Edit Content
Bizwit-Logo-Final

Bizwit Research & Consulting LLP is a global provider of business intelligence & consulting services. We have a strong primary base of key industry leaders along with the chain of industry analysts to analyze the market trends and its future impact in order to estimates and forecast different business segments and markets. 

Global Big Data in E-commerce Market to reach USD XX billion by the end of 2029.

Global Big Data in E-commerce Market Size study & Forecast, 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) and Regional Analysis, 2022-2029

Product Code: ICTE-57152101
Publish Date: 9-05-2023
Page: 200

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:
Ø 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.
Ø 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

Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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

Chapter 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.
Chapter 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

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

Need Assistance

Contact Person -
Krishant Mennon
Call us @
+ 91 99931 15879
Email: sales@bizwitresearch.com

Checkout

Why Choose Us?

Quality over Quantity

Backed by 60+ paid data sources our reports deliver crisp insights with no compromise quality.

Analyst Support

24x7 Chat Support plus
free analyst hours with every purchase

Flawless Methodology

Our 360-degree approach of market study, our research methods leave stones unturned.

Enquiry Now