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시장보고서
상품코드
1862514
세계의 컨텐츠 추천 엔진 - 시장 점유율과 순위, 전체 판매량 및 수요 예측(2025-2031년)Content Recommendation Engines - Global Market Share and Ranking, Overall Sales and Demand Forecast 2025-2031 |
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QYResearch
컨텐츠 추천 엔진 시장 규모는 2024년에 104억 700만 달러로 평가되었고, 2025-2031년의 예측 기간에 CAGR 31.2%로 성장하여 2031년까지 663억 4,000만 달러에 달할 것으로 예측됩니다.
컨텐츠 추천 엔진은 데이터 분석과 알고리즘 모델을 활용하여 사용자의 관심사, 취향, 행동에 따라 개인화된 컨텐츠를 자동으로 제안하는 지능형 시스템입니다. 검색 기록, 클릭, 검색, 좋아요, 구매, 컨텐츠 열람 시간 등의 데이터를 수집하고 분석하여 패턴과 사용자의 의도를 파악합니다. 이후 이 정보를 사용 가능한 컨텐츠 속성 및 문맥적 신호와 비교하여 가장 관련성이 높고 매력적인 추천을 제공합니다.
컨텐츠 추천 엔진 시장의 성장은 주로 개인화에 대한 수요 증가와 상업적 전환 효율성 개선에 대한 요구가 주도하고 있습니다. 디지털 컨텐츠의 양이 급증하는 가운데, 사용자들은 개인의 관심사에 맞는 관련 정보를 필터링하여 제공하는 플랫폼에 대한 의존도가 높아지고 있으며, 사용자 경험을 향상시키기 위한 추천 기술의 보급을 촉진하고 있습니다. 동시에 디지털 플랫폼은 추천 엔진을 사용자 참여도 향상, 세션 시간 연장, 클릭 및 구매 촉진을 위한 중요한 도구로 활용하고 있습니다. 이러한 시스템은 사용자와 컨텐츠의 매칭을 최적화하여 만족도를 높일 뿐만 아니라, 트래픽 수익화, 타겟팅 광고 게재, 데이터 기반의 정밀한 운영을 가능하게 하는 중요한 기반이 되며, 컨텐츠 경제와 지능형 디지털 서비스의 확장에 따른 꾸준한 성장을 뒷받침하고 있습니다. 컨텐츠 경제와 지능형 디지털 서비스의 맥락에서 꾸준한 성장을 뒷받침하고 있습니다.
현재 주요 세계 기업으로는 Taboola, Outbrain, Dynamic Yield(McDonald), Amazon Web Services, Adobe, Kibo Commerce, Optimizely, Salesforce(Evergage), Zeta Global, Emarsys(SAP), Algonomy, ThinkAnalytics, Alibaba Cloud, Tencent, Baidu, ByteDance(Volcano Engine) 등이 있습니다. 이 중 Taboola는 2024년 시장 점유율 30.76%를 차지할 것으로 예측됩니다.
이 보고서는 컨텐츠 추천 엔진 세계 시장에 대해 총 매출액, 주요 기업의 시장 점유율 및 순위에 초점을 맞추고, 지역/국가별, 도입 형태별, 용도별 분석을 종합적으로 제시하는 것을 목적으로 합니다.
컨텐츠 추천 엔진 시장 규모, 추정 및 예측은 2024년을 기준 연도로 하여 2020년에서 2031년까지의 기간의 과거 데이터와 예측 데이터를 포함하는 매출액으로 제시되었습니다. 정량적, 정성적 분석을 통해 독자들이 비즈니스/성장 전략 수립, 시장 경쟁 평가, 현재 시장에서의 포지셔닝 분석, 컨텐츠 추천 엔진에 대한 정보에 입각한 비즈니스 의사결정을 내릴 수 있도록 돕습니다.
시장 세분화
기업별
- Taboola
- Outbrain
- Dynamic Yield
- Amazon Web Services
- Adobe
- Kibo Commerce
- Optimizely
- Salesforce
- Zeta Global
- SAP Emarsys
- Algonomy
- ThinkAnalytics
- Alibaba Cloud
- Tencent.
- Baidu
- Byte Dance
도입 형태별 부문
- 로컬 도입
- 클라우드 도입
용도별 부문
- 뉴스 및 미디어
- 엔터테인먼트 및 게임
- 전자상거래
- 금융
- 기타
지역별
- 북미
- 미국
- 캐나다
- 아시아태평양
- 중국
- 일본
- 한국
- 동남아시아
- 인도
- 호주
- 기타 아시아태평양
- 유럽
- 독일
- 프랑스
- 영국
- 이탈리아
- 네덜란드
- 북유럽 국가
- 기타 유럽
- 라틴아메리카
- 멕시코
- 브라질
- 기타 라틴아메리카
- 중동 및 아프리카
- 튀르키예
- 사우디아라비아
- 아랍에미리트(UAE)
- 기타 중동 및 아프리카
자주 묻는 질문
The global market for Content Recommendation Engines was estimated to be worth US$ 10407 million in 2024 and is forecast to a readjusted size of US$ 66340 million by 2031 with a CAGR of 31.2% during the forecast period 2025-2031.
A Content Recommendation Engine is an intelligent system that leverages data analysis and algorithmic models to automatically suggest personalized content to users based on their interests, preferences, and behavior. By collecting and analyzing data such as browsing history, clicks, searches, likes, purchases, and time spent on content, the engine identifies patterns and user intent. It then matches this information with available content attributes and contextual signals to deliver the most relevant and engaging recommendations.
The growth of the content recommendation engine market is primarily driven by the rising demand for personalization and the need to improve commercial conversion efficiency. As the volume of digital content continues to surge, users increasingly rely on platforms to filter and deliver relevant information tailored to their individual interests, prompting widespread adoption of recommendation technologies to enhance user experience. At the same time, digital platforms are leveraging recommendation engines as essential tools to boost user engagement, increase session duration, and drive clicks and purchases. By optimizing the match between users and content, these systems not only enhance satisfaction but also serve as critical infrastructure for monetizing traffic, delivering targeted ads, and enabling data-driven, precision operations-fueling steady growth in the context of an expanding content economy and intelligent digital services.
Currently, major global companies 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, ByteDance (Volcano Engine), etc. Among them, Taboola accounting for 30.76% of the market share in 2024.
This report aims to provide a comprehensive presentation of the global market for Content Recommendation Engines, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Content Recommendation Engines by region & country, by Deployment Mode, and by Application.
The Content Recommendation Engines market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2024 as the base year, with history and forecast data for the period from 2020 to 2031. 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.
Market Segmentation
By Company
- Taboola
- Outbrain
- Dynamic Yield
- Amazon Web Services
- Adobe
- Kibo Commerce
- Optimizely
- Salesforce
- Zeta Global
- SAP Emarsys
- Algonomy
- ThinkAnalytics
- Alibaba Cloud
- Tencent.
- Baidu
- Byte Dance
Segment by Deployment Mode
- Local Deployment
- Cloud Deployment
Segment by Application
- News and Media
- Entertainment and Games
- E-commerce
- Finance
- others
By Region
- North America
- United States
- Canada
- Asia-Pacific
- China
- Japan
- South Korea
- Southeast Asia
- India
- Australia
- Rest of Asia-Pacific
- Europe
- Germany
- France
- U.K.
- Italy
- Netherlands
- Nordic Countries
- Rest of Europe
- Latin America
- Mexico
- Brazil
- Rest of Latin America
- Middle East & Africa
- Turkey
- Saudi Arabia
- UAE
- Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Content Recommendation Engines company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Deployment Mode, 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 by 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: Revenue of Content Recommendation Engines in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of Content Recommendation Engines in country level. It provides sigmate data by Deployment Mode, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.
Table of Contents
1 Market Overview
- 1.1 Content Recommendation Engines Product Introduction
- 1.2 Global Content Recommendation Engines Market Size Forecast (2020-2031)
- 1.3 Content Recommendation Engines Market Trends & Drivers
- 1.3.1 Content Recommendation Engines Industry Trends
- 1.3.2 Content Recommendation Engines Market Drivers & Opportunity
- 1.3.3 Content Recommendation Engines Market Challenges
- 1.3.4 Content Recommendation Engines Market Restraints
- 1.4 Assumptions and Limitations
- 1.5 Study Objectives
- 1.6 Years Considered
2 Competitive Analysis by Company
- 2.1 Global Content Recommendation Engines Players Revenue Ranking (2024)
- 2.2 Global Content Recommendation Engines Revenue by Company (2020-2025)
- 2.3 Key Companies Content Recommendation Engines Manufacturing Base Distribution and Headquarters
- 2.4 Key Companies Content Recommendation Engines Product Offered
- 2.5 Key Companies Time to Begin Mass Production of Content Recommendation Engines
- 2.6 Content Recommendation Engines Market Competitive Analysis
- 2.6.1 Content Recommendation Engines Market Concentration Rate (2020-2025)
- 2.6.2 Global 5 and 10 Largest Companies by Content Recommendation Engines Revenue in 2024
- 2.6.3 Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Content Recommendation Engines as of 2024)
- 2.7 Mergers & Acquisitions, Expansion
3 Segmentation by Deployment Mode
- 3.1 Introduction by Deployment Mode
- 3.1.1 Local Deployment
- 3.1.2 Cloud Deployment
- 3.2 Global Content Recommendation Engines Sales Value by Deployment Mode
- 3.2.1 Global Content Recommendation Engines Sales Value by Deployment Mode (2020 VS 2024 VS 2031)
- 3.2.2 Global Content Recommendation Engines Sales Value, by Deployment Mode (2020-2031)
- 3.2.3 Global Content Recommendation Engines Sales Value, by Deployment Mode (%) (2020-2031)
4 Segmentation by Application
- 4.1 Introduction by Application
- 4.1.1 News and Media
- 4.1.2 Entertainment and Games
- 4.1.3 E-commerce
- 4.1.4 Finance
- 4.1.5 others
- 4.2 Global Content Recommendation Engines Sales Value by Application
- 4.2.1 Global Content Recommendation Engines Sales Value by Application (2020 VS 2024 VS 2031)
- 4.2.2 Global Content Recommendation Engines Sales Value, by Application (2020-2031)
- 4.2.3 Global Content Recommendation Engines Sales Value, by Application (%) (2020-2031)
5 Segmentation by Region
- 5.1 Global Content Recommendation Engines Sales Value by Region
- 5.1.1 Global Content Recommendation Engines Sales Value by Region: 2020 VS 2024 VS 2031
- 5.1.2 Global Content Recommendation Engines Sales Value by Region (2020-2025)
- 5.1.3 Global Content Recommendation Engines Sales Value by Region (2026-2031)
- 5.1.4 Global Content Recommendation Engines Sales Value by Region (%), (2020-2031)
- 5.2 North America
- 5.2.1 North America Content Recommendation Engines Sales Value, 2020-2031
- 5.2.2 North America Content Recommendation Engines Sales Value by Country (%), 2024 VS 2031
- 5.3 Europe
- 5.3.1 Europe Content Recommendation Engines Sales Value, 2020-2031
- 5.3.2 Europe Content Recommendation Engines Sales Value by Country (%), 2024 VS 2031
- 5.4 Asia Pacific
- 5.4.1 Asia Pacific Content Recommendation Engines Sales Value, 2020-2031
- 5.4.2 Asia Pacific Content Recommendation Engines Sales Value by Region (%), 2024 VS 2031
- 5.5 South America
- 5.5.1 South America Content Recommendation Engines Sales Value, 2020-2031
- 5.5.2 South America Content Recommendation Engines Sales Value by Country (%), 2024 VS 2031
- 5.6 Middle East & Africa
- 5.6.1 Middle East & Africa Content Recommendation Engines Sales Value, 2020-2031
- 5.6.2 Middle East & Africa Content Recommendation Engines Sales Value by Country (%), 2024 VS 2031
6 Segmentation by Key Countries/Regions
- 6.1 Key Countries/Regions Content Recommendation Engines Sales Value Growth Trends, 2020 VS 2024 VS 2031
- 6.2 Key Countries/Regions Content Recommendation Engines Sales Value, 2020-2031
- 6.3 United States
- 6.3.1 United States Content Recommendation Engines Sales Value, 2020-2031
- 6.3.2 United States Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
- 6.3.3 United States Content Recommendation Engines Sales Value by Application, 2024 VS 2031
- 6.4 Europe
- 6.4.1 Europe Content Recommendation Engines Sales Value, 2020-2031
- 6.4.2 Europe Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
- 6.4.3 Europe Content Recommendation Engines Sales Value by Application, 2024 VS 2031
- 6.5 China
- 6.5.1 China Content Recommendation Engines Sales Value, 2020-2031
- 6.5.2 China Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
- 6.5.3 China Content Recommendation Engines Sales Value by Application, 2024 VS 2031
- 6.6 Japan
- 6.6.1 Japan Content Recommendation Engines Sales Value, 2020-2031
- 6.6.2 Japan Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
- 6.6.3 Japan Content Recommendation Engines Sales Value by Application, 2024 VS 2031
- 6.7 South Korea
- 6.7.1 South Korea Content Recommendation Engines Sales Value, 2020-2031
- 6.7.2 South Korea Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
- 6.7.3 South Korea Content Recommendation Engines Sales Value by Application, 2024 VS 2031
- 6.8 Southeast Asia
- 6.8.1 Southeast Asia Content Recommendation Engines Sales Value, 2020-2031
- 6.8.2 Southeast Asia Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
- 6.8.3 Southeast Asia Content Recommendation Engines Sales Value by Application, 2024 VS 2031
- 6.9 India
- 6.9.1 India Content Recommendation Engines Sales Value, 2020-2031
- 6.9.2 India Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
- 6.9.3 India Content Recommendation Engines Sales Value by Application, 2024 VS 2031
7 Company Profiles
- 7.1 Taboola
- 7.1.1 Taboola Profile
- 7.1.2 Taboola Main Business
- 7.1.3 Taboola Content Recommendation Engines Products, Services and Solutions
- 7.1.4 Taboola Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.1.5 Taboola Recent Developments
- 7.2 Outbrain
- 7.2.1 Outbrain Profile
- 7.2.2 Outbrain Main Business
- 7.2.3 Outbrain Content Recommendation Engines Products, Services and Solutions
- 7.2.4 Outbrain Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.2.5 Outbrain Recent Developments
- 7.3 Dynamic Yield
- 7.3.1 Dynamic Yield Profile
- 7.3.2 Dynamic Yield Main Business
- 7.3.3 Dynamic Yield Content Recommendation Engines Products, Services and Solutions
- 7.3.4 Dynamic Yield Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.3.5 Dynamic Yield Recent Developments
- 7.4 Amazon Web Services
- 7.4.1 Amazon Web Services Profile
- 7.4.2 Amazon Web Services Main Business
- 7.4.3 Amazon Web Services Content Recommendation Engines Products, Services and Solutions
- 7.4.4 Amazon Web Services Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.4.5 Amazon Web Services Recent Developments
- 7.5 Adobe
- 7.5.1 Adobe Profile
- 7.5.2 Adobe Main Business
- 7.5.3 Adobe Content Recommendation Engines Products, Services and Solutions
- 7.5.4 Adobe Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.5.5 Adobe Recent Developments
- 7.6 Kibo Commerce
- 7.6.1 Kibo Commerce Profile
- 7.6.2 Kibo Commerce Main Business
- 7.6.3 Kibo Commerce Content Recommendation Engines Products, Services and Solutions
- 7.6.4 Kibo Commerce Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.6.5 Kibo Commerce Recent Developments
- 7.7 Optimizely
- 7.7.1 Optimizely Profile
- 7.7.2 Optimizely Main Business
- 7.7.3 Optimizely Content Recommendation Engines Products, Services and Solutions
- 7.7.4 Optimizely Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.7.5 Optimizely Recent Developments
- 7.8 Salesforce
- 7.8.1 Salesforce Profile
- 7.8.2 Salesforce Main Business
- 7.8.3 Salesforce Content Recommendation Engines Products, Services and Solutions
- 7.8.4 Salesforce Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.8.5 Salesforce Recent Developments
- 7.9 Zeta Global
- 7.9.1 Zeta Global Profile
- 7.9.2 Zeta Global Main Business
- 7.9.3 Zeta Global Content Recommendation Engines Products, Services and Solutions
- 7.9.4 Zeta Global Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.9.5 Zeta Global Recent Developments
- 7.10 SAP Emarsys
- 7.10.1 SAP Emarsys Profile
- 7.10.2 SAP Emarsys Main Business
- 7.10.3 SAP Emarsys Content Recommendation Engines Products, Services and Solutions
- 7.10.4 SAP Emarsys Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.10.5 SAP Emarsys Recent Developments
- 7.11 Algonomy
- 7.11.1 Algonomy Profile
- 7.11.2 Algonomy Main Business
- 7.11.3 Algonomy Content Recommendation Engines Products, Services and Solutions
- 7.11.4 Algonomy Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.11.5 Algonomy Recent Developments
- 7.12 ThinkAnalytics
- 7.12.1 ThinkAnalytics Profile
- 7.12.2 ThinkAnalytics Main Business
- 7.12.3 ThinkAnalytics Content Recommendation Engines Products, Services and Solutions
- 7.12.4 ThinkAnalytics Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.12.5 ThinkAnalytics Recent Developments
- 7.13 Alibaba Cloud
- 7.13.1 Alibaba Cloud Profile
- 7.13.2 Alibaba Cloud Main Business
- 7.13.3 Alibaba Cloud Content Recommendation Engines Products, Services and Solutions
- 7.13.4 Alibaba Cloud Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.13.5 Alibaba Cloud Recent Developments
- 7.14 Tencent.
- 7.14.1 Tencent. Profile
- 7.14.2 Tencent. Main Business
- 7.14.3 Tencent. Content Recommendation Engines Products, Services and Solutions
- 7.14.4 Tencent. Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.14.5 Tencent. Recent Developments
- 7.15 Baidu
- 7.15.1 Baidu Profile
- 7.15.2 Baidu Main Business
- 7.15.3 Baidu Content Recommendation Engines Products, Services and Solutions
- 7.15.4 Baidu Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.15.5 Baidu Recent Developments
- 7.16 Byte Dance
- 7.16.1 Byte Dance Profile
- 7.16.2 Byte Dance Main Business
- 7.16.3 Byte Dance Content Recommendation Engines Products, Services and Solutions
- 7.16.4 Byte Dance Content Recommendation Engines Revenue (US$ Million) & (2020-2025)
- 7.16.5 Byte Dance Recent Developments
8 Industry Chain Analysis
- 8.1 Content Recommendation Engines Industrial Chain
- 8.2 Content Recommendation Engines Upstream Analysis
- 8.2.1 Key Raw Materials
- 8.2.2 Raw Materials Key Suppliers
- 8.2.3 Manufacturing Cost Structure
- 8.3 Midstream Analysis
- 8.4 Downstream Analysis (Customers Analysis)
- 8.5 Sales Model and Sales Channels
- 8.5.1 Content Recommendation Engines Sales Model
- 8.5.2 Sales Channel
- 8.5.3 Content Recommendation Engines Distributors
9 Research Findings and Conclusion
10 Appendix
- 10.1 Research Methodology
- 10.1.1 Methodology/Research Approach
- 10.1.1.1 Research Programs/Design
- 10.1.1.2 Market Size Estimation
- 10.1.1.3 Market Breakdown and Data Triangulation
- 10.1.2 Data Source
- 10.1.2.1 Secondary Sources
- 10.1.2.2 Primary Sources
- 10.1.1 Methodology/Research Approach
- 10.2 Author Details
- 10.3 Disclaimer
