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Big Data Analytics in Retail Market Size, Share, Statistics & Trends Analysis Report By Component, Deployment, Enterprise Size, Application, and Segment Forecasts, 2021-2028
Published Date: March 2021
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Report Code: ALLI-Manu-1H34
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Big Data Analytics in Retail Industry

Big Data Analytics in Retail Market Size, Share, Statistics & Trends Analysis Report By Component, Deployment, Enterprise Size, Application, and Segment Forecasts, 2021-2028

Code: ALLI-Manu-1H34
Report
March 2021
274 Pages
Allied Market Research
Region: Global,
Description
Table of Content
Tables & Figures

Big Data Analytics in Retail Market Statistics 2028

The global big data analytics in retail market size was valued at $4,854 million in 2020, and is projected to reach $25,560 million by 2028, registering a CAGR of 23.1% from 2021 to 2028. An increase in spending on big data analytics tools, rise in need to deliver personalized customer experience to increase sales, and increase in growth of the e-commerce sector are some of the major factors that are driving the growth of the global market. However, issues in collecting and collating the data from disparate systems and challenges in capturing customer data are anticipated to restrict the big data analytics in retail market growth. On the contrary, integration of new technologies such as IoT, AI and machine learning in big data analytics in retail, and rise in demand for predictive analytics in retail are anticipated to provide lucrative growth opportunities for the global big data analytics in retail market during the analysis period.

Big Data Analytics in Retail Market

Big data describes a large volume of data that is used to reveal patterns, trends, and associations, especially related to human behavior and interactions. For the retail industry, big data is used for getting greater understanding of consumer shopping habits and how to attract new customers. Big data analytics in retail enables companies to create customer recommendations based on their purchase history, resulting in personalized shopping experiences. These big data analytics solutions also help in forecasting trends and making strategic decisions based on market analysis.

The report focuses on growth prospects, restraints, and big data analytics in retail market analysis. The study provides Porter’s five forces analysis of the internet advertising industry to understand impact of various factors such as bargaining power of suppliers, competitive intensity of competitors, threat of new entrants, threat of substitutes, and bargaining power of buyers on the big data analytics in retail market trends.

Impact of COVID-19 on Big Data Analytics in Retail Market (Pre and Post Analysis):

Size of the big data analytics in retail market is estimated to grow from 5,955 million in 2021, and is projected to reach $25,560 million by 2028, at a CAGR of 23.1%. The current estimation of 2028 is projected to be higher than pre-COVID-19 estimates. COVID-19 pandemic has bought a positive impact on the big data analytics in retail market, achieving a growth rate of 3–5% in the 2021. The global big data in retail analytics market witnessed significant growth in the recent past, and is expected to exhibit similar trend in the coming years. Although the retail sector has witnessed decline in growth rate, retail companies are still focused on studying customer trends and analyzing future market dynamics. Thus, retail companies are expected to continue their investments on big data analytics. Retail data analytics can help companies stay ahead of shopper trends by applying customer analytics in retail to uncover, interpret, and act on meaningful data insights, including in-store and online shopper patterns. In addition, strong awareness about data analytics benefits, unaffected data analytics budget by enterprises, and need to analyze risks has increased demand for predictive analytics at a significant rate, which, in turn, supports growth of the market. Economically, it generated $4,437.3 million in 2019, and is expected to reach $17,851.7 in 2027.

Big Data Analytics in Retail Industry

Big Data Analytics in Retail Market Segment Review

The big data analytics in retail market is segmented on the basis of component, deployment, organization size, application, and region. By component, the market is categorized into software and services. On the basis of deployment, it is classified into on-premise and cloud. As per organization size, market is divided into large enterprises and small & medium sized enterprises (SMEs). Depending on application, it is divided into sales & marketing analytics, supply chain operations management, merchandising analytics, customer analytics, and others. By region, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

In 2019, the global big data analytics in retail market share was dominated by the software segment, and is expected to maintain its dominance in the upcoming years the software segment includes different big data analytics tools and platforms for storing, managing, and analyzing valuable information collected form large data sets in retail companies. These solutions help organizations leverage best return from their data, either by making better decisions or bringing in more revenue. Retail companies are presently focused on traditional descriptive and exploratory analytics to automated decision making driven by advanced analytics and machine learning. These new big data analytics in retail software are improving personalization at a transformational scale by allowing retail companies to enhance customer experience and provide more customized recommendations to customers. Thus, integration of advanced technologies such as AI is expected to boost growth of this segment in the coming years.

Big Data Analytics in Retail Market by Component

By deployment, the on-premise deployment model for big data analytics in retail enables installation of software and permits applications to run on systems present in premises of an organization instead of putting on server space or cloud. These types of software offer enhanced security features, which drive their adoption in largescale financial institutions and other data sensitive organizations, where security is priority. On-premise-based software is known for better maintenance of servers and continuous system facilitates implementation of these big data analytics in retail. In addition, on-premise deployment mode is considered widely useful in large enterprises as it involves a significant investment to implement and organizations need to purchase interconnected servers as well as software to manage the system. Furthermore, better security of data as compared to cloud-based software promotes its adoption among organizations.

Big Data Analytics in Retail Market by Deployment Mode

Asia-Pacific is one of the fastest growing regions, owing to adoption of cloud-enabled big data analytics in retail software are expected to witness growth in this region, owing to increase in popularity of fast internet connectivity including 4G connections, growing

adoption of smartphones, increase in popularity of e-commerce companies, change in customer purchase patterns, and strong & growing competition among retail vendors in the region. These technologies have led to a great amount of data exchange on mobile and internet networks, and thus, enables enterprises to capture huge volumes of information about customer interactions. Further, many retail analytics vendors who have a strong presence in North America are expanding their business across Asia-Pacific, which creates lucrative opportunities for the big data analytics in retail market.

Big Data Analytics in Retail Market Enterprise Size

The key players operating in the global big data analytics in retail market analysis include Alteryx Inc., IBM, Microsoft, Microstrategy Inc., Oracle Corporation, Qlik Technologies Inc., RetailNext, SAP SE, SAS institute, and Teradata.

Top Market Impacting Factors

Increase in spending on big data analytics tools

Retailers across the world are increasingly adopting big data technologies to generate more value and data-driven decision making. Companies are leveraging data generated to improve customer facing experiences, employee productivity, operational improvement, product innovation and more. The retailers are mostly seeking to utilize these tools for forecasting, personalization, marketing, price optimization, merchandizing, and others. As per a study conducted by Forbes in 2015, customer analytics, operational analytics and fraud & compliance are some of the top use cases for big data in retail industry. It has emerged as the most important information system for CEOs, and they are now viewing data and analytics software as directly contributing to organizational profitability. Thus, retail companies are increasingly focusing on adopting big data analytics software and embedding them in the existing workflows of organizations.

Increasing growth of e-commerce sector

With big data analytics in retail software, retailers can improve the performance of their online stores to generate more revenue. Utilizing website analytics, clickstream data and heatmap studies, retailers can optimize product landing pages to ensure better engagement and conversion rates. Personalized product recommendations and offers based on the historic web footprints of customers increases the chances of clickthroughs and sales. Items can be promoted by inspecting data points such as product browsing activity by region, user feedback and reviews, saved wishlists, or items in abandoned shopping carts. Further, today’s customers are more connected than ever before due to proliferation of smartphones. Thus, customers can access any information to consumer products using channels such as mobile, social media, e-commerce sites etc. Thus, for understanding customer’s buying decisions companies are utilizing customer journey analytics. This is further driving the growth of the big data analytics in retail market.

Growing demand of predictive analytics in retail

Predictive analytics in retail can enable extracting valuable information from the massive data which will be useful for retail companies in future. Analytical data helps retailers to offer precise insights, improve the existing process, future customer buying process, and more. It also helps in forming the retail sales strategy and increases the ROI of marketing activities. Predictive analytics for retail industry helps to optimize the supply chain and increase collaboration internally and across trading partners. The analytics report generated from using this software helps to guide new product development and launches. Further, it enables a curated relationship over a tailored conversation between retailers and suppliers. It also facilitates to improve conversion rate, lessening customer churn, and reducing customer acquisition costs. It also provides fast feedback on consumer tastes and preferences. Thus, owing to all these functionalities, the demand for predictive analytics in retail is on the rise, which is offering lucrative opportunities for the market.

Key Benefits for Stakeholders

  • This study includes the analytical depiction of the global big data analytics in retail market forecast and trends to determine the imminent investment pockets.
  • The report presents information related to key drivers, restraints, and big data analytics in retail market opportunity.
  • The current market size is quantitatively analyzed from 2019 to 2027 to highlight the financial competency of the big data analytics in retail industry.
  • Porter’s five forces analysis illustrates the potency of buyers & suppliers in the market

Scope of the Big Data Analytics in Retail Market Report

Report Metric

Details

Report Name

Big Data Analytics in Retail Market

The market size in 2020

$4,854 Million

The market size in 2021

$5,955 Million

The revenue forecast in 2028

$25,560 Million

Growth Rate

CAGR of 23.1% from 2021-2028

Forecast period

2021-2028

Forecast units

Value (USD)

Segments covered

Component, Deployment, Enterprise Size, Application, Regions, Key Players

by Component

Software and Services

by Deployment

On-premise and Cloud

by Enterprise Size

Large Enterprises and Small & Medium-sized Enterprises

by Application

Sales & Marketing Analytics, Supply Chain Operations Management, Merchandising Analytics, Customer Analytics, and Others

Report coverage

Revenue & volume forecast, company share, competitive landscape, growth factors, and trends

Geographic regions covered

North America, Europe, Asia Pacific, Latin America, Middle East & Africa

Key Players

Alteryx Inc., IBM, Microsoft, Microstrategy Inc., Oracle Corporation, Qlik Technologies Inc., RetailNext, SAP SE, SAS institute, and Teradata

Big Data Analytics in Retail Market Segments

Big Data Analytics in Retail Market by Component

  • Software
  • Services  

Big Data Analytics in Retail Market by Deployment

  • On-premise
  • Cloud    

Big Data Analytics in Retail Market by Enterprise Size

  • Large Enterprises 
  • Small & Medium Enterprises (SMEs)

Big Data Analytics in Retail Market by Application

  • Sales and marketing analytics
  • Supply chain operations management
  • Merchandising analytics
  • Customer analytics
  • Others

Big Data Analytics in Retail Market by Region

  • North America
    • U.S.
    • Canada
  • Europe 
    • UK
    • Germany
    • France
    • Rest of Europe
  • Asia-Pacific 
    • China
    • India
    • Japan
    • Australia
    • Rest of Asia-Pacific
  • LAMEA
    • Latin America
    • Middle East
    • Africa

Major Players in the Big Data Analytics in Retail Market

  • Alteryx Inc.
  • IBM
  • Microsoft
  • Microstrategy Inc.
  • Oracle Corporation
  • Qlik Technologies Inc.
  • RetailNext
  • SAP SE
  • SAS institute
  • Teradata

Frequently Asked Questions About This Report

1. How big is Big Data Analytics in Retail Market?

Ans. The global big data analytics in retail market size was valued at $4,854 million in 2020, and is projected to reach $25,560 million by 2028, registering a CAGR of 23.1% from 2021 to 2028.

2. Which region is fastest growing in the global Big Data Analytics in Retail Market?

Ans. Asia-Pacific is one of the fastest-growing regions, owing to the adoption of cloud-enabled big data analytics in retail software are expected to witness growth in this region, owing to increase in popularity of fast internet connectivity including 4G connections, growing

3. Who are the leading players in Big Data Analytics in Retail Market?

Ans. The key players operating in the global big data analytics in retail market analysis include Alteryx Inc., IBM, Microsoft, Microstrategy Inc., Oracle Corporation, Qlik Technologies Inc., RetailNext, SAP SE, SAS institute, and Teradata.

4. Which market by component segment dominates the Big Data Analytics in Retail Market Share?

Ans. Big Data Analytics in Retail Software segment is projected as one of the most lucrative segments during the forecast period.

5. What would be the forecast period in the Big Data Analytics in Retail Market report?

Ans. The forecast period for the Big Data Analytics in Retail Market is 2021 to 2028

6. What are the market drivers in the global Big Data Analytics in Retail Market?

Ans. Increase in spending on big data analytics tools, rise in need to deliver personalized customer experience to increase sales, and increase in growth of the e-commerce sector are some of the major factors that are driving the growth of the global market

TABLE OF CONTENT

 

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
2.1.2.Top investment pockets

2.2.CXO PERSPECTIVE

CHAPTER 3:MARKET OVERVIEW

3.1.MARKET DEFINITION AND SCOPE
3.2.PORTER’S 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 & FIGURES

 

TABLE 01.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT, 2019–2027 ($MILLION)
TABLE 02.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR SOFTWARE, BY REGION, 2019–2027 ($MILLION)
TABLE 03.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR SERVICE, BY REGION , 2019–2027 ($MILLION)
TABLE 04.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT 2019–2027 ($MILLION)
TABLE 05.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR ON PREMISE, BY REGION, 2019–2027 ($MILLION)
TABLE 06.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR CLOUD, BY REGION, 2019–2027 ($MILLION)
TABLE 07.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE 2019–2027 ($MILLION)
TABLE 08.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR LARGE ENTERPRISES, BY REGION, 2019–2027 ($MILLION)
TABLE 09.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR SMES, BY REGION, 2019–2027 ($MILLION)
TABLE 10.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019–2027 ($MILLION)
TABLE 11.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR SALES AND MARKETING ANALYTICS, BY REGION, 2019–2027 ($MILLION)
TABLE 12.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR SUPPLY CHAIN OPERATIONS MANAGEMENT, BY REGION, 2019–2027 ($MILLION)
TABLE 13.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR MERCHANDISING ANALYTICS, BY REGION, 2019–2027 ($MILLION)
TABLE 14.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR RISK AND CUSTOMER ANALYTICS, BY REGION, 2019–2027 ($MILLION)
TABLE 15.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE FOR OTHERS, BY REGION, 2019–2027 ($MILLION)
TABLE 16.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY REGION, 2019–2027 ($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 ($MILLION)
TABLE 92.AFRICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019-2027 ($MILLION)
TABLE 93.KEY NEW PRODUCT LAUNCHES (2016-2019)
TABLE 94.PARTNERSHIP (2016-2019)
TABLE 95.ACQUISTION (2016-2019)
TABLE 96.PRODUCT DEVELOPMENT (2016-2019)
TABLE 97.KEY EXPANSIONS (2016-2019)
TABLE 98.COLLABORATION (2016-2019)
TABLE 99.AGREEMENT (2016-2019)
TABLE 100.ADOBE INC.: KEY EXECUTIVES
TABLE 101.ADOBE INC.: COMPANY SNAPSHOT
TABLE 102.ADOBE INC.: OPERATING SEGMENTS
TABLE 103.ADOBE INC.: PRODUCT PORTFOLIO
TABLE 104.CISCO SYSTEMS, INC.: KEY EXECUTIVES
TABLE 105.CISCO SYSTEMS, INC.: COMPANY SNAPSHOT
TABLE 106.CISCO SYSTEMS, INC.: PRODUCT PORTFOLIO
TABLE 107.INTERNATIONAL BUSINESS MACHINES CORPORATION: KEY EXECUTIVES
TABLE 108.INTERNATIONAL BUSINESS MACHINES CORPORATION: COMPANY SNAPSHOT
TABLE 109.INTERNATIONAL BUSINESS MACHINES CORPORATION: OPERATING SEGMENTS
TABLE 110.INTERNATIONAL BUSINESS MACHINES CORPORATION: PRODUCT PORTFOLIO
TABLE 111.ORACLE CORPORATION: KEY EXECUTIVES
TABLE 112.ORACLE CORPORATION: COMPANY SNAPSHOT
TABLE 113.ORACLE CORPORATION: OPERATING SEGMENTS
TABLE 114.ORACLE CORPORATION: PRODUCT PORTFOLIO
TABLE 115.ORACLE CORPORATION: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 116.SAP SE: KEY EXECUTIVES
TABLE 117.SAP SE: COMPANY SNAPSHOT
TABLE 118.SAP SE: OPERATING SEGMENTS
TABLE 119.SAP SE: PRODUCT PORTFOLIO
TABLE 120.SAS INSTITUTE INC.: KEY EXECUTIVES
TABLE 121.SAS INSTITUTE INC.: COMPANY SNAPSHOT
TABLE 122.SAS INSTITUTE INC.: PRODUCT PORTFOLIO
TABLE 123.SISENSE INC.: KEY EXECUTIVES
TABLE 124.SISENSE INC.: COMPANY SNAPSHOT
TABLE 125.SISENSE INC.: PRODUCT PORTFOLIO
TABLE 126.TERADATA CORPORATION: COMPANY SNAPSHOT
TABLE 127.TERADATA CORPORATION: PRODUCT PORTFOLIO
TABLE 128.TIBCO SOFTWARE INC.: KEY EXECUTIVES
TABLE 129.TIBCO SOFTWARE INC.: COMPANY SNAPSHOT
TABLE 130.TIBCO SOFTWARE INC.: PRODUCT PORTFOLIO
TABLE 131.TABLEAU SOFTWARE: KEY EXECUTIVES
TABLE 132.TABLEAU SOFTWARE: COMPANY SNAPSHOT
TABLE 133.TABLEAU SOFTWARE: PRODUCT PORTFOLIO

LIST OF FIGURES

FIGURE 01.KEY MARKET SEGMENTS
FIGURE 02.BIG DATA ANALYTICS IN RETAIL MARKET, 2019–2027
FIGURE 03.BIG DATA ANALYTICS IN RETAIL MARKET, BY REGION, 2019-2027
FIGURE 04.TOP IMPACTING FACTORS
FIGURE 05.TOP INVESTMENT POCKETS
FIGURE 06.MODERATE BARGAINING POWER OF SUPPLIERS
FIGURE 07.LOW-TO-MODERATE BARGAINING POWER OF BUYERS
FIGURE 08.LOW-TO-MODERATE THREAT OF SUBSTITUTES
FIGURE 09.MODERATE-TO-HIGH THREAT OF NEW ENTRANTS
FIGURE 10.LOW-TO-HIGH COMPETITIVE RIVALRY
FIGURE 11.BIG DATA ANALYTICS IN RETAIL ANLYTICS MARKET: KEY PLAYER POSITIONING
FIGURE 12.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY COMPONENT, 2019–2027($BILLION)
FIGURE 13.COMPARATIVE SHARE ANALYSIS OF BIG DATA ANALYTICS IN RETAIL MARKET FOR SOFTWARE, BY REGION,  2019 & 2027 (%)
FIGURE 14.COMPARATIVE SHARE ANALYSIS OF BIG DATA ANALYTICS IN RETAIL MARKET FOR SERVICE, BY REGION,  2019 & 2027 (%)
FIGURE 15.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY DEPLOYMENT, 2019–2027($BILLION)
FIGURE 16.COMPARATIVE SHARE ANALYSIS OF BIG DATA ANALYTICS IN RETAIL MARKET FOR ON PREMISE, BY REGION,  2019 & 2027 (%)
FIGURE 17.COMPARATIVE SHARE ANALYSIS OF BIG DATA ANALYTICS IN RETAIL MARKET FOR CLOUD, BY REGION, 2019 & 2027 (%)
FIGURE 18.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY ORGANIZATION SIZE, 2019–2027($BILLION)
FIGURE 19.COMPARATIVE SHARE ANALYSIS OF BIG DATA ANALYTICS IN RETAIL MARKET FOR LARGE ENTERPRISES, BY REGION,  2019 & 2027 (%)
FIGURE 20.COMPARATIVE SHARE ANALYSIS OF BIG DATA ANALYTICS IN RETAIL MARKET FOR SMES, BY REGION, 2019 & 2027 (%)
FIGURE 21.BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, BY APPLICATION, 2019–2027($BILLION)
FIGURE 22.COMPARATIVE SHARE ANALYSIS OF BIG DATA ANALYTICS IN RETAIL MARKET FOR SALES AND MARKETING ANALYTICS, BY REGION, 2019 & 2027 (%)
FIGURE 23.COMPARATIVE SHARE ANALYSIS OF BIG DATA ANALYTICS IN RETAIL MARKET FOR SUPPLY CHAIN OPERATIONS MANAGEMENT, BY REGION, 2019 & 2027 (%)
FIGURE 24.COMPARATIVE SHARE ANALYSIS OF BIG DATA ANALYTICS IN RETAIL MARKET FOR MERCHANDISING ANALYTICS, BY REGION, 2019 & 2027 (%)
FIGURE 25.COMPARATIVE SHARE ANALYSIS OF BIG DATA ANALYTICS IN RETAIL MARKET FOR CUSTOMER ANALYTICS, BY REGION, 2019 & 2027 (%)
FIGURE 26.COMPARATIVE SHARE ANALYSIS OF BIG DATA ANALYTICS IN RETAIL MARKET FOR OTHERS, BY REGION, 2019 & 2027 (%)
FIGURE 27.U.S. BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 28.CANADA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 29.UK BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 30.GERMANY BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 31.FRANCE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 32.REST OF EUROPE BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 33.CHINA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 34.INDIA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 35.JAPAN BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 36.AUSTRALIA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 37.REST OF ASIA-PACIFIC BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 38.LATIN AMERICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 39.MIDDLE EAST BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 40.AFRICA BIG DATA ANALYTICS IN RETAIL MARKET REVENUE, 2019-2027 ($MILLION)
FIGURE 41.COMPETITIVE DASHBOARD
FIGURE 42.COMPETITIVE DASHBOARD
FIGURE 43.COMPETITIVE HEATMAP OF KEY PLAYERS
FIGURE 44.TOP WINNING STRATEGIES, BY YEAR, 2016-2019
FIGURE 45.TOP WINNING STRATEGIES, BY DEVELOPMENT, 2016-2019
FIGURE 46.TOP WINNING STRATEGIES, BY COMPANY, 2016-2019
FIGURE 47.R&D EXPENDITURE, 2016–2018 ($MILLION)
FIGURE 48.ADOBE INC.: REVENUE, 2016–2018 ($MILLION)
FIGURE 49.ADOBE INC.: REVENUE SHARE BY SEGMENT, 2018 (%)
FIGURE 50.ADOBE INC.: REVENUE SHARE BY REGION, 2018 (%)
FIGURE 51.R&D EXPENDITURE, 2016–2018 ($MILLION)
FIGURE 52.CISCO SYSTEMS, INC.: REVENUE, 2016–2018 ($MILLION)
FIGURE 53.CISCO SYSTEMS, INC.: REVENUE SHARE BY REGION, 2018 (%)
FIGURE 54.R&D EXPENDITURE, 2016–2018 ($MILLION)
FIGURE 55.INTERNATIONAL BUSINESS MACHINES CORPORATION: REVENUE, 2016–2018 ($MILLION)
FIGURE 56.INTERNATIONAL BUSINESS MACHINES CORPORATION: REVENUE SHARE BY SEGMENT, 2018 (%)
FIGURE 57.INTERNATIONAL BUSINESS MACHINES CORPORATION: REVENUE SHARE BY REGION, 2018 (%)
FIGURE 58.R&D EXPENDITURE, 2016–2018 ($MILLION)
FIGURE 59.ORACLE CORPORATION: REVENUE, 2016–2018 ($MILLION)
FIGURE 60.ORACLE CORPORATION: REVENUE SHARE BY SEGMENT, 2018 (%)
FIGURE 61.ORACLE CORPORATION: REVENUE SHARE BY REGION, 2018 (%)
FIGURE 62.R&D EXPENDITURE, 2016–2018 ($MILLION)
FIGURE 63.SAP SE: REVENUE, 2016–2018 ($MILLION)
FIGURE 64.SAP SE: REVENUE SHARE BY SEGMENT, 2018 (%)
FIGURE 65.SAP SE: REVENUE SHARE BY REGION, 2018 (%)
FIGURE 66.SAS INSTITUTE INC.: REVENUE, 2016–2018 ($MILLION)
FIGURE 67.TERADATA CORPORATION: KEY EXECUTIVES
FIGURE 68.R&D EXPENDITURE, 2016–2018 ($MILLION)
FIGURE 69.TABLEAU SOFTWARE: REVENUE, 2016–2018 ($MILLION)
FIGURE 70.TABLEAU SOFTWARE: REVENUE SHARE BY REGION, 2018 (%)

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