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市场调查报告书
商品编码
1862514

内容推荐引擎－2025-2031年全球市占率及排名、总营收及需求预测

Content Recommendation Engines - Global Market Share and Ranking, Overall Sales and Demand Forecast 2025-2031
出版日期: | 出版商: QYResearch | 英文 128 Pages | 商品交期: 2-3个工作天内

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全球内容推荐引擎市场预计在 2024 年达到 104.07 亿美元，预计到 2031 年将达到 663.4 亿美元，2025 年至 2031 年的复合年增长率为 31.2%。

内容推荐引擎是一种智慧系统，它利用数据分析和演算法模型，根据用户的兴趣、偏好和行为自动提案个人化内容。透过收集和分析浏览历史记录、点击、搜寻、按讚、购买和观看时长等数据，该引擎能够识别用户行为模式和意图。然后，它将这些资讯与可用的内容属性和上下文讯号进行匹配，从而提供最相关、最吸引人的推荐内容。

内容推荐引擎市场的成长主要受个人化需求和提升商业性转换效率的需求所驱动。随着数位内容量的持续飙升，用户越来越依赖平台来筛选和推送与其个人兴趣相关的客製化讯息，这推动了推荐技术的广泛应用，从而提升用户体验。同时，数位平台也利用推荐引擎作为关键工具，以提高用户参与度、延长会话时间并增加点击量和购买量。透过优化用户与内容的匹配，这些系统不仅提升了用户满意度，也为流量变现、定向广告投放和精准数据驱动营运奠定了基础，从而在不断扩展的内容经济和智慧数位服务的背景下，支撑着平台的稳步增长。

目前，全球主要公司包括 Taboola、Outbrain、Dynamic Yield（麦当劳）、亚马逊网路服务、Adobe、Kibo Commerce、Optimizely、Salesforce（Evergage）、Zeta Global、Emarsys（SAP）、Algonomy、ThinkAnalytics、阿里云、腾讯、百度、canoine）。其中，Taboola 预计到 2024 年将占据 30.76% 的市占率。

本报告旨在按地区/国家、部署类型和应用程式对全球内容推荐引擎市场进行全面分析，重点关注总收入、市场份额和主要企业的排名。

内容推荐引擎市场规模、估算和预测均以销售收入为指标，以 2024 年为基准年，并包含 2020 年至 2031 年的历史数据和预测数据。我们运用定量和定性分析，帮助读者制定业务和成长策略，评估市场竞争，分析自身在当前市场中的地位，并就内容推荐引擎做出明智的商业决策。

市场区隔

公司

  • Taboola
  • Outbrain
  • Dynamic Yield
  • Amazon Web Services
  • Adobe
  • Kibo Commerce
  • Optimizely
  • Salesforce
  • Zeta Global
  • SAP Emarsys
  • Algonomy
  • ThinkAnalytics
  • Alibaba Cloud
  • Tencent.
  • Baidu
  • Byte Dance

按实施类型分類的细分市场

  • 本地部署
  • 云端采用

应用领域

  • 新闻媒体
  • 娱乐和游戏
  • 电子商务
  • 金融
  • 其他的

按地区

  • 北美洲
    • 美国
    • 加拿大
  • 亚太地区
    • 中国
    • 日本
    • 韩国
    • 东南亚
    • 印度
    • 澳洲
    • 亚太其他地区
  • 欧洲
    • 德国
    • 法国
    • 英国
    • 义大利
    • 荷兰
    • 北欧国家
    • 其他欧洲
  • 拉丁美洲
    • 墨西哥
    • 巴西
    • 其他拉丁美洲
  • 中东和非洲
    • 土耳其
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 其他中东和非洲地区

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
  • 10.2 Author Details
  • 10.3 Disclaimer

List of Tables

  • Table 1. Content Recommendation Engines Market Trends
  • Table 2. Content Recommendation Engines Market Drivers & Opportunity
  • Table 3. Content Recommendation Engines Market Challenges
  • Table 4. Content Recommendation Engines Market Restraints
  • Table 5. Global Content Recommendation Engines Revenue by Company (2020-2025) & (US$ Million)
  • Table 6. Global Content Recommendation Engines Revenue Market Share by Company (2020-2025)
  • Table 7. Key Companies Content Recommendation Engines Manufacturing Base Distribution and Headquarters
  • Table 8. Key Companies Content Recommendation Engines Product Type
  • Table 9. Key Companies Time to Begin Mass Production of Content Recommendation Engines
  • Table 10. Global Content Recommendation Engines Companies Market Concentration Ratio (CR5 and HHI)
  • Table 11. Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Content Recommendation Engines as of 2024)
  • Table 12. Mergers & Acquisitions, Expansion Plans
  • Table 13. Global Content Recommendation Engines Sales Value by Deployment Mode: 2020 VS 2024 VS 2031 (US$ Million)
  • Table 14. Global Content Recommendation Engines Sales Value by Deployment Mode (2020-2025) & (US$ Million)
  • Table 15. Global Content Recommendation Engines Sales Value by Deployment Mode (2026-2031) & (US$ Million)
  • Table 16. Global Content Recommendation Engines Sales Market Share in Value by Deployment Mode (2020-2025)
  • Table 17. Global Content Recommendation Engines Sales Market Share in Value by Deployment Mode (2026-2031)
  • Table 18. Global Content Recommendation Engines Sales Value by Application: 2020 VS 2024 VS 2031 (US$ Million)
  • Table 19. Global Content Recommendation Engines Sales Value by Application (2020-2025) & (US$ Million)
  • Table 20. Global Content Recommendation Engines Sales Value by Application (2026-2031) & (US$ Million)
  • Table 21. Global Content Recommendation Engines Sales Market Share in Value by Application (2020-2025)
  • Table 22. Global Content Recommendation Engines Sales Market Share in Value by Application (2026-2031)
  • Table 23. Global Content Recommendation Engines Sales Value by Region, (2020 VS 2024 VS 2031) & (US$ Million)
  • Table 24. Global Content Recommendation Engines Sales Value by Region (2020-2025) & (US$ Million)
  • Table 25. Global Content Recommendation Engines Sales Value by Region (2026-2031) & (US$ Million)
  • Table 26. Global Content Recommendation Engines Sales Value by Region (2020-2025) & (%)
  • Table 27. Global Content Recommendation Engines Sales Value by Region (2026-2031) & (%)
  • Table 28. Key Countries/Regions Content Recommendation Engines Sales Value Growth Trends, (US$ Million): 2020 VS 2024 VS 2031
  • Table 29. Key Countries/Regions Content Recommendation Engines Sales Value, (2020-2025) & (US$ Million)
  • Table 30. Key Countries/Regions Content Recommendation Engines Sales Value, (2026-2031) & (US$ Million)
  • Table 31. Taboola Basic Information List
  • Table 32. Taboola Description and Business Overview
  • Table 33. Taboola Content Recommendation Engines Products, Services and Solutions
  • Table 34. Revenue (US$ Million) in Content Recommendation Engines Business of Taboola (2020-2025)
  • Table 35. Taboola Recent Developments
  • Table 36. Outbrain Basic Information List
  • Table 37. Outbrain Description and Business Overview
  • Table 38. Outbrain Content Recommendation Engines Products, Services and Solutions
  • Table 39. Revenue (US$ Million) in Content Recommendation Engines Business of Outbrain (2020-2025)
  • Table 40. Outbrain Recent Developments
  • Table 41. Dynamic Yield Basic Information List
  • Table 42. Dynamic Yield Description and Business Overview
  • Table 43. Dynamic Yield Content Recommendation Engines Products, Services and Solutions
  • Table 44. Revenue (US$ Million) in Content Recommendation Engines Business of Dynamic Yield (2020-2025)
  • Table 45. Dynamic Yield Recent Developments
  • Table 46. Amazon Web Services Basic Information List
  • Table 47. Amazon Web Services Description and Business Overview
  • Table 48. Amazon Web Services Content Recommendation Engines Products, Services and Solutions
  • Table 49. Revenue (US$ Million) in Content Recommendation Engines Business of Amazon Web Services (2020-2025)
  • Table 50. Amazon Web Services Recent Developments
  • Table 51. Adobe Basic Information List
  • Table 52. Adobe Description and Business Overview
  • Table 53. Adobe Content Recommendation Engines Products, Services and Solutions
  • Table 54. Revenue (US$ Million) in Content Recommendation Engines Business of Adobe (2020-2025)
  • Table 55. Adobe Recent Developments
  • Table 56. Kibo Commerce Basic Information List
  • Table 57. Kibo Commerce Description and Business Overview
  • Table 58. Kibo Commerce Content Recommendation Engines Products, Services and Solutions
  • Table 59. Revenue (US$ Million) in Content Recommendation Engines Business of Kibo Commerce (2020-2025)
  • Table 60. Kibo Commerce Recent Developments
  • Table 61. Optimizely Basic Information List
  • Table 62. Optimizely Description and Business Overview
  • Table 63. Optimizely Content Recommendation Engines Products, Services and Solutions
  • Table 64. Revenue (US$ Million) in Content Recommendation Engines Business of Optimizely (2020-2025)
  • Table 65. Optimizely Recent Developments
  • Table 66. Salesforce Basic Information List
  • Table 67. Salesforce Description and Business Overview
  • Table 68. Salesforce Content Recommendation Engines Products, Services and Solutions
  • Table 69. Revenue (US$ Million) in Content Recommendation Engines Business of Salesforce (2020-2025)
  • Table 70. Salesforce Recent Developments
  • Table 71. Zeta Global Basic Information List
  • Table 72. Zeta Global Description and Business Overview
  • Table 73. Zeta Global Content Recommendation Engines Products, Services and Solutions
  • Table 74. Revenue (US$ Million) in Content Recommendation Engines Business of Zeta Global (2020-2025)
  • Table 75. Zeta Global Recent Developments
  • Table 76. SAP Emarsys Basic Information List
  • Table 77. SAP Emarsys Description and Business Overview
  • Table 78. SAP Emarsys Content Recommendation Engines Products, Services and Solutions
  • Table 79. Revenue (US$ Million) in Content Recommendation Engines Business of SAP Emarsys (2020-2025)
  • Table 80. SAP Emarsys Recent Developments
  • Table 81. Algonomy Basic Information List
  • Table 82. Algonomy Description and Business Overview
  • Table 83. Algonomy Content Recommendation Engines Products, Services and Solutions
  • Table 84. Revenue (US$ Million) in Content Recommendation Engines Business of Algonomy (2020-2025)
  • Table 85. Algonomy Recent Developments
  • Table 86. ThinkAnalytics Basic Information List
  • Table 87. ThinkAnalytics Description and Business Overview
  • Table 88. ThinkAnalytics Content Recommendation Engines Products, Services and Solutions
  • Table 89. Revenue (US$ Million) in Content Recommendation Engines Business of ThinkAnalytics (2020-2025)
  • Table 90. ThinkAnalytics Recent Developments
  • Table 91. Alibaba Cloud Basic Information List
  • Table 92. Alibaba Cloud Description and Business Overview
  • Table 93. Alibaba Cloud Content Recommendation Engines Products, Services and Solutions
  • Table 94. Revenue (US$ Million) in Content Recommendation Engines Business of Alibaba Cloud (2020-2025)
  • Table 95. Alibaba Cloud Recent Developments
  • Table 96. Tencent. Basic Information List
  • Table 97. Tencent. Description and Business Overview
  • Table 98. Tencent. Content Recommendation Engines Products, Services and Solutions
  • Table 99. Revenue (US$ Million) in Content Recommendation Engines Business of Tencent. (2020-2025)
  • Table 100. Tencent. Recent Developments
  • Table 101. Baidu Basic Information List
  • Table 102. Baidu Description and Business Overview
  • Table 103. Baidu Content Recommendation Engines Products, Services and Solutions
  • Table 104. Revenue (US$ Million) in Content Recommendation Engines Business of Baidu (2020-2025)
  • Table 105. Baidu Recent Developments
  • Table 106. Byte Dance Basic Information List
  • Table 107. Byte Dance Description and Business Overview
  • Table 108. Byte Dance Content Recommendation Engines Products, Services and Solutions
  • Table 109. Revenue (US$ Million) in Content Recommendation Engines Business of Byte Dance (2020-2025)
  • Table 110. Byte Dance Recent Developments
  • Table 111. Key Raw Materials Lists
  • Table 112. Raw Materials Key Suppliers Lists
  • Table 113. Content Recommendation Engines Downstream Customers
  • Table 114. Content Recommendation Engines Distributors List
  • Table 115. Research Programs/Design for This Report
  • Table 116. Key Data Information from Secondary Sources
  • Table 117. Key Data Information from Primary Sources

List of Figures

  • Figure 1. Content Recommendation Engines Product Picture
  • Figure 2. Global Content Recommendation Engines Sales Value, 2020 VS 2024 VS 2031 (US$ Million)
  • Figure 3. Global Content Recommendation Engines Sales Value (2020-2031) & (US$ Million)
  • Figure 4. Content Recommendation Engines Report Years Considered
  • Figure 5. Global Content Recommendation Engines Players Revenue Ranking (2024) & (US$ Million)
  • Figure 6. The 5 and 10 Largest Companies in the World: Market Share by Content Recommendation Engines Revenue in 2024
  • Figure 7. Content Recommendation Engines Market Share by Company Type (Tier 1, Tier 2, and Tier 3): 2020 VS 2024
  • Figure 8. Local Deployment Picture
  • Figure 9. Cloud Deployment Picture
  • Figure 10. Global Content Recommendation Engines Sales Value by Deployment Mode (2020 VS 2024 VS 2031) & (US$ Million)
  • Figure 11. Global Content Recommendation Engines Sales Value Market Share by Deployment Mode, 2024 & 2031
  • Figure 12. Product Picture of News and Media
  • Figure 13. Product Picture of Entertainment and Games
  • Figure 14. Product Picture of E-commerce
  • Figure 15. Product Picture of Finance
  • Figure 16. Product Picture of others
  • Figure 17. Global Content Recommendation Engines Sales Value by Application (2020 VS 2024 VS 2031) & (US$ Million)
  • Figure 18. Global Content Recommendation Engines Sales Value Market Share by Application, 2024 & 2031
  • Figure 19. North America Content Recommendation Engines Sales Value (2020-2031) & (US$ Million)
  • Figure 20. North America Content Recommendation Engines Sales Value by Country (%), 2024 VS 2031
  • Figure 21. Europe Content Recommendation Engines Sales Value, (2020-2031) & (US$ Million)
  • Figure 22. Europe Content Recommendation Engines Sales Value by Country (%), 2024 VS 2031
  • Figure 23. Asia Pacific Content Recommendation Engines Sales Value, (2020-2031) & (US$ Million)
  • Figure 24. Asia Pacific Content Recommendation Engines Sales Value by Region (%), 2024 VS 2031
  • Figure 25. South America Content Recommendation Engines Sales Value, (2020-2031) & (US$ Million)
  • Figure 26. South America Content Recommendation Engines Sales Value by Country (%), 2024 VS 2031
  • Figure 27. Middle East & Africa Content Recommendation Engines Sales Value, (2020-2031) & (US$ Million)
  • Figure 28. Middle East & Africa Content Recommendation Engines Sales Value by Country (%), 2024 VS 2031
  • Figure 29. Key Countries/Regions Content Recommendation Engines Sales Value (%), (2020-2031)
  • Figure 30. United States Content Recommendation Engines Sales Value, (2020-2031) & (US$ Million)
  • Figure 31. United States Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
  • Figure 32. United States Content Recommendation Engines Sales Value by Application (%), 2024 VS 2031
  • Figure 33. Europe Content Recommendation Engines Sales Value, (2020-2031) & (US$ Million)
  • Figure 34. Europe Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
  • Figure 35. Europe Content Recommendation Engines Sales Value by Application (%), 2024 VS 2031
  • Figure 36. China Content Recommendation Engines Sales Value, (2020-2031) & (US$ Million)
  • Figure 37. China Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
  • Figure 38. China Content Recommendation Engines Sales Value by Application (%), 2024 VS 2031
  • Figure 39. Japan Content Recommendation Engines Sales Value, (2020-2031) & (US$ Million)
  • Figure 40. Japan Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
  • Figure 41. Japan Content Recommendation Engines Sales Value by Application (%), 2024 VS 2031
  • Figure 42. South Korea Content Recommendation Engines Sales Value, (2020-2031) & (US$ Million)
  • Figure 43. South Korea Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
  • Figure 44. South Korea Content Recommendation Engines Sales Value by Application (%), 2024 VS 2031
  • Figure 45. Southeast Asia Content Recommendation Engines Sales Value, (2020-2031) & (US$ Million)
  • Figure 46. Southeast Asia Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
  • Figure 47. Southeast Asia Content Recommendation Engines Sales Value by Application (%), 2024 VS 2031
  • Figure 48. India Content Recommendation Engines Sales Value, (2020-2031) & (US$ Million)
  • Figure 49. India Content Recommendation Engines Sales Value by Deployment Mode (%), 2024 VS 2031
  • Figure 50. India Content Recommendation Engines Sales Value by Application (%), 2024 VS 2031
  • Figure 51. Content Recommendation Engines Industrial Chain
  • Figure 52. Content Recommendation Engines Manufacturing Cost Structure
  • Figure 53. Channels of Distribution (Direct Sales, and Distribution)
  • Figure 54. Bottom-up and Top-down Approaches for This Report
  • Figure 55. Data Triangulation
  • Figure 56. Key Executives Interviewed