It is self-evident that customers are essential to business enterprises. This was already the case when the ﬁrst barter transaction in history was concluded. What is relatively new is the ability of many businesses now, particularly those who charge a subscription fee for their services, to track their customers, identify their preferences, customize products to people’s tastes, and learn about their experiences and satisfaction level. This wealth of information derived from the footprints of customers of Internet service providers, media and entertainment ﬁrms, and insurance companies, among other sectors, radically transformed corporate customer management. But this transformation is a work in process with lots of unanswered questions for both corporate managers and their shareholders. That is the reason this book on customer accounting is such a welcome addition to the literature of management, marketing, operations research, and of course accounting. The core of the book is the introduction of the highly useful concept of a company’s lifetime value of customers, which for many enterprises is their largest and most consequential, value-creating asset. The computation of customers’ value (customer equity) and the various uses of this important metric in management and capital market investment decisions are clearly discussed in this book. The many real-life examples provided by the authors, both experts on the subject, demonstrate the power of this new metric and make the book fun to read. Customer value and the related measures introduced and demonstrated by the authors are particularly important to investors, given the sharp decline in the usefulness and relevance of the traditional accounting and ﬁnancial variables used in investment analysis. In this book, both managers and investors will ﬁnd new measures and methods to manage customers and enhance corporate value. Who will beneﬁt from this book? Corporate executives responsible for the management of their customers to create corporate value and also CFOs; ﬁnancial analysts and investors striving to value business enterprises and frustrated with the traditional, failed ﬁnancial measures based on accounting asset and earnings; and
last but not least, business students, both at the undergraduate and graduate (MBA) levels, will beneﬁt considerably from this book in ﬁnance, marketing, and accounting courses. Philip Bardes Professor of Accounting and Finance NYU Stern School of Business New York, NY, USA
The primary function of a business is to serve the customer and the primary goal of your business is to create customers. —Peter Drucker
Customer-Centricity in a Fast-Evolving Landscape
During the Nineties, the business environment was affected by technological advances resulting from “combinatorial innovations” triggered by liberalization of the telecommunication industry and the Internet (Varian et al. 2004). Those innovations created the basis for many of the innovative services introduced over the past decade, such as cell phones, satellite radio, cable TV, ﬁnancial services (e.g. direct banking) and internet services (games, music, entertainment, etc.) (Libai et al. 2009). At the same time, the information technology (IT) revolution introduced extraordinary improvements in methods of collecting, storing, analyzing, and transmitting huge amounts of information (Varian 2006, 2009). Firms realized that this presented great opportunities to invest in IT to manage customer relationships, since data could reveal actual customer preferences rather than merely their intentions, making sampling unnecessary since information on customer behavior became available for the entire population of customers (Gupta et al. 2006). For instance, advertising models evolved from a focus on “brand awareness” to “direct and measurable” customer acquisitions (Economist 2006a, b, 2007; Epstein 2007; Epstein and Yuthas 2007; French 2007). Unlike television advertising, Internet advertisers paid only when a user clicked through to their website, gaining a reliable measurement of customer acquisition costs (Court 2005; Laffey 2007; Mulhern 2009). In recent years, ﬁrms have continued witnessing a period of transformative developments that emphasize the central role of customers in all industries. We
provide below a few examples showing customer power and the trends shaping the future of marketing decisions into the next decade: • Half of the ﬁrms listed in the DAX 30 and DJIA 30 explicitly mention in their mission statements or company strategies the notion of value creation for customers (Kumar and Reinartz 2016) • According to a 2017 Forrester report, we are now fully within the ‘Age of the Customer’, in which newly empowered customers place elevated expectations on every interaction they have with brands. • The 2017 Salesforce report “State of the Connected Customer”, revealed that 70% of consumers now believe technology has made it easier than ever to switch brands to ﬁnd experiences that matches their expectations. • The results of a 2016 global survey by Forbes Insights showed that ﬁrms who increased their spending on retention in the last 1–3 years had nearly a 200% higher likelihood of increasing their market share in the last year compared to those spending more on acquisition. • An online survey by TECH at Harvard revealed that in 2016, increasing customer experience received the highest priority among 908 IT decision makers at global ﬁrms. These latest examples clearly indicate that consumers hold far more power than ever before in today’s ultracompetitive and fast evolving business landscape. The transition from a product-centric, transaction-focused business model to a more relationship-oriented or customer-centric view appears as a necessary condition to sustain long-term business performance (Sheth et al. 2000; Shah et al. 2006; Ramani and Kumar 2008). This transition necessitates a radical shift that aggressively relies on interaction response capacity and customer value management (Kumar et al. 2008; Ramani and Kumar 2008). Interaction response capacity is the degree to which a ﬁrm can provide successful products and services by exploiting the feedback of a speciﬁc customer. At the same time, through customer value management a ﬁrm can deﬁne and dynamically measure individual customer data and use this information as a guiding principle for tactical and strategic resource allocation decisions. Customer-centric ﬁrms thus understand not only what the customer values but, more importantly, the value the customer adds to their bottom line. Customercentricity implies a carefully deﬁned and quantiﬁed customer segmentation strategy in which a ﬁrm’s operations aim at delivering the greatest value to the best customers for the least cost (Sheth et al. 2000; Shah et al. 2006; Ramani and Kumar 2008; Libai et al. 2009; Fader 2012). Shah et al. (2006) and Fader (2012) emphasize that customer centricity is a necessary condition for twenty-ﬁrst-century ﬁrms that need to address key strategic issues (Kumar and Rajan 2012; Cokins 2015) such as: • Do we push for volume or for margin with a speciﬁc customer? How many products can we sell to a speciﬁc customer? • How can we develop proﬁtable relationships over a long time span?
1.1 Customer-Centricity in a Fast-Evolving Landscape
Table 1.1 Comparison of the product-centric and customer-centric approach (source: Bonacchi and Perego 2012) Basic philosophy Business orientation Product positioning and selling approach Organizational focus
Highlight product’s beneﬁts in terms of meeting individual customer needs Externally focused. Customer relationship development, proﬁtability through customer loyalty. Employees are customer advocates How many products can we sell to this customer?
Internally focused. New product development, new account development, market share growth, and customer trelations are issues for the marketing department How many customers can we sell this product to?
Source: Authors’ elaboration adapted from Kumar (2008a); Ramani and Kumar (2008); Shah et al. (2006)
• How can we identify proﬁtable customer segments and business processes with higher productivity? • Can we inﬂuence our customers to alter their behavior to interact differently (and more proﬁtably) with us? In Table 1.1, we summarize the main differences between product-centric and customer-centric orientations after a review of several sources in marketing and management literature (Sheth et al. 2000; Egol et al. 2004; Shah et al. 2006; Kumar 2008a, b; Ryals 2008). In this context, disruptive developments in digital technology, Internet of Things (IoT), sensor data and the social media have accelerated the shift towards customercentricity on an unprecedented scale and pace. In a short time, ﬁrms in several industries have started to collect very large quantities of data from their own operations, supply chains, production processes, and customer interactions. The scale and diversity of customer data provide Internet-based ﬁrms such as Facebook, Google, Amazon and Netﬂix rich new sources of business insights, allowing ﬁrms to understand and engage with customers in novel ways to both better serve them and maximize proﬁtability. Beyond a basic transaction history, companies currently track marketing interactions, clicks, web or mobile navigation patterns, and online and ofﬂine behaviors, on their own platforms or on social media. They also receive large amounts of data from connected objects owned by customers (e.g. mobile phones, tablets, tracking devices). Traditional databases cannot handle such volumes of information and variety of formats, but this is where ‘Big Data’ solutions step in. We are currently witnessing a shift in the breadth and depth of ﬁrms’ customer accounting systems. In this book, we use the label customer analytics to broadly denote the metrics, processes and technologies that provide
ﬁrms the insight into customers necessary to deliver offers that are anticipated, relevant and timely. Numerous examples are emerging of the potential impact of customer analytics in traditional companies: Tesco and IBM, among other large ﬁrms, make increasing use of Big Data to deliver contextual insights about purchase behaviors and marketing response. Several ﬁrms are also spinning up new investigative computing or data science practices rooted in artiﬁcial intelligence (AI), deep learning and other highly dynamic and multidimensional forms of advanced analytics. Half a decade ago, none of these disrupting technologies were anywhere close to being used in daily practices. In the closing chapter of this book, we will point at these developments further.
Motivation and Objectives of This Book
An increasing number of academic papers in marketing have examined how a customer-centric focus can provide competitive advantages and emphasized the beneﬁts of providing differentially tailored responses to marketing initiatives, such that the contribution from each customer to overall proﬁtability is maximized (e.g., Verhoef and Lemon 2013). The marketing literature has also started to highlight the organizational steps and barriers critical to initiate and sustain customer centricity (Shah et al. 2006; Kumar et al. 2008). However, there is a dearth of knowledge about the business processes with which CFOs and management accountants interact and coordinate with other CMOs and marketing managers to monitor the attraction, conversion and retention of customers through marketing campaigns and reliance on customer data. Interested readers should refer to recent reviews of the literature dedicated to the marketing-accounting interface (Gleaves et al. 2008; Roslender and Wilson 2008; Kraus et al. 2015). The apparent disjunction between these two core functions emerges clearly in the developments of the accounting literature on customer accounting, deﬁned as “all accounting techniques that measure individual customer’s and/or customer segments’ contributions to ﬁrm proﬁtability” (Holm et al. 2016). On one hand, accounting textbooks seem to cover traditional techniques of customer proﬁtability analysis and only marginally treat contemporary topics in customer value management (Gleaves et al. 2008; Bates and Whittington 2009). On the other hand, the academic literature on customer accounting is still embryonic when compared to marketing, pointing at a relevant gap between current practice and theory-driven research in this rapidly changing business area (Guilding and McManus 2002; McManus and Guilding 2008). We will provide a review of this literature in subsequent chapters. In sum, whilst the volume and complexity of customer data today require sophisticated analytic methods that go beyond traditional measurement and reporting, accounting research and accounting textbook knowledge on these topics lag behind. In this book, we contribute to ﬁlling this void by examining fundamental issues, challenges and opportunities that typically a CFO or a manager in the accounting & ﬁnance function would face when dealing with customer-centricity and the role of
customer analytics in extracting valuable business insights at all stages of the customer lifecycle. To logically map and structure the various implications, we draw upon a theoretical framework that allows an analysis of the main levers involved in the implementation of a customer-centric strategy. Such a conceptualization, labeled ‘organizational architecture’, relies on research conducted in organizational economics and management accounting (Wruck and Jensen 1994; Brickley et al. 1995; Ittner and Larcker 2001; Brickley et al. 2004; Brickley et al. 2009) and has the advantage of being broadly generalizable to several business contexts and industries. In the next section, we provide a deﬁnition and a few examples of the three components of organizational architecture relevant for customer-centricity.
The organizational architecture framework provides the infrastructure with which business processes are deployed and ensures that the organization’s core capabilities are realized across business processes. A key issue is ensuring that decision makers not only have the relevant (i.e. accurate and useful) information required to make decisions, but that they must also be provided with the appropriate incentives to use that information to achieve organizational objectives. Thus, the fundamental tenet behind organizational architecture is that value creation depends on coherence among three primary organizational components, namely, the assignment of decision rights, the choice of performance measures, and the design of compensation and incentive systems, as depicted in Fig. 1.1. The extent to which top management chooses how to design an organizational architecture differs greatly among ﬁrms. Such differences are not random but vary in systematic ways with underlying characteristics of the ﬁrms themselves. Drawing on the contingency theory of organizations in management (Brickley et al. 1995, 2004; Brickley et al. 2009) and management accounting research (e.g. Gong and Ferreira 2014), consistent relationships and alignment among the three components should ensure the most effective ﬁt with a ﬁrm’s business environment and inherent strategy (Ittner and Larcker 1997; Langﬁeld-Smith 1997; Chenhall 2003; Widener et al. 2008; Lee and Yang 2011; Grabner and Moers 2013). Kaplan and Norton (2004) state that “unless an organization links its strategy to its governance and operational processes, it won’t be able to sustain its success”. Put simply, failure to properly design and incorporate the three levers (hence the ‘three-legged stool’ label of the model) in internal decision-making and control systems, is likely reﬂected in lower organizational performance. Previous management accounting studies recognize that these three key organizational elements are jointly determined and complementary (Nagar 2002; Abernethy et al. 2004; Widener et al. 2008). The role of strategy is indeed a crucial part of a contingency framework, although, as noted by Chenhall (2003: 150), “it is not an element of the context, it is a means whereby managers can inﬂuence the nature of the external environment, the technologies of the organization, the structural arrangement, the control culture and the
Contingent Variables Technology − Fast pace of change − Big Data, AI, Social media
− Rising power of the Customer − Diffusion of services
− Deregulation − Globalization
Business Strategy Customer-Centric Strategy
Organizational Architecture Allocation of decision rights
Financial performance Fig. 1.1 Conceptual framework: organizational architecture (source: Adapted from Brickley et al. 1995)
management control system.” The marketing literature similarly sees in customercentric strategies a solution to adapt to the new competitive environment characterized by rapid changes in technology, market forces and regulation. In particular, Shah et al. (2006), Fader (2012) and Cokins (2015) emphasize that customer centricity is a necessary condition for twenty-ﬁrst-century ﬁrms that need to address key strategic issues (Kumar and Rajan 2012), such as: • How many products can we sell to the customer? • How can we develop proﬁtable relationships? • How can we identify proﬁtable customer segments? Following this rationale, customer-centric ﬁrms should deliberately design and develop features in their organizational architecture that differentiate them from those typical of traditional product-centric ﬁrms. The performance measurement system (how a ﬁrm’s performance is conceptualized, tracked and evaluated) involves the choice of performance measures to coordinate the efforts of decision makers, to provide feedback to top management for evaluating progress toward strategic objectives and to employees for learning purposes. A critical component of the performance measurement system for
customer-centric organizations is determining how to collect customer-related data to provide a uniﬁed, comprehensive, and organization-wide view of a ﬁrm’s customer base, irrespective of the products purchased or channels employed by the customer. This entails a substantial IT-related investment commitment to set up an information infrastructure for collecting, tracking, and integrating data at the individual-customer and transaction level. Jayachandran et al. (2005) speciﬁed how several information system-related activities can be integrated and allow customercentric ﬁrms to successfully build a viable relationship with their customers. Such an integrated database is then made accessible to those responsible for managing the customer relationship to analyze past performance with the goal of understanding the “why” behind customer behavior (Shah et al. 2006). One of the reasons many organizations struggle to deliver value from customer data is the excessive number of possible integration points among the number of different data management and analysis technologies. In recent years, the advent of disrupting digital technologies and Big Data has accelerated and opened up a variety of technical solutions to measure customer-related performance data. Several ﬁrms today have multiple data warehouses, data marts, data caches, and operational data stores aimed at a timely collection of customer information. The allocation of decision-making authority (that is, who in the organization is given the authority to make decisions) reﬂects the contention that delegation and empowering people with speciﬁc knowledge is a critical determinant of organizational success. A typical product-centric company that is organized around functional silos deﬁned by product types is not conducive to customer centricity, as each product/sales manager may end up pushing different product offerings to the same customer without ﬁrst determining what the customer’s true needs are. On the contrary, it can be posited that a customer-centric organization has its functional activities integrated and aligned to successfully serve its customers. The ﬁrst stage of this organizational realignment is the emergence of lateral coordinating activities that aim to overcome the traditional deﬁciencies of products or functional silos. This may be achieved by setting up a horizontal organization structure, in which information ﬂows are readily shared among team members (Shah et al. 2006). In this context, ensuring an interface between the Marketing and the Accounting and Finance (A&F) functions becomes crucial. For example, more than a dozen Fortune 1000 ﬁrms, such as Coca Cola, Hershey, Intel, HP, and JD Edwards, have created a specialized function, labeled as Chief Customer Ofﬁcer, to acknowledge the importance of customer-centricity-related issues in the boardroom (Shah et al. 2006; Rust et al. 2009). Wells Fargo has successfully realigned its organization by creating a two-tiered sales structure whereby a relationship manager ensures an interaction orientation (external focus) and a product specialist provides the technical input for product development (internal focus). In this context, the interface between Marketing and A&F is crucial to provide decision makers with relevant information on customer proﬁtability. The third element of an organizational architecture refers to the formal incentive and compensation systems (how a ﬁrm rewards its management for success). Incentive systems seek to motivate managers and employees to be more productive,
to focus on organizational objectives and to learn. A broad consensus from a variety of disciplines concludes that the presence of incentives inﬂuences behavior. With regard to customer-centric organizations, ﬁrms should include selected customer metrics among the key performance indicators that are regularly reported to the top management and the board. Moreover, it is essential to synchronize incentive and reward systems by linking the formal evaluation of employees with customer-centric metrics and targets. For instance, sales/account managers could be rewarded for increasing customer equity, while relationship managers could be incentivized to extend the proﬁtable lifetime duration of the customers. For example, Texas Instruments is reported to have successfully introduced a reward system that includes three marketing metrics tracking the following dimensions: marketing gains for three consecutive years, efﬁcient and timely services and better understanding of customers (Kumar 2008b). In sum, a customer-centric strategy should shape ﬁrms in ways that radically deviate from transaction- and product-centric business models. The speciﬁc architecture choices in the three dimensions of organizational design likely have an impact on the proﬁtability of the ﬁrm. Incorporating several customer data sources into customer analytics, properly allocating decision-rights to move quickly from data to decision, and aligning incentives to avoid dysfunctional triggers remain fundamentally difﬁcult tasks contingent upon the business environment, the industry and the technological developments in which a ﬁrm operates.
Outline of This Book
We acknowledge that the organizational architecture (similarly to other organizational design frameworks) is an abstraction of the complex interdependencies, simultaneous choices, and feedback loops found in practice. However, it provides a useful framework for categorizing the main organizational dimensions and business processes involved in customer-centric ﬁrms and the main effects thereof. In this book, we will therefore rely upon the organizational architecture to structure our analysis along two lines: • The current state-of-the-art academic literature: our focus will predominantly be on accounting studies, although we will also highlight main trends and ﬁndings in the marketing literature; • Practical applications or ﬁeld studies that serve the purpose of illustrating with concrete examples and research insights how customer accounting can inﬂuence organizations interested to shift towards customer-centricity. We will initially point to the recent developments in the dimension of performance measurement as a foundational element of the organizational architecture required to pursue a speciﬁc business strategy—in our setting a customer-centric strategy. The label and contents we adopt will be customer analytics to more appropriately convey the combination of the wide range of data sources and customer
1.4 Outline of This Book
metrics with the analytic capabilities used to engage with customers. To determine the relative analytics proﬁciency of an organization, MIT Sloan Management Review developed the Analytics Core Index based on the organization’s core analytics capabilities in: • ingesting data (capturing, aggregating, and integrating data); • analyzing (descriptive analytics, predictive analytics, and prescriptive analytics); • applying insights (disseminating data insights and incorporating them into automated processes). Our aim is not to dissect every analytic capability; we will focus instead on essential features that are more relevant for the typical challenges faced by a CFOs and CMOs in developing a suitable set of customer analytics. Chapter 2 provides deﬁnitions of the most widely diffused customer metrics, namely Customer Proﬁtability (CP), Customer Lifetime Value (CLV), Customer Equity (CE). We refer to the marketing literature that extensively covers these metrics and illustrate their interrelationships. We point at applications in business settings that have a contractual, subscription-based model and mention potential challenges to compute CLV in non-contractual settings. To illustrate the implementation and impact of customer metrics in a real-world context, we provide a case study focused on the computation of CLV in an Internet-based, subscription-based company. The case presents a simulation that applies cohort analysis in an attempt to ﬁll the void between theoretical CLV models and its implementation in practice. The main rationale is to provide CFOs and CMOs a better understanding of new and latent customer preferences in a typical subscription-based business model by directly observing the customer’s purchase behavior and subsequently linking this data to estimate CLV and ﬁrm performance. In Chap. 3 we offer a critical evaluation of the literature in accounting that examined the role of customer metrics in internal decision-making and control purposes. We draw on the relationships theorized in the organizational architecture outlined in Chap. 1 to structure our selective review and emphasize key critical gaps in our knowledge, especially vis-à-vis extant developments in the marketing literature. The chapter then presents two empirical studies aimed at generating insights on the adoption of customer metrics for internal decision-making and control purposes. The ﬁrst study is a qualitative case study conducted within a subscription-based enterprise (SBE). The second study reports a survey about the diffusion of customer metrics in a sample of SBEs. In combination, the empirical evidence highlights relevant take-away points and current challenges about the actual use of customer metrics in performance measurement and management control systems. Chapter 4 reiterates a common critique about current ﬁnancial accounting models (e.g. IFRS/US GAAP), namely that they cannot capture Customer Franchise as key value creator intangible asset. We approach this issue by characterizing the business model of subscription-based enterprises (SBEs) that offer a for-fee-per-period access to products or services. Speciﬁcally, we show how to aggregate publicly available data into a measure of a ﬁrm’s Customer Equity value, which incorporates the major value drivers of SBEs, and empirically examine its properties. We build on the idea
that the acquisition and retention of proﬁtable customers is crucial for SBEs to identify the fundamental elements of their business model (e.g., customer base, revenues and service cost per user, and customer turnover). We further argue that companies should disclose in the Management Discussion and Analysis (MD&A) section of their annual report a set of customer metrics useful to investors, such as new subscriber acquisitions, revenue per subscriber, customer dropouts, and cost of customer acquisition. Chapter 5 concludes the book and provides a glimpse on managerial, technological and institutional trends that will likely affect the way customer metrics will be deployed to create business value in the next decade.
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Customer Analytics: Deﬁnitions, Measurement and Models
The world’s most valuable resource is no longer oil, but data. —The Economist
Customer Analytics: Deﬁnitions of CP, CLV and CE
In a recent discussion on the future of management accounting, Cokins (2013, 2014) pointed out that cost accounting techniques like Activity-Based Costing were conceived as causal cost tracing approaches to manage the complexity caused by increasingly diverse types of products, services, channels and customers. He labelled the period from 1980 to date as the ‘consumer era’ and suggested moving forward into the predictive analytics era, with a shift in emphasis from a backward-looking to forward-looking perspective of strategy and operations. Cokins (2013: 25) identiﬁed the expansion from product to channel and customer proﬁtability analysis and called for management accounting to support the sales and marketing function to ﬁnd “the best types of customer to retain, grow, win back and acquire” in order to maximize shareholder value. Consistently, with such a call to expand the toolkit of traditional cost accounting techniques, a survey by Deloitte in 2016 found that more than half of responding North American CFOs, broadly speaking, were investing substantially in (or were planning to invest in) customer analytics, with ﬁnance/accounting analytics running a close second in terms of priority. The central tenet behind any performance measurement system is the type and sophistication of tailored business performance metrics or indicators that allow managers to gauge a ﬁrm’s performance against targets. Literature reviews by Kumar and George (2007), Villanueva and Hanssens (2007), Kumar (2008a) and Petersen et al. (2009) provided exhaustive coverage of a new generation of customer-metrics in the marketing literature. Three core marketing-related indicators have been crucial in ensuring the shift towards a customer-centric strategy:
2 Customer Analytics: Deﬁnitions, Measurement and Models
• Customer Proﬁtability; • Customer Lifetime Value; • Customer Equity. We brieﬂy deﬁne these fundamental customer analytics consistently with the marketing and accounting literature and will rely on these deﬁnitions accordingly for the remainder of this book (Pfeifer et al. 2005; Villanueva and Hanssens 2007; Gleaves et al. 2008; Kumar 2008a; Kumar and Shah 2009). Customer Proﬁtability (CP) is deﬁned as the difference between the revenue earned from, and the cost associated with, a customer relationship during a speciﬁed period (Smith 1993; Smith and Dikolli 1995; Foster et al. 1996). This metric is usually gauged in one accounting period (e.g. monthly, quarterly, half yearly and/or yearly) in which all revenues and costs have to be traced or allocated to customers. CP belongs to the necessary toolkit that helps to make decisions about: (a) which customers to select for targeting; (b) determining the level of resources to be allocated to the selected customers; and (c) selecting the customers to be nurtured to increase future proﬁtability (Kumar 2008a). Customer Lifetime Value (CLV) in its classical deﬁnition is the value of future cash ﬂow attributed to a single customer or a group of customers, discounted using the average cost of capital of the ﬁrm (Kumar 2008a). It is a leading informative indicator that drives customer proﬁtability (Kumar and Rajan 2009). CLV can also be deﬁned in terms of proﬁt instead of cash ﬂow (see, Gupta and Lehmann 2005). If we assume that cash ﬂow equals proﬁt, CP becomes a special case of CLV with the lifetime period set at one accounting period (Gleaves et al. 2008). CLV is measured using three main components, namely customer retention rate, margin per customer, and cost of customer acquisition. CLV is a pivotal metric that is useful both for customer proﬁtability analysis and in valuing companies (cf. Chaps. 3 and 4 of this book). Customer proﬁtability is positively associated with the forward-looking perspective offered by CLV (Kumar and Bharath 2009), in particular, when a ﬁrm has to decide which customers to acquire/retain because CLV is the upper limit of what one should be willing to spend to acquire/retain a customer unless one wants to lose money. CLV allows assessing which customers to nurture, with the underlying tenet that management should focus on customers with high CLV. Finally, the incorporation of CLV in decision-making should improve resource allocation, with marketing resources that should strive to maximize CLV. Similarly, equity valuation will beneﬁt because CLV offers the algorithm that helps to estimate one of the most important assets of a company: the value of its customer base. In fact, CLV provides a valuation model that allows understanding of the mechanisms by which individual customer metrics (i.e. ARPU, churn, cost of customer acquisition) affect a ﬁrm’s sales/earnings, and ultimately its stock return (Bonacchi et al. 2015). We elaborate further on this topic in Chap. 4 of this book. Finally, Customer Equity (CE) is a combination of a ﬁrm’s current customer assets and the value of the ﬁrm’s potential customer assets (Villanueva and Hanssens 2007). CE is deﬁned as the sum of the CLV of all a ﬁrms’ existing and potential customers. In other words, CLV is a disaggregate measure of customer proﬁtability,
2.1 Customer Analytics: Deﬁnitions of CP, CLV and CE
while CE is an aggregate measure. CE is an intangible asset of the ﬁrm inﬂuenced by the ability to acquire, retain, and increase the customer base (Gupta et al. 2004; Kumar and Shah 2009; Bonacchi et al. 2015). In sum, the key distinctions between these three concepts, which all measure customer value, relate to the timescale under consideration (1 year, multiple years), and to whether the analysis refers to one or all of a ﬁrm’s customers. For a visual representation of the inter-relation among Customer Proﬁtability, Customer Lifetime Value, and Customer Equity, refer to Fig. 2.1 and Gleaves et al. (2008). For the sake of completeness, the ﬁgure also shows the operating proﬁt that, under the assumption that all costs have been traced to customers, is the sum of the customer proﬁtability from all customers the ﬁrm has served within a single accounting period. According to past reviews (Kumar and George 2007; Villanueva and Hanssens 2007; Kumar 2008a; Petersen et al. 2009), the literature on CLV and other customer metrics in mainstream marketing research presently provides a rather consolidated stream of research concerned with the development and reﬁnement of modelling approaches in various business settings. The ﬁrst modelling stream attempts to use deterministic equations in which some inputs are entered into the equation in order to calculate CLV (for a review of these models see Berger and Nasr (1998)). More recently, in order to control for some endogenous parameters, researchers have proposed stochastic models to estimate CE. Inherent in all these models which try to value the long-run ﬁnancial contribution of a customer, is the expected length of the relationship. The most interesting are statistical models used to predict the probability of churn (or retention) (see Villanueva and Hanssens (2007) for a review of these models). Some researchers have also developed a parsimonious model in which the parameters can be easily obtained, even in Microsoft Excel (Fader and Hardie 2007b). With regards to practitioner’s literature that contains applications of CLV, initial evidence is currently available in recent books such as Gupta and Lehmann (2005), Kumar (2008b) and Ryals (2008). Case studies written with pedagogical purposes Fig. 2.1 Classiﬁcation of customer metrics All customers
A single customer
Customer Lifetime Value
Current accounƟng Period
All Future accounƟng periods
2 Customer Analytics: Deﬁnitions, Measurement and Models
about best-practice CLV techniques are also emerging (Ofek 2002; Asis and Narayanan 2007). Bendle and Bagga (2017) provide an exhaustive list of relevant cases, notes and teaching materials on CLV. The next paragraph outlines the formulae applicable to compute CLV.
CLV Formulae: Sources and Variations
For exhaustive reviews of CLV models and their underlying logic, we invite the reader to refer to Jain and Singh (2010), Ascarza et al. (2017) and Kumar (2018) as excellent literature reviews of the marketing literature. The deﬁnitions available on how to compute CLV vary depending on underlying assumptions and different notations (Fader and Hardie 2012; Bendle and Bagga 2017). A quite commonly used deﬁnition of CLV is the one provided by Rust et al. (2009): “The customer lifetime value metric evaluates the future proﬁts generated from a customer, properly discounted to reﬂect the time value of money”. Despite the variation and at times inconsistency across deﬁnitions, the rationale behind CLV computation therefore resembles the Net Present Value in ﬁnance, where a constant series of cash ﬂows over time is discounted to take into account the time value of money (d ). Most common CLV deﬁnitions therefore assume the following equation, with m the (average) contribution margin generated from a customer (or customer segment/ channel) in a year or other period (cf. Steenburgh and Avery 2017). mr mr2 þ þ ÁÁÁ ð1 þ d Þ ð1 þ d Þ2
t 1 X r ¼m 1þd t¼0
CLV ¼ m þ
A fundamental element of any CLV computation refers to the churn rate (r), deﬁned as the percentage of customers who end their relationship with the company in a given period. The churn rate is typically deﬁned at the segment level, and it is implicitly assumed that all individuals in that segment have the same probability of ending the relationship with the ﬁrm. In each subsequent period, the probability that a customer leaves is modelled as a survival probability function that decreases over time along the entire lifetime of the customer. The series of survival probabilities thus determine the expected cash ﬂows (proxied by the periodic contribution margin) in a given period. If we sum the discounted expected contribution margins over a customer’s lifetime, for the properties of inﬁnite geometric series we obtain a simpliﬁed version of the CLV formula that nevertheless may differ depending on two factors: