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Highlights The global Content Recommendation Engines market is projected to reach US$ million by 2029 from an estimated US$ million in 2023, at a CAGR of % during 2024 and 2029. The top two companies in Content Recommendation Engines Global Market are Taboola and Outbrain with over 50% in total. Comparing by regions, North America and Europe take a huge proportion of over 80% of the global market. Report Scope This report aims to provide a comprehensive presentation of the global market for Content Recommendation Engines, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Content Recommendation Engines. The Content Recommendation Engines market size, estimations, and forecasts are provided in terms of and revenue ($ millions), considering 2022 as the base year, with history and forecast data for the period from 2018 to 2029. This report segments the global Content Recommendation Engines market comprehensively. Regional market sizes, concerning products by types, by application, and by players, are also provided. The influence of COVID-19 and the Russia-Ukraine War were considered while estimating market sizes. For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments. The report will help the Content Recommendation Engines companies, new entrants, and industry chain related companies in this market with information on the revenues for the overall market and the sub-segments across the different segments, by company, product type, application, and regions. Key Companies & Market Share Insights In this section, the readers will gain an understanding of the key players competing. This report has studied the key growth strategies, such as innovative trends and developments, intensification of product portfolio, mergers and acquisitions, collaborations, new product innovation, and geographical expansion, undertaken by these participants to maintain their presence. Apart from business strategies, the study includes current developments and key financials. The readers will also get access to the data related to global revenue by companies for the period 2017-2022. This all-inclusive report will certainly serve the clients to stay updated and make effective decisions in their businesses. Some of the prominent players reviewed in the research report include: Taboola Outbrain Dynamic Yield (McDonald) Amazon Web Services Adobe Kibo Commerce Optimizely Salesforce (Evergage) Zeta Global Emarsys (SAP) Algonomy ThinkAnalytics Alibaba Cloud Tencent. Baidu Byte Dance Product Type Insights Global markets are presented by Content Recommendation Engines type, along with growth forecasts through 2029. Estimates on revenue are based on the price in the supply chain at which the Content Recommendation Engines are procured by the companies. This report has studied every segment and provided the market size using historical data. They have also talked about the growth opportunities that the segment may pose in the future. This study bestows revenue data by type, and during the historical period (2018-2023) and forecast period (2024-2029). Content Recommendation Engines segment by Deployment Mode Local Deployment Cloud Deployment Application Insights This report has provided the market size (revenue data) by application, during the historical period (2018-2023) and forecast period (2024-2029). This report also outlines the market trends of each segment and consumer behaviors impacting the Content Recommendation Engines market and what implications these may have on the industry's future. This report can help to understand the relevant market and consumer trends that are driving the Content Recommendation Engines market. Content Recommendation Engines Segment by Application News and Media Entertainment and Games E-commerce Finance others Regional Outlook This section of the report provides key insights regarding various regions and the key players operating in each region. Economic, social, environmental, technological, and political factors have been taken into consideration while assessing the growth of the particular region/country. The readers will also get their hands on the revenue data of each region and country for the period 2018-2029. The market has been segmented into various major geographies, including North America, Europe, Asia-Pacific, South America, Middle East & Africa. Detailed analysis of major countries such as the USA, Germany, the U.K., Italy, France, China, Japan, South Korea, Southeast Asia, and India will be covered within the regional segment. For market estimates, data are going to be provided for 2022 because of the base year, with estimates for 2023 and forecast revenue for 2029. North America United States Canada Europe Germany France UK Italy Russia Nordic Countries Rest of Europe Asia-Pacific China Japan South Korea Southeast Asia India Australia Rest of Asia Latin America Mexico Brazil Rest of Latin America Middle East & Africa Turkey Saudi Arabia UAE Rest of MEA Key Drivers & Barriers High-impact rendering factors and drivers have been studied in this report to aid the readers to understand the general development. Moreover, the report includes restraints and challenges that may act as stumbling blocks on the way of the players. This will assist the users to be attentive and make informed decisions related to business. Specialists have also laid their focus on the upcoming business prospects. COVID-19 and Russia-Ukraine War Influence Analysis The readers in the section will understand how the Content Recommendation Engines market scenario changed across the globe during the pandemic, post-pandemic and Russia-Ukraine War. The study is done keeping in view the changes in aspects such as demand, consumption, transportation, consumer behavior, supply chain management. The industry experts have also highlighted the key factors that will help create opportunities for players and stabilize the overall industry in the years to come. Reasons to Buy This Report This report will help the readers to understand the competition within the industries and strategies for the competitive environment to enhance the potential profit. The report also focuses on the competitive landscape of the global Content Recommendation Engines market, and introduces in detail the market share, industry ranking, competitor ecosystem, market performance, new product development, operation situation, expansion, and acquisition. etc. of the main players, which helps the readers to identify the main competitors and deeply understand the competition pattern of the market. This report will help stakeholders to understand the global industry status and trends of Content Recommendation Engines and provides them with information on key market drivers, restraints, challenges, and opportunities. This report will help stakeholders to understand competitors better and gain more insights to strengthen their position in their businesses. The competitive landscape section includes the market share and rank (in volume and value), competitor ecosystem, new product development, expansion, and acquisition. This report stays updated with novel technology integration, features, and the latest developments in the market This report helps stakeholders to understand the COVID-19 and Russia-Ukraine War Influence on the Content Recommendation Engines industry. This report helps stakeholders to gain insights into which regions to target globally This report helps stakeholders to gain insights into the end-user perception concerning the adoption of Content Recommendation Engines. This report helps stakeholders to identify some of the key players in the market and understand their valuable contribution. Core Chapters Chapter 1: Research objectives, research methods, data sources, data cross-validation; Chapter 2: Introduces the report scope of the report, executive summary of different market segments (product type, application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term. Chapter 3: Provides the analysis of various market segments product types, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments. Chapter 4: Provides the analysis of various market segments application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets. Chapter 5: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry. Chapter 6: Detailed analysis of Content Recommendation Engines companies’ competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc. Chapter 7, 8, 9, 10, 11: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world. Chapter 12: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc. Chapter 13: The main points and conclusions of the report. Frequently Asked Questions What factors will challenge the Product Name market growth? Which end-use segment will expand at the fastest CAGR in the Product Name market? Which are the emerging players in the Product Name market? How concentrated is the Product Name market? Which factors are positively contributing to the Product Name market growth? Which are the novel product innovations in the Product Name market? Which product segment will emerge as the most lucrative in the Product Name market? Which factors are increasing the competition in the Product Name market? Which are the strategic measures taken by the Product Name industry players? Which region will witness inactive growth during the forecast period? What key trends are likely to emerge in the Product Name market in the coming years?
1 Preface 1.1 Scope of Report 1.2 Reasons for Doing This Study 1.3 Research Methodology 1.4 Research Process 1.5 Data Source 1.5.1 Secondary Sources 1.5.2 Primary Sources 2 Market Overview 2.1 Product Definition 2.2 Content Recommendation Engines by Deployment Mode 2.2.1 Market Value Comparison by Deployment Mode (2018 VS 2022 VS 2029) 1.2.2 Local Deployment 1.2.3 Cloud Deployment 2.3 Content Recommendation Engines by Application 2.3.1 Market Value Comparison by Application (2018 VS 2022 VS 2029) 2.3.2 News and Media 2.3.3 Entertainment and Games 2.3.4 E-commerce 2.3.5 Finance 2.3.6 others 2.4 Assumptions and Limitations 3 Content Recommendation Engines Breakdown Data by Deployment Mode 3.1 Global Content Recommendation Engines Historic Market Size by Deployment Mode (2018-2023) 3.2 Global Content Recommendation Engines Forecasted Market Size by Deployment Mode (2023-2028) 4 Content Recommendation Engines Breakdown Data by Application 4.1 Global Content Recommendation Engines Historic Market Size by Application (2018-2023) 4.2 Global Content Recommendation Engines Forecasted Market Size by Application (2018-2023) 5 Global Growth Trends 5.1 Global Content Recommendation Engines Market Perspective (2018-2029) 5.2 Global Content Recommendation Engines Growth Trends by Region 5.2.1 Global Content Recommendation Engines Market Size by Region: 2018 VS 2022 VS 2029 5.2.2 Content Recommendation Engines Historic Market Size by Region (2018-2023) 5.2.3 Content Recommendation Engines Forecasted Market Size by Region (2024-2029) 5.3 Content Recommendation Engines Market Dynamics 5.3.1 Content Recommendation Engines Industry Trends 5.3.2 Content Recommendation Engines Market Drivers 5.3.3 Content Recommendation Engines Market Challenges 5.3.4 Content Recommendation Engines Market Restraints 6 Market Competitive Landscape by Players 6.1 Global Top Content Recommendation Engines Players by Revenue 6.1.1 Global Top Content Recommendation Engines Players by Revenue (2018-2023) 6.1.2 Global Content Recommendation Engines Revenue Market Share by Players (2018-2023) 6.2 Global Content Recommendation Engines Industry Players Ranking, 2021 VS 2022 VS 2023 6.3 Global Key Players of Content Recommendation Engines Head office and Area Served 6.4 Global Content Recommendation Engines Players, Product Type & Application 6.5 Global Content Recommendation Engines Players, Date of Enter into This Industry 6.6 Global Content Recommendation Engines Market CR5 and HHI 6.7 Global Players Mergers & Acquisition 7 North America 7.1 North America Content Recommendation Engines Market Size (2018-2029) 7.2 North America Content Recommendation Engines Market Growth Rate by Country: 2018 VS 2022 VS 2029 7.3 North America Content Recommendation Engines Market Size by Country (2018-2023) 7.4 North America Content Recommendation Engines Market Size by Country (2024-2029) 7.5 United States 7.6 Canada 8 Europe 8.1 Europe Content Recommendation Engines Market Size (2018-2029) 8.2 Europe Content Recommendation Engines Market Growth Rate by Country: 2018 VS 2022 VS 2029 8.3 Europe Content Recommendation Engines Market Size by Country (2018-2023) 8.4 Europe Content Recommendation Engines Market Size by Country (2024-2029) 7.4 Germany 7.5 France 7.6 U.K. 7.7 Italy 7.8 Russia 7.9 Nordic Countries 9 Asia-Pacific 9.1 Asia-Pacific Content Recommendation Engines Market Size (2018-2029) 9.2 Asia-Pacific Content Recommendation Engines Market Growth Rate by Country: 2018 VS 2022 VS 2029 9.3 Asia-Pacific Content Recommendation Engines Market Size by Country (2018-2023) 9.4 Asia-Pacific Content Recommendation Engines Market Size by Country (2024-2029) 8.4 China 8.5 Japan 8.6 South Korea 8.7 Southeast Asia 8.8 India 8.9 Australia 10 Latin America 10.1 Latin America Content Recommendation Engines Market Size (2018-2029) 10.2 Latin America Content Recommendation Engines Market Growth Rate by Country: 2018 VS 2022 VS 2029 10.3 Latin America Content Recommendation Engines Market Size by Country (2018-2023) 10.4 Latin America Content Recommendation Engines Market Size by Country (2024-2029) 9.4 Mexico 9.5 Brazil 11 Middle East & Africa 11.1 Middle East & Africa Content Recommendation Engines Market Size (2018-2029) 11.2 Middle East & Africa Content Recommendation Engines Market Growth Rate by Country: 2018 VS 2022 VS 2029 11.3 Middle East & Africa Content Recommendation Engines Market Size by Country (2018-2023) 11.4 Middle East & Africa Content Recommendation Engines Market Size by Country (2024-2029) 10.4 Turkey 10.5 Saudi Arabia 10.6 UAE 12 Players Profiled 11.1 Taboola 11.1.1 Taboola Company Detail 11.1.2 Taboola Business Overview 11.1.3 Taboola Content Recommendation Engines Introduction 11.1.4 Taboola Revenue in Content Recommendation Engines Business (2017-2022) 11.1.5 Taboola Recent Development 11.2 Outbrain 11.2.1 Outbrain Company Detail 11.2.2 Outbrain Business Overview 11.2.3 Outbrain Content Recommendation Engines Introduction 11.2.4 Outbrain Revenue in Content Recommendation Engines Business (2017-2022) 11.2.5 Outbrain Recent Development 11.3 Dynamic Yield (McDonald) 11.3.1 Dynamic Yield (McDonald) Company Detail 11.3.2 Dynamic Yield (McDonald) Business Overview 11.3.3 Dynamic Yield (McDonald) Content Recommendation Engines Introduction 11.3.4 Dynamic Yield (McDonald) Revenue in Content Recommendation Engines Business (2017-2022) 11.3.5 Dynamic Yield (McDonald) Recent Development 11.4 Amazon Web Services 11.4.1 Amazon Web Services Company Detail 11.4.2 Amazon Web Services Business Overview 11.4.3 Amazon Web Services Content Recommendation Engines Introduction 11.4.4 Amazon Web Services Revenue in Content Recommendation Engines Business (2017-2022) 11.4.5 Amazon Web Services Recent Development 11.5 Adobe 11.5.1 Adobe Company Detail 11.5.2 Adobe Business Overview 11.5.3 Adobe Content Recommendation Engines Introduction 11.5.4 Adobe Revenue in Content Recommendation Engines Business (2017-2022) 11.5.5 Adobe Recent Development 11.6 Kibo Commerce 11.6.1 Kibo Commerce Company Detail 11.6.2 Kibo Commerce Business Overview 11.6.3 Kibo Commerce Content Recommendation Engines Introduction 11.6.4 Kibo Commerce Revenue in Content Recommendation Engines Business (2017-2022) 11.6.5 Kibo Commerce Recent Development 11.7 Optimizely 11.7.1 Optimizely Company Detail 11.7.2 Optimizely Business Overview 11.7.3 Optimizely Content Recommendation Engines Introduction 11.7.4 Optimizely Revenue in Content Recommendation Engines Business (2017-2022) 11.7.5 Optimizely Recent Development 11.8 Salesforce (Evergage) 11.8.1 Salesforce (Evergage) Company Detail 11.8.2 Salesforce (Evergage) Business Overview 11.8.3 Salesforce (Evergage) Content Recommendation Engines Introduction 11.8.4 Salesforce (Evergage) Revenue in Content Recommendation Engines Business (2017-2022) 11.8.5 Salesforce (Evergage) Recent Development 11.9 Zeta Global 11.9.1 Zeta Global Company Detail 11.9.2 Zeta Global Business Overview 11.9.3 Zeta Global Content Recommendation Engines Introduction 11.9.4 Zeta Global Revenue in Content Recommendation Engines Business (2017-2022) 11.9.5 Zeta Global Recent Development 11.10 Emarsys (SAP) 11.10.1 Emarsys (SAP) Company Detail 11.10.2 Emarsys (SAP) Business Overview 11.10.3 Emarsys (SAP) Content Recommendation Engines Introduction 11.10.4 Emarsys (SAP) Revenue in Content Recommendation Engines Business (2017-2022) 11.10.5 Emarsys (SAP) Recent Development 11.11 Algonomy 11.11.1 Algonomy Company Detail 11.11.2 Algonomy Business Overview 11.11.3 Algonomy Content Recommendation Engines Introduction 11.11.4 Algonomy Revenue in Content Recommendation Engines Business (2017-2022) 11.11.5 Algonomy Recent Development 11.12 ThinkAnalytics 11.12.1 ThinkAnalytics Company Detail 11.12.2 ThinkAnalytics Business Overview 11.12.3 ThinkAnalytics Content Recommendation Engines Introduction 11.12.4 ThinkAnalytics Revenue in Content Recommendation Engines Business (2017-2022) 11.12.5 ThinkAnalytics Recent Development 11.13 Alibaba Cloud 11.13.1 Alibaba Cloud Company Detail 11.13.2 Alibaba Cloud Business Overview 11.13.3 Alibaba Cloud Content Recommendation Engines Introduction 11.13.4 Alibaba Cloud Revenue in Content Recommendation Engines Business (2017-2022) 11.13.5 Alibaba Cloud Recent Development 11.14 Tencent. 11.14.1 Tencent. Company Detail 11.14.2 Tencent. Business Overview 11.14.3 Tencent. Content Recommendation Engines Introduction 11.14.4 Tencent. Revenue in Content Recommendation Engines Business (2017-2022) 11.14.5 Tencent. Recent Development 11.15 Baidu 11.15.1 Baidu Company Detail 11.15.2 Baidu Business Overview 11.15.3 Baidu Content Recommendation Engines Introduction 11.15.4 Baidu Revenue in Content Recommendation Engines Business (2017-2022) 11.15.5 Baidu Recent Development 11.16 Byte Dance 11.16.1 Byte Dance Company Detail 11.16.2 Byte Dance Business Overview 11.16.3 Byte Dance Content Recommendation Engines Introduction 11.16.4 Byte Dance Revenue in Content Recommendation Engines Business (2017-2022) 11.16.5 Byte Dance Recent Development 13 Report Conclusion 14 Disclaimer