3.1.2 The Customer Asset Approach Is Designed To Aid Decisions 32
3.1.3 What Is Relevant And Irrelevant? 35
3.1.4 Acquisition Costs 38
3.1.5 A Prediction of the Future 44
3.2 Prior Perspectives 46
3.2.1 Brand Assets versus Customer Assets 46
3.2.2 Customers Differ 47
3.2.3 The Shadow of Customer Equity 50
3.3 Estimating CLV 55
3.3.1 The Lost For Good Approach 59
3.3.2 Always a Share Approach 61
3.3.3 Bespoke Predictions 62
3.3.4 Predicting Customer Life Value with AI 65
3.4 Defining CLV When Customers Are Seen As Assets 67
3.5 Time and CLV 70
3.5.1 Historic CLV 71
3.5.2 Full Life CLV 72
3.5.3 Prospect Lifetime Value 73
3.6 Does CLV Change Over Time? 73
3.7 A General Approach to CLV 74
3.8 The Customer Asset and Beyond 75
3.8.1 Increasing Direct Financial Contributions 77
3.8.2 Indirect Financial Contributions 78
3.8.3 Non-Financial Value 81
4 Who Is The Customer Assets Approach For? 84
4.1 Marketers 84
4.2 Strategists 85
4.3 Senior Managers 86
4.4 Data Analysts 86
4.5 IT Managers 87
4.6 Managerial Accountants 87
4.7 Small Investors 89
4.8 Major Investors and Creditors 90
4.9 Financial Accountants 90
5 Applying the Customer Asset 94
5.1 V: The Customer Asset Approach to Corporate Valuation 94
5.1.1 Corporate Valuation 95
5.1.2 Valuation for Mergers and Acquisition (Part 1) 100
5.2 A: The Customer Asset Approach to Acquisition 101
5.3 R: The Customer Asset Approach to Retention Decisions 104
5.3.1 Retention Decisions for a Firm That Knows Who Its Customers Are 105
5.3.2 Strategic Decisions That Impact Retention 109
5.3.3 Firing Your Customers 110
5.3.4 The Customer asset approach To Developing Your Customers 114
5.4 I: The Customer Asset Approach to Internal Reporting 116
5.4.1 Support of Managerial Decisions Is Not Covered By GAAP 116
5.4.2 Managing the Customer Business 118
5.5 E: The Customer Asset Approach to External Reporting 119
5.5.1 Adding Customers Assets to the Balance Sheet 120
5.5.2 Valuation for Mergers and Acquisition (Part 2) 124
5.5.3 Beyond the Core Financial Statements 126
5.6 D: Data Flexibility 129
5.6.1 Minimalist: A Clear, Consistent, and Flexible Approach 129
5.6.2 Incentive-Compatible: The Interests of Those Adopting It Matter 130
5.6.3 Communicable: Easy To Explain and Consistent With Other Terms 131
5.6.4 Auditable: Credible Because It Can Be Checked 132
5.6.5 Practical: Now Possible To Deliver 133
6 Communication about the Customer Asset Approach 138
6.1 Communicating With Skeptics 139
6.2 Communicating With Accountants 140
6.3 Communicating With Marketers 141
7 Conclusion 144
8 References 146
Neil Bendle is Associate Professor of Marketing in the Terry College of Business at the University of Georgia, USA. He is a fellow of the Association of Chartered Certified Accountants and a director of the Marketing Accountability Standards Board (MASB).
Shane Wang is Professor of Marketing at Virginia Tech Univiersity, USA. His research focuses on artificial intelligence and machine learning techniques with applications in business and social media analytics, firm strategy and management.
This book delves into the concept of customers as financial assets, explaining how firms can assess investments in customer relationships. The authors present the VARIED framework for quantifying the customer asset, enabling marketers to devise strategies that enhance its value. Crucially, these strategies' advantages can be communicated in financial terms to non-marketers, instilling accountability in marketing and augmenting firm value through well-informed investment decisions. This methodology offers a practical avenue to enact the strategic concept of customer centricity. It will resonate with marketers, accountants, and all managers eager to demonstratecustomers' financial worth to the organization.
Neil Bendle is Associate Professor of Marketing in the Terry College of Business at the University of Georgia, USA. He is a fellow of the Association of Chartered Certified Accountants and a director of the Marketing Accountability Standards Board (MASB).
Shane Wang is Professor of Marketing at Virginia Tech Univiersity, USA. His research focuses on artificial intelligence and machine learning techniques with applications in business and social media analytics, firm strategy and management.