Introduction |
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1 | (10) |
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1 | (3) |
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The Perils of Personalization |
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4 | (1) |
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Rise of the Avoidant Customer |
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5 | (1) |
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The Disconnected Data Dilemma |
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6 | (1) |
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Crossing the Customer Data Chasm |
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7 | (1) |
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Customer Data Platform (CDP) |
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8 | (3) |
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Chapter 1 The Customer Data Conundrum |
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11 | (18) |
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11 | (3) |
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14 | (1) |
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Customer Relationship Management (CRM) |
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15 | (1) |
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15 | (1) |
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16 | (1) |
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16 | (3) |
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Cross-Device Identity Management (CDIM) |
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19 | (1) |
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Connecting the Known and Unknown |
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20 | (1) |
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21 | (1) |
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22 | (2) |
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Customer-Driven Thinker: Kevin Mannion |
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24 | (2) |
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Summary: The Customer Data Problem |
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26 | (3) |
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Chapter 2 The Brief, Wondrous Life of Customer Data Management |
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29 | (18) |
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Customer Data on Cards and Tape? |
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29 | (2) |
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Direct Mail and Email: The Prototypes of Modern Marketing |
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31 | (1) |
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A Brief History of Customer Data Management |
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32 | (3) |
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34 | (1) |
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The Rise of CRM and Marketing Automation |
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35 | (3) |
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36 | (1) |
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Improved User Interface (UI) |
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37 | (1) |
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The Multichannel Multiverse of the Thoroughly Modern Marketer |
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38 | (5) |
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38 | (2) |
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40 | (1) |
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Today's Martech Frankenstack |
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41 | (2) |
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Customer-Driven Thinker: Scott Brinker |
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43 | (1) |
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Summary: The Brief, Wondrous Life of Customer Data Management |
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44 | (3) |
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Chapter 3 What Is a CDP, Anyway? |
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47 | (22) |
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Rise of the Customer Data Platform |
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47 | (5) |
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What Marketers Really Want from the CDP |
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51 | (1) |
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52 | (2) |
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"We Want a Platform, Not a Product" |
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53 | (1) |
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Building a Platform Solution |
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54 | (1) |
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54 | (4) |
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54 | (1) |
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55 | (1) |
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56 | (1) |
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Segmentation and Activation |
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56 | (1) |
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57 | (1) |
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The Two (Actually Three) Types of CDPs |
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58 | (1) |
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58 | (2) |
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60 | (2) |
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The Third Type: Enterprise Holistic CDP |
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62 | (2) |
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Known and Unknown (CDMP) Data Must Be Unified |
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62 | (1) |
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A Business-User Friendly UI |
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62 | (1) |
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63 | (1) |
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64 | (1) |
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Customer-Driven Thinker: David Raab |
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65 | (1) |
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66 | (3) |
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Chapter 4 Organizing Customer Data |
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69 | (22) |
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Munging Data in the Midwest |
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69 | (2) |
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Elements of a Data Pipeline |
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71 | (1) |
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72 | (12) |
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72 | (2) |
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74 | (1) |
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Using an Information Model |
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75 | (1) |
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76 | (1) |
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Benefits of Identity Management |
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77 | (1) |
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78 | (1) |
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Identity Management in Practice |
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79 | (1) |
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79 | (3) |
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The Importance of Attributes |
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82 | (1) |
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83 | (1) |
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84 | (1) |
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Different Spheres of Influence |
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84 | (2) |
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Customer-Driven Thinker: Brad Feinberg |
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86 | (2) |
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Summary: Organizing Customer Data |
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88 | (3) |
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Chapter 5 Build a First-Party Data Asset with Consent |
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91 | (16) |
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Privacy-First Is Customer-Driven |
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91 | (2) |
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Privacy Police: Browsers and Regulators |
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93 | (1) |
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Web Browsers and Standards Bodies |
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93 | (2) |
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Intelligent Tracking Prevention |
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94 | (1) |
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Enhanced Tracking Prevention and Brave |
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94 | (1) |
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94 | (1) |
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95 | (1) |
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96 | (3) |
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How Can a Marketer Gain Trust? |
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98 | (1) |
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Attitudes Around the World |
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99 | (1) |
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100 | (2) |
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What Exactly Is the Privacy Paradox? |
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101 | (1) |
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How Do You Solve the Paradox? |
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101 | (1) |
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Four Privacy Tactics to Try |
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102 | (1) |
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Customer-Driven Thinker: Sebastian Baltruszewicz |
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103 | (1) |
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Summary: Build a First-Party Data Asset with Consent |
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104 | (3) |
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Chapter 6 Building a Customer-Driven Marketing Machine |
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107 | (24) |
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Know, Personalize, Engage, and Measure |
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107 | (1) |
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Know ("the Right Person ") |
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108 | (1) |
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Personalize ("the Right Message") |
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109 | (2) |
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Engage ("the Right Channel") |
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111 | (2) |
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113 | (1) |
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Organizational Transformation |
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114 | (1) |
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114 | (5) |
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114 | (2) |
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116 | (1) |
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116 | (1) |
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117 | (1) |
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118 | (1) |
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The People at the Center (the Center of Excellence Model) |
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119 | (4) |
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120 | (1) |
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121 | (1) |
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122 | (1) |
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123 | (1) |
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How to Get There from Here: A Working Maturity Model |
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124 | (4) |
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Channel Coordination Stages |
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126 | (1) |
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Engagement Maturity Stages |
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126 | (1) |
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Touchpoints: That Was Then |
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127 | (1) |
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127 | (1) |
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Experiences: This Is the Future |
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128 | (1) |
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Summary: Build a Customer-Driven Marketing Machine |
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128 | (3) |
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Chapter 7 Adtech and the Data Management Platform |
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131 | (10) |
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131 | (1) |
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Background/Evolution of the DMP |
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132 | (1) |
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Five Sources of Value in DMP |
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133 | (1) |
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Advertising as Part of the Marketing Mix |
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134 | (1) |
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Role of Pseudonymous IDs in the Enterprise |
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135 | (1) |
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Advertising in "Walled Gardens" with First-Party Data |
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135 | (1) |
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End-to-end Journey Management: The CDMP |
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136 | (1) |
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Customer-Driven Thinker: Ron Amram |
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137 | (1) |
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Summary: Adtech and the Data Management Platform |
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138 | (3) |
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Chapter 8 Beyond Marketing |
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141 | (14) |
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The Expanding Role of Customer Data Across the Enterprise |
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141 | (5) |
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Service: Frontline Engagement with the Customer |
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144 | (2) |
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Commerce: The Storefront and the Nexus of Response |
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146 | (3) |
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Use of Commerce Data for Modeling and Scoring |
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147 | (2) |
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Sales: The B2B Context, and What That Means for Customer Data |
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149 | (2) |
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150 | (1) |
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150 | (1) |
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151 | (1) |
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Marketing: The Brand Stewards, Revenue, and the Engagement Engine |
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151 | (1) |
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Customer-Driven Thinker: Kumar Subramanyam |
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152 | (1) |
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Summary: Beyond Marketing: Putting Sales, Service, and Commerce Data to Work |
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153 | (2) |
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Chapter 9 Machine Learning and Artificial Intelligence |
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155 | (20) |
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Once Upon a Time... in Silicon Valley |
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155 | (1) |
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156 | (3) |
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157 | (1) |
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157 | (2) |
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Customer-Driven Machine Learning and AI |
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159 | (1) |
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Data Science in Marketing |
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160 | (1) |
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Machine Learning Vs. Artificial Intelligence? |
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161 | (1) |
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What Does a Marketing Data Scientist Do? |
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161 | (1) |
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Customer Data and Experimental Design |
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161 | (1) |
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Customer Data, Machine Learning, and AI |
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162 | (3) |
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162 | (1) |
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Labeled Vs. Unlabeled Data |
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162 | (1) |
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162 | (1) |
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163 | (1) |
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163 | (1) |
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163 | (1) |
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164 | (1) |
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164 | (1) |
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164 | (1) |
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164 | (1) |
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Applying Machine Learning and AI in Marketing |
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165 | (4) |
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Machine-Learned Segmentation |
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165 | (2) |
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Machine-Learned Attribution |
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167 | (1) |
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Image Recognition and Natural Language Processing (NLP) |
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168 | (1) |
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Importance of Customer Data for AI |
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169 | (1) |
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AI/ML in the Organization: Data Science Teams |
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170 | (1) |
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Customer-Driven Thinker: Alysia Borsa |
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171 | (2) |
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Summary: Machine Learning and Artificial Intelligence |
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173 | (2) |
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Chapter 10 Orchestrating a Personalized Customer Journey |
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175 | (10) |
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The Rise of Context Marketing |
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175 | (2) |
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177 | (1) |
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178 | (2) |
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Real-Time Interaction Management (RTIM) Journeys |
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180 | (1) |
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Customer-Driven Thinker: Laura Lisowski Cox |
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181 | (2) |
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Summary: Orchestrating a Personalized Customer Journey |
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183 | (2) |
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Chapter 11 Connected Data for Analytics |
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185 | (16) |
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Customer Data for Marketing Analytics |
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185 | (3) |
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188 | (1) |
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188 | (1) |
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189 | (1) |
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190 | (7) |
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Marketing/Email Analytics |
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190 | (1) |
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191 | (1) |
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Multitouch Attribution (MTA) |
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192 | (1) |
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193 | (1) |
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Marketing Analytics Platforms |
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194 | (1) |
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195 | (2) |
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Customer-Driven Thinker: Vinny Rinaldi |
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197 | (2) |
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Summary: Connected Data for Analytics |
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199 | (2) |
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Chapter 12 Summary and Looking Ahead |
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201 | (8) |
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201 | (3) |
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204 | (1) |
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205 | (1) |
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Aggregate-Level Data and "FLOCtimization" |
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206 | (1) |
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A Fresh Start for Multitouch Attribution |
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206 | (1) |
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207 | (1) |
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208 | (1) |
Further Reading |
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209 | (2) |
Acknowledgments |
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211 | (2) |
About the Authors |
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213 | (2) |
Index |
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215 | |