This book introduces computational advertising, and Internet monetization. It provides a macroscopic understanding of how consumer products in the Internet era push user experience and monetization to the limit. Part One of the book focuses on the basic problems and background knowledge of online advertising. Part Two targets the product, operations, and sales staff, as well as high-level decision makers of the Internet products. It explains the market structure, trading models, and the main products in computational advertising. Part Three targets systems, algorithms, and architects, and focuses on the key technical challenges of different advertising products.
Features
· Introduces computational advertising and Internet monetization
· Covers data processing, utilization, and trading
· Uses business logic as the driving force to explain online advertising products and technology advancement
· Explores the products and the technologies of computational advertising, to provide insights on the realization of personalization systems, constrained optimization, data monetization and trading, and other practical industry problems
· Includes case studies and code snippets
Autorentext
Dr. Liu Peng is senior director and chief architect of business products at Qihoo 360. He is
also responsible for product and engineering for monetization of 360. After receiving his
PhD from Tsinghua University in 2005, he joined Microsoft Research Asia and studied
cutting-edge artificial intelligence technologies. In 2009, he participated in the founding of
Yahoo! Labs Beijing as a senior scientist. He was also chief scientist of MediaV. Dr. Liu
Peng is devoted to products and technologies related to big data and computational
advertising. His public online course "computational advertising" has attracted more than
30,000 students on Netease.com, and has been adopted as a basic training material in
many related companies. Moreover, this course has been selected by Peking University,
Tsinghua University and Beihang University for their graduates.
Wang Chao received his master's degree from Peking University, and then worked at
Weibo and Autohome's advertising department for some years. He is now a tech leader in
the query recommendation group at Baidu's portal search department. His work focuses on
machine learning algorithms in computational advertising, and he has won 7th place among
718 participants in "predict click-through rates on display ads" organized by Kaggle and
Criteo. He is also interested in contributing code for open source machine learning tools
such as xgboost.
Inhalt
Contents
Figures, xxi
Tables, xxvii
Foreword, xxix
Preface (1), xxxvii
Preface (2), xxxix
Preface (3), xli
Authors, xliii
PART 1 Market and Background of Online Advertising 1
CHAPTER 1 Overview of Online Advertising 3
1.1 FREE MODE AND CORE ASSETS OF THE INTERNET 4
1.2 RELATIONSHIP BETWEEN BIG DATA AND ADVERTISING 5
1.3 DEFINITION AND PURPOSE OF ADVERTISING 8
1.4 PRESENTATION FORMS OF ONLINE ADVERTISING 10
1.5 BRIEF HISTORY OF ONLINE ADVERTISING 18
CHAPTER 2 Basis for Computational Advertising 25
2.1 ADVERTISING EFFECTIVENESS THEORY 26
2.2 TECHNICAL FEATURES OF THE INTERNET ADVERTISING 29
2.3 CORE ISSUE OF COMPUTATIONAL ADVERTISING 30
2.3.1 Breakdown of Advertising Return 32
2.3.2 Relationship between Billing Models and eCPM Estimation 33
2.4 BUSINESS ORGANIZATIONS IN THE ONLINE ADVERTISING
INDUSTRY 36
2.4.1 Interactive Advertising Bureau 37
2.4.2 American Association of Advertising Agencies 38
2.4.3 Association of National Advertisers 38
PART 2 Product Logic of Online Advertising 39
CHAPTER 3 Overview of Online Advertising Products 41
3.1 DESIGN PHILOSOPHY FOR COMMERCIAL PRODUCTS 43
3.2 PRODUCT INTERFACE OF ADVERTISING SYSTEM 44
3.2.1 Demand-Side Management Interface 44
3.2.2 Supply-Side Management Interface 47
3.2.3 Multiple Forms of Interface between Supply and Demand Sides 48
CHAPTER 4 Agreement-Based Advertising 51
4.1 AD SPACE AGREEMENT 52
4.2 AUDIENCE TARGETING 53
4.2.1 Overview of Audience Targeting Technologies 54
4.2.2 Audience Targeting Tag System 57
4.2.3 Design Principles for Tag System 59
4.3 DISPLAY QUANTITY AGREEMENT 60
4.3.1 Traffic Forecasting 61
4.3.2 Traffic Shaping 61
4.3.3 Online Allocation 62
4.3.4 Product Cases 63
4.3.4.1 Yahoo! GD 63
CHAPTER 5 Search Ad and Auction-Based Advertising 65
5.1 SEARCH AD 67
5.1.1 Products of Search Advertising 67
5.1.2 New Forms of Search Ads 70
5.1.3 Product Strategy of Search Advertising 73
5.1.4 Product Cases 76
5.2 POSITION AUCTION AND MECHANISM DESIGN 79
5.2.1 Market Reserve Price 80
5.2.2 Pricing Problem 81
5.2.3 Squashing 83
5.2.4 Myerson Optimal Auction 84
5.2.5 Examples of Pricing Results 85
5.3 AUCTION-BASED ADN 85
5.3.1 Forms of ADN Products 86
5.3.2 Product Strategy for ADN 88
5.3.3 Product Cases 89
5.4 DEMAND-SIDE PRODUCTS IN AUCTION-BASED ADVERTISING 90
5.4.1 Search Engine Marketing 90
5.4.2 Trading Desk 91
5.4.3 Product Cases 91
5.5 COMPARISON BETWEEN AUCTION-BASED AND
AGREEMENT-BASED ADVERTISING 93
CHAPTER 6 Programmatic Trade Advertising 95
6.1 RTB 97
6.1.1 RTB Process 98
6.2 OTHER MODES OF PROGRAMMED TRADE 100
6.2.1 Preferred Deal 100
6.2.2 Private Marketplace 101
6.2.3 Programmatic Direct Buy 102
6.2.4 Spectrum of Advertising Transactions 103
6.3 AD EXCHANGE 104
6.3.1 Product Samples 104
6.4 DEMAND-SIDE PLATFORM 105
6.4.1 DSP Product Strategy 106
6.4.2 Bidding Strategy 106
6.4.3 Bidding and Pricing Processes 108
6.4.4 Retargeting 108
6.4.5 Look-Alike 111
6.4.6 Product Cases 112
6.5 SUPPLY-SIDE PLATFORM 113
6.5.1 SSP Product Strategy 114
6.5.2 Header Bidding 115
6.5.3 Product Cases 117
CHAPTER 7 Data Processing and Exchange 119
7.1 VALUABLE DATA SOURCES 120
7.2 DATA MANAGEMENT PLATFORM 123
7.2.1 Tripartite Data Partitioning 123
7.2.2 First-Party DMP 123
7.2.3 Third-Party DMP 124
7.2.4 Product Cases 125
7.3 BASIC PROCESS OF DATA TRADING 129
7.4 PRIVACY PROTECTION AND DATA SECURITY 131
7.4.1 Privacy Protection 131
7.4.2 Data Security in Programmatic Trade 134
7.4.3 General Data Protection Regulations 136
CHAPTER 8 News Feed Ad and Native Ad 139
8.1 STATUS QUO AND CHALLENGES IN MOBILE ADVERTISING 140
8.1.1 Characteristics of Mobile Advertising 141
8.1.2 Traditional Creative of Mobile Advertising 142
8.1.3 Challenges in Front of Mobile Advertising 144
8.2 NEWS FEED AD 146
8.2.1 Definition of News Feed Ad 146
8.2.2 Key Points about News Feed Ad 149
8.3 OTHER NATIVE AD-RELATED PRODUCTS 150
8.3.1 Search Ad 150
8.3.2 Advertorial 151
8.3.3 Affiliate network 151
8.4 NATIVE ADVERTISING PLATFORM 151
8.4.1 Native Display and Native Scenario 152
8.4.2 Scenario Perception and Application 153
8.4.3 Product Placement Native Ad 154
8.4.4 Product Cases 157
8.5 NATIVE AD AND PROGRAMMATIC TRADE 161
PART 3 Key Technologies for Computational Advertising 163
CHAPTER 9 Technological Overview 165
9.1 PERSONALIZED SYSTEM FRAMEWORK 166
9.2 OP…