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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.

Target Audience:
Big Data Service providers
Big Box and Online retailers
Big Data technology developers
Telecommunications service providers
Online and Mobile Marketing companies
E-commerce and Mobile Commerce providers

Table Of Contents

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