Abstract
Summary
Global Big Data in E-commerce Market is valued at approximately USD XX billion in 2021 and is anticipated to grow with a healthy growth rate of more than XX% over the forecast period 2022-2029. In the e-commerce market, Big Data refers to the vast and complex sets of structured and unstructured data generated by online transactions, customer interactions, and other digital sources. This data includes customer behavior, preferences, purchase history, website traffic, and social media activity, among others. The major driving factor for the Global Big Data in E-commerce Market is increased data generation and growing demand for personalized shopping experiences.
The government around the world is supporting the use of big data in e-commerce and excelling the digitalization in the country. For instance, in 2019, the Australian government launched the "Digital Economy Strategy," which includes plans to promote the development of Big Data infrastructure and promote the use of Big Data in various industries, including the E-commerce market. Moreover, technological advancements and rising government support for the adoption of big data in e-commerce is creating a lucrative growth opportunity for the market over the forecast period 2022-2029. However, the high cost of Big Data in E-commerce stifles market growth throughout the forecast period of 2022-2029.
The key regions considered for the Global Big Data in E-commerce Market study includes Asia Pacific, North America, Europe, Latin America, and Rest of the World. North America is one of the leading regions when it comes to the adoption of Big Data in e-commerce. The region has a large number of established e-commerce players who have invested heavily in Big Data analytics to gain insights into customer behavior, preferences, and purchasing patterns. Big Data is also being used to improve supply chain management, optimize pricing strategies, and enhance the overall customer experience. The Asia-Pacific region is rapidly catching up in terms of Big Data adoption in e-commerce. The region has many fast-growing e-commerce companies such as Alibaba, JD.com, and Flipkart which are using Big Data to gain a competitive edge. Big Data is being used to improve product recommendations, personalize marketing campaigns, and optimize pricing strategies.
Major market player included in this report are:
Amazon Web Services, Inc.
Data Inc.
Dell Inc.
Facebook
Hitachi, Ltd.
International Business Machines Corporation
Microsoft Corp.
Oracle Corp.
Palantir Technologies, Inc.
SAS Institute Inc.
Recent Developments in the Market:
In July 2020, Shopify launched a new feature called Shopify Balance, which uses big data to help small business owners manage their finances and cash flow more effectively.
In January 2020, Zara's parent company Inditex announced plans to use big data and artificial intelligence to improve its supply chain operations and reduce waste.
Global Big Data in E-commerce Market Report Scope:
Historical Data 2019-2020-2021
Base Year for Estimation 2021
Forecast period 2022-2029
Report Coverage Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
Segments Covered Component, Deployment, Type, End-use, Region
Regional Scope North America; Europe; Asia Pacific; Latin America; Rest of the World
Customization Scope Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*
The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.
The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and Component offerings of key players. The detailed segments and sub-segment of the market are explained below:
By Component:
Big Data Software in the E-commerce
Big Data Hardware in the E-commerce
By Deployment:
Cloud-based
On-premises
By Type:
Structured Big Data in the E-commerce
Unstructured Big Data in the E-commerce
Semi-structured Big Data in the E-commerce
By End-use:
Online Classifieds in the E-commerce
Online Education
Online Financials
Online Retail
Online Travel and Leisure
Other End Uses
By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE
Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World
Table of Contents
1 Executive Summary
1.1 Market Snapshot
1.2 Global & Segmental Market Estimates & Forecasts, 2019-2029 (USD Billion)
1.2.1 Big Data in E-commerce Market, by Region, 2019-2029 (USD Billion)
1.2.2 Big Data in E-commerce Market, by Component, 2019-2029 (USD Billion)
1.2.3 Big Data in E-commerce Market, by Deployment, 2019-2029 (USD Billion)
1.2.4 Big Data in E-commerce Market, by Type, 2019-2029 (USD Billion)
1.2.5 Big Data in E-commerce Market, by End-use, 2019-2029 (USD Billion)
1.3 Key Trends
1.4 Estimation Methodology
1.5 Research Assumption
2 Global Big Data in E-commerce Market Definition and Scope
2.1 Objective of the Study
2.2 Market Definition & Scope
2.2.1 Scope of the Study
2.2.2 Industry Evolution
2.3 Years Considered for the Study
2.4 Currency Conversion Rates
3 Global Big Data in E-commerce Market Dynamics
3.1 Big Data in E-commerce Market Impact Analysis (2019-2029)
3.1.1 Market Drivers
- 3.1.1.1 Increased Data Generation
- 3.1.1.2 Growing demand for personalized shopping experiences
3.1.2 Market Challenges
- 3.1.2.1 High Cost of Big Data in E-commerce
3.1.3 Market Opportunities
- 3.1.3.1 Technological Advancements
- 3.1.3.2 Rising Government Support
4 Global Big Data in E-commerce Market Industry Analysis
4.1 Porter’s 5 Force Model
4.1.1 Bargaining Power of Suppliers
4.1.2 Bargaining Power of Buyers
4.1.3 Threat of New Entrants
4.1.4 Threat of Substitutes
4.1.5 Competitive Rivalry
4.2 Futuristic Approach to Porter’s 5 Force Model (2019-2029)
4.3 PEST Analysis
4.3.1 Political
4.3.2 Economical
4.3.3 Social
4.3.4 Technological
4.4 Investment Adoption Model
4.5 Analyst Recommendation & Conclusion
4.6 Top investment opportunity
4.7 Top winning strategies
5 Risk Assessment: COVID-19 Impact
5.1 Assessment of the overall impact of COVID-19 on the industry
5.2 Pre COVID-19 and post COVID-19 Market scenario
6 Global Big Data in E-commerce Market, by Component
6.1 Market Snapshot
6.2 Global Big Data in E-commerce Market by Component, Performance - Potential Analysis
6.3 Global Big Data in E-commerce Market Estimates & Forecasts by Component 2019-2029 (USD Billion)
6.4 Big Data in E-commerce Market, Sub Segment Analysis
6.4.1 Big Data Software in the E-commerce
6.4.2 Big Data Hardware in the E-commerce
7 Global Big Data in E-commerce Market, by Deployment
7.1 Market Snapshot
7.2 Global Big Data in E-commerce Market by Deployment, Performance - Potential Analysis
7.3 Global Big Data in E-commerce Market Estimates & Forecasts by Deployment 2019-2029 (USD Billion)
7.4 Big Data in E-commerce Market, Sub Segment Analysis
7.4.1 Cloud-based
7.4.2 On-premises
8 Global Big Data in E-commerce Market, by Type
8.1 Market Snapshot
8.2 Global Big Data in E-commerce Market by Type, Performance - Potential Analysis
8.3 Global Big Data in E-commerce Market Estimates & Forecasts by Type 2019-2029 (USD Billion)
8.4 Big Data in E-commerce Market, Sub Segment Analysis
8.4.1 Structured Big Data in the E-commerce
8.4.2 Unstructured Big Data in the E-commerce
8.4.3 Semi-structured Big Data in the E-commerce
9 Global Big Data in E-commerce Market, by End-use
9.1 Market Snapshot
9.2 Global Big Data in E-commerce Market by End-use, Performance - Potential Analysis
9.3 Global Big Data in E-commerce Market Estimates & Forecasts by End-use 2019-2029 (USD Billion)
9.4 Big Data in E-commerce Market, Sub Segment Analysis
9.4.1 Online Classifieds in the E-commerce
9.4.2 Online Education
9.4.3 Online Financials
9.4.4 Online Retail
9.4.5 Online Travel and Leisure
9.4.6 Other End Uses
10 Global Big Data in E-commerce Market, Regional Analysis
10.1 Big Data in E-commerce Market, Regional Market Snapshot
10.2 North America Big Data in E-commerce Market
10.2.1 U.S. Big Data in E-commerce Market
- 10.2.1.1 Component breakdown estimates & forecasts, 2019-2029
- 10.2.1.2 Deployment breakdown estimates & forecasts, 2019-2029
- 10.2.1.3 Type breakdown estimates & forecasts, 2019-2029
- 10.2.1.4 End-use breakdown estimates & forecasts, 2019-2029
10.2.2 Canada Big Data in E-commerce Market
10.3 Europe Big Data in E-commerce Market Snapshot
10.3.1 U.K. Big Data in E-commerce Market
10.3.2 Germany Big Data in E-commerce Market
10.3.3 France Big Data in E-commerce Market
10.3.4 Spain Big Data in E-commerce Market
10.3.5 Italy Big Data in E-commerce Market
10.3.6 Rest of Europe Big Data in E-commerce Market
10.4 Asia-Pacific Big Data in E-commerce Market Snapshot
10.4.1 China Big Data in E-commerce Market
10.4.2 India Big Data in E-commerce Market
10.4.3 Japan Big Data in E-commerce Market
10.4.4 Australia Big Data in E-commerce Market
10.4.5 South Korea Big Data in E-commerce Market
10.4.6 Rest of Asia Pacific Big Data in E-commerce Market
10.5 Latin America Big Data in E-commerce Market Snapshot
10.5.1 Brazil Big Data in E-commerce Market
10.5.2 Mexico Big Data in E-commerce Market
10.6 Rest of The World Big Data in E-commerce Market
11 Competitive Intelligence
11.1 Top Market Strategies
11.2 Company Profiles
11.2.1 Amazon Web Services, Inc
- 11.2.1.1 Key Information
- 11.2.1.2 Overview
- 11.2.1.3 Financial (Subject to Data Availability)
- 11.2.1.4 Product Summary
- 11.2.1.5 Recent Developments
11.2.2 Data Inc
11.2.3 Dell Inc
11.2.4 Facebook
11.2.5 Hitachi, Ltd
11.2.6 International Business Machines Corporation
11.2.7 Microsoft Corp
11.2.8 Oracle Corp
11.2.9 Palantir Technologies, Inc
11.2.10 SAS Institute Inc
12 Research Process
12.1 Research Process
12.1.1 Data Mining
12.1.2 Analysis
12.1.3 Market Estimation
12.1.4 Validation
12.1.5 Publishing
12.2 Research Attributes
12.3 Research Assumption