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Big Data Analytics in Retail Market by Component (Software and Services), Deployment (On-premise and Cloud), Enterprise Size (Large Enterprises and Small & Medium-Sized Enterprises), and Application (Sales & Marketing Analytics, Supply Chain Operations Management, Merchandising Analytics, Customer Analytics, and Others): Global Opportunity Analysis and Industry Forecast, 2020–2027

A02451
Pages: 274
May 2021 | 6332 Views
   
Author(s) : Abhijith Nair
Tables: 133
Charts: 70
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COVID-19

Pandemic disrupted the entire world and affected many industries.

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Big Data Analytics in Retail Market Insights - 2027

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. Big data analytics in retail enables detecting customer behavior, discovering customer shopping patterns and trends, improving quality of customer service, and achieving better customer retention and satisfaction. It can be used by retailers for customer segmentation, customer loyalty analysis, pricing analysis, cross selling, supply chain management, demand forecasting, market basket analysis, and finance and fixed asset management.

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.

In accordance with several interviews that were conducted of top level CXOs, adoption of big data analytics in retail software has increased over time to propel decision-making capability of organizations and to improve business insights of retail companies. In addition, ability of big data analytics in retail software to provide different opportunities for business and gain new insights to run business efficiently is increasing its popularity among end users.

Big-Data-in-Retail-Market,-2021-2028

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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.

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.

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.

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.

Top Impacting Factors

Increase in spending on big data analytics tools, rise in need to deliver personalized customer experience to increase sales, increase in growth of e-commerce sector, growth in demand for predictive analytics in retail, and integration of new technologies such as IoT, AI, and machine learning in big data analytics in retail are the major factors that propel the market growth. However, issues in collecting and collating data from disparate systems are expected to hamper the market growth during the forecast period.

Big Data Analytics in Retail Market
By Component

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Service segment is projected as one of the most lucrative segments.

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Growth of E-Commerce Sector 

With big data analytics in retail software, retailers can improve performance of their online stores to generate more revenue. Utilizing website analytics, clickstream data and heatmaps studies, retailers can optimize product landing pages to ensure better engagement and conversion rates. Personalized product recommendations and offers based on historic web footprints of customers increase chances of click throughs and sales. Items can be promoted by inspecting data points such as product browsing activity by region, user feedback and reviews, saved wish lists, or items in abandoned shopping carts. Further, presently, customers are more connected than ever before, owing to proliferation of smartphones. Thus, customers can access any information to consumer products using channels such as mobile, social media, and e-commerce sites. Thus, to understand buying decisions of customers, companies are utilizing customer journey analytics. This, in turn, drives growth of the big data analytics in retail market.

Increase in spending on big data analytics tools

Retailers across the globe 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, and product innovation. As per a survey of 100 U.S. retailers published by Microsoft, 33% retailers invested in analytics capabilities in 2017, and 29% respondents were planning to invest in big data analytics in 2018. Mostly these retailers were seeking to utilize these tools for forecasting, personalization, marketing, price optimization, and merchandizing. As per a study conducted by Forbes in 2018, customer analytics, operational analytics, and fraud & compliance are some of the top use cases for big data in the 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 focused on adopting big data analytics software and embedding it in existing workflows of organizations.

Big Data Analytics in Retail Market
By Region

2028
North America 
Europe
Asia-pacific
Lamea

Asia-Pacific is projected as one of the most significant region.

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Key Benefits For Stakeholders

  • The study provides an in-depth analysis of the big data analytics in retail market along with current trends and future estimations to elucidate imminent investment pockets.
  • Information about key drivers, restrains, and opportunities and their impact analysis on the market size is provided in the report.
  • Porter’s five forces analysis illustrates the potency of buyers and suppliers operating in the industry.
  • The quantitative analysis of big data in retail storage market for the period 2020-2028 is provided to determine the market potential.

Key Market Segments

By Component    

  • Software
  • Services

By Deployment Type

  • On-Premise
  • Cloud

By Organization Size

  • Large Enterprise
  • Small & Medium Enterprise

By Applications

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

By Region

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

Key Market Players

  • Alteryx Inc
  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • Qlik Technologies Inc.,
  • RetailNext
  • SAP SE
  • SAS Institute
  • Teradata
 

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.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

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 (%)

 
 

According to CXOs of major companies, the big data in retail market is experiencing a rapid growth, as retailers across the globe 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, and product innovation. As per a survey of 100 U.S. retailers published by Microsoft, 33% retailers invested in analytics capabilities in 2018 and 29% respondents were planning to invest in big data analytics in 2019. Mostly these retailers were seeking to utilize these tools for forecasting, personalization, marketing, price optimization, and merchandizing. As per a study conducted by Forbes in 2017, customer analytics, operational analytics, and fraud & compliance are some of the top use cases for big data in the retail industry. It has emerged as 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 focused on adopting big data analytics software and embedding it in existing workflows of organizations.

According to CXOs of leading companies, the big data in retail market is experiencing a colossal shift. With big data analytics in retail software, retailers can improve performance of their online stores to generate more revenue. In addition, by utilizing website analytics, clickstream data, and heat map studies, retailers can optimize product landing pages to ensure better engagement and conversion rates. Personalized product recommendations and offers based on historic web footprints of customers increase chances of click through and sales. Items can be promoted by inspecting data points such as product browsing activity by region, user feedback and reviews, saved wish lists, or items in abandoned shopping carts. Further, presently, customers are more connected than ever before, owing to proliferation of smartphones. Thus, customers can access any information to consumer products using channels such as mobile, social media, and e-commerce sites. Thus, to understand buying decisions of customers, companies are utilizing customer journey analytics. This, in turn, drives growth of the big data analytics in retail market.

The big data in retail market is competitive and comprises a number of regional and global vendors competing based on factors such as cost of solutions & services, reliability, efficiency of products, and support services. The market is concentrated with major players consuming 30–45% of the share. The degree of concentration is expected to remain same during the forecast period. Furthermore, as IoT becomes more significant, increasing number retailers are starting to equip their stores with sensors that can sense when a nearby consumer has the store’s app installed on their smart device. From this, retailers are able to send timely offers to influence a shopper’s decision to purchase their products or introduce customers to their new products. Thus, owing to such advantages of big data analytics in retail software, the market is expected to grow during the forecast period.

 

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