FULL HOUSE Turning Data into Audiences - Creative New Zealand

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SECTION TWO – BUILDING THE RESOURCE 37 4. Customer Information 39 Data Versus Information 40 Three Steps to Building Data 42 The Potential Customer Record 43 Name 44 Position 44 Address 44 Postcode 45 Phone 45 Email 46 Age 46 Purchase History 46 Performances/Events/Activities Attended 47 Event Categorisations 48 Case 6: ADUK Categorisation of Events 48 Segmentation Criteria 50 Preferences 50 Relationship with the Customer 51 Ticket Type 52 Group or Party Bookings 54 Price Paid or Payment Level 54 Time of Booking 55 Payment Method 57 How They Found Out 57 5. Permission Marketing and Privacy 59 Opting Out Versus Opting In 61 Legislation 61 Chronology of Developments in Privacy and Data Access in New Zealand 62 The Privacy Act 63 Extent of Application 64 Privacy Policy Compliance for Different Booking Methods 64 The Unsolicited Electronic Messages Bill 66 Email Marketing Standards 67 Case 7: A Strategic Approach to Email Communication 68 Compliance Steps 69 Email Address Collection: Good Practice for Box Offices 71 Case 8: Ticket Agencies – Access to Customer Data 72 Data Ownership and Access 73 Case 9: Whose Customer Is It? 74 Sharing Data 75 Published Codes and Guidelines 76 Guidelines for Best Practice 78 Privacy Best Practice 78 EMarketing Best Practice 79 6. Beyond Ticketing 80 Membership Matters 82 Loyalty Schemes and ‘Frequent Attender’ Schemes 84 Case 10: Accumulating Theatre Loyalty Points 84 Season Tickets 85 Subscriptions 86 CONTENTS
SECTION THREE – MANAGING THE RESOURCE 89 7. Expanding the Horizons 91 Types of Data 92 Supplementary Data 93 Postcode Address File (PAF) 93 National Postcode Directory 94 Address Verification Software 94 National Databases and Profiling Systems 95 Geodemographic Profiling 95 Using Profiling Tools 95 Statistics New Zealand Microsegments 96 Case 11: Slicing and Dicing 97 Mapping 98 Market Opportunity 98 Household Economic Survey 100 Profiling in Action 103 List Cleaning Services 104 Proprietary Mapping and Market Analysis Systems 106 8. Processing the Information 109 The Mean 110 The Mode 110 The Median 111 Range and Dispersion 112 Standard Deviation 113 Frequency Distribution 114 Graphic Presentation 114 Bar Charts 116 Line Graphs 118 Understanding Data 119 Analysis of Variance 124 Significance Levels and Probability Values 125 Correlation and Regression 125 Segmenting Customers Using Multivariate Analysis 126 Trend Analysis and Index Numbers 127 In Summary 130 9. Reports 133 Configuration 134 Data Integrity 134 Standard Reports 135 Exception Reports 136 How Are Bookings Going? 136 Analysis of Booking Patterns 138 Touring 138 Timing 139 Hierarchy of Comparisons 140 Case 12: Audience Crossover Analysis 142 Index Tables 143 Comparisons 144 Penetration and Geographical Reach 145 vi FULL HOUSE: Turning Data into Audiences © Roger Tomlinson & Tim Roberts
Page 1 and 2: FULL HOUSE Turning Data into Audien
Page 3 and 4: Published by Creative New Zealand O
Page 5: CONTENTS FOREWORD INTRODUCTION 20 R
Page 9 and 10: INTRODUCTION This new Manual is fun
Page 11 and 12: How to Use this Manual ? Each secti
Page 13 and 14: xii FULL HOUSE: Turning Data into A
Page 15 and 16: 1. The heart of marketing The custo
Page 17 and 18: Detailed knowledge of the current a
Page 19 and 20: DO YOU KNOW THE ANSWERS? The key st
Page 21 and 22: This Manual opens with the statemen
Page 23 and 24: AVAILABLE AUDIENCES Diagram 2-1: At
Page 25 and 26: In a study of ticketing data for 28
Page 27 and 28: CUSTOMER RELATIONSHIP MANAGEMENT Th
Page 29 and 30: LIFETIME VALUE The effort in reachi
Page 31 and 32: For first-time attenders, it is use
Page 33 and 34: In summary, organisations should as
Page 35 and 36: 20 Things Sydney Symphony Orchestra
Page 37 and 38: Unrealistic perhaps, but the spirit
Page 39 and 40: ! REVIEW . Is your organisation ‘

FULL HOUSE Turning Data into Audiences - Creative New Zealand

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