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Customer accounting creating value with customer analytics


Massimiliano Bonacchi
Paolo Perego

Creating Value with
Customer Analytics

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SpringerBriefs in Accounting
Series editors
Peter Schuster, Schmalkalden, Germany
Robert Luther, Bristol, UK

More information about this series at http://www.springer.com/series/11900

Massimiliano Bonacchi • Paolo Perego

Customer Accounting
Creating Value with Customer Analytics

Massimiliano Bonacchi
Faculty of Economics and Management
Free University of Bozen-Bolzano
Bolzano, Italy

Paolo Perego
Faculty of Economics and Management
Free University of Bozen-Bolzano
Bolzano, Italy

ISSN 2196-7873
ISSN 2196-7881 (electronic)
SpringerBriefs in Accounting
ISBN 978-3-030-01970-9
ISBN 978-3-030-01971-6 (eBook)
Library of Congress Control Number: 2018957984
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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

Baruch Lev



Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1 Customer-Centricity in a Fast-Evolving Landscape . . . . . . . . . . . .
1.2 Motivation and Objectives of This Book . . . . . . . . . . . . . . . . . . .
1.3 Theoretical Framework: Organizational Architecture . . . . . . . . . . .
1.4 Outline of This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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. 4
. 5
. 8
. 10


Customer Analytics: Definitions, Measurement and Models . . . . . . .
2.1 Customer Analytics: Definitions of CP, CLV and CE . . . . . . . . . .
2.2 CLV Formulae: Sources and Variations . . . . . . . . . . . . . . . . . . . .
2.3 Applications of CLV in Subscription-Based Business Settings . . . .
2.4 CLV Scorecard and Cohort Analysis: An Application in an SBE . .
2.4.1 The CLV Scorecard as a Performance Measurement
System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.2 Benefits of CLV Scorecard . . . . . . . . . . . . . . . . . . . . . . . .
2.4.3 CLV Cohort Analysis: Rationale . . . . . . . . . . . . . . . . . . . .
2.4.4 CLV Cohort Analysis: A Practical Illustration . . . . . . . . . .
2.5 Conclusions and Implications . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .








Customer Analytics for Internal Decision-Making and Control . . . .
3.1 Review of Accounting and Marketing Literature . . . . . . . . . . . . . .
3.2 Evaluation of the Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 A Case Study on the Adoption of Customer Analytics . . . . . . . . .
3.3.1 Case Background and Research Methodology . . . . . . . . . .
3.3.2 Organizational Structure . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.3 The Performance Measurement System . . . . . . . . . . . . . . .
3.3.4 The Reward System . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.5 Conclusions and Implications from the Case Study . . . . . .
3.4 An Exploratory Cross-Sectional Survey on the Adoption
of Customer Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3.4.1 Sample and Data Collection . . . . . . . . . . . . . . . . . . . . . . .
3.4.2 Descriptive Statistics and Univariate Analysis . . . . . . . . . .
3.4.3 Multivariate Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4.4 Conclusions and Implications from the Survey . . . . . . . . . .
Appendix Chapter 3: Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


Customer Equity for External Reporting and Valuation . . . . . . . . . .
4.1 Customers as the Most Valuable (Intangible) Asset . . . . . . . . . . . .
4.2 Customer Franchise Is Missing in IFRS/US GAAP Financial
Statements: How to Value It? . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Describing SBEs Business Model Using Customer Metrics . . . . . .
4.4 Valuing SBEs Using Publicly Disclosed Customer Metrics:
A Parsimonious Model to Estimate Customer Equity . . . . . . . . . .
4.5 Customer Equity and Stock Returns: Empirical Evidence . . . . . . .
4.6 Beyond GAAP: Customer Metrics Reporting . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Conclusions and Trends to Look Forward . . . . . . . . . . . . . . . . . . . .
5.1 Looking Back and Looking Ahead . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Linking Online with Offline Commerce . . . . . . . . . . . . . . . . . . . .
5.3 Enhanced Forms of Corporate Non–financial Reporting . . . . . . . .
5.4 The Rising Impact of Artificial Intelligence on Modeling Customer
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


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Chapter 1


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

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2019
M. Bonacchi, P. Perego, Customer Accounting, SpringerBriefs in Accounting,



1 Introduction

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
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
positioning and
selling approach

Selling approach

Product-centric approach
Sell products
Transaction oriented

Customer-centric approach
Serve customers
Relationship oriented

Highlight product features
and advantages

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
How many customers can we sell this
product to?

Source: Authors’ elaboration adapted from Kumar (2008a); Ramani and Kumar (2008); Shah et al.

• 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


1 Introduction

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

1.3 Theoretical Framework: Organizational Architecture


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.


Theoretical Framework: Organizational Architecture

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


1 Introduction

Contingent Variables
− 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



Fig. 1.1 Conceptual framework: organizational architecture (source: Adapted from Brickley et al.

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

1.3 Theoretical Framework: Organizational Architecture


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,


1 Introduction

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


1 Introduction

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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1 Introduction

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Chapter 2

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:

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2019
M. Bonacchi, P. Perego, Customer Accounting, SpringerBriefs in Accounting,



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





Current accounƟng

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).
ð1 þ d Þ ð1 þ d Þ2


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:

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