GLOBAL BIG DATA IN E-COMMERCE MARKET FORECAST 2020-2028

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GLOBAL BIG DATA IN E-COMMERCE MARKET FORECAST 2020-2028

The global big data in e-commerce market was valued at $xx billion in 2019, and is projected to reach $9.98 billion by 2028, registering a CAGR of 14.17% during the forecast period. The base year considered for the study is 2019, while the estimated period is between 2020 and 2028.

GLOBAL BIG DATA IN E-COMMERCE MARKET FORECAST 2020-2028

Global Big Data in E-commerce Market by Component (Software, Hardware) by Deployment Model (Cloud Based, on-premises)  by Type (Structured, Unstructured, Semi-structured)  by Solution (Content Analytics, Customer Analytics, Fraud Detection, Risk Management)  by End-user (Online Classified, Online Education, Online Financial, Online Retail, Online Travel and Leisure, Other End-users) and by Geography.

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The global big data in e-commerce market was valued at $xx billion in 2019, and is projected to reach $9.98 billion by 2028, registering a CAGR of 14.17% during the forecast period. The base year considered for the study is 2019, while the estimated period is between 2020 and 2028.

Key factors propelling the growth of the market are:

  • Utilization of big data for improving sales & customer satisfaction
  • Use of big data by e-commerce companies to drive product customizations
  • Growing inclination towards various online payment methods

Global Big Data in E-commerce Market

The use of big data and analytics is constantly upgrading the e-commerce business by helping the organizations in determining the choice of product offerings, their price, and advertising with an aim to maximize their profits. Using real time analytics, retailers can get the information related to day to day operations and offer customers with many preferences while shopping on their site. The use of big data in the e-commerce field helps in building better customer experiences, predicting the buying patterns of the consumers, personalizing emails and increasing conversion rates, and most importantly, fixing up prices and constantly changing them to keep up with competition using real-time analytics.

In 2019, the majority of the market share of 47.55% was captured by customer analytics category in the market by solution segment. In addition, it is anticipated to be the fastest-growing solution in the big data in e-commerce market. Customer analytics has now emerged as a comprehensive tool to identify the requirements of today’s competitive business environment. The majority of the organizations today are targeting online users to collect the needful information about the consumers’ online behavior. The customer analytics help the e-commerce business to dynamically gather and understand customer interaction and combine the data with abstract groups to identify the evolving trends and patterns. These insights are derived from various interactions over diverse channels such as call center, web, mobile, emails, campaigns, and others. Moreover, the rising number of social media users and the improving scenario of big data are further increasing the popularity of the application of customer analytics.

However, the privacy of big data is a huge concern due to the distinctive characteristics of its application in the e-commerce environment. The high concentration and volume of data increase the possibilities of hacking activities. The high diversity of big data information usually leads to organizations lacking the ability of effectively managing and solving these data, thereby resulting in third parties having opportunities to access such information. These third parties may not comply with all data protection regulations, thereby posing a threat to privacy issues.

The report on the global big data in e-commerce includes segmentation analysis on the basis of components, deployment, type, solutions, and end user.

Market by Component:

  • Software
  • Hardware

Market by Deployment Model:

  • Cloud Based
    • Private Cloud
    • Public Cloud
  • On-premises

Market by Type:

  • Structured
  • Unstructured
  • Semi-structured

Market by Solution:

  • Content Analytics
  • Customer Analytics
  • Fraud Detection
  • Risk Management

Market by End-user:

  • Online Classified
  • Online Education
  • Online Financial
    • Banking Services/Wallets
    • Financial Services
  • Online Retail
  • Online Travel and Leisure
  • Other End Users

Geographically, the global big data in e-commerce market has been segmented on the basis of four major regions, which includes:

  • North America: the United States and Canada
  • Europe: the United Kingdom, Germany, France, Italy, Russia, Belgium, Poland, and Rest of Europe
  • Asia Pacific: China, Japan, India, South Korea, Indonesia, Thailand, Vietnam, Australia & New Zealand, and Rest of Asia Pacific
  • Rest of World: Latin America, the Middle East & Africa

Regionally, Asia Pacific accounted for the largest share of 42.57% in the big data in e-commerce market in 2019, and is estimated to be the fastest-growing market in the forthcoming years. The primary drivers of big data technology in retail are the need for the customer and behavioral analytics, smartphone penetration, growing urbanization development in machine learning, algorithm development, and the explosion of data due to increasing rates of the internet. The growth is also projected to be driven by the growing adoption of technology and the significant expansion of e-commerce applications of big data in China, India, and the ASEAN countries. As per 2018 statistics, this region is now home to over 2 billion internet users, with an internet penetration rate of about 48%. Guizhou Province, which is one of the least developed regions in China, has become one of the leading hubs for the big data industry. Similarly, at present, India is among the top 10 big data analytics markets.

The key players involved in the global big data in e-commerce market are:

  • Amazon Web Services Inc
  • Data USA
  • International Business Machines Corporation (IBM)
  • SAS Institute
  • Microsoft Corporation
  • SAP SE
  • Others

Key strategies adopted by some of these companies;

In February 2020, DataRow, formerly known as TeamSQL, was acquired by Amazon Web Services (AWS). Similarly, in September 2019, Cloudera entered into a definitive agreement to Arcadia Data, a supplier of AI-powered business intelligence and real-time analytics based on the cloud. In the same month, Splunk Inc launched Splunk Ventures and two other introductory funds ($100 million Innovation Fund and $50 million Social Impact Fund) to help the Next Generation of Data Analytics.

Key findings of the global big data in e-commerce market:

  • One of the prominent trends in the big data in the e-commerce industry includes contextual and programmatic advertising, which will require the use of a huge volume of data sets to identify the target customers.
  • The unstructured data type in big data in the e-commerce market is projected to record the fastest growth during the forecasted period. The unstructured data type is primarily driven by the growing unstructured data generated by humans and the adoption of platforms to manage these data.
  • The e-commerce industry is expected to witness a rise in subscription-based business models, where, in exchange for regular payments from the customer, the company provides its ongoing services on a regular basis.
  1. RESEARCH SCOPE & METHODOLOGY
    • STUDY OBJECTIVES
    • SCOPE OF STUDY
    • METHODOLOGY
    • ASSUMPTIONS & LIMITATIONS
  2. EXECUTIVE SUMMARY
    • MARKET SIZE & ESTIMATES
    • MARKET OVERVIEW
  3. MARKET DYNAMICS
    • PARENT MARKET ANALYSIS: BIG DATA MARKET
    • DEVELOPMENT OF BIG DATA
    • MARKET DEFINITION
    •    KEY DRIVERS
      • UTILIZATION OF BIG DATA FOR IMPROVING SALES & CUSTOMER SATISFACTION
      • USE OF BIG DATA BY E-COMMERCE COMPANIES TO DRIVE PRODUCT CUSTOMIZATIONS
      • GROWING INCLINATION TOWARDS VARIOUS ONLINE PAYMENT METHODS
    •    KEY RESTRAINTS
      • SHORTAGE OF TRAINED BIG DATA EXPERTS
      • CONCERNS RELATED TO DATA ACCURACY & PRIVACY
  1. KEY ANALYTICS
    • KEY INVESTMENT INSIGHTS
    •    PORTER’S FIVE FORCE ANALYSIS
      • BUYER POWER
      • SUPPLIER POWER
      • SUBSTITUTION
      • NEW ENTRANTS
      • INDUSTRY RIVALRY
    • OPPORTUNITY MATRIX
    • VENDOR LANDSCAPE
    • VALUE CHAIN ANALYSIS
  2. MARKET BY COMPONENT
    • SOFTWARE
    • HARDWARE
  3. MARKET BY DEPLOYMENT MODEL
    •    CLOUD BASED
      • PRIVATE CLOUD
      • PUBLIC CLOUD
    • ON-PREMISES
  4. MARKET BY TYPE
    • STRUCTURED
    • UNSTRUCTURED
    • SEMI-STRUCTURED
  5. MARKET BY SOLUTION
    • CONTENT ANALYTICS
    • CUSTOMER ANALYTICS
    • FRAUD DETECTION
    • RISK MANAGEMENT
  6. MARKET BY END-USER
    • ONLINE CLASSIFIED
    • ONLINE EDUCATION
    •    ONLINE FINANCIAL
      • BANKING SERVICES/WALLETS
      • FINANCIAL SERVICES
    • ONLINE RETAIL
    • ONLINE TRAVEL AND LEISURE
    • OTHER END-USERS
  7. GEOGRAPHICAL ANALYSIS
    •    NORTH AMERICA
      • UNITED STATES
      • CANADA
    •    EUROPE
      • UNITED KINGDOM
      • GERMANY
      • FRANCE
      • ITALY
      • RUSSIA
      • BELGIUM
      • POLAND
      • REST OF EUROPE
    •    ASIA PACIFIC
      • CHINA
      • JAPAN
      • INDIA
      • SOUTH KOREA
      • INDONESIA
      • THAILAND
      • VIETNAM
      • AUSTRALIA & NEW ZEALAND
      • REST OF ASIA PACIFIC
    •    REST OF WORLD
      • LATIN AMERICA
      • MIDDLE EAST & AFRICA
  1. COMPANY PROFILES
    • AMAZON WEB SERVICES INC
    • CLOUDERA INC
    • DATA USA
    • DELL INC
    • GUAVUS INC
    • HEWLETT PACKARD ENTERPRISE COMPANY
    • HITACHI LTD
    • INTERNATIONAL BUSINESS MACHINES CORPORATION (IBM)
    • MICROSOFT CORPORATION
    • ORACLE CORPORATION
    • PALANTIR TECHNOLOGIES
    • SAP SE
    • SAS INSTITUTE
    • SPLUNK INC
    • TERADATA CORPORATION

TABLE LIST

TABLE 1: MARKET SNAPSHOT – BIG DATA IN E-COMMERCE

TABLE 2: DEVELOPMENT OF BIG DATA

TABLE 3: MOST COMMONLY USED MOBILE PAYMENT OPTIONS WITH THEIR APPROXIMATE USERS

TABLE 4: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY COMPONENT, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 5: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY COMPONENT, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 6: GLOBAL SOFTWARE MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 7: GLOBAL SOFTWARE MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 8: GLOBAL HARDWARE MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 9: GLOBAL HARDWARE MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 10: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY DEPLOYMENT MODEL, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 11: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY DEPLOYMENT MODEL, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 12: GLOBAL CLOUD BASED MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 13: GLOBAL CLOUD BASED MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 14: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY CLOUD BASED, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 15: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY CLOUD BASED, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 16: GLOBAL PRIVATE CLOUD MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 17: GLOBAL PRIVATE CLOUD MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 18: GLOBAL PUBLIC CLOUD MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 19: GLOBAL PUBLIC CLOUD MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 20: GLOBAL ON-PREMISES MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 21: GLOBAL ON-PREMISES MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 22: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY TYPE, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 23: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY TYPE, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 24: GLOBAL STRUCTURED MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 25: GLOBAL STRUCTURED MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 26: GLOBAL UNSTRUCTURED MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 27: GLOBAL UNSTRUCTURED MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 28: GLOBAL SEMI-STRUCTURED MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 29: GLOBAL SEMI-STRUCTURED MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 30: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY SOLUTION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 31: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY SOLUTION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 32: GLOBAL CONTENT ANALYTICS MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 33: GLOBAL CONTENT ANALYTICS MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 34: GLOBAL CUSTOMER ANALYTICS MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 35: GLOBAL CUSTOMER ANALYTICS MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 36: GLOBAL FRAUD DETECTION MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 37: GLOBAL FRAUD DETECTION MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 38: GLOBAL RISK MANAGEMENT MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 39: GLOBAL RISK MANAGEMENT MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 40: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY END-USER, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 41: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY END-USER, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 42: GLOBAL ONLINE CLASSIFIED MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 43: GLOBAL ONLINE CLASSIFIED MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 44: GLOBAL ONLINE EDUCATION MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 45: GLOBAL ONLINE EDUCATION MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 46: GLOBAL ONLINE FINANCIAL MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 47: GLOBAL ONLINE FINANCIAL MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 48: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY ONLINE FINANCIAL, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 49: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY ONLINE FINANCIAL, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 50: GLOBAL ONLINE RETAIL MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 51: GLOBAL ONLINE RETAIL MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 52: GLOBAL ONLINE TRAVEL AND LEISURE MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 53: GLOBAL ONLINE TRAVEL AND LEISURE MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 54: GLOBAL OTHER END USERS MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 55: GLOBAL OTHER END USERS MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 56: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY GEOGRAPHY, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 57: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY GEOGRAPHY, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 58: NORTH AMERICA BIG DATA IN E-COMMERCE MARKET, BY COUNTRY, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 59: NORTH AMERICA BIG DATA IN E-COMMERCE MARKET, BY COUNTRY, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 60: EUROPE BIG DATA IN E-COMMERCE MARKET, BY COUNTRY, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 61: EUROPE BIG DATA IN E-COMMERCE MARKET, BY COUNTRY, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 62: ASIA PACIFIC BIG DATA IN E-COMMERCE MARKET, BY COUNTRY, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 63: ASIA PACIFIC BIG DATA IN E-COMMERCE MARKET, BY COUNTRY, FORECAST YEARS, 2020-2028 (IN $ MILLION)

TABLE 64: REST OF WORLD BIG DATA IN E-COMMERCE MARKET, BY REGION, HISTORICAL YEARS, 2016-2019 (IN $ MILLION)

TABLE 65: REST OF WORLD BIG DATA IN E-COMMERCE MARKET, BY REGION, FORECAST YEARS, 2020-2028 (IN $ MILLION)

FIGURE LIST

FIGURE 1: PAYMENT METHOD SHARE IN E-COMMERCE TRANSACTIONS, 2018 & 2022 (IN %)

FIGURE 2: KEY INVESTMENT INSIGHTS

FIGURE 3: REGION WISE MOBILE DATA TRAFFIC GENERATION (IN %)

FIGURE 4: WORLDWIDE MOBILE DATA TRAFFIC FORECASTS, 2016-2021 (EXABYTES PER MONTH)

FIGURE 5: MOBILE DATA TRAFFIC IN KEY GEOGRAPHIES, 2017-2021 (EXABYTES PER MONTH)

FIGURE 6: PORTER’S FIVE FORCE ANALYSIS

FIGURE 7: OPPORTUNITY MATRIX

FIGURE 8: VENDOR LANDSCAPE

FIGURE 9: VALUE CHAIN ANALYSIS

FIGURE 10: GLOBAL BIG DATA IN E-COMMERCE MARKET, GROWTH POTENTIAL, BY COMPONENT, IN 2019

FIGURE 11: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY SOFTWARE, 2020-2028 (IN $ MILLION)

FIGURE 12: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY HARDWARE, 2020-2028 (IN $ MILLION)

FIGURE 13: GLOBAL BIG DATA IN E-COMMERCE MARKET, GROWTH POTENTIAL, BY DEPLOYMENT MODEL, IN 2019

FIGURE 14: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY CLOUD BASED, 2020-2028 (IN $ MILLION)

FIGURE 15: GLOBAL BIG DATA IN E-COMMERCE MARKET, GROWTH POTENTIAL, BY CLOUD BASED, IN 2019

FIGURE 16: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY PRIVATE CLOUD, 2020-2028 (IN $ MILLION)

FIGURE 17: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY PUBLIC CLOUD, 2020-2028 (IN $ MILLION)

FIGURE 18: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY ON-PREMISES, 2020-2028 (IN $ MILLION)

FIGURE 19: GLOBAL BIG DATA IN E-COMMERCE MARKET, GROWTH POTENTIAL, BY TYPE, IN 2019

FIGURE 20: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY STRUCTURED, 2020-2028 (IN $ MILLION)

FIGURE 21: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY UNSTRUCTURED, 2020-2028 (IN $ MILLION)

FIGURE 22: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY SEMI-STRUCTURED, 2020-2028 (IN $ MILLION)

FIGURE 23: GLOBAL BIG DATA IN E-COMMERCE MARKET, GROWTH POTENTIAL, BY SOLUTION, IN 2019

FIGURE 24: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY CONTENT ANALYTICS, 2020-2028 (IN $ MILLION)

FIGURE 25: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY CUSTOMER ANALYTICS, 2020-2028 (IN $ MILLION)

FIGURE 26: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY FRAUD DETECTION, 2020-2028 (IN $ MILLION)

FIGURE 27: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY RISK MANAGEMENT, 2020-2028 (IN $ MILLION)

FIGURE 28: GLOBAL BIG DATA IN E-COMMERCE MARKET, GROWTH POTENTIAL, BY END-USER, IN 2019

FIGURE 29: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY ONLINE CLASSIFIED, 2020-2028 (IN $ MILLION)

FIGURE 30: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY ONLINE EDUCATION, 2020-2028 (IN $ MILLION)

FIGURE 31: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY ONLINE FINANCIAL, 2020-2028 (IN $ MILLION)

FIGURE 32: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY ONLINE RETAIL, 2020-2028 (IN $ MILLION)

FIGURE 33: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY ONLINE TRAVEL AND LEISURE, 2020-2028 (IN $ MILLION)

FIGURE 34: GLOBAL BIG DATA IN E-COMMERCE MARKET, BY OTHER END-USERS, 2020-2028 (IN $ MILLION)

FIGURE 35: NORTH AMERICA BIG DATA IN E-COMMERCE MARKET, REGIONAL OUTLOOK, 2019 & 2028 (IN %)

FIGURE 36: UNITED STATES BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 37: CANADA BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 38: EUROPE BIG DATA IN E-COMMERCE MARKET, REGIONAL OUTLOOK, 2019 & 2028 (IN %)

FIGURE 39: UNITED KINGDOM BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 40: GERMANY BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 41: FRANCE BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 42: ITALY BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 43: RUSSIA BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 44: BELGIUM BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 45: POLAND BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 46: REST OF EUROPE BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 47: ASIA PACIFIC BIG DATA IN E-COMMERCE MARKET, REGIONAL OUTLOOK, 2019 & 2028 (IN %)

FIGURE 48: CHINA BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 49: JAPAN BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 50: INDIA BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 51: SOUTH KOREA BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 52: INDONESIA BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 53: THAILAND BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 54: VIETNAM BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 55: AUSTRALIA & NEW ZEALAND BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 56: REST OF ASIA PACIFIC BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION

FIGURE 57: REST OF WORLD BIG DATA IN E-COMMERCE MARKET, REGIONAL OUTLOOK, 2019 & 2028 (IN %)

FIGURE 58: LATIN AMERICA BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

FIGURE 59: MIDDLE EAST & AFRICA BIG DATA IN E-COMMERCE MARKET, 2020-2028 (IN $ MILLION)

  1. MARKET BY COMPONENT
    • SOFTWARE
    • HARDWARE
  2. MARKET BY DEPLOYMENT MODEL
    •    CLOUD BASED
      • PRIVATE CLOUD
      • PUBLIC CLOUD
    • ON-PREMISES
  3. MARKET BY TYPE
    • STRUCTURED
    • UNSTRUCTURED
    • SEMI-STRUCTURED
  4. MARKET BY SOLUTION
    • CONTENT ANALYTICS
    • CUSTOMER ANALYTICS
    • FRAUD DETECTION
    • RISK MANAGEMENT
  5. MARKET BY END-USER
    • ONLINE CLASSIFIED
    • ONLINE EDUCATION
    •    ONLINE FINANCIAL
      • BANKING SERVICES/WALLETS
      • FINANCIAL SERVICES
    • ONLINE RETAIL
    • ONLINE TRAVEL AND LEISURE
    • OTHER END-USERS
  6. GEOGRAPHICAL ANALYSIS
    • NORTH AMERICA
      • UNITED STATES
      • CANADA
    • EUROPE
      • UNITED KINGDOM
      • GERMANY
      • FRANCE
      • ITALY
      • RUSSIA
      • BELGIUM
      • POLAND
      • REST OF EUROPE
    • ASIA PACIFIC
      • CHINA
      • JAPAN
      • INDIA
      • SOUTH KOREA
      • INDONESIA
      • THAILAND
      • VIETNAM
      • AUSTRALIA & NEW ZEALAND
      • REST OF ASIA PACIFIC
    • REST OF WORLD
      • LATIN AMERICA
      • MIDDLE EAST & AFRICA

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