Worldwide Retail Analytics Industry to 2025 - Players Include Microsoft, IBM and Oracle Among Others

DUBLIN, Dec. 11, 2020 /PRNewswire/ -- The "Retail Analytics Market by Application (Merchandising Analysis, Customer Analytics, and Promotional Analysis and Planning), Business Function (Finance and Operations), Component, Organization Size, End User, and Region - Global Forecast to 2025" report has been added to ResearchAndMarkets.com's offering.

The global retail analytics market size to grow from USD 4.3 billion in 2020 to USD 11.1 billion by 2025, at a CAGR of 21.2% during the forecast period.

Major factors contributing to the growth of the retail analytics market are the rising demand for dashboards for data visualization, growing adoption of cloud, and continuous increase in data generation. The objective of the report is to define, describe, and forecast the retail analytics market size based on component, business function, application, organization size, end-user, and region.

The COVID-19 has impacted all elements of the technology sector. It has slowed down the growth of IT infrastructure owing to disruptions in the hardware supply chain and reduced manufacturing activities. This health crisis has had an unprecedented impact on businesses across industries; while some are struggling, others are thriving. Rising support from governments and several data analytics companies can help in the fight against this highly contagious disease. Businesses providing retail analytics solutions and services are also expected to witness a decline in their growth for a short span of time. However, the adoption of collaborative applications, IoT, analytics, security solutions, and AI is set to increase in the remaining part of 2020.

The service segment to grow at a higher CAGR during the forecast period

The services segment is expected to grow at the highest CAGR during the forecast period. The growing concern of organizations to gain visibility for diagnosing and troubleshooting problems before they impact operations or end-user experiences will drive the adoption of retail analytics solutions and services.

The operation business function segment to grow at the highest CAGR during the forecast period

The operations segment is projected to grow at the highest CAGR during the forecast period. The growth can be attributed to the rising need of retailers to generate deeper insights across the entire value chain of retail operations, including procurement, supply chain, sales and marketing, store operations, and customer management.

The inventory analysis segment to grow at the highest CAGR during the forecast period

The retail analytics market is segmented into various applications, such as merchandising analysis, pricing analysis, customer analytics, promotional analysis and planning, yield analysis, inventory analysis, and others (order management, transportation management, assortment and cluster planning, and real-estate planning). The inventory analysis segment is projected to register a higher CAGR during the forecast period due to the growing need to enhance business processes by keeping track of stocked goods and ensure surplus inventory.

Among regions, Asia Pacific (APAC) to grow at the highest CAGR during the forecast period

APAC is expected to record the highest CAGR during the forecast period, as it is home to many developed and emerging economies, which offer major opportunities for the growth of retail stores and technology development. China, India, and Japan in particular are focusing on the management of data to enable data-based business decisions and enhance business processes in the retail market.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights
4.1 Attractive Opportunities in Retail Analytics Market
4.2 Market: Top Three Applications
4.3 Market, by Region
4.4 Market, by Business Function and Application

5 Market Overview and Industry Trends
5.1 Introduction
5.2 Retail Analytics: Evolution
5.3 Retail Analytics: Ecosystem
5.4 Market Dynamics
5.4.1 Drivers
5.4.1.1 Digitalizing to Enhance Customer Experience and Retail Operations
5.4.1.2 Rising Adoption of Disruptive Technologies to Forecast Future Market Trends
5.4.1.3 Growing Number of Smartphones, and Increasing Use of M-Commerce
5.4.2 Restraints
5.4.2.1 Lack of Technology Adoption by Unorganized Retail Sector
5.4.2.2 Legal Concerns and Data Privacy Issues
5.4.3 Opportunities
5.4.3.1 Growing Adoption of Cloud Services in Retail
5.4.3.2 Proliferation of Data Analytics to Understand Customer Data During COVID-19
5.4.4 Challenges
5.4.4.1 Retailers Face a Data Deficit in the Wake of the COVID-19 Pandemic
5.4.5 Cumulative Growth Analysis
5.5 Retail Analytics Market: COVID-19 Impact
5.6 Case Study Analysis
5.6.1 a Global Retail Chain Used Advanced Analytics & Machine Learning to Forecast New Store Locations and Revenues
5.6.2 a Fortune 500 Retailer Used Customer Genome to Deliver Personalized Interaction to Customers and Drive More Revenue
5.6.3 a Global Retailer Leveraged Manthan's Smart Analytics Solution to Take Data-Driven Business Decisions
5.6.4 The Retailer Used Bridgei2I to Deliver an Enhanced Customer Experience
5.6.5 The Client Adopted Sas Analytics to Understand Customer Needs
5.6.6 Groupo Merza Leveraged Sap Solutions for Market Basket Analysis
5.6.7 Peter England Adopted Capillary's Customer Acquisition Platform to Analyze Customer Footfall
5.6.8 a Global It Company Increased Customer Base and Improved Loyalty
5.6.9 a Home Improvement Retailer Enhanced Sales and Workforce Optimization
5.7 Patent Analysis
5.7.1 Patents Filed: Retail Analytics, by Application, 2019-2020
5.8 Value Chain Analysis
5.9 Technology Analysis
5.9.1 Voice Search
5.9.2 In-Store Digital Display
5.9.3 Social Shopping
5.9.4 Geo-Location Services
5.9.5 Visual Search
5.9.6 Smart Fitting Room
5.9.7 Ai and Ml in Retail
5.9.8 IoT in Retail
5.9.9 Big Data in Retail
5.1 Pricing Analysis
5.11 Retail Analytics, Key Performance Indicators (KPIs)

6 Retail Analytics Market, by Component
6.1 Introduction
6.1.1 Components: COVID-19 Impact
6.2 Solutions
6.2.1 Solutions: Market Drivers
6.3 Services
6.3.1 Services: Retail Analytics Market Drivers
6.3.2 Professional Services
6.3.2.1 Training and Support
6.3.2.2 Implementation and Consulting
6.3.3 Managed Services

7 Retail Analytics Market, by Business Function
7.1 Introduction
7.1.1 Business Functions: COVID-19 Impact
7.1.2 Business Functions: Market Drivers
7.2 Finance
7.3 Marketing and Sales
7.4 Human Resources
7.5 Operations

8 Retail Analytics Market, by Application
8.1 Introduction
8.1.1 Applications: COVID-19 Impact
8.1.2 Applications: Market Drivers
8.2 Merchandising Analysis
8.3 Pricing Analysis
8.4 Customer Analytics
8.5 Promotional Analysis and Planning
8.6 Yeild Analysis
8.7 Inventory Analysis
8.8 Others

9 Retail Analytics Market, by Organization Size
9.1 Introduction
9.1.1 Organization Size: COVID-19 Impact
9.1.2 Organization Size: Market Drivers
9.2 Large Enterprises
9.3 Small and Medium-Sized Enterprises

10 Retail Analytics Market, by End-user
10.1 Introduction
10.1.1 End-user: COVID-19 Impact
10.1.2 End-user: Market Drivers
10.2 Offline
10.3 Online

11 Retail Analytics Market, by Region
11.1 Introduction
11.2 North America
11.3 Europe
11.4 Asia-Pacific
11.5 Middle East & Africa
11.6 Latin America

12 Competitive Landscape
12.1 Overview
12.2 Market Evaluation Framework
12.3 Market Share, 2019
12.4 Historic Revenue Analysis of Key Market Players
12.5 Key Market Developments
12.5.1 New Product Launches and Product Enhancements
12.5.2 Business Expansions
12.5.3 Mergers and Acquisitions
12.5.4 Partnerships, Agreements, Contracts, and Collaborations
12.6 Company Evaluation Matrix, 2020
12.6.1 Star
12.6.2 Emerging Leader
12.6.3 Pervasive
12.6.4 Participant
12.7 Startup/SME Evaluation Matrix, 2020
12.7.1 Progressive Companies
12.7.2 Responsive Companies
12.7.3 Dynamic Companies
12.7.4 Starting Blocks
12.8 Market Ranking Analysis, by Company

13 Company Profiles
13.1 Introduction
13.2 Microsoft
13.3 IBM
13.4 Oracle
13.5 Salesforce
13.6 SAP
13.7 AWS
13.8 SAS Institute
13.9 Qlik
13.10 Manthan
13.11 Bridgei2I
13.12 Microstrategy
13.13 Teradata
13.14 HCL
13.15 Fujitsu
13.16 Domo
13.17 Google
13.18 Flir Systems
13.19 Information Builders
13.20 1010Data
13.21 Capillary
13.22 Retailnext
13.23 WNS
13.24 True Fit
13.25 Vend
13.26 Fit Analytics
13.27 Edited
13.28 Decision6
13.29 Cubelizer
13.30 Thinkinside
13.31 DOR Technologies
13.32 Glimpse Analytics
13.33 Pygmalios
13.34 Orenda Software Solutions

14 Appendix

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