Global Big Data Analytics in Retail 麻豆原创 Research Report 2023

Publication ID:

PB502235857

Publication Date:

February 7, 2023

Pages:

Publisher:

MRRPB5

Countries:

Global [1]

USD 2,900.00

IT Services [2]

Hardware (Computing & Electronics) [3]

Computing & Electronics [4]

Description:

Highlights
The global Big Data Analytics in Retail market was valued at US$ 3415.6 million in 2022 and is anticipated to reach US$ 8766.1 million by 2029, witnessing a CAGR of 17.0% during the forecast period 2023-2029. The influence of COVID-19 and the Russia-Ukraine War were considered while estimating market sizes.
Big data is a massive volume of structured and unstructured data stored on daily basis. Big data analytics technique is used to search hidden patterns, market trends, and other useful information that may help organizations to take effective business decisions.
Report Scope
This report aims to provide a comprehensive presentation of the global market for Big Data Analytics in Retail, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Big Data Analytics in Retail.
The Big Data Analytics in Retail market size, estimations, and forecasts are provided in terms of and revenue ($ millions), considering 2022 as the base year, with history and forecast data for the period from 2018 to 2029. This report segments the global Big Data Analytics in Retail market comprehensively. Regional market sizes, concerning products by type, by application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Big Data Analytics in Retail companies, new entrants, and industry chain related companies in this market with information on the revenues for the overall market and the sub-segments across the different segments, by company, by type, by application, and by regions.
By Company
IBM
SAP
Microsoft
Oracle
SAS
Adobe
Microstrategy
Information Builders
Tableau Software
Qlik Technologies
RetailNext
Duozhun
Segment by Type
Software & Service
Platform
Segment by Application
Merchandising & In-store Analytics
麻豆原创ing & Customer Analytics
Supply Chain Analytics
Others
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Core Chapters
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by type, application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Big Data Analytics in Retail companies鈥� competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the key companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.

Please Note - This is an on demand report and will be delivered in 2 business days (48 hours) post payment.

Table Of Contents:

1 Report Overview
1.1 Study Scope
1.2 麻豆原创 Analysis by Type
1.2.1 Global Big Data Analytics in Retail 麻豆原创 Size Growth Rate by Type: 2018 VS 2022 VS 2029
1.2.2 Software & Service
1.2.3 Platform
1.3 麻豆原创 by Application
1.3.1 Global Big Data Analytics in Retail 麻豆原创 Growth by Application: 2018 VS 2022 VS 2029
1.3.2 Merchandising & In-store Analytics
1.3.3 麻豆原创ing & Customer Analytics
1.3.4 Supply Chain Analytics
1.3.5 Others
1.4 Study Objectives
1.5 Years Considered
1.6 Years Considered
2 Global Growth Trends
2.1 Global Big Data Analytics in Retail 麻豆原创 Perspective (2018-2029)
2.2 Big Data Analytics in Retail Growth Trends by Region
2.2.1 Global Big Data Analytics in Retail 麻豆原创 Size by Region: 2018 VS 2022 VS 2029
2.2.2 Big Data Analytics in Retail Historic 麻豆原创 Size by Region (2018-2023)
2.2.3 Big Data Analytics in Retail Forecasted 麻豆原创 Size by Region (2024-2029)
2.3 Big Data Analytics in Retail 麻豆原创 Dynamics
2.3.1 Big Data Analytics in Retail Industry Trends
2.3.2 Big Data Analytics in Retail 麻豆原创 Drivers
2.3.3 Big Data Analytics in Retail 麻豆原创 Challenges
2.3.4 Big Data Analytics in Retail 麻豆原创 Restraints
3 Competition Landscape by Key Players
3.1 Global Top Big Data Analytics in Retail Players by Revenue
3.1.1 Global Top Big Data Analytics in Retail Players by Revenue (2018-2023)
3.1.2 Global Big Data Analytics in Retail Revenue 麻豆原创 Share by Players (2018-2023)
3.2 Global Big Data Analytics in Retail 麻豆原创 Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by Big Data Analytics in Retail Revenue
3.4 Global Big Data Analytics in Retail 麻豆原创 Concentration Ratio
3.4.1 Global Big Data Analytics in Retail 麻豆原创 Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Big Data Analytics in Retail Revenue in 2022
3.5 Big Data Analytics in Retail Key Players Head office and Area Served
3.6 Key Players Big Data Analytics in Retail Product Solution and Service
3.7 Date of Enter into Big Data Analytics in Retail 麻豆原创
3.8 Mergers & Acquisitions, Expansion Plans
4 Big Data Analytics in Retail Breakdown Data by Type
4.1 Global Big Data Analytics in Retail Historic 麻豆原创 Size by Type (2018-2023)
4.2 Global Big Data Analytics in Retail Forecasted 麻豆原创 Size by Type (2024-2029)
5 Big Data Analytics in Retail Breakdown Data by Application
5.1 Global Big Data Analytics in Retail Historic 麻豆原创 Size by Application (2018-2023)
5.2 Global Big Data Analytics in Retail Forecasted 麻豆原创 Size by Application (2024-2029)
6 North America
6.1 North America Big Data Analytics in Retail 麻豆原创 Size (2018-2029)
6.2 North America Big Data Analytics in Retail 麻豆原创 Growth Rate by Country: 2018 VS 2022 VS 2029
6.3 North America Big Data Analytics in Retail 麻豆原创 Size by Country (2018-2023)
6.4 North America Big Data Analytics in Retail 麻豆原创 Size by Country (2024-2029)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Big Data Analytics in Retail 麻豆原创 Size (2018-2029)
7.2 Europe Big Data Analytics in Retail 麻豆原创 Growth Rate by Country: 2018 VS 2022 VS 2029
7.3 Europe Big Data Analytics in Retail 麻豆原创 Size by Country (2018-2023)
7.4 Europe Big Data Analytics in Retail 麻豆原创 Size by Country (2024-2029)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Big Data Analytics in Retail 麻豆原创 Size (2018-2029)
8.2 Asia-Pacific Big Data Analytics in Retail 麻豆原创 Growth Rate by Region: 2018 VS 2022 VS 2029
8.3 Asia-Pacific Big Data Analytics in Retail 麻豆原创 Size by Region (2018-2023)
8.4 Asia-Pacific Big Data Analytics in Retail 麻豆原创 Size by Region (2024-2029)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Big Data Analytics in Retail 麻豆原创 Size (2018-2029)
9.2 Latin America Big Data Analytics in Retail 麻豆原创 Growth Rate by Country: 2018 VS 2022 VS 2029
9.3 Latin America Big Data Analytics in Retail 麻豆原创 Size by Country (2018-2023)
9.4 Latin America Big Data Analytics in Retail 麻豆原创 Size by Country (2024-2029)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Big Data Analytics in Retail 麻豆原创 Size (2018-2029)
10.2 Middle East & Africa Big Data Analytics in Retail 麻豆原创 Growth Rate by Country: 2018 VS 2022 VS 2029
10.3 Middle East & Africa Big Data Analytics in Retail 麻豆原创 Size by Country (2018-2023)
10.4 Middle East & Africa Big Data Analytics in Retail 麻豆原创 Size by Country (2024-2029)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 IBM
11.1.1 IBM Company Detail
11.1.2 IBM Business Overview
11.1.3 IBM Big Data Analytics in Retail Introduction
11.1.4 IBM Revenue in Big Data Analytics in Retail Business (2018-2023)
11.1.5 IBM Recent Development
11.2 SAP
11.2.1 SAP Company Detail
11.2.2 SAP Business Overview
11.2.3 SAP Big Data Analytics in Retail Introduction
11.2.4 SAP Revenue in Big Data Analytics in Retail Business (2018-2023)
11.2.5 SAP Recent Development
11.3 Microsoft
11.3.1 Microsoft Company Detail
11.3.2 Microsoft Business Overview
11.3.3 Microsoft Big Data Analytics in Retail Introduction
11.3.4 Microsoft Revenue in Big Data Analytics in Retail Business (2018-2023)
11.3.5 Microsoft Recent Development
11.4 Oracle
11.4.1 Oracle Company Detail
11.4.2 Oracle Business Overview
11.4.3 Oracle Big Data Analytics in Retail Introduction
11.4.4 Oracle Revenue in Big Data Analytics in Retail Business (2018-2023)
11.4.5 Oracle Recent Development
11.5 SAS
11.5.1 SAS Company Detail
11.5.2 SAS Business Overview
11.5.3 SAS Big Data Analytics in Retail Introduction
11.5.4 SAS Revenue in Big Data Analytics in Retail Business (2018-2023)
11.5.5 SAS Recent Development
11.6 Adobe
11.6.1 Adobe Company Detail
11.6.2 Adobe Business Overview
11.6.3 Adobe Big Data Analytics in Retail Introduction
11.6.4 Adobe Revenue in Big Data Analytics in Retail Business (2018-2023)
11.6.5 Adobe Recent Development
11.7 Microstrategy
11.7.1 Microstrategy Company Detail
11.7.2 Microstrategy Business Overview
11.7.3 Microstrategy Big Data Analytics in Retail Introduction
11.7.4 Microstrategy Revenue in Big Data Analytics in Retail Business (2018-2023)
11.7.5 Microstrategy Recent Development
11.8 Information Builders
11.8.1 Information Builders Company Detail
11.8.2 Information Builders Business Overview
11.8.3 Information Builders Big Data Analytics in Retail Introduction
11.8.4 Information Builders Revenue in Big Data Analytics in Retail Business (2018-2023)
11.8.5 Information Builders Recent Development
11.9 Tableau Software
11.9.1 Tableau Software Company Detail
11.9.2 Tableau Software Business Overview
11.9.3 Tableau Software Big Data Analytics in Retail Introduction
11.9.4 Tableau Software Revenue in Big Data Analytics in Retail Business (2018-2023)
11.9.5 Tableau Software Recent Development
11.10 Qlik Technologies
11.10.1 Qlik Technologies Company Detail
11.10.2 Qlik Technologies Business Overview
11.10.3 Qlik Technologies Big Data Analytics in Retail Introduction
11.10.4 Qlik Technologies Revenue in Big Data Analytics in Retail Business (2018-2023)
11.10.5 Qlik Technologies Recent Development
11.11 RetailNext
11.11.1 RetailNext Company Detail
11.11.2 RetailNext Business Overview
11.11.3 RetailNext Big Data Analytics in Retail Introduction
11.11.4 RetailNext Revenue in Big Data Analytics in Retail Business (2018-2023)
11.11.5 RetailNext Recent Development
11.12 Duozhun
11.12.1 Duozhun Company Detail
11.12.2 Duozhun Business Overview
11.12.3 Duozhun Big Data Analytics in Retail Introduction
11.12.4 Duozhun Revenue in Big Data Analytics in Retail Business (2018-2023)
11.12.5 Duozhun Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.2 Data Source
13.2 Disclaimer
13.3 Author Details

Companies Mentioned:

IBM
SAP
Microsoft
Oracle
SAS
Adobe
Microstrategy
Information Builders
Tableau Software
Qlik Technologies
RetailNext
Duozhun

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