime are three factors that were taken into account. Comparisons between different learning methodswere accomplished as they were applied to each normalization method. A new matrix was designed tocheck for the best normalization method based on the factors and their priorities. Recommendationswere concluded.
: The simulation variance in the estimation of mixed logit parameters is found, in our application, to be lower with 100 Halton draws than with 1000 random draws. This finding confirms Bhat's (1999a) results and implies significant reduction in run times for mixed logit estimation. Further investigation is needed to assure that the result is not quixotic or masking other issues. I. Introduction Choice probabilities in mixed logit models take the form of a multidimensional integral over a mixing distribution (see, e.g., Brownstone and Train, 1999). The integral does not have a closed form in general, and so it must be evaluated numerically. In applications, the integral has been approximated though simulation using random draws from the mixing distribution. A large number of draws is usually needed to assure reasonably low simulation error in the estimated parameters. The large number of draws translates into long computer run-times. Estimation can require 2-3 hours for moderately siz...
Service churn and retention rates remain central as constructs in marketing activities, such as valuation of service subscribers and resource allocation. Although extant approaches have been proposed to relate service churn to external factors, such as reported satisfaction, marketing-mix activities, and so on, managers often face situations in which the only information available is the duration for which subscribers have had service. In such cases, can they forecast service churn and understand the contributing factors, which may allow for subsequent intervention? The authors propose a framework to examine factors that may underlie service retention in a contractual setting. Specifically, they use a model of retention that accounts for (1) duration dependence, (2) promotional effects, (3) subscriber heterogeneity, (4) cross-cohort effects, and (5) calendar-time effects (e.g., seasonality). Then, they apply the framework to subscription databases of seven services offered by a telecommunications provider, mirroring the format commonly used to forecast future service churn (and to make managerial decisions). Across all seven services, the inclusion of promotional effects always improves the forecast accuracy of retention behavior, whereas including cross-cohort effects does not significantly improve it. In five of the services, customer heterogeneity, calendar-time effects, and duration dependence also contribute to improved forecasts. The authors use these results to understand how the expected value of a subscription differs across model specifications. They find considerable variation across model specifications, indicating that model misspecification can affect resource allocation decisions and other marketing efforts that are important to a firm.
Market response models based on field-generated data need to address potential endogeneity in the regressors
to obtain consistent parameter estimates. Another requirement is that market response models predict
well in a holdout sample. With both requirements combined, it may seem reasonable to subject an endogeneity- corrected model to a holdout prediction task, and this is quite common in the academic marketing literature.
One may be inclined to expect that the consistent parameter estimates obtained via instrumental variables (IV)
estimation predict better than the biased ordinary least squares (OLS) estimates. This paper shows that this
expectation is incorrect. That is, if the holdout sample is similar to the estimation sample so that the regressors
are endogenous in both samples, holdout sample validation favors regression estimates that are not corrected
for endogeneity (i.e., OLS) over estimates that are corrected for endogeneity (i.e., IV estimation). We also discuss ways in which holdout samples may be used sensibly in the presence of endogeneity. A key takeaway is that if consistent parameter estimates are the primary model objective, the model should be validated with an
exogenous (rather than endogenous) holdout sample.
Since the start of the 21St century, a new breed of shareholder-the activist hedge fund-has frequently played a decisive role in interactions between corporations and markets. The game of these activists is simple: They buy stocks they view as undervalued and pressure management to do things they believe will raise the value, such as giving more cash back to shareholders or shedding divisions that the activists think are driving down the stock price. With increasing frequency they get deeply involved in governance-demanding board seats, replacing CEOs, and advocating specific business strategies. The authors have identified six ways in which to fend off activist challenges or use them to improve your organization: (1) Have a clear strategic focus and stick to it. (2) Analyze your business as an activist would. (3) Have your external advisers lined up in advance and familiar with your company. (4) Build board chemistry. (5) Perform in the short run against declared goals. (6) Don't dismiss activist ideas out of hand.
Researchers and managers typically recognize “customer risk” by considering the probability of customer defection, as well as by discounting cash flows using the cost of capital. However, in the finance literature, risk is associated with unexpected variability in future cash flows. An understanding of cash flow variability is critical for firms to effectively forecast, allocate resources, manage customer equity and prepare for the future. The variability or unpredictability of future cash flows arises from underlying customer interactions, consumption patterns and purchase behavior – as well as from organizational processes that support them. This chapter summarizes alternative approaches to reducing variability in future cash flows without adversely affecting cash flow levels. First, firms can target customers with low cash flow variability or low risk. Second, since customers do not have fixed cash flow characteristics, the variability of individual customers’ future cash flows can be decreased through appropriate management actions. Third, firms can allocate resources to specific customers and market segments (through their marketing plan) to yield future cash flows that complement each other and thereby decrease aggregate variability of future cash flows. This chapter offers practical ways for firms to optimize their customer base by balancing return and risk and describes avenues for future research.
We introduce a measure of customer franchise value for subscription–based companies — a fast growing and vital sector of the economy. This measure is based on information voluntarily disclosed by some, but not all, firms. Controlling for self-selection, we examine the measure's information content and find that customer value is significantly positively associated with stock price and this association is incremental to both GAAP and a set of non-GAAP variables typically considered in valuation tests. Furthermore, we show that the customer value measure is positively associated with future earnings and analysts’ forecast errors. Importantly, we find that the documented results are robust to controlling for the individual inputs used to derive the measure, highlighting the need to consider the interaction between stand-alone value drivers in assessing a firm's performance. These findings indicate that the proposed measure of customer value is an important valuation tool that quantifies and summarizes the main trends and factors underlying the performance of subscription-based enterprises. This study informs researchers and investors, as well as accounting policymakers, about a major value–generating asset currently missing from corporate financial reports.This article is protected by copyright. All rights reserved.
Many of the products introduced during the past two decades have been services rather than goods. An important influence on the growth and long-term profits of these services is customer attrition, which can occur at the category level (disadoption) or between firms (churn). However, the literature has rarely modeled how services penetrate a market and has not evaluated the effect of attrition on growth. The authors combine diffusion modeling with a customer relationship approach to investigate the influence of attrition on growth in service markets. In particular, the authors model the effects of disadoption and churn on evolution of a category and on growth of individual firms in a competitive environment. The authors show how neglecting disadoption can bias parameter estimation and, especially, market potential. They also derive an expression for the customer equity of a growing service firm and apply it to valuation of firms operating in competitive industries. The results for six of seven firms in four service categories are remarkably close to stock market valuations, an indicator for the role of customer equity in valuations of growing service firms.
Marketers have long struggled with developing forecasts for new products before their launch. We focus on one data source-advance purchase orders-that has been available to retailers for many years but has rarely been tied together with postlaunch sales data. We put forth a duration model that incorporates the basic concepts of new product diffusion, using a mixture of two distributions: one representing the behavior of innovators (i.e., those who place advance orders) and one representing the behavior of followers (i.e., those who wait for the mass market to emerge). The resulting mixed-Weibull model specification can accommodate a wide variety of possible sales patterns. This flexibility is what makes the model well-suited for an experiential product category (e.g., movies, music, etc.) in which we frequently observe very different sales diffusion patterns, ranging from a rapid exponential decline (which is most typical) to a gradual buildup characteristic of "sleeper" products. We incorporate product-specific covariates and use hierarchical Bayes methods to link the two customer segments together while accommodating heterogeneity across products. We find that this model fits a variety of sales patterns far better than do a pair of benchmark models. More importantly, we demonstrate the ability to forecast new album sales before the actual launch of the album, based only on the pattern of advance orders.
Customer acquisition and retention are not independent processes. However, because of data limitations, customer management decisions are frequently based only on an analysis of acquired customers. This analysis shows that these decisions can be biased and misleading. The author presents a modeling approach that estimates the length of a customer's lifetime and adjusts for this bias. Using the model, the author shows the financial impact of not accounting for the effect of acquisition on customer retention.
A great many people provided comments on early versions of this paper which led to major improvements in the exposition. In addition to the referees, who were most helpful, the author wishes to express his appreciation to Dr. Harry Markowitz of the RAND Corporation, Professor Jack Hirshleifer of the University of California at Los Angeles, and to Professors Yoram Barzel, George Brabb, Bruce Johnson, Walter Oi and R. Haney Scott of the University of Washington.
Recently, Penman and Sougiannis (1998) and Francis, Olsson, and Oswald (2000) compared the bias and accuracy of the discounted cash flow model (DCF) and Edwards‐Bell‐Ohlson residual income model (RIM) in explaining the relation between value estimates and observed stock prices. Both studies report that, with non‐price‐based terminal values, RIM outperforms DCF.
Our first research objective is to explore the question whether, over a five‐year valuation horizon, DCF and RIM are empirically equivalent when Penman's (1997) theoretically “ideal” terminal value expressions are employed in each model. Using Value Line terminal stock price forecasts at the horizon to proxy for such values, we find empirical support for the prediction of equivalence between these valuation models. Thus, the apparent superiority of RIM does not hold in a level playing field comparison.
Our second research objective is to demonstrate that, within each class of the DCF and RIM valuation models, the model that employs Value Line forecasted price in the terminal value expression generates the lowest prediction errors, compared with models that employ non‐price‐based terminal values under arbitrary growth assumptions. The results indicate that, for both DCF and RIM, price‐based valuation models outperform the corresponding non‐price‐based models by a wide margin. These results imply that researchers should exercise care in interpreting findings from models using ad hoc terminal value expressions.
When defection is unobserved, latent attrition models provide useful insights about customer behavior and accurate forecasts of customer value. Yet extant models ignore direct marketing efforts. Response models incorporate the effects of direct marketing, but because they ignore latent attrition, they may lead firms to waste resources on inactive customers.
We propose a parsimonious model that allows direct marketing to impact three relevant behaviors in latent attrition models—the frequency with which customers conduct transactions, the size of the transactions, and the duration for which customers remain active. Our model also accounts for how the organization targets its direct marketing across individuals and over time.
Using donation data from a nonprofit organization, we find that direct marketing increases donation incidence for active donors. However, our analysis also shows that direct marketing has the potential to shorten the length of a donor's relationship. We find that our proposed model offers superior predictive performance compared with models that ignore the impact of direct marketing activity or latent attrition. We demonstrate the managerial applicability of our modeling approach by estimating the impact of direct marketing on donation behavior and identifying those donors most likely to conduct transactions in the future.
It is increasingly apparent that the financial value of a firm depends on off-balance-sheet intangible assets. In this article, the authors focus on the most critical aspect of a firm: its customers. Specifically, they demonstrate how valuing customers makes it feasible to value firms, including high growth firms with negative earnings. The authors define the value of a customer as the expected sum of discounted future earnings. They demonstrate their valuation method by using publicly available data for five firms. They find that a 1% improvement in retention, margin, or acquisition cost improves firm value by 5%, 1% and .1% respectively. They also find that a 1% improvement in retention has almost five times greater impact on firm value than a 1% change in discount rate or cost of capital. The results show that the linking of marketing concepts to shareholder value is both possible and insightful.
We examine the valuation performance of a comprehensive list of value drivers and find that multiples derived from forward earnings explain stock prices remarkably well: pricing errors are within 15 percent of stock prices for about half our sample. In terms of relative performance, the following general rankings are observed consistently each year: forward earnings measures are followed by historical earnings measures, cash flow measures and book value of equity are tied for third, and sales performs the worst. Curiously, performance declines when we consider more complex measures of intrinsic value based on short-cut residual income models. Contrary to the popular view that different industries have different “best” multiples, these overall rankings are observed consistently for almost all industries examined. Since we require analysts’ earnings and growth forecasts and positive values for all measures, our results may not be representative of the many firm-years excluded from our sample.
This article is concerned with counting and identifying those customers who are still active. The issue is important in at least three settings: monitoring the size and growth rate of a firm's ongoing customer base, evaluating a new product's success based on the pattern of trial and repeat purchases, and targeting a subgroup of customers for advertising and promotions. We develop a model based on the number and timing of the customers' previous transactions. This approach allows computation of the probability that any particular customer is still active. Several numerical examples are used to illustrate applications of the model.
Schmittlein and Mahajan (Schmittlein, D. C., V. Mahajan. 1982. Maximum likelihood estimation for an innovation diffusion model of new product acceptance. (Winter) 57–78.) made an important improvement in the estimation of the Bass (Bass, F. M. 1969. A new product growth model for consumer durables. (January) 215–227.) diffusion model by appropriately aggregating the continuous time model over the time intervals represented by the data. However, by restricting consideration to only sampling errors and ignoring all other errors (such as the effects of excluded marketing variables), their Maximum Likelihood Estimation (MLE) seriously underestimates the standard errors of the estimated parameters. This note uses an additive error term to model sampling and other errors in the Schmittlein and Mahajan formulation. The proposed Nonlinear Least Squares (NLS) approach produces valid standard error estimates. The fit and the predictive validity are roughly comparable for the two approaches. Although the empirical applications reported in this paper are in the context of the Bass diffusion model, the NLS approach is also applicable to other diffusion models for which cumulative adoption can be expressed as an explicit function of time.
We model the diffusion of innovations in markets with two segments: who are more in touch with new developments and who affect another segment of whose own adoptions do not affect the influentials. This two-segment structure with asymmetric influence is consistent with several theories in sociology and diffusion research, as well as many “viral” or “network” marketing strategies. We have four main results. (1) Diffusion in a mixture of influentials and imitators can exhibit a dip or “chasm” between the early and later parts of the diffusion curve. (2) The proportion of adoptions stemming from influentials need not decrease monotonically, but may first decrease and then increase. (3) Erroneously specifying a mixed-influence model to a mixture process where influentials act independently from each other can generate systematic changes in the parameter values reported in earlier research. (4) Empirical analysis of 33 different data series indicates that the two-segment model fits better than the standard mixed-influence, the Gamma/Shifted Gompertz, and the Weibull-Gamma models, especially in cases where a two-segment structure is likely to exist. Also, the two-segment model fits about as well as the Karmeshu-Goswami mixed-influence model, in which the coefficients of innovation and imitation vary across potential adopters in a continuous fashion.
A consistent pattern observed for really new household consumer durables is a takeoff or dramatic increase in sales early in their history. The takeoff tends to appear as an elbow-shaped discontinuity in the sales curve showing an average sales increase of over 400%. In contrast, most marketing textbooks as well as diffusion models generally depict the growth of new consumer durables as a smooth sales curve.
Our discussions with managers indicate that they have little idea about the takeoff and its associated characteristics. Many managers did not even know that most successful new consumer durables had a distinct takeoff. Their sales forecasts tend to show linear growth. Yet, knowledge about the takeoff is crucial for managers to decide whether to maintain, increase, or withdraw support of new products. It is equally important for industry analysts who advise investors and manufacturers of complementary and substitute products.
Although previous studies have urged researchers to examine the takeoff, no research has addressed this critical event. While diffusion models are commonly used to study new product sales growth, they do not explicitly consider a new product's takeoff in sales. Indeed, diffusion researchers frequently use data only from the point of takeoff. Therefore, nothing is known about the takeoff or models appropriate for this event. Our study provides the first analysis of the takeoff. In particular, we address three key questions: (i) How much time does a newly introduced product need to takeoff? (ii) Does the takeoff have any systematic patterns? (iii) Can we predict the takeoff?
We begin our study by developing an operational measure to determine when the takeoff occurs. We found that when the base level of sales is small, a relatively large percentage increase could occur without signaling the takeoff. Conversely, when the base level of sales is large, the takeoff sometimes occurs with a relatively small percentage increase in sales. Therefore, we developed a “threshold for takeoff.” This is a plot of percentage sales growth relative to a base level of sales, common across all categories. We define the takeoff as the first year in which an individual category's growth rate relative to base sales crosses this threshold. The threshold measure correctly identifies the takeoff in over 90% of our categories.
We model the takeoff with a hazard model because of its advantages for analyzing time-based events. We consider three primary independent variables: price, year of introduction, and market penetration, as well as several control variables. The hazard model fits the pattern of takeoffs very well, with price and market penetration being strong correlates of takeoff.
Our results provide potential generalizations about the time to takeoff and the price reduction, nominal price, and penetration at takeoff. In particular, we found that:
• On average for 16 post-World War II categories:
— the price at takeoff is 63% of the introductory price;
— the time to takeoff from introduction is six years;
— the penetration at takeoff is 1.7%.
• The time to takeoff is decreasing for more recent categories. For example, the time to takeoff is 18 years for categories introduced before World War II, but only six years for those introduced after World War II.
• Many of the products in our sample had a takeoff near three specific price points (in nominal dollars): $1000, $500 and $100.
In addition, we show how the hazard model can be used to predict the takeoff. The model predicts takeoff one year ahead with an expected average error of 1.2 years. It predicts takeoff at a product's introduction with an expected average error of 1.9 years. Even against the simple mean time to takeoff of six years for recent categories, the model's performance represents a tremendous improvement in prediction. It represents an immeasurable improvement in prediction for managers who currently have no idea about how long it takes for a new product to takeoff. The threshold rule for determining takeoff can be used to distinguish between a large increase in sales and a real takeoff.
Some limitations of this study could provide fruitful areas for future research. Our independent variables suffer from endogeneity bias, so alternative variables or methods could address this limitation. Also, the takeoff may be related to additional variables such as relative advantage over substitutes and the presence of complementary products. Finally, examination of sales from takeoff to their leveling off could be done with an integrated model of takeoff and sales growth or with the hazard model we propose. Generalizations about this period of sales growth could also be of tremendous importance to managers of new products.
Many businesses track repeat transactions on a discrete-time basis. These include: (1) companies where transactions can only occur at fixed regular intervals, (2) firms that frequently associate transactions with specific events (e.g., a charity that records whether or not supporters respond to a particular appeal), and (3) organizations that simply use discrete reporting periods even though the transactions can occur at any time. Furthermore, many of these businesses operate in a noncontractual setting, so they have a difficult time differentiating between those customers who have ended their relationship with the firm versus those who are in the midst of a long hiatus between transactions. We develop a model to predict future purchasing patterns for a customer base that can be described by these structural characteristics. Our beta-geometric/beta-Bernoulli (BG/BB) model captures both of the underlying behavioral processes (i.e., customers' purchasing while "alive", and time until each customer permanently "dies"). The model is easy to implement in a standard spreadsheet environment, and yields relatively simple closed-form expressions for the expected number of future transactions conditional on past observed behavior (and other quantities of managerial interest). We apply this discrete-time analog of the well-known Pareto/NBD model to a dataset on donations made by the supporters of a public radio station located in the Midwestern United States. Our analysis demonstrates the excellent ability of the BG/BB model to describe and predict the future behavior of a customer base.
The past few years have seen increasing interest in taking the notion of customer lifetime value (CLV) and extending it to value a customer base (with subsequent links to corporate valuation). The application of standard textbook discussions of CLV sees us performing such calculations using a single aggregate retention rate. However, at the cohort level, retention rates typically increase over time. We suggest that these observed dynamics are due, in large part, to a sorting effect in a heterogeneous population. We show that failing to recognize these dynamics yields a downward-biased estimate of the residual value of the customer base. We also explore the implications of failing to account for retention dynamics when computing retention elasticities, and find that the frequently reported estimates underestimate the true effect of increases in underlying retention rates in a heterogeneous world.
Managers have recently begun to think of good marketing as good conversation, as a process of drawing customers into progressively more satisfying relationships with a company. And just as the art of conversation follows two steps--first striking up a conversation with a likely partner and then maintaining the flow--so the new marketing naturally divides itself into the work of customer acquisition and the work of customer retention. But how can managers determine the optimal balance between spending on acquisition and spending on retention? Robert Blattberg and John Deighton use decision calculus to help managers answer that question. That is, they ask managers to approach the large, complex problem through several smaller, more manageable questions on the same topic. Then they use a formal model to turn those smaller judgments into an answer to the larger question. The ultimate goal, the authors say, is to grow the company's customer equity the sum of all the conversations-to its fullest potential. Recognizing that managers must constantly reassess the spending points determined by the decision-calculus model, the authors also provide a series of guidelines and suggestions to help frame the issues that affect acquisition, retention, and customer equity. When managers strive to grow customer equity rather than a brand's sales or profits, they put a primary indicator of the health of the business at the fore front of their strategic thinking: the quality of customer relationships.
A number of researchers have developed models that use test market data to generate forecasts of a new product's performance. However, most of these models have ignored the effects of marketing covariates. In this paper we examine what impact these covariates have on a model's forecasting performance and explore whether their presence enables us to reduce the length of the model calibration period (i.e. shorten the duration of the test market). We develop from first principles a set of models that enable us to systematically explore the impact of various model 'components' on forecasting performance. Furthermore, we also explore the impact of the length of the test market on forecasting performance. We find that it is critically important to capture consumer heterogeneity, and that the inclusion of covariate effects can improve forecast accuracy, especially for models calibrated on fewer than 20 weeks of data. Copyright © 2003 John Wiley & Sons, Ltd.
- Bauer Hans H.
- Fader Peter S.
- Schweidel David A.
Halton Sequences for Mixed Logit,” working paper
- Train Kenneth
Grande Expectations: A Year in the Life of Starbucks' Stock
- Karen Blumenthal
Blumenthal, Karen (2008), Grande Expectations: A Year in the Life of
Starbucks' Stock. New York: Crown Publishing.
Marketing Metrics: The Definitive Guide to Measuring Marketing Performance
- Paul W Farris
- T Neil
- Phillip E Bendle
- David J Pfeifer
- Reibstein
Farris, Paul W., Neil T. Bendle, Phillip E. Pfeifer, and David J. Reibstein
(2010), Marketing Metrics: The Definitive Guide to Measuring Marketing Performance. Upper Saddle River, NJ: Pearson Education.