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Big Data Analytics in Retail Market Analysis- Industry Size, Share, Research Report, Insights, Covid-19 Impact, Statistics, Trends, Growth and Forecast 2022-2030

Published Date: July, 2022
No of Pages: 169
Delivery Format: PDF + Excel

$2,950.00

Description of the Report

The global big data analytics retail market was estimated at $4,854 million by 2020 and is expected to grow to $25,560 million by 2028. This will result in a growth rate of 23.1 percent from 2021 to 2028. Retail big data analytics can assist in monitoring customer behavior, finding patterns and trends in customers’ shopping habits, improving customer service quality, and improving levels of customer satisfaction and retention. Retailers can utilize it for segmentation of customers as well as customer loyalty, pricing, cross-selling, and supply chain management—forecasting of demand market basket analysis and managing fixed assets and finance.

According to a variety of interviews conducted with the top CXOs, big data analytics’ use in software used for retail has grown in recent years to boost organizations’ decision-making capacity and enhance retailers’ business insight. Additionally, the ability to use extensive data analysis in software for retail to offer various opportunities for businesses and provide new understanding to improve business efficiency is growing in popularity among the end-users.

Segment review

In this Big Data Analytics, the retail market is divided according to the component, deployment, size, the size of the application, and geography. Based on the component market is classified into services and software. Based on the deployment type, it’s organized into cloud and on-premise. The market is split into large, small, and medium enterprises (SMEs). Its purpose is divided into sales and marketing analytics, Supply logistics management and merchandising, customer analytics, and many more. Based on region, it’s examined throughout North America, Europe, Asia-Pacific, and LAMEA.

In 2019 the world market for big data analytics in the retail market was heavily dominated by the software segment. It is anticipated to continue its position in the coming years. The software market includes tools for big data analytics and platforms used for storing, organizing, and analyzing the critical data gathered from large datasets in retail businesses. These solutions allow organizations to get the maximum value from their data by making better decisions or generating more revenue. Retailers are currently focusing on exploratory and descriptive analytics. They are moving towards automated decision-making supported by advanced machine learning and analytics. Big data analytics within retail software have improved personalization on a massive scale by enabling retailers to improve customer service and offer more personalized suggestions to their customers. Therefore, the integration of modern technology like A.I. is predicted to accelerate the growth of this market shortly.

Deployment using the on-premise deployment model that is used for analytics using big data in retail permits the installation of software and allows the software to be run by the systems that are in the facilities of the company instead of being placed on cloud or server space. These kinds of software provide additional security features that encourage their use in large-scale financial institutions and other sensitive businesses in which security is a top priority. On-premise-based software is renowned for its better maintenance of servers, and the continuous system helps in the implementation of big analytics on data in retail. Furthermore, the deployment on-premise method is widely regarded as beneficial in large organizations since it is expensive to set up and requires interconnected servers and programs to control the systems. Additionally, greater data security, when opposed to cloud-based software, encourages its acceptance by organizations.

Asia-Pacific is among the fastest-growing regions because of the adoption of big data analytics within retail software. Are anticipated to see growth in this region due to the increasing the demand for fast internet connectivity and 4G connectivity that are increasing

the use of smartphones, the rise in popularity of online retailers as well as changes in consumer purchase patterns, and the intense and growing competition between retailers across the region. These developments have resulted in abundant data exchange over mobile and online networks, allowing businesses to collect vast quantities of information regarding customer interactions. Furthermore, numerous retail analytics providers with an established market presence in North America are expanding their business into Asia-Pacific and creating lucrative opportunities for the vast data analytics within the retail market.

The report is focused on growth opportunities, restrictions, and extensive data analysis in the analysis of retail markets. The report provides Porter’s five force analysis on the web advertising industry to comprehend the effect of various elements like the bargaining strength of suppliers, the competition intensity of competitors, the risk of new players as well as the threat of substitutes, and the bargaining power of buyers about extensive data analysis on trends in the retail market.

Effect of COVID-19’s big data analytics on the Retail Market (Pre and Post Analysis):

The size of big data analytics within the retail market is expected to increase from 5,955 million by 2021 and grow to $25,560 million in 2028, with an increase of 23.1 percent. The estimated 2028 figure is predicted to be greater than estimates before COVID-19. The COVID-19 pandemic has improved big data analytics of the retail markets, reaching a growth rate of 35% by 2021. The big data market in the world retail analytics market has recently experienced significant growth and is predicted to continue growing shortly. While the retail industry has experienced a decrease in its growth rates, retail businesses are still keen on studying trends in customer behavior and the future of market dynamics. Therefore, retailers are expected to keep their spending on large-scale data analysis. Data analytics in retail can help businesses stay ahead of trends in shopper behavior using customer analytics to discover the meaning of data, to interpret and then act upon meaningful data insights, which include the patterns of shoppers in stores and online.

Furthermore, businesses’ growing awareness of the benefits of analytics, the unaffected budget for data analytics, and the necessity to evaluate risks have boosted the need for predictive analytics at a rapid growth rate. This, in turn, bolsters the expansion of the industry. In terms of economics, it generated $4,437.3 million in 2019 and is projected to grow to $17,851.7 by 2027.

Top Factors Impacting HTML0

The increase in spending on tools to analyze significant data increase the demand to offer a more personalized customer experience to increase sales, growth in the growth of the e-commerce market and the growing demand for predictive analytics within retail, and the integration of cutting-edge technologies such as IoT, A.I., and machine learning for big data analytics within retail are among the main elements driving the market expansion. But, the challenges of collecting and integrating data from various systems could hinder the market’s growth over the forecast time.

Development in the E-Commerce Sector

With the help of big data analytics in retail software, retailers can enhance the efficiency of their online stores to earn more money. By using web analytics, including clickstream data, heatmaps, and clickstream research, stores can improve the pages for their products to guarantee higher conversion rates and better engagement. Individualized product recommendations and special offers based on the historical customer web footprints increase the likelihood of clicks and sales. Products can be promoted by looking at data points like shopping activity for products by location review and customer feedback, as well as saved wish lists or items left in empty shopping carts. In addition, today, consumers can be more in touch than ever due to the proliferation of smartphones. Therefore, consumers can get information about consumer products through mobile phones, social media sites, and e-commerce websites. Therefore, to better understand the buying choices of their customers, businesses use analytics of the customer’s journey. This is, in turn, driving the development of big data analytics within retail.

Spending increases on analytics tools for big data

Global retailers increasingly embrace big-data technologies to create more value and benefit from data-driven decision-making. Businesses are using the data they collect to improve customer experience efficiency, employee productivity, efficiency improvement, and even the development of new products. According to the survey from 100 U.S. retailers published by Microsoft, 33% of retailers invested in analytics in 2017, and 29% of respondents planned to invest in analytics using big data for 2018. Most retailers wanted to use the tools to forecast marketing, personalization prices, price optimization, and selling. According to a research by Forbes in 2018, operational analytics, customer analytics, fraud, and compliance are among the most frequently used applications for big data in the retail business. It is now the primary data system for CEOs, and they’re now focusing on analytics software and data directly impacting their organizations’ profitability. Retail companies are increasingly focusing on adopting analytics software that can handle big data and integrating it into their organizations’ workflows.

Big Data Analytics in Retail Market Report Scope and Segmentation

ATTRIBUTESDETAILS
ESTIMATED YEAR2022
BASE YEAR2021
FORECAST YEAR2030
HISTORICAL YEAR2017-2021
UNITValue (USD Million/Billion)
BY REGIONNorth America, Europe, Asia Pacific, Latin America, Middle East and Africa

Asia-Pacific is expected to be one of the largest regions.

The Benefits for Stakeholders

  • The report provides a thorough study of the massive analysis of data analytics in retail, as well as recent trends and future estimates to identify the most likely investment pockets.
  • Details on essential drivers, constraints, and opportunities, as well as their impact on the market’s size, are included in the report.
  • Porter’s Five Forces analysis shows the potential of buyers and suppliers in the business.
  • A quantitative analysis of the big data from the retail store market during 2020-2028 is presented to assess the market’s potential.

Market Segments Key Market Segments

    • On Application
      • Marketing analytics and sales
      • Management of supply chain operations
      • Analytical Merchandising
      • Customer analytics
      • Other
    • By the size of an organization
      • Large Enterprise
      • Small & Medium Enterprise
    • By Component
      • Software
      • Services
    • Through Deployment
      • On-Premise
      • Cloud
    • By Region
      • North America
        • US
        • Canada
        • Mexico
      • Europe
        • Germany
        • Italy
        • France
        • UK
        • Spain
        • Poland
        • Russia
        • The Netherlands
        • Norway
        • Czech Republic
        • Rest of Europe
      • Asia Pacific
        • China
        • Japan
        • India
        • South Korea
        • Indonesia
        • Malaysia
        • Thailand
        • Singapore
        • Australia & New Zealand
        • Rest of Asia Pacific
      • South America
        • Brazil
        • Argentina
        • Colombia
        • Rest of South America
      • The Middle East & Africa
        • Saudi Arabia
        • UAE
        • South Africa
        • Northern Africa
        • Rest of MEA

 

Principal Market Participants

  • ADOBE INC.
  • CISCO Systems, INC.
  • RETAILNEXT
  • INTERNATIONAL BUSINESS MACHINES CORPORATION
  • ORACLE CORPORATION
  • SAP SE
  • SAS INSTITUTE INC.
  • SISENSE INC.
  • TERADATA CORPORATION
  • TIBCO SOFTWARE INC.
  • TABLEAU SOFTWARE

Table of Contents

CHAPTER 1: INTRODUCTION

1.1. REPORT DESCRIPTION
1.2.KEY BENEFITS FOR STAKEHOLDERS
1.3.KEY MARKET SEGMENTS
1.4.RESEARCH METHODOLOGY

1.4.1. Secondary research
1.4.2.Primary research
1.4.3.Analyst tools & models

CHAPTER 2: EXECUTIVE SUMMARY

2.1.KEY FINDINGS

2.1.1.Top impacting factors
2.1.2Top investment pockets

2.2.CXO PERSPECTIVE

CHAPTER 3:MARKET OVERVIEW

3.1. MARKET DEFINITION AND SCOPE
3.2.PORTERS FIVE FORCES ANALYSIS
3.3.KEY PLAYER POSITIONING
3.4.CASE STUDIES

3.4.1. Case Study 01
3.4.2.Case Study 02

3.5.MARKET DYNAMICS

3.5.1.Drivers

3.5.1.1.Increase in spending on big data analytics tools
3.5.1.2.Rise in need to deliver personalized customer experience to increase sales
3.5.1.3.Increasing growth of e-commerce sector

3.5.2.Restraints

3.5.2.1. Collecting and collating the data from disparate systems
3.5.2.2.To capture customer data

3.5.3.Opportunity

3.5.3.1.Integration of new technologies such as IoT, AI and machine learning in big data analytics in retail
3.5.3.2.Growing demand of predictive analytics in retail

3.6.IMPACT ANALYSIS: COVID-19 ON BIG DATA IN RETAIL ANALYTICS MARKET

3.6.1.Impact on market size
3.6.2.Consumer trends, preferences, and budget impact
3.6.3.Regulatory framework
3.6.4.Economic impact
3.6.5.Key player strategies to tackle negative impact
3.6.6.Opportunity window (due to COVID outbreak)

CHAPTER 4: BIG DATA ANALYTICS IN RETAIL MARKET, BY COMPONENT

4.1. OVERVIEW
4.2.SOFTWARE

4.2.1.Key market trends, growth factors, and opportunities
4.2.2.Market size and forecast, by region
4.2.3.Market analysis, by region

4.3.SERVICE

4.3.1. Key market trends, growth factors, and opportunities
4.3.2.Market size and forecast, by region
4.3.3.Market analysis, by region

CHAPTER 5: BIG DATA ANALYTICS IN RETAIL MARKET, BY DEPLOYMENT

5.1.OVERVIEW
5.2.ON PREMISE

5.2.1.Key market trends, growth factors, and opportunities
5.2.2.Market size and forecast, by region
5.2.3.Market analysis, by region

5.3.CLOUD

5.3.1. Key market trends, growth factors, and opportunities
5.3.2.Market size and forecast, by region
5.3.3.Market analysis, by region

CHAPTER 6: BIG DATA ANALYTICS IN RETAIL MARKET, BY ORGANIZATION SIZE

6.1.OVERVIEW
6.2.LARGE ENTERPRISES

6.2.1. Key market trends, growth factors, and opportunities
6.2.2.Market size and forecast, by region
6.2.3.Market analysis, by region

6.3.SMES

6.3.1. Key market trends, growth factors, and opportunities
6.3.2.Market size and forecast, by region
6.3.3.Market analysis, by region

CHAPTER 7: BIG DATA ANALYTICS IN RETAIL MARKET, BY APPLICATION

7.1.OVERVIEW
7.2.SALES AND MARKETING ANALYTICS

7.2.1. Key market trends, growth factors, and opportunities
7.2.2.Market size and forecast, by region
7.2.3.Market analysis, by region

7.3.SUPPLY CHAIN OPERATIONS MANAGEMENT

7.3.1. Key market trends, growth factors, and opportunities
7.3.2.Market size and forecast, by region
7.3.3.Market analysis, by region

7.4.MERCHANDISING ANALYTICS

7.4.1.Key market trends, growth factors, and opportunities
7.4.2.Market size and forecast, by region
7.4.3.Market analysis, by region

7.5.CUSTOMER ANALYTICS

7.5.1.Key market trends, growth factors, and opportunities
7.5.2.Market size and forecast, by region
7.5.3.Market analysis, by region

7.6.OTHERS

7.6.1. Key market trends, growth factors, and opportunities
7.6.2.Market size and forecast, by region
7.6.3.Market analysis, by region

CHAPTER 8: BIG DATA ANALYTICS IN RETAIL MARKET, BY REGION

8.1.OVERVIEW
8.2.NORTH AMERICA

8.2.1.Key market trends, growth factors and opportunities
8.2.2.Market size and forecast, by component
8.2.3.Market size and forecast, by deployment
8.2.4.Market size and forecast, by organization size
8.2.5.Market size and forecast, by application
8.2.6.Market analysis by country

8.2.6.1. U.S.

8.2.6.1.1.Market size and forecast, by component
8.2.6.1.2.Market size and forecast, by deployment
8.2.6.1.3.Market size and forecast, by organization size
8.2.6.1.4.Market size and forecast, by application

8.2.6.2.Canada

8.2.6.2.1.Market size and forecast, by component
8.2.6.2.2.Market size and forecast, by deployment
8.2.6.2.3.Market size and forecast, by organization size
8.2.6.2.4.Market size and forecast, by application

8.3.EUROPE

8.3.1.Key market trends, growth factors and opportunities
8.3.2.Market size and forecast, by component
8.3.3.Market size and forecast, by deployment
8.3.4.Market size and forecast, by organization size
8.3.5.Market size and forecast, by application
8.3.6.Market analysis by country

8.3.6.1.UK

8.3.6.1.1.Market size and forecast, by component
8.3.6.1.2.Market size and forecast, by deployment
8.3.6.1.3.Market size and forecast, by organization size
8.3.6.1.4.Market size and forecast, by application

8.3.6.2.Germany

8.3.6.2.1.Market size and forecast, by component
8.3.6.2.2.Market size and forecast, by deployment
8.3.6.2.3.Market size and forecast, by organization size
8.3.6.2.4.Market size and forecast, by application

8.3.6.3.France

8.3.6.3.1.Market size and forecast, by component
8.3.6.3.2.Market size and forecast, by deployment
8.3.6.3.3.Market size and forecast, by organization size
8.3.6.3.4.Market size and forecast, by application

8.3.6.4.Rest of Europe

8.3.6.4.1.Market size and forecast, by component
8.3.6.4.2.Market size and forecast, by deployment
8.3.6.4.3.Market size and forecast, by organization size
8.3.6.4.4.Market size and forecast, by application

8.4.ASIA-PACIFIC

8.4.1.Key market trends, growth factors and opportunities
8.4.2.Market size and forecast, by component
8.4.3.Market size and forecast, by deployment
8.4.4.Market size and forecast, by organization size
8.4.5.Market size and forecast, by application
8.4.6.Market analysis by country

8.4.6.1.China

8.4.6.1.1.Market size and forecast, by component
8.4.6.1.2.Market size and forecast, by deployment
8.4.6.1.3.Market size and forecast, by organization size
8.4.6.1.4.Market size and forecast, by application

8.4.6.2.India

8.4.6.2.1.Market size and forecast, by component
8.4.6.2.2.Market size and forecast, by deployment
8.4.6.2.3.Market size and forecast, by organization size
8.4.6.2.4.Market size and forecast, by application

8.4.6.3.Japan

8.4.6.3.1.Market size and forecast, by component
8.4.6.3.2.Market size and forecast, by deployment
8.4.6.3.3.Market size and forecast, by organization size
8.4.6.3.4.Market size and forecast, by application

8.4.6.4.Australia

8.4.6.4.1.Market size and forecast, by component
8.4.6.4.2.Market size and forecast, by deployment
8.4.6.4.3.Market size and forecast, by organization size
8.4.6.4.4.Market size and forecast, by application

8.4.6.5.Rest of Asia-Pacific

8.4.6.5.1.Market size and forecast, by component
8.4.6.5.2.Market size and forecast, by deployment
8.4.6.5.3.Market size and forecast, by organization size
8.4.6.5.4.Market size and forecast, by application

8.5.LAMEA

8.5.1.Key market trends, growth factors and opportunities
8.5.2.Market size and forecast, by component
8.5.3.Market size and forecast, by deployment
8.5.4.Market size and forecast, by organization size
8.5.5.Market size and forecast, by application
8.5.6.Market analysis by country

8.5.6.1.Latin America

8.5.6.1.1.Market size and forecast, by component
8.5.6.1.2.Market size and forecast, by deployment
8.5.6.1.3.Market size and forecast, by organization size
8.5.6.1.4.Market size and forecast, by application

8.5.6.2.Middle East

8.5.6.2.1.Market size and forecast, by component
8.5.6.2.2.Market size and forecast, by deployment
8.5.6.2.3.Market size and forecast, by organization size
8.5.6.2.4.Market size and forecast, by application

8.5.6.3.Africa

8.5.6.3.1.Market size and forecast, by component
8.5.6.3.2.Market size and forecast, by deployment
8.5.6.3.3.Market size and forecast, by organization size
8.5.6.3.4.Market size and forecast, by application

CHAPTER 9: COMPETITIVE LANDSCAPE

9.1. COMPETITIVE DASHBOARD
9.2.TOP WINNING STRATEGIES
9.3.KEY DEVELOPMENTS

9.3.1.New product launches
9.3.2.Partnership
9.3.3.Acquisition
9.3.4.Product development
9.3.5.Business expansion
9.3.6.Collaboration
9.3.7.Agreement

CHAPTER 10: COMPANY PROFILE

10.1. ADOBE INC.

10.1.1.Company overview
10.1.2.Key Executives
10.1.3.Company snapshot
10.1.4.Operating business segments
10.1.5.Product portfolio
10.1.6.R&D Expenditure
10.1.7.Business performance
10.1.8.Key strategic moves and developments

10.2.CISCO SYSTEMS, INC.

10.2.1. Company overview
10.2.2.Key Executives
10.2.3.Company snapshot
10.2.4.Product portfolio
10.2.5.R&D Expenditure
10.2.6.Business performance
10.2.7.Key strategic moves and developments

10.3.INTERNATIONAL BUSINESS MACHINES CORPORATION

10.3.1.Company overview
10.3.2.Key Executives
10.3.3.Company snapshot
10.3.4.Operating business segments
10.3.5.Product portfolio
10.3.6.R&D Expenditure
10.3.7.Business performance
10.3.8.Key strategic moves and developments

10.4.ORACLE CORPORATION

10.4.1. Company overview
10.4.2.Key executives
10.4.3.Company snapshot
10.4.4.Operating business segments
10.4.5.Product portfolio
10.4.6.R&D expenditure
10.4.7.Business performance
10.4.8.Key strategic moves and developments

10.5.SAP SE

10.5.1.Company overview
10.5.2.Key Executives
10.5.3.Company snapshot
10.5.4.Operating business segments
10.5.5.Product portfolio
10.5.6.R&D Expenditure
10.5.7.Business performance
10.5.8.Key strategic moves and developments

10.6.SAS INSTITUTE INC.

10.6.1. Company overview
10.6.2.Key Executives
10.6.3.Company snapshot
10.6.4.Product portfolio
10.6.5.Business performance
10.6.6.Key strategic moves and developments

10.7.SISENSE INC.

10.7.1. Company overview
10.7.2.Key Executives
10.7.3.Company snapshot
10.7.4.Product portfolio
10.7.5.Key strategic moves and developments

10.8.TERADATA CORPORATION

10.8.1. Company overview
10.8.2.Key Executives
10.8.3.Company snapshot
10.8.4.Product portfolio
10.8.5.Key strategic moves and developments

10.9.TIBCO SOFTWARE INC.

10.9.1. Company overview
10.9.2.Key Executives
10.9.3.Company snapshot
10.9.4.Product portfolio
10.9.5.Key strategic moves and developments

10.10.TABLEAU SOFTWARE

10.10.1. Company overview
10.10.2.Key Executives
10.10.3.Company snapshot
10.10.4.Product portfolio
10.10.5.R&D Expenditure
10.10.6.Business performance
10.10.7.Key strategic moves and developments

LIST OF TABLES

TABLE 01.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT, 20192027 ($MILLION)
TABLE 02.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR SOFTWARE, BY REGION, 20192027 ($MILLION)
TABLE 03.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR SERVICE, BY REGION , 20192027 ($MILLION)
TABLE 04.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT 20192027 ($MILLION)
TABLE 05.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR ON PREMISE, BY REGION, 20192027 ($MILLION)
TABLE 06.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR CLOUD, BY REGION, 20192027 ($MILLION)
TABLE 07.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE 20192027 ($MILLION)
TABLE 08.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR LARGE ENTERPRISES, BY REGION, 20192027 ($MILLION)
TABLE 09.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR SMES, BY REGION, 20192027 ($MILLION)
TABLE 10.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 20192027 ($MILLION)
TABLE 11.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR SALES AND MARKETING ANALYTICS, BY REGION, 20192027 ($MILLION)
TABLE 12.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR SUPPLY CHAIN OPERATIONS MANAGEMENT, BY REGION, 20192027 ($MILLION)
TABLE 13.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR MERCHANDISING ANALYTICS, BY REGION, 20192027 ($MILLION)
TABLE 14.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR RISK AND CUSTOMER ANALYTICS, BY REGION, 20192027 ($MILLION)
TABLE 15.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR OTHERS, BY REGION, 20192027 ($MILLION)
TABLE 16.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY REGION, 20192027 ($MILLION)
TABLE 17.NORTH AMERICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 18.NORTH AMERICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 19.NORTH AMERICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE 2019-2027 ($MILLION)
TABLE 20.NORTH AMERICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 21.NORTH AMERICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COUNTRY, 2019-2027 ($MILLION)
TABLE 22.U.S. BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 23.U.S. BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 24.U.S. BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 25.U.S. BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 26.CANADA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 27.CANADA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 28.CANADA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 29.CANADA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 30.EUROPE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 31.EUROPE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 32.EUROPE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE 2019-2027 ($MILLION)
TABLE 33.EUROPE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 34.EUROPE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COUNTRY, 2019-2027 ($MILLION)
TABLE 35.UK BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 36.UK BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 37.UK BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 38.UK BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 39.GERMANY BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 40.GERMANY BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 41.GERMANY BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 42.GERMANY BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 43.FRANCE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 44.FRANCE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 45.FRANCE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 46.FRANCE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 47.REST OF EUROPE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 48.REST OF EUROPE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 49.REST OF EUROPE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 50.REST OF EUROPE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 51.ASIA-PACIFIC BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 52.ASIA-PACIFIC BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 53.ASIA-PACIFIC BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE 2019-2027 ($MILLION)
TABLE 54.ASIA-PACIFIC BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 55.ASIA-PACIFIC BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COUNTRY, 2019-2027 ($MILLION)
TABLE 56.CHINA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 57.CHINA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 58.CHINA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 59.CHINA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 60.INDIA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 61.INDIA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 62.INDIA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 63.INDIA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 64.JAPAN BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 65.JAPAN BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 66.JAPAN BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 67.JAPAN BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 68.AUSTRALIA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 69.AUSTRALIA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 70.AUSTRALIA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 71.AUSTRALIA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 72.REST OF ASIA-PACIFIC BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 73.REST OF ASIA-PACIFIC BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 74.REST OF ASIA-PACIFIC BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 75.REST OF ASIA-PACIFIC BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 76.LAMEA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 77.LAMEA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 78.LAMEA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE 2019-2027 ($MILLION)
TABLE 79.LAMEA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 80.LAMEA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COUNTRY, 2017-2025 ($MILLION)
TABLE 81.LATIN AMERICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 82.LATIN AMERICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 83.LATIN AMERICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 84.LATIN AMERICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 85.MIDDLE EAST BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 86.MIDDLE EAST BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 87.MIDDLE EAST BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027 ($MILLION)
TABLE 88.MIDDLE EAST BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 89.AFRICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT 2019-2027 ($MILLION)
TABLE 90.AFRICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019-2027 ($MILLION)
TABLE 91.AFRICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019-2027

Major Companies

  • ADOBE INC.
  • CISCO Systems, INC.
  • RETAILNEXT
  • INTERNATIONAL BUSINESS MACHINES CORPORATION
  • ORACLE CORPORATION
  • SAP SE
  • SAS INSTITUTE INC.
  • SISENSE INC.
  • TERADATA CORPORATION
  • TIBCO SOFTWARE INC.
  • TABLEAU SOFTWARE

Major Segmentation

  • On Application
    • Marketing analytics and sales
    • Management of supply chain operations
    • Analytical Merchandising
    • Customer analytics
    • Other
  • By the size of an organization
    • Large Enterprise
    • Small & Medium Enterprise
  • By Component
    • Software
    • Services
  • Through Deployment
    • On-Premise
    • Cloud

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