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市場調查報告書

零售的巨量資料 2015年:市場分析、企業、解決方案、預測 2015-2020年

Big Data in Retail 2015: Market Analysis, Companies, Solutions, and Forecasts 2015 - 2020

出版商 Mind Commerce 商品編碼 274987
出版日期 內容資訊 英文 100 Pages
商品交期: 最快1-2個工作天內
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零售的巨量資料 2015年:市場分析、企業、解決方案、預測 2015-2020年 Big Data in Retail 2015: Market Analysis, Companies, Solutions, and Forecasts 2015 - 2020
出版日期: 2015年09月01日 內容資訊: 英文 100 Pages
簡介

本報告提供零售的巨量資料相關整體分析,支撐零售產業的巨量資料技術,市場展望,預測,及相關利益者的建議等彙整資料。

第1章 摘要整理

第2章 簡介

第3章 資料管理及零售數位轉換

  • 從多通路向全方位流通管道
  • 即日發送
  • 策略的實行
  • 展示廳現象(Showrooming)
  • SoLoMoMe (社群+當地+行動+個性化)
  • 預測分析
  • 全方位流通管道客戶體驗
  • 巨量資料分析
  • 全方位流通管道經驗的利用案例
  • 全方位流通管道預測分析
  • 客戶行為分析
  • 全方位流通管道策略的開發

第4章 零售的巨量資料:技術、解決方案及方法

  • 對零售來說巨量資料意味什麼?
  • 零售分析方法的變異
  • 零售的資料管理及巨量資料應用
  • 零售巨量資料分析的優點
  • 零售的行動
  • 零售商務上巨量資料的4個V
  • 零售商務上4個V的影響
  • 巨量資料技術
  • 零售的巨量資料所扮演的角色、重要性
  • 零售的巨量資料分析所扮演的角色、重要性

第5章 零售市場上巨量資料分析

  • 目前市場趨勢
  • 巨量資料及預測的零售促進成長要素
  • 巨量資料市場課題
  • 網路購物市場課題
  • 巨量資料的風險
  • 引進障礙
  • 市場機會
  • 市場投資機會

第6章 零售的巨量資料的生態系統

  • 巨量資料的相關利益者
  • 經營模式

第7章 零售的巨量資料的案例研究

  • 家電
  • 家用
  • 包含食品的一般消費品
  • 包含奢侈品、運動的流行
  • 實際生活的影響

第8章 零售的巨量資料供應商

  • 個性化
  • 動態價格設定
  • 客戶服務
  • 欺詐管理
  • 供應鏈的能見度
  • 預測分析
  • 主要企業

第9章 零售市場上巨量資料的預測

  • 零售市場上巨量資料的收益
  • 零售市場上巨量資料的收益:各類型
  • 零售市場上巨量資料的收益:各子類型
  • 零售市場上HADOOP 形式巨量資料解決方案的收益
  • 零售市場上巨量資料的收益:各地區
  • 零售市場上巨量資料的收益:各國
  • 前五名的巨量資料收益
  • 巨量資料的成長

第10章 結論、建議

  • 整體建議
  • 巨量資料供應商的建議
  • 零售的建議

圖表

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目錄

Overview:

The retail industry makes up a sizable part of the world economy and covers a large ecosystem. The industry has faced massive disruption through the advent of significant online competitors such as Amazon. In addition, the smartphone has facilitated smart shopping, which enables "showrooming". These factors have forced retailers to get smarter through an in-depth real time analysis of massive data being spewed on a daily basis for quick insight to make informed decisions for corporate strategies and business operations. The use of Big Data, analytics, and reporting have proven valuable to retails through insights that determine future solutions and opportunities to improve sales operations, customer loyalty, company revenues and profitability.

This report provides comprehensive analysis of Big Data in Retail. The report analyzes Big Data technologies deployed to support the retail industry with an associated assessment of various companies in the ecosystem including key vendor solutions. The report provides a view into the future of retail as it leverages Big Data with associated market outlook, forecasts through 2020, and recommendations for Big Data stakeholders. 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.

Table of Contents

1.0. EXECUTIVE SUMMARY

2.0. INTRODUCTORY CONCEPTS

  • 2.1. WHAT IS BIG DATA?
  • 2.2. SOURCES OF BIG DATA
  • 2.3. DATA MANGEMENT AND THE FOUR V'S OF BIG DATA
  • 2.4. BIG DATA PRODUCT AND SERVICES
  • 2.5. BIG DATA ANALYTICS
  • 2.6. BIG DATA APPROACHES FOR ANALYTICS
  • 2.7. RETAIL ANALYTICS
  • 2.8. RETAIL USE CASE OF ANALYTICS
  • 2.9. OMNI CHANNEL PLATFORM
  • 2.10. CUSTOMER CENTRIC ANALYTICS

3.0. DATA MANAGEMENT AND RETAIL DIGITAL TRANSFORMATION

  • 3.1. MULTICHANNEL TO OMNI-CHANNEL
  • 3.2. SAME DAY DELIVERY
  • 3.3. EXECUTION OF STRATEGY
  • 3.4. SHOWROOMING
  • 3.5. SOLOMOME
  • 3.6. PREDICTIVE ANALYTICS
  • 3.7. OMNI-CHANNEL CUSTOMER EXPERIENCE
  • 3.8. BIG DATA ANALYTICS
  • 3.9. OMNI-CHANNEL EXPERIENCE USE CASES
  • 3.10. OMNI-CHANNEL PREDICTIVE ANALYTICS
  • 3.11. CUSTOMER BEHAVIORAL ANALYTICS
  • 3.12. DEVELOPING AN OMNI-CHANNEL STRATEGY

4.0. BIG DATA IN RETAIL: TECHNOLOGIES, SOLUTIONS, AND APPROACH

  • 4.1. WHAT DOES BIG DATA MEAN FOR RETAIL?
  • 4.2. VARIANT OF RETAIL ANALYTIC APPROACH
    • 4.2.1. DESCRIPTIVE ANALYTICS
    • 4.2.2. INQUISITIVE OR DIAGNOSTIC ANALYTICS
    • 4.2.3. PREDICTIVE ANALYTICS
    • 4.2.4. PRESCRIPTIVE AANALYTICS
    • 4.2.5. PRE-EMPTIVE ANALYTICS
  • 4.3. DATA MANAGEMENT AND BIG DATA APPS IN RETAIL
    • 4.3.1. DIRECT MAIL MARKETING
    • 4.3.2. CUSTOMER RELATIONSHIP MANAGEMENT
    • 4.3.3. CATEGORY MANAGEMENT AND INVENTORY CONTROL
    • 4.3.4. MARKET BASKET ANALYSIS
    • 4.3.5. WEBSITE ANALYSIS AND PERSONALIZATION
    • 4.3.6. ADDITIONAL POSSIBLE RETAIL APPLICATIONS
  • 4.4. BENEFITS FROM BIG DATA ANALYTICS FOR RETAILERS
  • 4.5. RETAILERS BEHAVIOR
    • 4.5.1. INNOVATORS
    • 4.5.2. UNLOCKING BIG DATA
    • 4.5.3. MAXIMIZE TECHNOLOGY USE
    • 4.5.4. USE ANALYTICS TO PERSONALIZE PRODUCTS
    • 4.5.5. OMNI-CHANNEL ORIENTED
    • 4.5.6. MEASURE WHAT MATTERS
    • 4.5.7. STAY TRUE TO THEIR COMPANY STRATEGY
  • 4.6. FOUR V'S OF BIG DATA IN RETAIL BUSINESS
    • 4.6.1. VOLUME
    • 4.6.2. VELOCITY
    • 4.6.3. VARIETY
    • 4.6.4. VALUE
  • 4.7. IMPACT OF FOUR V'S IN RETAIL BUSINESS
    • 4.7.1. RIGHT PRODUCT
    • 4.7.2. RIGHT PLACE
    • 4.7.3. RIGHT TIME
    • 4.7.4. RIGHT PRICE
  • 4.8. BIG DATA TECHNOLOGY
    • 4.8.1. SENSORS
    • 4.8.2. COMPUTER NETWORKS
    • 4.8.3. DATA STORAGE
    • 4.8.4. CLUSTER COMPUTER SYSTEMS
    • 4.8.5. CLOUD COMPUTING FACILITIES
    • 4.8.6. DATA ANALYSIS ALGORITHMS
    • 4.8.7. BIG DATA TECHNOLOGY STACK
  • 4.9. ROLE AND IMPORTANCE OF BIG DATA IN RETAIL 4
    • 4.9.1. PATTERN DISCOVERY
    • 4.9.2. DECISION MAKING
    • 4.9.3. PROCESS INVENTION
    • 4.9.4. INCREASING REVENUE
  • 4.10. ROLE AND IMPORTANCE OF BIG DATA ANALYTICS IN RETAIL
    • 4.10.1. INTELLIGENT ENTERPRISE

5.0. BIG DATA IN RETAIL MARKET ANALYSIS

  • 5.1. CURRENT MARKET TRENDS
    • 5.1.1. HEAVY INFLUENCE OF BOOMERS AND MILLENNIALS
    • 5.1.2. SOCIAL NETWORKS AS SHOPPING PLATFORMS
    • 5.1.3. DOUBLING TREND OF CORPORATE SOCIAL RESPONSIBILITY
    • 5.1.4. GAMIFICATION LOYALTY
    • 5.1.5. EXPERIMENT WITH TEHCNOLOGY
    • 5.1.6. DATA DRIVEN METRICS
    • 5.1.7. BETTER WAYS TO MANAGE RISK AND PROTECT CUSTOMERS
    • 5.1.8. CONTROL OVER VALUE CHAIN AND IMPROVE ORDER FULFILLMENT
    • 5.1.9. ECOMMERCE TO OFFLINE SHOP
    • 5.1.10. LOCALIZATION OF PRODUCT MIX AND STORE FORMATS
    • 5.1.11. MOBILE SHOPPING
    • 5.1.12. STORES WITH OMNICHANNEL STRATEGIES
  • 5.2. BIG DATA AND ANTICIPATED RETAIL GROWTH DRIVERS
    • 5.2.1. AWARENESS
    • 5.2.2. SOFTWARE
    • 5.2.3. SERVICES
    • 5.2.4. INVESTMENT
    • 5.2.5. OTHER DRIVERS
  • 5.3. BIG DATA MARKET CHALLENGES
    • 5.3.1. DATA CHALLENGES
    • 5.3.2. PROCESS CHALLENGES
    • 5.3.3. MANAGEMENT CHALLENGES
  • 5.4. ONLINE SHOPPING MARKET CHALLENGES
  • 5.5. BIG DATA RISKS
    • 5.5.1. GOVERNANCE
    • 5.5.2. MANAGEMENT
    • 5.5.3. ARCHITECTURE
    • 5.5.4. USAGE
    • 5.5.5. QUALITY
    • 5.5.6. SECURITY
    • 5.5.7. PRIVACY
  • 5.6. ADOPTION BARRIERS
  • 5.7. MARKET OPPORTUNITY
  • 5.8. MARKET INVESTMENT OPPORTUNITY
    • 5.8.1. INVESTMENT WITHIN HADOOP
    • 5.8.2. SPLUNK CAPITALIZING BIG DATA
    • 5.8.3. TERADATA EXPECTING BIG GROWTH
    • 5.8.4. HORTONWORKS COMMERCIALIZES HADOOP
    • 5.8.5. MAPR DISTRIBUTION OF HADOOP

6.0. BIG DATA ECOSYSTEM IN RETAIL

  • 6.1. BIG DATA STAKEHOLDERS
  • 6.2. BUSINESS MODELS

7.0. CASE STUDIES OF BIG DATA IN RETAIL

  • 7.1. CONSUMER ELECTRONICS
    • 7.1.1. BEST BUY
    • 7.1.2. INSOURCESM SOLUTION FROM EXPERIAN
  • 7.2. FOR THE HOME
    • 7.2.1. BED BATH AND BEYOND (BBB)
  • 7.3. GENERAL CONSUMER ITEMS INCLUDING FOOD
    • 7.3.1. WALMART
    • 7.3.2. SOCIAL GENOME
    • 7.3.3. SHOPPYCAT
    • 7.3.4. GET ON THE SHELF
    • 7.3.5. MACY'S
    • 7.3.6. SAS® BUSINESS ANALYTICS
    • 7.3.7. DEBENHAMS
    • 7.3.8. SKY IQ
    • 7.3.9. WILLIAMS-SONOMA
  • 7.4. LUXURY AND FASHION INCLUDING SPORTS
    • 7.4.1. LUXOTTICA
    • 7.4.2. ELIE TAHARI
  • 7.5. REAL LIFE IMPACT
    • 7.5.1. TESCO
    • 7.5.2. KROGER
    • 7.5.3. DELHAIZE
    • 7.5.4. FOOD LION
    • 7.5.5. RED ROOF
    • 7.5.6. PIZZA CHAIN
    • 7.5.7. EMI
    • 7.5.8. FINANCIAL SERVICES COMPANY
    • 7.5.9. TARGET

8.0. BIG DATA VENDORS IN RETAIL

  • 8.1. PERSONALIZATION
    • 8.1.1. SYNQERA
    • 8.1.2. NGDATA
  • 8.2. DYNAMIC PRICING
    • 8.2.1. ALTIERRE
  • 8.3. CUSTOMER SERVICE
    • 8.3.1. RETENTION SCIENCE
  • 8.4. FRAUD MANAGEMENT
    • 8.4.1. RSA
  • 8.5. SUPPLY CHAIN VISIBILITY
    • 8.5.1. OPERA SUPPLY CHAIN SOLUTIONS
  • 8.6. PREDICTIVE ANALYTICS
    • 8.6.1. SUMALL
  • 8.7. KEY PLAYERS
    • 8.7.1. 1010DATA
    • 8.7.2. IBM
    • 8.7.3. TERADATA
    • 8.7.4. ORACLE
    • 8.7.5. HP

9.0. BIG DATA IN RETAIL MARKET FORECASTS 2015 - 2020

  • 9.1. BIG DATA IN RETAIL MARKET REVENUE 2015 - 2020
  • 9.2. BIG DATA IN RETAIL MARKET REVENUE BY TYPE 2015 - 2020
  • 9.3. BIG DATA IN RETAIL MARKET REVENUE BY SUB-TYPE 2015 - 2020
  • 9.4. HADOOP BASED BIG DATA SOLUTION REVENUE RETAIL MARKET 2015 - 2020
  • 9.5. BIG DATA IN RETAIL MARKET REVENUE BY REGION 2015 - 2020
  • 9.6. BIG DATA IN RETAIL MARKET REVENUE BY COUNTRY 2015 - 2020
  • 9.7. BIG DATA REVENUE OF TOP FIVE LEADERS 2013 - 2014
  • 9.8. DATA GROWTH 2008 - 2020

10.0. CONCLUSIONS AND RECOMMENDATIONS

  • 10.1. GENERAL RECOMMENDATIONS
  • 10.2. RECOMMENDATIONS TO BIG DATA VENDORS
  • 10.3. RECOMMENDATION TO RETAILERS

Figures

  • Figure 1: Big Data in SMAC Ecosystem
  • Figure 2: Big Data Sources
  • Figure 3: Four V Framework of Big Data
  • Figure 4: Big Data for Analytics: Sources, Projections and Contribution
  • Figure 5: Customer Journey in In-Store Analytics Framework
  • Figure 6: Use Case Framework for Retail Analytics
  • Figure 7: Omni-channel Customers and New Retail IT Model
  • Figure 8: Customer Centric Analytics Framework
  • Figure 9: Consumer Goods Value Chain
  • Figure 10 Transformation of Age from Manufacturing to Customer
  • Figure 11: Customer Experience Framework
  • Figure 12: Omni-Channel Micro Strategy
  • Figure 13: Customer Intelligence Appliance
  • Figure 14: In-Store Customers in Big Data Retail Framework
  • Figure 15: Big Data Situation in Retail Industry
  • Figure 16: Big Data Retail Analytics Variant and Actions
  • Figure 17: Goals of Using Big Data Application in Retail
  • Figure 18: Big Data Customer Insight Framework for Time Engagement
  • Figure 19: Big Data Technology Stack
  • Figure 20: Value Generation of Big Data Analytics
  • Figure 21: Big Data Analytic Value Chain
  • Figure 22: Nordstrom Using Like2Buy Button on Instagram
  • Figure 23: Walgreens Gamified Health Activities in Retail
  • Figure 24: Birchbox Ecommerce to Offline Shop
  • Figure 25: Bird on a Wire on Mobi2Go Solution
  • Figure 26: Big Data Business Model: Information Based Framework
  • Figure 27: Lily Interactive Big Data Framework
  • Figure 28: Big Data in Retail Market Revenue $ Billion 2015 - 2020
  • Figure 29: Data Growth in Zettabytes 2008 - 2020
  • Figure 30: Big Data Implementation Framework
  • Figure 31: Big Data Implementation Steps

Tables

  • Table 1: New Online Shopping Dynamics for Retail Marchant
  • Table 2: Big Data Technology and Services Vendors to Watch
  • Table 3: Big Data in Retail Revenue by H/W vs. S/W vs. Services 2015 - 2020
  • Table 4: BD in Retail Rev by Database, Analytics, Services, Cloud 2015 - 2020
  • Table 5: Hadoop Based BD Retail Revenue 2015 - 2020
  • Table 6: Big Data in Retail Market Revenue by Region 2015 - 2020
  • Table 7: Big Data in Retail Revenue in Top 4 Countries 2015 - 2020
  • Table 8: BD Retail Revenue by Country as % of Total BD Market by Region
  • Table 9: Big Data Revenue of Top Five Leaders 2013 - 2014
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