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市場調查報告書

從內建型遊戲的消費行為產生利益: 遊戲化及巨量資料 - 2015∼2020年

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

出版商 Mind Commerce 商品編碼 332282
出版日期 內容資訊 英文 190 Pages
商品交期: 最快1-2個工作天內
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從內建型遊戲的消費行為產生利益: 遊戲化及巨量資料 - 2015∼2020年 Capitalizing on Consumer Behavior in Embedded Gaming: Gamification and Big Data 2015 - 2020
出版日期: 2015年06月10日 內容資訊: 英文 190 Pages
簡介

將一般消費者當作客戶的產業中的2個重要趨勢,為加深客戶參與用的遊戲化,和讀取消費行為的模式,還有預測利用的巨量資料及分析技術等。遊戲化的目標是透過樂趣和有趣,加深企業品牌和與用戶的互相交流,針對其企業品牌/產品最大化用戶參與度。遊戲化不僅成為解決用戶忠誠度問題的手段,巨量資料和分析流入各種資料系統之用戶回饋有助於商務實際課題的解決。

本報告提供遊戲化相關企業及解決方案的評估,和重要趨勢相關的市場分析、2015年到2020年的市場預測。

第1章 遊戲化、範例相關的序論

第2章 遊戲化市場趨勢分析

  • 核心、遊戲平台 vs. 遊戲化 (遊戲化平台)
  • 忠誠度這個報酬,GaaS (遊戲化即服務) 、及App內遊戲化
  • 客戶獲得、客戶參與、忠誠度,及遊戲化
  • 社群網站工程和遊戲化
  • 地理定位有效利用服務網路 (LBSN) 和遊戲化
  • F-貿易、社群網路,及遊戲化
  • 社群革新和創業者精神的推動
  • 社群商品產業和遊戲化
  • 社群商務、Start-Ups企業和遊戲化
  • 遊戲化的投資趨勢
  • 遊戲化和巨量資料、分析
  • 生產率提高的遊戲化
  • 虛擬實境 (VR) 和遊戲化
  • 穿戴式、無線設備和自我遊戲化
  • 重要職位與IT領導者的企業學習
  • 語意網和遊戲化
  • 千禧新生代和遊戲化

第3章 遊戲化全球市場的評估

  • 遊戲化全球市場預測 - 2015∼2020年
  • 各地區遊戲化市場 - 2020年
  • 各終端用戶遊戲化市場 - 2020年
  • 各垂直產業遊戲化市場 - 2020年

第4章 遊戲化技術解決方案

  • 遊戲風格行銷
  • 遊戲化 vs. seriousness遊戲
  • 穿戴式遊戲化
  • 行動社群遊戲化
  • 遊戲層的有效利用
  • 雲端遊戲化

第5章 遊戲化企業分析

第6章 結論及建議

  • 對品牌與廣告業者的建議
  • 對店主與商店內策略的建議
  • 對IT領導者與應用開發企業的建議
目錄

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

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

1.0 INTRODUCTION TO THE GAMIFICATION PARADIGM

2.0 GAMIFICATION MARKET TREND ANALYSIS

  • 2.1 CORE GAMING PLATFORMS VS. GAMIFICATION (GAMIFIED PLATFORMS)
  • 2.2 LOYALTY REWARD, GAAS (GAMIFICATION AS A SERVICE), AND IN-APP GAMIFICATION
  • 2.3 CUSTOMER ACQUISITION, ENGAGEMENT, LOYALTY AND GAMIFICATION
  • 2.4 SOCIAL WEB ENGINEERING & GAMIFICATION
  • 2.5 LOCATION BASED SERVICE NETWORK (LBSN) & GAMIFICATION
  • 2.6 F-COMMERCE, SOCIAL NETWORK, AND GAMIFICATION
  • 2.7 BOOST UP SOCIAL INNOVATIONS AND ENTREPRENEURSHIP
  • 2.8 SOCIAL GOODS INDUSTRY AND GAMIFICATION
  • 2.9 SOCIAL BUSINESS STARTUP AND CLOUD GAMIFICATION
  • 2.10 INVESTMENT TREND IN GAMIFICATION
  • 2.11 GAMIFICATION AND BIG DATA ANALYTICS
  • 2.12 GAMIFICATION FOR PRODUCTIVITY
  • 2.13 VIRTUAL REALITY AND GAMIFICATION
  • 2.14 WEARABLE WIRELESS AND SELF GAMIFICATION
  • 2.15 CORPORATE LEARNING FOR EXECUTIVE AND IT LEADERS
  • 2.16 SEMANTIC WEB & GAMIFICATION
  • 2.17 MILLENNIAL AND GAMIFICATION

3.0 GLOBAL GAMIFICATION MARKET ASSESSSMENT

  • 3.1 GLOBAL GAMIFICATION MARKET PROJECTIONS 2015 - 2020
  • 3.2 GAMIFICATION MARKET BY GEOGRAPHY 2020
  • 3.3 GAMIFICATION MARKET BY END-USER 2020
  • 3.4 GAMIFICATION MARKET BY INDUSTRY VERTICAL 2020

4.0 GAMIFICATION TECHNOLOGY SOLUTIONS

  • 4.1 GAME STYLE MARKETING
  • 4.2 GAMIFICATION VS. SERIOUS GAMING
  • 4.3 WEARABLE GAMIFICATION
  • 4.4 MOBILE SOCIAL GAMIFICATION
  • 4.5 USING GAME LAYER
  • 4.6 CLOUD GAMIFICATION

5.0 GAMIFICATION COMPANY ANALYSIS

  • 5.1 42 TERABYTES
  • 5.2 500 FRIENDS
  • 5.3 ACTAPI 24
  • 5.4 ACTIPLAY
  • 5.5 BADGEVILLE
  • 5.6 BANKERSLAB
  • 5.7 BELLY
  • 5.8 BENNU
  • 5.9 BIGDOOR
  • 5.10 BITOON DIGITAL
  • 5.11 BIZPART ENGAGE
  • 5.12 BLACK INK STUDIO
  • 5.13 BLUE TELESCOPE
  • 5.14 BOOMBOX
  • 5.15 BRANDGAME
  • 5.16 BUNCHBALL
  • 5.17 CATALYSTS
  • 5.18 CHALLENGERA
  • 5.19 CI&T
  • 5.20 CLIC&GAIN
  • 5.21 COMARCH
  • 5.22 CRMGAMIFIED
  • 5.23 CROWDTWIST
  • 5.24 CUSTOMERADVOCACY
  • 5.25 DESIGNING DIGITALLY
  • 5.26 DOPAMINE
  • 5.27 DOPAWIN
  • 5.28 DSXGROUP, LLC
  • 5.29 DYNAMIA
  • 5.30 ECHO.IT
  • 5.31 EMEE
  • 5.32 ENTHUSE
  • 5.33 EXPERTOFFICE
  • 5.34 FANTASYSALESTEAM
  • 5.35 FRIENDEFI
  • 5.36 FUNIFIER
  • 5.37 GAME CRAFT
  • 5.38 GAME ON! LEARNING
  • 5.39 GAMEFFECTIVE
  • 5.40 GAMIFICATION NATION
  • 5.41 GAMIFIED LABS
  • 5.42 GAMINSIDE
  • 5.43 G-ERA
  • 5.44 GIGYA
  • 5.45 IACTIONABLE
  • 5.46 LEADERBOARDED
  • 5.47 LEVELUP
  • 5.48 LOYALTYMATCH
  • 5.49 MINDSPACE
  • 5.50 MINDTICKLE
  • 5.51 PAKRA
  • 5.52 PLAYBASIS
  • 5.53 PLAYGEN
  • 5.54 PUGPHARM
  • 5.55 PUNCHCARD
  • 5.56 PUNCHTAB
  • 5.57 SALESFORCE
  • 5.58 SAP
  • 5.59 SERIOSITY
  • 5.60 TEMBOSOCIAL
  • 5.61 THE GAMIFIERS
  • 5.62 WONNOVA
  • 5.63 WORK BANDITS (FIDUP)

6.0 CONCLUSIONS AND RECOMMENDATIONS

  • 6.1 RECOMMENDATIONS FOR BRANDS AND ADVERTISING AGENCIES
  • 6.2 RECOMMENDATIONS FOR MERCHANTS AND INSTORE STRATEGIES
  • 6.3 RECOMMENDATIONS FOR IT LEADERS AND APPLICATION DEVELOPERS

Figures

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

Tables

  • Table 1: Gamification and Business Objectives in App Design

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

1 Introduction

  • 1.1 Executive Summary
  • 1.2 Topics Covered
  • 1.3 Select Findings
  • 1.4 Target Audience
  • 1.5 Companies Mentioned

2 Big Data Technology & Business Case

  • 2.1 Defining Big Data
  • 2.2 Key Characteristics of Big Data
    • 2.2.1 Volume
    • 2.2.2 Variety
    • 2.2.3 Velocity
    • 2.2.4 Variability
    • 2.2.5 Complexity
  • 2.3 Big Data Technology
    • 2.3.1 Hadoop
    • 2.3.2 Other Apache Projects
    • 2.3.3 NoSQL
      • 2.3.3.1 Hbase
      • 2.3.3.2 Cassandra
      • 2.3.3.3 Mongo DB
      • 2.3.3.4 Riak
      • 2.3.3.5 CouchDB
    • 2.3.4 MPP Databases
    • 2.3.5 Others and Emerging Technologies
      • 2.3.5.1 Storm
      • 2.3.5.2 Drill
      • 2.3.5.3 Dremel
      • 2.3.5.4 SAP HANA
      • 2.3.5.5 Gremlin & Giraph
    • 2.3.6 New Paradigms and Techniques
      • 2.3.6.1 Streaming Analytics
      • 2.3.6.2 Cloud Technology
      • 2.3.6.3 Google Search 30
      • 2.3.6.4 Customize Analytical Tools
      • 2.3.6.5 Internet Keywords
      • 2.3.6.6 Gamification
  • 2.4 Big Data Roadmap
  • 2.5 Market Drivers
    • 2.5.1 Data Volume & Variety
    • 2.5.2 Increasing Adoption of Big Data by Enterprises and Telecom
    • 2.5.3 Maturation of Big Data Software
    • 2.5.4 Continued Investments in Big Data by Web Giants
    • 2.5.5 Business Drivers
  • 2.6 Market Barriers
    • 2.6.1 Privacy and Security: The 'Big' Barrier
    • 2.6.2 Workforce Re-skilling and Organizational Resistance
    • 2.6.3 Lack of Clear Big Data Strategies
    • 2.6.4 Technical Challenges: Scalability & Maintenance
    • 2.6.5 Big Data Development Expertise

3 Key Investment Sectors for Big Data

  • 3.1 Industrial Internet and Machine-to-Machine
    • 3.1.1 Big Data in M2M
    • 3.1.2 Vertical Opportunities
  • 3.2 Retail and Hospitality
    • 3.2.1 Improving Accuracy of Forecasts & Stock Management
    • 3.2.2 Determining Buying Patterns
    • 3.2.3 Hospitality Use Cases
    • 3.2.4 Personalized Marketing
  • 3.3 Media
    • 3.3.1 Social Media
    • 3.3.2 Social Gaming Analytics
    • 3.3.3 Usage of Social Media Analytics by Other Verticals
    • 3.3.4 Internet Keyword Search
  • 3.4 Utilities
    • 3.4.1 Analysis of Operational Data
    • 3.4.2 Application Areas for the Future
  • 3.5 Financial Services
    • 3.5.1 Fraud Analysis, Mitigation & Risk Profiling
    • 3.5.2 Merchant-Funded Reward Programs
    • 3.5.3 Customer Segmentation
    • 3.5.4 Customer Retention & Personalized Product Offering
    • 3.5.5 Insurance Companies
  • 3.6 Healthcare and Pharmaceutical
    • 3.6.1 Drug Development
    • 3.6.2 Medical Data Analytics
    • 3.6.3 Case Study: Identifying Heartbeat Patterns
  • 3.7 Telecommunications
    • 3.7.1 Telco Analytics: Customer/Usage Profiling and Service Optimization
    • 3.7.2 Big Data Analytic Tools
    • 3.7.3 Speech Analytics
    • 3.7.4 New Products and Services
  • 3.8 Government and Homeland Security
    • 3.8.1 Big Data Research
    • 3.8.2 Statistical Analysis
    • 3.8.3 Language Translation
    • 3.8.4 Developing New Applications for the Public
    • 3.8.5 Tracking Crime
    • 3.8.6 Intelligence Gathering
    • 3.8.7 Fraud Detection & Revenue Generation
  • 3.9 Other Sectors
    • 3.9.1 Aviation
    • 3.9.2 Transportation & Logistics: Optimizing Fleet Usage
    • 3.9.3 Sports: Real-Time Processing of Statistics
    • 3.9.4 Education
    • 3.9.5 Manufacturing

4 The Big Data Value Chain

  • 4.1 How Fragmented is the Big Data Value Chain?
  • 4.2 Data Acquisitioning & Provisioning
  • 4.3 Data Warehousing & Business Intelligence
  • 4.4 Analytics & Virtualization
  • 4.5 Actioning and Business Process Management
  • 4.6 Data Governance

5 Big Data Analytics

  • 5.1 What is Big Data Analytics?
  • 5.2 The Importance of Big Data Analytics
  • 5.3 Reactive vs. Proactive Analytics
  • 5.4 Technology and Implementation Approaches
    • 5.4.1 Grid Computing
    • 5.4.2 In-Database processing
    • 5.4.3 In-Memory Analytics
    • 5.4.4 Data Mining
    • 5.4.5 Predictive Analytics
    • 5.4.6 Natural Language Processing
    • 5.4.7 Text Analytics
    • 5.4.8 Visual Analytics
    • 5.4.9 Association rule learning
    • 5.4.10 Classification tree analysis
    • 5.4.11 Machine Learning
      • 5.4.11.1 Neural networks
      • 5.4.11.2 Multilayer Perceptron (MLP)
      • 5.4.11.3 Radial Basis Functions
      • 5.4.11.4 Support vector machines
      • 5.4.11.5 Naïve Bayes
      • 5.4.11.6 k-nearest neighbours
      • 5.4.11.7 Geospatial predictive modelling
    • 5.4.12 Regression Analysis
    • 5.4.13 Social Network Analysis

6 Standardization and Regulatory Initiatives

  • 6.1 Cloud Standards Customer Council - Big Data Working Group
  • 6.2 National Institute of Standards and Technology - Big Data Working Group
  • 6.3 OASIS
  • 6.4 Open Data Foundation
  • 6.5 Open Data Center Alliance
  • 6.6 Cloud Security Alliance - Big Data Working Group
  • 6.7 International Telecommunications Union
  • 6.8 International Organization for Standardization
  • 6.9 International Organization for Standardization)

7 Key Players in the Big Data Market

  • 7.1 Vendor Assessment Matrix
  • 7.2 1010Data
  • 7.3 Actuate Corporation
  • 7.4 Accenture
  • 7.5 Amazon
  • 7.6 Apache Software Foundation
  • 7.7 APTEAN (Formerly CDC Software)
  • 7.8 Booz Allen Hamilton
  • 7.9 Cap Gemini
  • 7.10 Cisco Systems
  • 7.11 Cloudera
  • 7.12 Computer Science Corporation
  • 7.13 DataDirect Network
  • 7.14 Dell
  • 7.15 Deloitte
  • 7.16 EMC
  • 7.17 Facebook
  • 7.18 Fujitsu
  • 7.19 General Electric
  • 7.20 GoodData Corporation
  • 7.21 Google
  • 7.22 Guavus
  • 7.23 Hitachi Data Systems
  • 7.24 Hortonworks
  • 7.25 HP
  • 7.26 IBM
  • 7.27 Informatica
  • 7.28 Intel
  • 7.29 Jaspersoft
  • 7.30 Juniper Networks
  • 7.31 Marklogic
  • 7.32 Microsoft
  • 7.33 MongoDB (Formerly 10Gen)
  • 7.34 MU Sigma
  • 7.35 Netapp
  • 7.36 NTT Data
  • 7.37 Opera Solutions
  • 7.38 Oracle
  • 7.39 Pentaho
  • 7.40 Platfora
  • 7.41 Qliktech
  • 7.42 Quantum
  • 7.43 Rackspace
  • 7.44 Revolution Analytics
  • 7.45 Salesforce
  • 7.46 SAP
  • 7.47 SAS Institute
  • 7.48 Sisense
  • 7.49 Software AG/Terracotta
  • 7.50 Splunk
  • 7.51 Sqrrl
  • 7.52 Supermicro
  • 7.53 Tableau Software
  • 7.54 Tata Consultancy Services
  • 7.55 Teradata
  • 7.56 Think Big Analytics
  • 7.57 TIBCO
  • 7.58 Tidemark Systems
  • 7.59 VMware (Part of EMC)
  • 7.60 Wipro
  • 7.61 Zettics

8 Market Analysis

  • 8.1 Big Data Revenue 2014 - 2020
  • 8.2 Big Data Revenue by Functional Area 2014 - 2020
    • 8.2.1 Supply Chain Management
    • 8.2.2 Business Intelligence
    • 8.2.3 Application Infrastructure & Middleware
    • 8.2.4 Data Integration Tools & Data Quality Tools
    • 8.2.5 Database Management Systems
    • 8.2.6 Big Data Social & Content Analytics
    • 8.2.7 Big Data Storage Management
    • 8.2.8 Big Data Professional Services
  • 8.3 Big Data Revenue by Region 2014 - 2020
    • 8.3.1 Asia Pacific
    • 8.3.2 Eastern Europe
    • 8.3.3 Latin & Central America
    • 8.3.4 Middle East & Africa
    • 8.3.5 North America
    • 8.3.6 Western Europe

Figures

  • Figure 1: NoSQL vs Legacy DB Performance Comparisons
  • Figure 2: 2014 Gartner Hype Cycle for Emerging Technologies
  • Figure 3: Roadmap Big Data Technologies 2014 - 2030
  • Figure 4: The Big Data Value Chain
  • Figure 5: Big Data Vendor Ranking Matrix
  • Figure 6: Big Data Revenue 2013 - 2020
  • Figure 7: Big Data Revenue by Functional Area 2013 - 2020
  • Figure 8: Big Data Supply Chain Management Revenue 2013 - 2020
  • Figure 9: Big Data Supply Business Intelligence Revenue 2013 - 2020
  • Figure 10: Big Data Application Infrastructure & Middleware Revenue 2013 - 2020
  • Figure 11: Big Data Integration and Quality Tools Revenue 2013 - 2020
  • Figure 12: Big Data DB Management Systems Revenue 2013 - 2020
  • Figure 13: Big Data Social & Content Analytics Revenue 2013 - 2020
  • Figure 14: Big Data Storage Management Revenue 2013 - 2020
  • Figure 15: Big Data Professional Services Revenue 2013 - 2020
  • Figure 16: Big Data Revenue by Region 2013 - 2020
  • Figure 17: Asia Pacific Big Data Revenue 2013 - 2020
  • Figure 18: Eastern Europe Big Data Revenue 2013 - 2020
  • Figure 19: Latin & Central America Big Data Revenue 2013 - 2020
  • Figure 20: Middle East & Africa Big Data Revenue 2013 - 2020
  • Figure 21: North America Big Data Revenue 2013 - 2020
  • Figure 22: Western Europe Big Data Revenue 2013 - 2020
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