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Overview:

Two significant trends for consumer-based industries are to leverage Gamification (embedded entertainment) for customer engagement and Big Data and related analytics techniques to mine patterns and predictions from consumer behaviors. The goal of Gamification is to maximum user brand/product engagement through facilitation of entertainment in which the user interacts with the brand in a fun/pleasurable manner.

We see gamification not only solving user loyalty problems for businesses but also tackling real-world problems for particular industries by producing significant user feedback that will flow into various data systems. Big Data and Analytics. Designers and developers are analyzing gamers' motivation and psyche on their actions and creating engaging content based on big data analytics. It is now considered as primary tools for business decision.

This research provides an assessment of the companies, solutions, and market analysis for these two dominant trends along with forecasts for 2015 - 2020. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:
Media Companies
Financial Institutions
Application Developers
Government Organizations
Mobile Network Operators
Gamification Platform Providers
Retail and Hospitality Companies
Content Providers and Intermediaries
Digital Marketing Agency or Consultants
Analytics and Data Reporting Companies
Brands, advertisers, and media companies

Table Of Contents

Capitalizing on Consumer Behavior in Embedded Gaming: Gamification and Big Data 2015 - 2020
Table of Contents:

Gamification Companies, Solutions, Market Outlook and Forecasts 2015 - 2020

1.0 INTRODUCTION TO THE GAMIFICATION PARADIGM 7
2.0 GAMIFICATION MARKET TREND ANALYSIS 8
2.1 CORE GAMING PLATFORMS VS. GAMIFICATION (GAMIFIED PLATFORMS) 8
2.2 LOYALTY REWARD, GAAS (GAMIFICATION AS A SERVICE), AND IN-APP GAMIFICATION 8
2.3 CUSTOMER ACQUISITION, ENGAGEMENT, LOYALTY AND GAMIFICATION 8
2.4 SOCIAL WEB ENGINEERING and GAMIFICATION 8
2.5 LOCATION BASED SERVICE NETWORK (LBSN) and GAMIFICATION 9
2.6 F-COMMERCE, SOCIAL NETWORK, AND GAMIFICATION 10
2.7 BOOST UP SOCIAL INNOVATIONS AND ENTREPRENEURSHIP 10
2.8 SOCIAL GOODS INDUSTRY AND GAMIFICATION 11
2.9 SOCIAL BUSINESS STARTUP AND CLOUD GAMIFICATION 12
2.10 INVESTMENT TREND IN GAMIFICATION 12
2.11 GAMIFICATION AND BIG DATA ANALYTICS 12
2.12 GAMIFICATION FOR PRODUCTIVITY 12
2.13 VIRTUAL REALITY AND GAMIFICATION 13
2.14 WEARABLE WIRELESS AND SELF GAMIFICATION 13
2.15 CORPORATE LEARNING FOR EXECUTIVE AND IT LEADERS 13
2.16 SEMANTIC WEB and GAMIFICATION 13
2.17 MILLENNIAL AND GAMIFICATION 14
3.0 GLOBAL GAMIFICATION MARKET ASSESSSMENT 15
3.1 GLOBAL GAMIFICATION MARKET PROJECTIONS 2015 - 2020 15
3.2 GAMIFICATION MARKET BY GEOGRAPHY 2020 15
3.3 GAMIFICATION MARKET BY END-USER 2020 16
3.4 GAMIFICATION MARKET BY INDUSTRY VERTICAL 2020 17
4.0 GAMIFICATION TECHNOLOGY SOLUTIONS 19
4.1 GAME STYLE MARKETING 19
4.2 GAMIFICATION VS. SERIOUS GAMING 20
4.3 WEARABLE GAMIFICATION 21
4.4 MOBILE SOCIAL GAMIFICATION 21
4.5 USING GAME LAYER 21
4.6 CLOUD GAMIFICATION 22
5.0 GAMIFICATION COMPANY ANALYSIS 23
5.1 42 TERABYTES 23
5.2 500 FRIENDS 23
5.3 ACTAPI 24
5.4 ACTIPLAY 24
5.5 BADGEVILLE 24
5.6 BANKERSLAB 25
5.7 BELLY 26
5.8 BENNU 26
5.9 BIGDOOR 26
5.10 BITOON DIGITAL 27
5.11 BIZPART ENGAGE 27
5.12 BLACK INK STUDIO 28
5.13 BLUE TELESCOPE 28
5.14 BOOMBOX 29
5.15 BRANDGAME 29
5.16 BUNCHBALL 29
5.17 CATALYSTS 30
5.18 CHALLENGERA 30
5.19 CIandT 31
5.20 CLICandGAIN 31
5.21 COMARCH 32
5.22 CRMGAMIFIED 32
5.23 CROWDTWIST 33
5.24 CUSTOMERADVOCACY 33
5.25 DESIGNING DIGITALLY 34
5.26 DOPAMINE 34
5.27 DOPAWIN 34
5.28 DSXGROUP, LLC 35
5.29 DYNAMIA 35
5.30 ECHO.IT 36
5.31 EMEE 36
5.32 ENTHUSE 37
5.33 EXPERTOFFICE 37
5.34 FANTASYSALESTEAM 37
5.35 FRIENDEFI 38
5.36 FUNIFIER 38
5.37 GAME CRAFT 39
5.38 GAME ON! LEARNING 39
5.39 GAMEFFECTIVE 39
5.40 GAMIFICATION NATION 40
5.41 GAMIFIED LABS 40
5.42 GAMINSIDE 41
5.43 G-ERA 41
5.44 GIGYA 41
5.45 IACTIONABLE 42
5.46 LEADERBOARDED 42
5.47 LEVELUP 43
5.48 LOYALTYMATCH 43
5.49 MINDSPACE 44
5.50 MINDTICKLE 44
5.51 PAKRA 45
5.52 PLAYBASIS 45
5.53 PLAYGEN 45
5.54 PUGPHARM 46
5.55 PUNCHCARD 46
5.56 PUNCHTAB 47
5.57 SALESFORCE 47
5.58 SAP 49
5.59 SERIOSITY 50
5.60 TEMBOSOCIAL 50
5.61 THE GAMIFIERS 51
5.62 WONNOVA 51
5.63 WORK BANDITS (FIDUP) 52
6.0 CONCLUSIONS AND RECOMMENDATIONS 53
6.1 RECOMMENDATIONS FOR BRANDS AND ADVERTISING AGENCIES 53
6.2 RECOMMENDATIONS FOR MERCHANTS AND INSTORE STRATEGIES 54
6.3 RECOMMENDATIONS FOR IT LEADERS AND APPLICATION DEVELOPERS 55

Figures

Figure 1: Flow Zone in Gamification Social Web Engineering 9
Figure 2: Zynga used LBSN concept for Times Square 9
Figure 3: BMW's Gamified Store 10
Figure 4: Pain Squad's Pain Parameters for Kids 11
Figure 5: SNN Gaming Interface 14
Figure 6: Global Gamification Market Forecast in $ billion 2015 - 2020 15
Figure 7: Global Gamification Market Percentage Share by Geography 2020 16
Figure 8: Global Gamification Market Percentage Share by End-user Type 2020 17
Figure 9: Global Gamification Market Percentage Share by Industry Vertical 2020 18
Figure 10: Cadbury Spots and Stripes: A Successful Game Style Marketing 20
Figure 11: Foursquare Leaderboard sponsored by Pepsi 21
Figure 12: Gaming Analytics and Statistics 55

Tables

Table 1: Gamification and Business Objectives in App Design 56

The Big Data Market: Business Case, Market Analysis and Forecasts 2015 - 2020

1 Introduction 10
1.1 Executive Summary 10
1.2 Topics Covered 12
1.3 Select Findings 13
1.4 Target Audience 14
1.5 Companies Mentioned 15
2 Big Data Technology and Business Case 20
2.1 Defining Big Data 20
2.2 Key Characteristics of Big Data 21
2.2.1 Volume 21
2.2.2 Variety 22
2.2.3 Velocity 22
2.2.4 Variability 23
2.2.5 Complexity 23
2.3 Big Data Technology 24
2.3.1 Hadoop 24
2.3.2 Other Apache Projects 26
2.3.3 NoSQL 26
2.3.3.1 Hbase 27
2.3.3.2 Cassandra 27
2.3.3.3 Mongo DB 28
2.3.3.4 Riak 28
2.3.3.5 CouchDB 28
2.3.4 MPP Databases 28
2.3.5 Others and Emerging Technologies 29
2.3.5.1 Storm 29
2.3.5.2 Drill 29
2.3.5.3 Dremel 29
2.3.5.4 SAP HANA 29
2.3.5.5 Gremlin and Giraph 30
2.3.6 New Paradigms and Techniques 30
2.3.6.1 Streaming Analytics 30
2.3.6.2 Cloud Technology 30
2.3.6.3 Google Search 30
2.3.6.4 Customize Analytical Tools 31
2.3.6.5 Internet Keywords 31
2.3.6.6 Gamification 32
2.4 Big Data Roadmap 34
2.5 Market Drivers 36
2.5.1 Data Volume and Variety 36
2.5.2 Increasing Adoption of Big Data by Enterprises and Telecom 36
2.5.3 Maturation of Big Data Software 36
2.5.4 Continued Investments in Big Data by Web Giants 36
2.5.5 Business Drivers 37
2.6 Market Barriers 38
2.6.1 Privacy and Security: The 'Big' Barrier 38
2.6.2 Workforce Re-skilling and Organizational Resistance 38
2.6.3 Lack of Clear Big Data Strategies 39
2.6.4 Technical Challenges: Scalability and Maintenance 39
2.6.5 Big Data Development Expertise 39
3 Key Investment Sectors for Big Data 40
3.1 Industrial Internet and Machine-to-Machine 40
3.1.1 Big Data in M2M 40
3.1.2 Vertical Opportunities 40
3.2 Retail and Hospitality 40
3.2.1 Improving Accuracy of Forecasts and Stock Management 41
3.2.2 Determining Buying Patterns 41
3.2.3 Hospitality Use Cases 41
3.2.4 Personalized Marketing 42
3.3 Media 44
3.3.1 Social Media 44
3.3.2 Social Gaming Analytics 44
3.3.3 Usage of Social Media Analytics by Other Verticals 45
3.3.4 Internet Keyword Search 45
3.4 Utilities 47
3.4.1 Analysis of Operational Data 47
3.4.2 Application Areas for the Future 47
3.5 Financial Services 48
3.5.1 Fraud Analysis, Mitigation and Risk Profiling 48
3.5.2 Merchant-Funded Reward Programs 48
3.5.3 Customer Segmentation 48
3.5.4 Customer Retention and Personalized Product Offering 48
3.5.5 Insurance Companies 50
3.6 Healthcare and Pharmaceutical 50
3.6.1 Drug Development 50
3.6.2 Medical Data Analytics 50
3.6.3 Case Study: Identifying Heartbeat Patterns 50
3.7 Telecommunications 51
3.7.1 Telco Analytics: Customer/Usage Profiling and Service Optimization 51
3.7.2 Big Data Analytic Tools 51
3.7.3 Speech Analytics 52
3.7.4 New Products and Services 52
3.8 Government and Homeland Security 53
3.8.1 Big Data Research 53
3.8.2 Statistical Analysis 55
3.8.3 Language Translation 55
3.8.4 Developing New Applications for the Public 56
3.8.5 Tracking Crime 56
3.8.6 Intelligence Gathering 56
3.8.7 Fraud Detection and Revenue Generation 56
3.9 Other Sectors 58
3.9.1 Aviation 58
3.9.2 Transportation and Logistics: Optimizing Fleet Usage 58
3.9.3 Sports: Real-Time Processing of Statistics 59
3.9.4 Education 59
3.9.5 Manufacturing 60
4 The Big Data Value Chain 66
4.1 How Fragmented is the Big Data Value Chain? 66
4.2 Data Acquisitioning and Provisioning 67
4.3 Data Warehousing and Business Intelligence 67
4.4 Analytics and Virtualization 67
4.5 Actioning and Business Process Management 68
4.6 Data Governance 68
5 Big Data Analytics 69
5.1 What is Big Data Analytics? 69
5.2 The Importance of Big Data Analytics 70
5.3 Reactive vs. Proactive Analytics 71
5.4 Technology and Implementation Approaches 73
5.4.1 Grid Computing 73
5.4.2 In-Database processing 73
5.4.3 In-Memory Analytics 75
5.4.4 Data Mining 75
5.4.5 Predictive Analytics 77
5.4.6 Natural Language Processing 80
5.4.7 Text Analytics 84
5.4.8 Visual Analytics 85
5.4.9 Association rule learning 86
5.4.10 Classification tree analysis 87
5.4.11 Machine Learning 87
5.4.11.1 Neural networks 88
5.4.11.2 Multilayer Perceptron (MLP) 89
5.4.11.3 Radial Basis Functions 90
5.4.11.4 Support vector machines 90
5.4.11.5 Naïve Bayes 90
5.4.11.6 k-nearest neighbours 91
5.4.11.7 Geospatial predictive modelling 92
5.4.12 Regression Analysis 92
5.4.13 Social Network Analysis 93
6 Standardization and Regulatory Initiatives 94
6.1 Cloud Standards Customer Council - Big Data Working Group 94
6.2 National Institute of Standards and Technology - Big Data Working Group 95
6.3 OASIS 96
6.4 Open Data Foundation 98
6.5 Open Data Center Alliance 99
6.6 Cloud Security Alliance - Big Data Working Group 100
6.7 International Telecommunications Union 101
6.8 International Organization for Standardization 101
6.9 International Organization for Standardization) 101
7 Key Players in the Big Data Market 102
7.1 Vendor Assessment Matrix 102
7.2 1010Data 102
7.3 Actuate Corporation 103
7.4 Accenture 103
7.5 Amazon 103
7.6 Apache Software Foundation 104
7.7 APTEAN (Formerly CDC Software) 104
7.8 Booz Allen Hamilton 104
7.9 Cap Gemini 105
7.10 Cisco Systems 105
7.11 Cloudera 105
7.12 Computer Science Corporation 105
7.13 DataDirect Network 106
7.14 Dell 107
7.15 Deloitte 107
7.16 EMC 107
7.17 Facebook 107
7.18 Fujitsu 108
7.19 General Electric 109
7.20 GoodData Corporation 110
7.21 Google 110
7.22 Guavus 110
7.23 Hitachi Data Systems 111
7.24 Hortonworks 111
7.25 HP 111
7.26 IBM 112
7.27 Informatica 112
7.28 Intel 112
7.29 Jaspersoft 112
7.30 Juniper Networks 113
7.31 Marklogic 113
7.32 Microsoft 114
7.33 MongoDB (Formerly 10Gen) 114
7.34 MU Sigma 114
7.35 Netapp 115
7.36 NTT Data 115
7.37 Opera Solutions 116
7.38 Oracle 116
7.39 Pentaho 116
7.40 Platfora 116
7.41 Qliktech 117
7.42 Quantum 117
7.43 Rackspace 117
7.44 Revolution Analytics 117
7.45 Salesforce 118
7.46 SAP 118
7.47 SAS Institute 118
7.48 Sisense 119
7.49 Software AG/Terracotta 119
7.50 Splunk 119
7.51 Sqrrl 120
7.52 Supermicro 120
7.53 Tableau Software 120
7.54 Tata Consultancy Services 121
7.55 Teradata 121
7.56 Think Big Analytics 121
7.57 TIBCO 121
7.58 Tidemark Systems 122
7.59 VMware (Part of EMC) 122
7.60 Wipro 122
7.61 Zettics 123
8 Market Analysis 124
8.1 Big Data Revenue 2014 - 2020 124
8.2 Big Data Revenue by Functional Area 2014 - 2020 125
8.2.1 Supply Chain Management 126
8.2.2 Business Intelligence 127
8.2.3 Application Infrastructure and Middleware 128
8.2.4 Data Integration Tools and Data Quality Tools 129
8.2.5 Database Management Systems 130
8.2.6 Big Data Social and Content Analytics 131
8.2.7 Big Data Storage Management 132
8.2.8 Big Data Professional Services 133
8.3 Big Data Revenue by Region 2014 - 2020 134
8.3.1 Asia Pacific 135
8.3.2 Eastern Europe 136
8.3.3 Latin and Central America 137
8.3.4 Middle East and Africa 138
8.3.5 North America 139
8.3.6 Western Europe 140

Figures

Figure 1: NoSQL vs Legacy DB Performance Comparisons 27
Figure 2: 2014 Gartner Hype Cycle for Emerging Technologies 34
Figure 3: Roadmap Big Data Technologies 2014 - 2030 35
Figure 4: The Big Data Value Chain 66
Figure 5: Big Data Vendor Ranking Matrix 102
Figure 6: Big Data Revenue 2013 - 2020 124
Figure 7: Big Data Revenue by Functional Area 2013 - 2020 125
Figure 8: Big Data Supply Chain Management Revenue 2013 - 2020 126
Figure 9: Big Data Supply Business Intelligence Revenue 2013 - 2020 127
Figure 10: Big Data Application Infrastructure and Middleware Revenue 2013 - 2020 128
Figure 11: Big Data Integration and Quality Tools Revenue 2013 - 2020 129
Figure 12: Big Data DB Management Systems Revenue 2013 - 2020 130
Figure 13: Big Data Social and Content Analytics Revenue 2013 - 2020 131
Figure 14: Big Data Storage Management Revenue 2013 - 2020 132
Figure 15: Big Data Professional Services Revenue 2013 - 2020 133
Figure 16: Big Data Revenue by Region 2013 - 2020 134
Figure 17: Asia Pacific Big Data Revenue 2013 - 2020 135
Figure 18: Eastern Europe Big Data Revenue 2013 - 2020 136
Figure 19: Latin and Central America Big Data Revenue 2013 - 2020 137
Figure 20: Middle East and Africa Big Data Revenue 2013 - 2020 138
Figure 21: North America Big Data Revenue 2013 - 2020 139
Figure 22: Western Europe Big Data Revenue 2013 - 2020 140

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