SPRINGER BRIEFS IN ACCOUNTING Massimiliano Bonacchi Paolo Perego Customer Accounting Creating Value with Customer Analytics 23 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) https://doi.org/10.1007/978-3-030-01971-6 Library of Congress Control Number: 2018957984 © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2019 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Foreword 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 v vi Foreword 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 Contents 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 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 13 13 16 17 19 20 24 25 28 33 33 37 37 41 42 42 43 45 47 48 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 50 vii viii Contents 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 67 67 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 53 54 56 58 59 64 68 69 71 77 79 81 83 83 84 85 86 87 Chapter Introduction The primary function of a business is to serve the customer and the primary goal of your business is to create customers —Peter Drucker 1.1 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, https://doi.org/10.1007/978-3-030-01971-6_1 Fig 4.2 Theoretical model for valuing customer base (source: Bonacchi et al 2015) 4.4 Valuing SBEs Using Publicly Disclosed Customer Metrics: A 73 Fig 4.3 Parsimonious model to estimate CEcur and CEtot using publicly available data (source: Bonacchi et al 2015) 74 Customer Equity for External Reporting and Valuation 4.4 Valuing SBEs Using Publicly Disclosed Customer Metrics: A 75 ! r CE cur ¼ n m ỵ i r ị 4:4ị In other words, our measure of CEcur is a margin (m) times a margin multiple that depends on the probability of customer retention and the company’s cost of capital.6 Value of future customers A challenge with estimating CEfut is forecasting the number of future customers We use a two-stage approach to model customer acquisition Step 1—Growth Period: For a finite time horizon, we forecast gross customer additions as the arithmetic mean of the gross addition for the last two quarters We estimate the length of the growth period as 1/churn (i.e number of months an average customer stays with the company) We take this approach under the conjecture that a company relying on a subscription-based model will not grow absent innovations in product/services Thus, we treat churn as a proxy for the degree of innovation, which is the main driver of growth (Thompson 2011).7 Step 2—Steady State: After the Growth Period, we model gross additions by setting them as equal to the number of customers that churn over an infinite time horizon In other words, absent innovation, the company maintains its customer base, acquiring customers to compensate for the churn Following the above rationale, future customer equity is the sum of customer equities from the growth and the steady state periods: r 1ỵir ị CE fut ẳ growth CE fut ỵ steady CE fut ð4:5Þ where growth_CEfut (steady_CEfut) is the number of new customers acquired in the growth (steady-state) period multiplied by the discounted margin multiple: growth CE fut ¼ growth n*gross a cịaỉ 4:6ị steady CE fut ẳ steady n*gross a cị i1 ỵ iịh 4:7ị hi where: growth_n*gross ¼ gross addition during the growth period steady_n*gross ¼ gross addition during the steady-state period In our model we estimate CEcur over an infinite time horizon This is desirable since we not need to specify arbitrarily the number of months that a customer will stay with the company In addition, the retention rate and discount rate account for future uncertainties by discounting accordingly future profit margins (Gupta et al 2004) Most of the companies employing a subscription-based business model grow under the recurring revenue model: “Sell it once and collect until it churns.” 76 Customer Equity for External Reporting and Valuation a¼ X tẳ1 rt r m t ẳ m ỵ i rị ỵ i ị h ị aỉ ẳ 11ỵi (the value of an annuity for h months) i hi r ¼ (1-churn) h ¼ number of months a customer stays with the firm; [1 / (1 – r)] rounded to an integer c ¼ cost of customer acquisition We estimate the gross additions in the growth and steady-state periods as follows: Growth Period: growth n*gross ¼ ÀÀ ngrosst ỵ ngrosst1 =2 =3 where the average gross additions for the two prior periods are scaled by three to convert the data from quarterly to monthly If a company does not disclose gross additions, we model it as the sum of the customers that churn and the net additions for the period: growth n* gross ¼ ððnt þ ntÀ1 Þ=2Þchurn þ ðnt À ntÀ1 Þ=3 Steady State steady n*gross ẳ nt r h ỵ growth n*gross h X ! r t churn t¼1 where: ntrh¼ number of current customers that remain after h months h X growth n*gross r t¼ number of customers acquired during the growth period that t¼1 remain after h months Combining CEcur and CEfut, we can express CEtot as: CEtot ¼ CE cur þ growth CE fut þ steady CE fut ð4:8Þ Using Eqs (4.5, 4.6 and 4.7), abstracting from the effect of taxes, and recognizing that, for r 6¼ 1, k X À Á r t ¼ r À r hỵ1ị =1 r ị ẳ ẵr=1 r ị À r k , t¼1 we can re-write (4.8) as: 4.5 Customer Equity and Stock Returns: Empirical Evidence CE tot, t ẳ nt a ỵ growth n*gross a cịaỉ ỵ steady n*gross a cị hi 77 i ỵ i ịh 4:9ị The graphical representation of the model in depicted in Fig 4.2 4.5 Customer Equity and Stock Returns: Empirical Evidence To estimate the value of a firm’s customer base, several inputs are required: number of customers, margin per customer, customer retention rate, and the cost of capital for the firm The number of customers refers to the active customer base at the end of the fiscal quarter Margin per customer is measured as the difference between average revenue per customer, ARPU, and cost of service Similar to the number of customers, most companies that disclose customer-related metrics provide sufficient data to infer ARPU.8 Some companies, however, not disclose cost of service per customer In these cases, we estimate the metric by applying to ARPU the ratio of “cost of service” to “service revenue” from the income statement When companies provide the disclosure by segment (e.g Post-Paid and Prepaid or U.S and Latin America), we use the weighted average of the customer metrics Turning to the customer retention rate, its estimation plays a critical role in the model, as it reflects the likelihood that a customer will defect in the future Analyses of parametric and non-parametric models to calculate customer lifetime (i.e how long a customer is expected to stay with the firm and create value) are beyond the scope of this study, so we project the historical churn to the future.9 In practical terms, we derive the probability of the current customer remaining active at time t as (1–churn) For the CEtot model, we also require the cost to acquire a new customer Customer-acquisition cost, when reported, is expressed as cost per gross addition However, more than 30 percent of the companies in our sample not report these data In these cases, we operationalize the acquisition cost by dividing marketing cost by the gross customer addition We this only when it is clear that the cost of customer acquisition is included in the marketing cost We acknowledge that some of the marketing cost is incurred for retention purpose, but we not have sufficient information to refine the measure Several studies, however, show that, in general, customer acquisition costs are significantly higher than customer retention cost (Reichheld and Teal 1996; Thomas 2001; Gupta et al 2004) The last model input is cost of capital In theory, cost of capital is a time- and firmspecific measure In practice, however, there is little consensus on how to measure When a company does not disclose ARPU, we calculate it by dividing subscriber revenues by the weighted average number of customers for the period Fader and Hardie (2007) and Rosset et al (2003) provide examples of projecting retention rate 78 Customer Equity for External Reporting and Valuation cost of capital For this study, we use a constant discount rate of 12 percent (Frankel and Lee 1998; Gupta et al 2004) As a robustness test, we repeat the analysis using a time-varying discount rate.10 We focused the main analysis on the future value of current, retained, customers (CEcur) The resulting customer equity model is a simplified version of Eq (4.1) (Gupta et al 2004) Under the assumptions that the profit margin and customer churn are constant and the acquisition of future customers is a zero net present value (NPV) project (Gupta and Lehmann 2005), customer equity could be expressed as: CE cur ¼ n X t¼1 ! rt r m ¼n m ỵ i r ị ỵ i ịt ð4:10Þ where n is the number of active customers at the end of the period (historic customer base); m is the profit margin per customer (revenue minus service cost) for period t; r is the retention rate for period t; i is the cost of capital; and t is the time period.11 Our main analysis focuses on CEcur and not on total customer equity (CEtot) for several reasons Forecasting the number of future customer acquisitions and their outcomes requires a higher degree of subjectivity, coming from three separate sources: (1) Customer growth: We forecast customer growth using an ad hoc model relying on historical growth A diffusion model is a natural candidate for estimation of the growth of the customers (Kim et al 1995; Gupta et al 2004) Such an approach requires the solution of nonlinear differential equations, and the resulting model is too complex to operationalize for a large sample (Pfeifer 2011) (2) Acquisition cost: The non-random loss of observations is likely to bias the reported results As discussed previously, more than one-third of companies not report these data, requiring the use of total marketing costs as a crude proxy, which could be an additional source of bias (3) Discount rate: Theoretically, the discount rate for future cashflows should be higher than the discount rate used for the current customers’ cashflows The discount rate is supposed to capture the risk inherent in the customer type: A current customer is more likely to stay with a company through good times and bad Furthermore, whether or not a company can acquire new customers is strongly impacted by macro and micro economic factors 10 We derive the time-varying discount rate as 10% + one-year LIBOR Using this rate instead of the static 12% does not qualitatively affect the results 11 The constant profit and retention rate assumptions, while not too strong, allow for the generation of a parsimonious model that is easily implementable in practice Separately, we not introduce taxes in the model: While the extension is analytically straightforward, the practical implementation presents challenges without contributing to the insights 4.6 Beyond GAAP: Customer Metrics Reporting 79 In summary, by focusing on the current customers of a company, we obtain a parsimonious and easy-to-implement model of customer equity.12 Despite the fact that our estimate of customer equity does not likely capture the entire customer intangible asset, we believe it is a useful practical valuation tool which provides a summary performance metric which managers and investors can track over time.13 To examine whether CEcur could be used in predicting future market performance, we examine the future returns of companies grouped by the “comprehensive to market value of equity” ratio (Gu and Lev 2011) In particular, we define comprehensive value as the sum of reported BVE and our estimate of CEcur, with the interpretation that comprehensive value (CV) that is higher (lower) than market value of equity (MVE) indicates underpriced (overpriced) stock In particular, we regress future returns (buy-and-hold market-adjusted returns) on a set of controls and our variable of interest is CV/MVE, which takes a value of 0, 0.5, and when a firm is in the lowest, middle, and highest tercile of the ratio, respectively, for the fiscal quarter.14 It is notable that the coefficient on CVq/MVE10Q,q is significantly positive and economically significant As an example, the hedge return from buying and holding (selling short) stocks in the top (bottom) tercile of the comprehensive to market value of equity—undervalued (overvalued) firms—yields 36.2, 41.2, and 76.5 percent return for 1, 2, and years after the investment, respectively 4.6 Beyond GAAP: Customer Metrics Reporting “Subscribers are the New, New Thing in Business” declared The Economist (April 11, 2018) The magazine says that “Subscription models are seen by many investors and executives as the holy grail, because they promise a recurring stream of revenue The attractions of subscription businesses are obvious Firms can predict the future better and build deeper relationships with customers who have less 12 One of this book’s authors applied the model outlined in the previous section in a paper co-authored with Baruch Lev and Kalin Kolev In this section, we explain how to apply the model previously outlined For further details, interested readers should refer to Bonacchi et al (2015) 13 Empirically, Silveira et al (2012) document that CEcur is a sufficiently close approximation of CEtot 14 The general model takes the following form: X RetqỵN ẳ ỵ CVq =MVE10Q, q ỵ Beta ỵ BM ỵ logMVE10Q ị ỵ Accruals Nẳ1 ỵ Momentum ỵ where Retq + N is the buy-and-hold market-adjusted return cumulated for 360, 720, and 1080 calendar days, starting days after the 10-Q filing date for the current quarter The set of controls aims to correct for previously documented determinants of stock returns A discussion of the general model is available in Doyle et al (2003) 80 Customer Equity for External Reporting and Valuation incentives to shop around.” Telecom companies, Internet service providers, media and entertainment firms, as well as insurance companies are the traditional, subscription-based enterprises, but the subscription model is fast expanding Many software producers offer subscription services to customers, and even Procter and Gamble sells detergents to subscribers Gillette markets razors on the basis of monthly fees, and Rolls-Royce, General Electric and Pratt & Whitney offer “power by the hour subscriptions.” And, as The Economist notes: “Several star firms floating their share this year have subscription models Dropbox, a cloudstorage firm, listed on NASDAQ on March 23rd and is now worth $13bn It boasts 500m registered users Spotify, has 159m users but derives its $27bn valuation from 71m “premium subscribers” who pay to listen without adverts.” “The subscription boom will doubtless continue” concludes The Economist In this chapter, we argue that customer metrics are useful to understand the business model of a firm in general and of a subscription-based enterprise in particular To this end, we introduce a model translating the business mechanism of subscription-based enterprises into a single measure of customer equity value We apply the estimation to a sample of companies that disclose customer-related metrics, and show that the measure of customer equity is positively and significantly associated with future returns Given the above we advocate for expanding the GAAP disclosure with a formalized and possible standardized Non-GAAP disclosure of customer metrics This will be useful to estimate Customer Equity What is needed as a minimum to estimate Customer Equity is listed in Table 4.2 To make the table more meaningful, we look at the customer metrics disclosed by Spotify in their prospectus and value the customer franchise of Premium subscribers as of December 2017.15 The estimated current subscriber value of $2.06 B is conservative, since it does not factor in the value of future customers (CEfut) and is limited to premium customers But even this conservative value is by far the largest asset owned by Spotify, yet not presented on its balance sheet Given Spotify’s market capitalization of $30 billion on June 29, its customer franchise multiple is 14.6 As Baruch Lev wrote recently: “Considering all the deficiencies of reported accounting earnings, the customer franchise multiple is a much more meaningful valuation metric than the widely used P/E ratio Both the customer value and its multiple can be examined over time and across peer companies to assess share over/under valuation for investment purposes.”16 Effective beyond-GAAP disclosure should be standardized and directly linked to performance and most importantly should describe the business model of the 15 The data are retrieved from the FORM F-1 SEC filing available at: https://investors.spotify.com/ financials/default.aspx In particular, we looked at the filing date of 28 February 2018: http:// d18rn0p25nwr6d.cloudfront.net/CIK-0001639920/6b71c48f-22b3-4c46-a206-5eb425d05e63.pdf 16 Interested readers should refer to Customer Franchise―The Most Valuable Asset: Here Is How to Value It, available from https://levtheendofaccountingblog.wordpress.com/2018/05/18/05-18-18-new-customer-fran chise%E2%80%95the-most-valuable-asset-here-is-how-to-value-it/ References 81 Table 4.2 Example of customer metrics at spotify (1) (2) Variables ARPU Cost of service ¼ Margin per subscriber (1–2) (4) Churn rate (5) ¼ (1/3) Lifetime (1/3) (6) Number of customers end of the quarter (7) ¼ (3*5*6) Current customer equity (CE) (3) Data extracted from FORM F-1 5.24 Pag 79 3.97 Pag 76 761/1019 ¼ 74.68% 1.27 5.1% 19.60 months 71 million Pag 79 Pag 79 1767 million euros 2058 million dollars (converted June 29, 2018) The data are retrieved from the FORM F-1 SEC filing available at: https://investors.spotify.com/ financials/default.aspx In particular, we looked at the filing date of 28 February 2018: http:// d18rn0p25nwr6d.cloudfront.net/CIK-0001639920/6b71c48f-22b3-4c46-a206-5eb425d05e63.pdf company We believe the above-mentioned examples in the subscription-based companies show that customer metrics are able to inform investors of the successful implementation of their strategies References Berger, P D., & Nasr, N I (1998) Customer lifetime value: Marketing models and applications Journal of Interactive Marketing, 12(1), 17–30 Bini, L., Dainelli, F., & Giunta, F (2016) Business model disclosure in the strategic 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lasting value Boston, MA: Harvard Business School Press Rosset, S., Neumann, E., Eick, U., & Vatnik, N (2003) Customer lifetime value models for decision support Data Mining and Knowledge Discovery, 7(3), 321–339 Schulze, C., Skiera, B., & Wiesel, T (2012) Linking customer and financial metrics to shareholder value: The leverage effect in customer-based valuation Journal of Marketing, 76(2), 17–32 Silveira, C S., de Oliveira, M O R., & Luce, F B (2012) Customer equity and market value: Two methods, same results? 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London: PwC Valuation Villanueva, J., & Hanssens, D (2007) Customer equity: Measurement management and research opportunities Foundations and Trends in Marketing, 1(1), 1–95 Chapter Conclusions and Trends to Look Forward What if you had a weather forecast for everything that happened in your life? —David Kenny (senior vice president of IBM Watson and Cloud Platform) 5.1 Looking Back and Looking Ahead As illustrated in cases and examples in previous chapters, the explosion of customerrelated data makes the role of data management and forecast much more prominent to guide marketing initiatives and plan firm’s business activities, with huge implications for the modern CFO function Our empirical evidence confirms that it is essential to ensure high quality of customer data A common step required in many firms to establish high standards of data quality is to involve various key corporate functions to validate existing sources, removing duplicates by matching records and poor data, and resolving inherent conflicts in data collection or data cleansing The responsibility to validate customer data should involve the CFO as one of the main user of this type of data for planning and control Once the right data is generated, CFOs can exploit customer-related information to make sense of customer preferences and meet their expectations Customer analytics like Customer Lifetime Value (CLV) and Customer Equity (CE) outlined in this monograph should enhance the opportunity to increase revenues with existing customers, but also secure new customers or increase margins This kind of analytics is fundamental, because it allows differentiating between customers that are worth spending time and budget on from those who are not In the next three sections, we highlight three trends that will likely shape the landscape of customer accounting in the next decade First, the ubiquity of consumers getting connected dramatically changes the landscape of e-commerce A major challenge is to understand and predict customer behaviour for the coming new era of business models that aim to integrate offline with online commerce Second, while © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2019 M Bonacchi, P Perego, Customer Accounting, SpringerBriefs in Accounting, https://doi.org/10.1007/978-3-030-01971-6_5 83 84 Conclusions and Trends to Look Forward the extant accounting literature documents the informational problems associated with financial reporting complexity, firms have at their disposal a variety of disclosure channels beyond financial statements that can be used to shape their information environment and influence investment decisions We outline the potential role of customer metrics as key leading, forward-looking indicators in enhanced forms of corporate reporting Finally, we close the monograph by flagging the opportunity to examine how current disruptive innovations in Artificial Intelligence, deep and machine learning will have a profound and radical impact on customer behaviour and related predictive models of marketing performance 5.2 Linking Online with Offline Commerce Technology advances have introduced customers to new buying behaviors, and especially the rise of e-commerce has provided retailers with novel avenues to reach those customers Past best-practices in marketing management relied on demographics to extrapolate key information about the characteristics and behavior of a firm’s customer base Because consumers’ digital trail can be increasingly collected and analysed in real-time, through the advent of the Internet and mobile device usage, currently firms can get much deeper than audience demographics and down into consumer psychographics The large amount of online data available provides CMOs and CFOs a much better battery of accurate and timely indicators regarding motivations, needs, and purchase behaviour It has never been easier to zoom in on what your individual customers are doing, and this is a major reason why enterprise products like Google Analytics and the like are getting easier to use and understand In their effort to use CLV as a decision-making basis for marketing management, especially companies operating an online store in a non-contractual relationship with customers face the issue of selecting the appropriate CLV model and other predictive customer analytics The challenge for academic research in the next years will remain to test predictions in various application contexts in online shopping, by exploiting the sophistication of CLV models in marketing literature (Jain and Singh 2010; Ascarza et al 2017) with more granular customer data at the individual transaction level (refer to these recent studies as examples: Verbraken et al 2013; Jasek et al 2018; Mzoughia et al 2018; Ĩskarsdóttir et al 2018) At the same time, the major research issue for retailers remains how to provide convenient service through different channels, with proposed “online to offline” and “offline to online” (O2O) commerce and many kinds of creative service models O2O Commerce can be defined as providing seamless shopping experience between online commerce and offline bricks-and-mortar with any connected device (e.g Tsai et al 2015) Scholars in marketing and accounting will need to address, among others, the following key research questions: 5.3 Enhanced Forms of Corporate Non–financial Reporting 85 • How to integrate new multi-channel services with traditional brick-and-mortar or e-commerce? • How to evaluate whether an O2O service is cost-effective and able to enhance the consumer satisfaction with the proposed new services? • What are the consequences of the integration of O2O services in the organizational architecture? What type of solutions in the allocation of decision rights and incentive systems enable a better fit with the challenges posed by O2O services? 5.3 Enhanced Forms of Corporate Non–financial Reporting Investors are poorly served by backward-looking accounting methods, as the ‘End of Accounting’ recent book by Lev and Gu argues (Lev and Gu 2016) Deficiencies of reported earnings make it especially hard to communicate the pervasive existence of intangible assets within corporations The rate of corporate investment in physical capital fell by 35% over the 1977–2012 period, whereas the rate of investment in intangible assets increased by 60% during the same period Lev and Gu (2016) suggest to improve the current corporate reporting landscape by including an expanded set of non-financial metrics with a higher predictive ability than traditional financial rations In this respect, customer metrics like CLV, customer equity, churn rate and customer acquisition costs are increasingly the new leading indicators to create and sustain corporate value in the twenty-first century intangible asset-based economy As we outlined in Chap 4, effective beyond-GAAP disclosure should be standardized and directly linked to performance and most importantly should describe the business model of the company We believe the examples presented in the subscription-based companies show that customer metrics are able to inform investors of the successful implementation of their strategies Among the recent international initiatives to spur enhanced forms of corporate reporting, Integrated Reporting (IR) attempts to merge in one document financial and non-financial data to overcome a potential disconnect in investors’ processing of the two types of information A salient feature of an IR is ‘integrated thinking’, defined as “the active consideration by an organization of the relationships between its various operating and financial units and the capitals that the organization uses or affects” (IIRC 2013) IR is therefore more than just a reporting framework It helps to better understand and link together disparate sources and drivers of longterm value by ‘telling the story’ of an underlying business model, both internally and to external financial markets (ACCA 2017) Connectivity is one of the six guiding principles that informs the content and presentation of the IR An IR should show, as a comprehensive value creation story, the combination, interrelatedness and dependencies between the components that are material to the company’s ability to create value over time (IIRC 2013) Customer metrics like CLV and Customer Equity should enable managers to consistently reach the customers that matter the most and focus on enhancing the connectivity among capitals whether through better targeting, value added services or improvements to customer experience 86 Conclusions and Trends to Look Forward In the near future, we further predict an increased role of customer metrics because of recent developments in regulations and legal frameworks intended to fix the drawbacks in the format and usefulness of current corporate financial reporting As a relevant example of this trend, the European Union Directive 2014/95/EU mandates the disclosure of non-financial information for approximately 6000 large companies starting from the financial year 2017 (European Commission 2017) Although customer data is not explicitly included in the new legislations, companies will face a mounting pressure to provide non-financial data related to business operations and ultimately to their impact on the customer base In their 2016 survey on the state-of-the-art in business reporting, KPMG found that only 41% of the companies examined reported detailed customer information beyond the traditional sales performance based on the financial statement, mostly as single period-data Measures capturing customer satisfaction and retention were present in 6% of the corporate disclosure investigated In the medium term, there are increasing opportunities to better align corporate reporting with key value-creation strategies, particularly by exploiting the forward-looking ability of customer data to anticipate future financial performance 5.4 The Rising Impact of Artificial Intelligence on Modeling Customer Data Three major “disruptions” have shaped past decades of technological revolution: “Moore’s Law” (1971), fitting increasingly smaller and more powerful transistors on integrated circuits—computers; “Metcalf’s Law” (1995), which revealed the power of networks; and “Digital transformation” of today business environment, where it is possible to apply a “weather forecast attitude” for everything that happens in your life The digital revolution is possible thanks to progress and exponential increase in computing power (Hyper-computing), large datasets available to train machine learning (from IoT, mobiles, social networks, and so forth), capillary diffusion of robotics Gartner predicts that by 2020 85% of customer interactions will be managed without a human Such developments have spurred sophisticated marketing applications relying on Artificial Intelligence (AI), an overarching concept which broadly refers to machines exhibiting intelligence inspired by human biological systems Under the AI broad label, there are variety of software and algorithm-driven approaches (combined with large amount of data) to simulate human cognitive functions Using advanced machine learning algorithms will increasingly allow to discover, classify and identify patterns in customer data, a trend commonly referred to as Deep Learning For example, a bank would be interested to apply Deep Learning to analyze customers’ payment transactions with the objective to identify and anticipate potential fraudulent behaviour In the hotel sector, machine learning is used in conjunction with advanced statistical methods to produce cutting-edge forecasting and decision References 87 optimisation to better understand the relationship between price and demand, and generate room rates that dynamically adapt and anticipate market fluctuations Furthermore, AI have put insurance companies in a better position to assess and manage the financial consequences associated with catastrophic storms Such disruptive AI techniques would definitely help marketing researchers and managers to overcome the limitations inherent in CLV stochastic models proposed by the marketing literature In particular, Deep Learning and neural network applications could be helpful to better model churn prediction as an effective and dynamic tool for companies that want to stay competitive in a rapidly growing market (see for example Sifa et al 2018) We encourage academic researchers from various disciplines in management and computer sciences to cooperate and engage in active collaborations with companies to exploit their rich data environment and ultimately test the validity of customer metrics modelling and their predictive ability Advances in theoretical knowledge and practical managerial applications in this dynamic area seem warranted for the next decade References ACCA (2017) Insights into integrated reporting London: Association of Chartered Certified Accountants Ascarza, E., Fader, P S., & Hardie, B G S (2017) Marketing models for the customer-centric firm In B Wierenga & R van der Lans (Eds.), Handbook of marketing decision models (pp 297–329) Cham: Springer European Commission (2017, September 30) Non-financial reporting 2017 Available from https:// ec.europa.eu/info/business-economy-euro/company-reporting-and-auditing/company-reporting/ non-financial-reporting_en IIRC (2013) The international framework London: International Integrated Reporting Council Jain, D C., & Singh, S S (2010) Measuring customer lifetime value In Review of marketing research (pp 37–62) Bingley: Emerald Group Publishing Jasek, P., Vrana, L., Sperkova, L., Smutny, Z., & Kobulsky, M (2018) Modeling and application of customer lifetime value in online retail Informatics, 5(1), Lev, B., & Gu, F (2016) The end of accounting and the path forward for investors and managers New York: Wiley Mzoughia, M B., Borle, S., & Limam, M (2018) A Mcmc approach for modeling customer lifetime behavior using the com-poisson distribution Applied Stochastic Models in Business and Industry, 34(2), 113–127 Ĩskarsdóttir, M., Baesens, B., & Vanthienen, J (2018) Profit-based model selection for customer retention using individual customer lifetime values Big Data, 6(1), 53–65 Sifa, R., J Runge, C Bauckhage, and D Klapper (2018) Customer lifetime value prediction in non-contractual freemium settings: Chasing high-value users using deep neural networks and smote Proceedings of the 51st Hawaii International Conference on System Sciences Tsai, T.-M., Wang, W.-N., Lin, Y.-T., & Choub, S.-C (2015) An O2O commerce service framework and its effectiveness analysis with application to proximity commerce Procedia Manufacturing, 3, 3498–3505 Verbraken, T., Verbeke, W., & Baesens, B (2013) A novel profit maximizing metric for measuring classification performance of customer churn prediction models IEEE Transactions on Knowledge and Data Engineering, 25(5), 961–973 ... written with pedagogical purposes Fig 2.1 Classification of customer metrics All customers A single customer Operating profit Customer Equity Customer Profitability Customer Lifetime Value Current... suitable set of customer analytics Chapter provides definitions of the most widely diffused customer metrics, namely Customer Profitability (CP), Customer Lifetime Value (CLV), Customer Equity... Perego, Customer Accounting, SpringerBriefs in Accounting, https://doi.org/10.1007/978-3-030-01971-6_2 13 14 Customer Analytics: Definitions, Measurement and Models • Customer Profitability; • Customer