Market Overview:
The global machine learning in retail market is expected to grow at a CAGR of 27.5% during the forecast period from 2018 to 2030. The growth of the market can be attributed to the increasing demand for machine learning in online and offline retail applications, and rising adoption of cloud-based solutions. North America is expected to hold the largest share of the global machine learning in retail market during the forecast period, followed by Europe and Asia Pacific.
Product Definition:
Machine learning is a subset of artificial intelligence that utilizes algorithms to enable computers to learn from data, without being explicitly programmed. In the retail industry, machine learning can be used for a variety of purposes, such as pricing optimization, fraud detection, and inventory management. By using machine learning algorithms, businesses can make predictions about customer behavior or trends in the market that would otherwise be difficult to discern. The importance of machine learning in retail lies in its ability to optimize business processes and improve decision-making.
Cloud Based:
Cloud-based solutions are gaining popularity in the retail industry owing to benefits such as flexibility, cost effectiveness and ease of use. Cloud-based services have been a boon for retailers that lack the infrastructure or budget to buy or maintain their own data center. The shift from on premise solutions to cloud based ones has been instrumental in driving market growth.
The advent of Artificial Intelligence (AI) and Machine Learning (ML) has transformed many aspects of modern life, including shopping patterns.
On-Premises:
On-premises is a term used for the software and hardware installation at the end-users site. The on-premises solutions are used to store data and perform analytics. It provides better control over systems as well as data privacy, which cannot be achieved in cloud computing.
Application Insights:
The online segment dominated the market in 2017 and is expected to continue its dominance over the forecast period. The growing use of ML techniques in various areas of e-commerce business including product, price, and promotion management is expected to drive the growth. Moreover, increasing penetration of smartphones has led to a significant increase in customer engagement through applications such as Facebook Messenger and Instagram which can be used for marketing products & services directly to customers thereby driving demand further.
Offline or prescriptive ML application witnessed considerable adoption owing to advantages such as real-time decision making with high accuracy rates that help eliminate human errors while offering precise data about consumer behavior patterns enabling businessesto develop more effective marketing campaigns thus driving growth across all regions.
Regional Analysis:
North America dominated the global market in 2017. The region is expected to maintain its position during the forecast period. This can be attributed to increasing investments in R&D and growing adoption of advanced technologies by companies operating in this region. Moreover, presence of prominent players such as IBM Corporation; Google LLC; Microsoft Corporation; and Amazon Web Services, Inc.; among others is also contributing towards growth of the regional market for machine learning in retail.
Asia Pacific is anticipated to witness significant growth over the forecast period owing to increasing adoption of cloud-based ML solutions by retailers operating in this region coupled with rising disposable income levels especially among millennials & Generation Z (born between 1995 & 2000). Furthermore, increased spending power enables consumers buy more expensive products which will contribute towards industry growth over next few years.
Growth Factors:
- Increasing demand for personalized recommendations and product suggestions
- Rising demand for omni-channel retailing
- Proliferation of big data and the increasing need to make sense of it all
- Advances in artificial intelligence and machine learning algorithms
- The growing popularity of experiential retail
Scope Of The Report
Report Attributes
Report Details
Report Title
Machine Learning in Retail Market Research Report
By Type
Cloud Based, On-Premises
By Application
Online, Offline
By Companies
IBM, Microsoft, Amazon Web Services, Oracle, SAP, Intel, NVIDIA, Google, Sentient Technologies, Salesforce, ViSenze
Regions Covered
North America, Europe, APAC, Latin America, MEA
Base Year
2021
Historical Year
2019 to 2020 (Data from 2010 can be provided as per availability)
Forecast Year
2030
Number of Pages
168
Number of Tables & Figures
118
Customization Available
Yes, the report can be customized as per your need.
Global Machine Learning in Retail Market Report Segments:
The global Machine Learning in Retail market is segmented on the basis of:
Types
Cloud Based, On-Premises
The product segment provides information about the market share of each product and the respective CAGR during the forecast period. It lays out information about the product pricing parameters, trends, and profits that provides in-depth insights of the market. Furthermore, it discusses latest product developments & innovation in the market.
Applications
Online, Offline
The application segment fragments various applications of the product and provides information on the market share and growth rate of each application segment. It discusses the potential future applications of the products and driving and restraining factors of each application segment.
Some of the companies that are profiled in this report are:
- IBM
- Microsoft
- Amazon Web Services
- Oracle
- SAP
- Intel
- NVIDIA
- Google
- Sentient Technologies
- Salesforce
- ViSenze
Highlights of The Machine Learning in Retail Market Report:
- The market structure and projections for the coming years.
- Drivers, restraints, opportunities, and current trends of market.
- Historical data and forecast.
- Estimations for the forecast period 2030.
- Developments and trends in the market.
- By Type:
- Cloud Based
- On-Premises
- By Application:
- Online
- Offline
- Market scenario by region, sub-region, and country.
- Market share of the market players, company profiles, product specifications, SWOT analysis, and competitive landscape.
- Analysis regarding upstream raw materials, downstream demand, and current market dynamics.
- Government Policies, Macro & Micro economic factors are also included in the report.
We have studied the Machine Learning in Retail Market in 360 degrees via. both primary & secondary research methodologies. This helped us in building an understanding of the current market dynamics, supply-demand gap, pricing trends, product preferences, consumer patterns & so on. The findings were further validated through primary research with industry experts & opinion leaders across countries. The data is further compiled & validated through various market estimation & data validation methodologies. Further, we also have our in-house data forecasting model to predict market growth up to 2030.
Regional Analysis
- North America
- Europe
- Asia Pacific
- Middle East & Africa
- Latin America
Note: A country of choice can be added in the report at no extra cost. If more than one country needs to be added, the research quote will vary accordingly.
The geographical analysis part of the report provides information about the product sales in terms of volume and revenue in regions. It lays out potential opportunities for the new entrants, emerging players, and major players in the region. The regional analysis is done after considering the socio-economic factors and government regulations of the countries in the regions.
How you may use our products:
- Correctly Positioning New Products
- Market Entry Strategies
- Business Expansion Strategies
- Consumer Insights
- Understanding Competition Scenario
- Product & Brand Management
- Channel & Customer Management
- Identifying Appropriate Advertising Appeals
8 Reasons to Buy This Report
- Includes a Chapter on the Impact of COVID-19 Pandemic On the Market
- Report Prepared After Conducting Interviews with Industry Experts & Top Designates of the Companies in the Market
- Implemented Robust Methodology to Prepare the Report
- Includes Graphs, Statistics, Flowcharts, and Infographics to Save Time
- Industry Growth Insights Provides 24/5 Assistance Regarding the Doubts in the Report
- Provides Information About the Top-winning Strategies Implemented by Industry Players.
- In-depth Insights On the Market Drivers, Restraints, Opportunities, and Threats
- Customization of the Report Available
Frequently Asked Questions?
Machine learning is a subset of artificial intelligence that allows computers to learn from data without being explicitly programmed. In retail, machine learning can be used to predict customer behavior and trends in order to improve the efficiency and effectiveness of marketing campaigns, product sales, and customer service.
Some of the major players in the machine learning in retail market are IBM, Microsoft, Amazon Web Services, Oracle, SAP, Intel, NVIDIA, Google, Sentient Technologies, Salesforce, ViSenze.
The machine learning in retail market is expected to register a CAGR of 27.5%.
Chapter 1 Executive Summary
Chapter 2 Assumptions and Acronyms Used
Chapter 3 Research Methodology
Chapter 4 Machine Learning in Retail Market Overview
4.1 Introduction
4.1.1 Market Taxonomy
4.1.2 Market Definition
4.1.3 Macro-Economic Factors Impacting the Market Growth
4.2 Machine Learning in Retail Market Dynamics
4.2.1 Market Drivers
4.2.2 Market Restraints
4.2.3 Market Opportunity
4.3 Machine Learning in Retail Market - Supply Chain Analysis
4.3.1 List of Key Suppliers
4.3.2 List of Key Distributors
4.3.3 List of Key Consumers
4.4 Key Forces Shaping the Machine Learning in Retail Market
4.4.1 Bargaining Power of Suppliers
4.4.2 Bargaining Power of Buyers
4.4.3 Threat of Substitution
4.4.4 Threat of New Entrants
4.4.5 Competitive Rivalry
4.5 Global Machine Learning in Retail Market Size & Forecast, 2018-2028
4.5.1 Machine Learning in Retail Market Size and Y-o-Y Growth
4.5.2 Machine Learning in Retail Market Absolute $ Opportunity
Chapter 5 Global Machine Learning in Retail Market Analysis and Forecast by Type
5.1 Introduction
5.1.1 Key Market Trends & Growth Opportunities by Type
5.1.2 Basis Point Share (BPS) Analysis by Type
5.1.3 Absolute $ Opportunity Assessment by Type
5.2 Machine Learning in Retail Market Size Forecast by Type
5.2.1 Cloud Based
5.2.2 On-Premises
5.3 Market Attractiveness Analysis by Type
Chapter 6 Global Machine Learning in Retail Market Analysis and Forecast by Applications
6.1 Introduction
6.1.1 Key Market Trends & Growth Opportunities by Applications
6.1.2 Basis Point Share (BPS) Analysis by Applications
6.1.3 Absolute $ Opportunity Assessment by Applications
6.2 Machine Learning in Retail Market Size Forecast by Applications
6.2.1 Online
6.2.2 Offline
6.3 Market Attractiveness Analysis by Applications
Chapter 7 Global Machine Learning in Retail Market Analysis and Forecast by Region
7.1 Introduction
7.1.1 Key Market Trends & Growth Opportunities by Region
7.1.2 Basis Point Share (BPS) Analysis by Region
7.1.3 Absolute $ Opportunity Assessment by Region
7.2 Machine Learning in Retail Market Size Forecast by Region
7.2.1 North America
7.2.2 Europe
7.2.3 Asia Pacific
7.2.4 Latin America
7.2.5 Middle East & Africa (MEA)
7.3 Market Attractiveness Analysis by Region
Chapter 8 Coronavirus Disease (COVID-19) Impact
8.1 Introduction
8.2 Current & Future Impact Analysis
8.3 Economic Impact Analysis
8.4 Government Policies
8.5 Investment Scenario
Chapter 9 North America Machine Learning in Retail Analysis and Forecast
9.1 Introduction
9.2 North America Machine Learning in Retail Market Size Forecast by Country
9.2.1 U.S.
9.2.2 Canada
9.3 Basis Point Share (BPS) Analysis by Country
9.4 Absolute $ Opportunity Assessment by Country
9.5 Market Attractiveness Analysis by Country
9.6 North America Machine Learning in Retail Market Size Forecast by Type
9.6.1 Cloud Based
9.6.2 On-Premises
9.7 Basis Point Share (BPS) Analysis by Type
9.8 Absolute $ Opportunity Assessment by Type
9.9 Market Attractiveness Analysis by Type
9.10 North America Machine Learning in Retail Market Size Forecast by Applications
9.10.1 Online
9.10.2 Offline
9.11 Basis Point Share (BPS) Analysis by Applications
9.12 Absolute $ Opportunity Assessment by Applications
9.13 Market Attractiveness Analysis by Applications
Chapter 10 Europe Machine Learning in Retail Analysis and Forecast
10.1 Introduction
10.2 Europe Machine Learning in Retail Market Size Forecast by Country
10.2.1 Germany
10.2.2 France
10.2.3 Italy
10.2.4 U.K.
10.2.5 Spain
10.2.6 Russia
10.2.7 Rest of Europe
10.3 Basis Point Share (BPS) Analysis by Country
10.4 Absolute $ Opportunity Assessment by Country
10.5 Market Attractiveness Analysis by Country
10.6 Europe Machine Learning in Retail Market Size Forecast by Type
10.6.1 Cloud Based
10.6.2 On-Premises
10.7 Basis Point Share (BPS) Analysis by Type
10.8 Absolute $ Opportunity Assessment by Type
10.9 Market Attractiveness Analysis by Type
10.10 Europe Machine Learning in Retail Market Size Forecast by Applications
10.10.1 Online
10.10.2 Offline
10.11 Basis Point Share (BPS) Analysis by Applications
10.12 Absolute $ Opportunity Assessment by Applications
10.13 Market Attractiveness Analysis by Applications
Chapter 11 Asia Pacific Machine Learning in Retail Analysis and Forecast
11.1 Introduction
11.2 Asia Pacific Machine Learning in Retail Market Size Forecast by Country
11.2.1 China
11.2.2 Japan
11.2.3 South Korea
11.2.4 India
11.2.5 Australia
11.2.6 South East Asia (SEA)
11.2.7 Rest of Asia Pacific (APAC)
11.3 Basis Point Share (BPS) Analysis by Country
11.4 Absolute $ Opportunity Assessment by Country
11.5 Market Attractiveness Analysis by Country
11.6 Asia Pacific Machine Learning in Retail Market Size Forecast by Type
11.6.1 Cloud Based
11.6.2 On-Premises
11.7 Basis Point Share (BPS) Analysis by Type
11.8 Absolute $ Opportunity Assessment by Type
11.9 Market Attractiveness Analysis by Type
11.10 Asia Pacific Machine Learning in Retail Market Size Forecast by Applications
11.10.1 Online
11.10.2 Offline
11.11 Basis Point Share (BPS) Analysis by Applications
11.12 Absolute $ Opportunity Assessment by Applications
11.13 Market Attractiveness Analysis by Applications
Chapter 12 Latin America Machine Learning in Retail Analysis and Forecast
12.1 Introduction
12.2 Latin America Machine Learning in Retail Market Size Forecast by Country
12.2.1 Brazil
12.2.2 Mexico
12.2.3 Rest of Latin America (LATAM)
12.3 Basis Point Share (BPS) Analysis by Country
12.4 Absolute $ Opportunity Assessment by Country
12.5 Market Attractiveness Analysis by Country
12.6 Latin America Machine Learning in Retail Market Size Forecast by Type
12.6.1 Cloud Based
12.6.2 On-Premises
12.7 Basis Point Share (BPS) Analysis by Type
12.8 Absolute $ Opportunity Assessment by Type
12.9 Market Attractiveness Analysis by Type
12.10 Latin America Machine Learning in Retail Market Size Forecast by Applications
12.10.1 Online
12.10.2 Offline
12.11 Basis Point Share (BPS) Analysis by Applications
12.12 Absolute $ Opportunity Assessment by Applications
12.13 Market Attractiveness Analysis by Applications
Chapter 13 Middle East & Africa (MEA) Machine Learning in Retail Analysis and Forecast
13.1 Introduction
13.2 Middle East & Africa (MEA) Machine Learning in Retail Market Size Forecast by Country
13.2.1 Saudi Arabia
13.2.2 South Africa
13.2.3 UAE
13.2.4 Rest of Middle East & Africa (MEA)
13.3 Basis Point Share (BPS) Analysis by Country
13.4 Absolute $ Opportunity Assessment by Country
13.5 Market Attractiveness Analysis by Country
13.6 Middle East & Africa (MEA) Machine Learning in Retail Market Size Forecast by Type
13.6.1 Cloud Based
13.6.2 On-Premises
13.7 Basis Point Share (BPS) Analysis by Type
13.8 Absolute $ Opportunity Assessment by Type
13.9 Market Attractiveness Analysis by Type
13.10 Middle East & Africa (MEA) Machine Learning in Retail Market Size Forecast by Applications
13.10.1 Online
13.10.2 Offline
13.11 Basis Point Share (BPS) Analysis by Applications
13.12 Absolute $ Opportunity Assessment by Applications
13.13 Market Attractiveness Analysis by Applications
Chapter 14 Competition Landscape
14.1 Machine Learning in Retail Market: Competitive Dashboard
14.2 Global Machine Learning in Retail Market: Market Share Analysis, 2019
14.3 Company Profiles (Details – Overview, Financials, Developments, Strategy)
14.3.1 IBM
14.3.2 Microsoft
14.3.3 Amazon Web Services
14.3.4 Oracle
14.3.5 SAP
14.3.6 Intel
14.3.7 NVIDIA
14.3.8 Google
14.3.9 Sentient Technologies
14.3.10 Salesforce
14.3.11 ViSenze
