Global Content Recommendation Engine Market Size study, by Component (Solution, Service), by Filtering Approach (Collaborative, Content-Based, Hybrid), by Vertical (E-Commerce, Media, Entertainment & Gaming, Retail, Hospitality, IT & Telecommunication, BFSI, Education & Training, Healthcare & Pharmaceutical, Others) and Regional Forecasts 2019-2026

  • Product Code:
    RP-ID-10085566
  • Published Date:
    17 Jul 2021
  • Region:
    Global
  • Pages:
    200
  • Category:
    ICT & Telecom
  • Publisher:
    Pub-ID-14

Impact Analysis on the Growth of Market

Activating Probable Propositions Amidst COVID-19 Crisis

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Market Overview:

Global Content Recommendation Engine Market valued approximately USD 1.58 billion in 2018 is anticipated to grow with a CAGR of 33.72% over the forecasted period of 2019-2026. The content recommendation engine is a software that analyzes available data to make suggestions for something that a website user might be interested in, such as a book, a video or a job, among other possibilities. Content Recommendation Engine has enabled the corporate world to work smarter, faster as well as doing more with significantly less. Increasing focus on enhancing customer experience, rapid digitalization, and need for analyzing large volumes of customer data are factors driving the market across the globe. It is essential to understand that Content Recommendation Engine includes several minor technologies like deep learning, machine learning, robotics, etc.

The regional analysis of Global Content Recommendation Engine Market is considered for the key regions such as Asia Pacific, North America, Europe, Latin America and Rest of the World. North America is the fastest growing region across the world in terms of market share. Whereas, owing to the countries such as China, Japan, and India, Asia Pacific region is anticipated to be the dominating region over the forecast period 2019-2026.

The leading market players mainly include-
IBM
Amazon Web Services
Revcontent
Taboola
Outbrain
Cxense
Dynamic Yield
Curata

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Component:

Solution
Service

By Filtering Approach:

Collaborative
Content-Based
Hybrid

By Vertical:

E-commerce
Media, Entertainment & Gaming
Retail
Hospitality
IT & Telecommunication
BFSI
Education & training
Healthcare & Pharmaceutical
Others

By Regions:
North America
U.S.
Canada
Europe
UK
Germany
Asia Pacific
China
India
Japan
Latin America
Brazil
Mexico
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2016, 2017
Base year – 2018
Forecast period – 2019 to 2026

Target Audience of the Global Content Recommendation Engine Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers
Investors


Geography Analysis:


The report further discusses the market opportunity, compound annual growth rate (CAGR) growth rate, competition, new technology innovations, market players analysis, government guidelines, export and import (EXIM) analysis, historical revenues, future forecasts etc. in the following regions and/or countries:

  • North America (U.S. & Canada) Market size, Y-O-Y growth, Market Players Analysis & Opportunity Outlook
  • Latin America (Brazil, Mexico, Argentina, Rest of Latin America) Market size, Y-O-Y growth & Market Players Analysis & Opportunity Outlook
  • Europe (U.K., Germany, France, Italy, Spain, Hungary, Belgium, Netherlands & Luxembourg, NORDIC(Finland, Sweden, Norway, Denmark), Ireland, Switzerland, Austria, Poland, Turkey, Russia, Rest of Europe), Poland, Turkey, Russia, Rest of Europe) Market size, Y-O-Y growth Market Players Analys  & Opportunity Outlook
  • Asia-Pacific (China, India, Japan, South Korea, Singapore, Indonesia, Malaysia, Australia, New Zealand, Rest of Asia-Pacific) Market size, Y-O-Y growth & Market Players Analysis & Opportunity Outlook
  • Middle East and Africa  (Israel, GCC (Saudi Arabia, UAE, Bahrain, Kuwait, Qatar, Oman), North Africa, South Africa, Rest of Middle East and Africa) Market size, Y-O-Y growth Market Players Analysis & Opportunity Outlook

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FREQUENTLY ASKED QUESTIONS

TABLE OF CONTENTS

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Key Trends
1.3. Global & Segmental Market Estimates & Forecasts, 2016-2026 (USD Billion)
1.3.1. Content Recommendation Engine Market, by Component, 2016-2026 (USD Billion)
1.3.2. Content Recommendation Engine Market, by Filtering Approach, 2016-2026 (USD Billion)
1.3.3. Content Recommendation Engine Market, by Vertical, 2016-2026 (USD Billion)
1.3.4. Content Recommendation Engine Market, by Region, 2016-2026 (USD Billion)
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Content Recommendation Engine Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Content Recommendation Engine Market Dynamics
3.1. See Saw Analysis
3.1.1. Market Drivers
3.1.2. Market Challenges
3.1.3. Market Opportunities
Chapter 4. Global Content Recommendation Engine Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Buyers
4.1.2. Bargaining Power of Suppliers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model
4.2. PEST Analysis
4.2.1. Political Scenario
4.2.2. Economic Scenario
4.2.3. Social Scenario
4.2.4. Technological Scenario
4.3. Key Buying Criteria
4.4. Regulatory Framework
4.5. Investment Vs Adoption Scenario
4.6. Analyst Recommendation & Conclusion
Chapter 5. Global Content Recommendation Engine Market, by Component
5.1. Market Snapshot
5.2. Market Performance - Potential Model
5.3. Content Recommendation Engine Market, Sub Segment Analysis
5.3.1. Solution
5.3.1.1. Market estimates & forecasts, 2016-2026 (USD Billion)
5.3.1.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
5.3.2. Service
5.3.2.1. Market estimates & forecasts, 2016-2026 (USD Billion)
5.3.2.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
Chapter 6. Global Content Recommendation Engine Market, by Filtering Approach
6.1. Market Snapshot
6.2. Market Performance - Potential Model
6.3. Content Recommendation Engine Market, Sub Segment Analysis
6.3.1. Collaborative
6.3.1.1. Market estimates & forecasts, 2016-2026 (USD Billion)
6.3.1.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
6.3.2. Content-Based
6.3.2.1. Market estimates & forecasts, 2016-2026 (USD Billion)
6.3.2.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
6.3.3. Hybrid
6.3.3.1. Market estimates & forecasts, 2016-2026 (USD Billion)
6.3.3.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
Chapter 7. Global Content Recommendation Engine Market, by Vertical
7.1. Market Snapshot
7.2. Market Performance - Potential Model
7.3. Content Recommendation Engine Market, Sub Segment Analysis
7.3.1. E-Commerce
7.3.1.1. Market estimates & forecasts, 2016-2026 (USD Billion)
7.3.1.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
7.3.2. Media, Entertainment & Gaming
7.3.2.1. Market estimates & forecasts, 2016-2026 (USD Billion)
7.3.2.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
7.3.3. Retail
7.3.3.1. Market estimates & forecasts, 2016-2026 (USD Billion)
7.3.3.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
7.3.4. Hospitality
7.3.4.1. Market estimates & forecasts, 2016-2026 (USD Billion)
7.3.4.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
7.3.5. IT & Telecommunication
7.3.5.1. Market estimates & forecasts, 2016-2026 (USD Billion)
7.3.5.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
7.3.6. BFSI
7.3.6.1. Market estimates & forecasts, 2016-2026 (USD Billion)
7.3.6.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
7.3.7. Education & Training
7.3.7.1. Market estimates & forecasts, 2016-2026 (USD Billion)
7.3.7.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
7.3.8. Healthcare & Pharmaceutical
7.3.8.1. Market estimates & forecasts, 2016-2026 (USD Billion)
7.3.8.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
7.3.9. Others
7.3.9.1. Market estimates & forecasts, 2016-2026 (USD Billion)
7.3.9.2. Regional breakdown estimates & forecasts, 2016-2026 (USD Billion)
Chapter 8. Global Content Recommendation Engine Market, by Regional Analysis
8.1. Content Recommendation Engine Market, Regional Market Snapshot (2016-2026)
8.2. North America Content Recommendation Engine Market Snapshot
8.2.1. U.S.
8.2.1.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.2.1.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.2.1.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.2.1.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.2.2. Canada
8.2.2.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.2.2.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.2.2.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.2.2.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.3. Europe Content Recommendation Engine Market Snapshot
8.3.1. U.K.
8.3.1.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.3.1.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.3.1.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.3.1.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.3.2. Germany
8.3.2.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.3.2.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.3.2.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.3.2.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.3.3. Rest of Europe
8.3.3.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.3.3.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.3.3.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.3.3.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4. Asia Content Recommendation Engine Market Snapshot
8.4.1. China
8.4.1.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.4.1.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4.1.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4.1.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4.2. India
8.4.2.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.4.2.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4.2.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4.2.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4.3. Japan
8.4.3.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.4.3.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4.3.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4.3.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4.4. Rest of Asia Pacific
8.4.4.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.4.4.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4.4.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.4.4.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.5. Latin America Content Recommendation Engine Market Snapshot
8.5.1. Brazil
8.5.1.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.5.1.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.5.1.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.5.1.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.5.2. Mexico
8.5.2.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.5.2.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.5.2.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.5.2.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.6. Rest of The World
8.6.1. South America
8.6.1.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.6.1.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.6.1.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.6.1.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.6.2. Middle East and Africa
8.6.2.1. Market estimates & forecasts, 2016-2026 (USD Billion)
8.6.2.2. Component breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.6.2.3. Filtering Approach breakdown estimates & forecasts, 2016-2026 (USD Billion)
8.6.2.4. Vertical breakdown estimates & forecasts, 2016-2026 (USD Billion)
Chapter 9. Competitive Intelligence
9.1. Company Market Share (Subject to Data Availability)
9.2. Top Market Strategies
9.3. Company Profiles
9.3.1. IBM
9.3.1.1. Overview
9.3.1.2. Financial (Subject to Data Availability)
9.3.1.3. Product Summary
9.3.1.4. Recent Developments
9.3.2. Amazon Web Services
9.3.3. Revcontent
9.3.4. Taboola
9.3.5. Outbrain
9.3.6. Cxense
9.3.7. Dynamic Yield
9.3.8. Curata
Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.1.6. Research Assumption
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IBM
Amazon Web Services
Revcontent
Taboola
Outbrain
Cxense
Dynamic Yield
Curata
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