+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)
Sale

Global Content Recommendation Engine Market by Type, Platform, Application - Forecast 2024-2030

  • PDF Icon

    Report

  • 192 Pages
  • March 2024
  • Region: Global
  • 360iResearch™
  • ID: 5887204
UP TO OFF until Dec 31st 2024
1h Free Analyst Time
1h Free Analyst Time

Speak directly to the analyst to clarify any post sales queries you may have.

The Content Recommendation Engine Market size was estimated at USD 1.67 billion in 2023, USD 1.84 billion in 2024, and is expected to grow at a CAGR of 15.15% to reach USD 4.49 billion by 2030.

FPNV Positioning Matrix

The FPNV Positioning Matrix is pivotal in evaluating the Content Recommendation Engine Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).

Market Share Analysis

The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the Content Recommendation Engine Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.

Key Company Profiles

The report delves into recent significant developments in the Content Recommendation Engine Market, highlighting leading vendors and their innovative profiles. These include ActiveCampaign, LLC, Algolia, Amazon Web Services, Inc., Braze, Inc., Dashword, Dynamic Yield Ltd, Google LLC, Gravity R&D, Hewlett Packard Enterprise Development LP, HubSpot, Inc., InData Labs, Intel Corporation, MarketMuse, Inc, Microsoft Corporation, Mushi Labs, Nexocod, Oracle Corporation, Recombee, Salesforce, Inc., SAP SE, Segmentify, Sentient.io, Taboola, Inc., and The International Business Machines Corporation.

Market Segmentation & Coverage

This research report categorizes the Content Recommendation Engine Market to forecast the revenues and analyze trends in each of the following sub-markets:

  • Type
    • Collaborative Filtering
    • Content-Based Filtering
    • Hybrid Recommendation Engine
  • Platform
    • E-mail & Newsletter Recommendation Engine
    • Mobile-based Recommendation Engine
    • Smart TV & Set-top Box Recommendation Engine
    • Web-based Recommendation Engine
  • Application
    • E-commerce & Retail
    • Gaming
    • Media & Entertainment
    • News & Content Aggregation
    • Social Media & Networking
  • Region
    • Americas
      • Argentina
      • Brazil
      • Canada
      • Mexico
      • United States
        • California
        • Florida
        • Illinois
        • New York
        • Ohio
        • Pennsylvania
        • Texas
    • Asia-Pacific
      • Australia
      • China
      • India
      • Indonesia
      • Japan
      • Malaysia
      • Philippines
      • Singapore
      • South Korea
      • Taiwan
      • Thailand
      • Vietnam
    • Europe, Middle East & Africa
      • Denmark
      • Egypt
      • Finland
      • France
      • Germany
      • Israel
      • Italy
      • Netherlands
      • Nigeria
      • Norway
      • Poland
      • Qatar
      • Russia
      • Saudi Arabia
      • South Africa
      • Spain
      • Sweden
      • Switzerland
      • Turkey
      • United Arab Emirates
      • United Kingdom

The report offers valuable insights on the following aspects

  1. Market Penetration: It presents comprehensive information on the market provided by key players.
  2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.
  3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.
  4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.
  5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.

The report addresses key questions such as

  1. What is the market size and forecast of the Content Recommendation Engine Market?
  2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Content Recommendation Engine Market?
  3. What are the technology trends and regulatory frameworks in the Content Recommendation Engine Market?
  4. What is the market share of the leading vendors in the Content Recommendation Engine Market?
  5. Which modes and strategic moves are suitable for entering the Content Recommendation Engine Market?

Please note: For this report, the purchase of an Enterprise license allows up to ten worldwide users of an organization access to the report

Please note: This report can be updated on request. Please contact our Customer Experience team using the Ask a Question widget on our website.

With the purchase of this report at the Multi-user License or greater level, you will have access to one hour with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This will need to be used within three months of purchase.

This report also includes a complimentary Excel file with data from the report for purchasers at the Site License or greater level.

Table of Contents

1. Preface
1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Limitations
1.7. Assumptions
1.8. Stakeholders
2. Research Methodology
2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update
3. Executive Summary
4. Market Overview
4.1. Introduction
4.2. Content Recommendation Engine Market, by Region
5. Market Insights
5.1. Market Dynamics
5.1.1. Drivers
5.1.1.1. Demand of digitalization and increased internet penetration for personalized user experience
5.1.1.2. Advantage over collaborative based filtering for user engagement
5.1.1.3. Increase in demand for data generation software solutions
5.1.2. Restraints
5.1.2.1. High costs associated with content recommendation engines
5.1.3. Opportunities
5.1.3.1. Advancement to provide personalized content to encourage optimized preferences and behaviors
5.1.3.2. Growing adoption of digital technologies in small and medium scale businesses
5.1.4. Challenges
5.1.4.1. Limited content analysis through platform
5.2. Market Segmentation Analysis
5.3. Market Trend Analysis
5.4. Cumulative Impact of High Inflation
5.5. Porter’s Five Forces Analysis
5.5.1. Threat of New Entrants
5.5.2. Threat of Substitutes
5.5.3. Bargaining Power of Customers
5.5.4. Bargaining Power of Suppliers
5.5.5. Industry Rivalry
5.6. Value Chain & Critical Path Analysis
5.7. Regulatory Framework
6. Content Recommendation Engine Market, by Type
6.1. Introduction
6.2. Collaborative Filtering
6.3. Content-Based Filtering
6.4. Hybrid Recommendation Engine
7. Content Recommendation Engine Market, by Platform
7.1. Introduction
7.2. E-mail & Newsletter Recommendation Engine
7.3. Mobile-based Recommendation Engine
7.4. Smart TV & Set-top Box Recommendation Engine
7.5. Web-based Recommendation Engine
8. Content Recommendation Engine Market, by Application
8.1. Introduction
8.2. E-commerce & Retail
8.3. Gaming
8.4. Media & Entertainment
8.5. News & Content Aggregation
8.6. Social Media & Networking
9. Americas Content Recommendation Engine Market
9.1. Introduction
9.2. Argentina
9.3. Brazil
9.4. Canada
9.5. Mexico
9.6. United States
10. Asia-Pacific Content Recommendation Engine Market
10.1. Introduction
10.2. Australia
10.3. China
10.4. India
10.5. Indonesia
10.6. Japan
10.7. Malaysia
10.8. Philippines
10.9. Singapore
10.10. South Korea
10.11. Taiwan
10.12. Thailand
10.13. Vietnam
11. Europe, Middle East & Africa Content Recommendation Engine Market
11.1. Introduction
11.2. Denmark
11.3. Egypt
11.4. Finland
11.5. France
11.6. Germany
11.7. Israel
11.8. Italy
11.9. Netherlands
11.10. Nigeria
11.11. Norway
11.12. Poland
11.13. Qatar
11.14. Russia
11.15. Saudi Arabia
11.16. South Africa
11.17. Spain
11.18. Sweden
11.19. Switzerland
11.20. Turkey
11.21. United Arab Emirates
11.22. United Kingdom
12. Competitive Landscape
12.1. FPNV Positioning Matrix
12.2. Market Share Analysis, By Key Player
12.3. Competitive Scenario Analysis, By Key Player
13. Competitive Portfolio
13.1. Key Company Profiles
13.1.1. ActiveCampaign, LLC
13.1.2. Algolia
13.1.3. Amazon Web Services, Inc.
13.1.4. Braze, Inc.
13.1.5. Dashword
13.1.6. Dynamic Yield Ltd
13.1.7. Google LLC
13.1.8. Gravity R&D
13.1.9. Hewlett Packard Enterprise Development LP
13.1.10. HubSpot, Inc.
13.1.11. InData Labs
13.1.12. Intel Corporation
13.1.13. MarketMuse, Inc
13.1.14. Microsoft Corporation
13.1.15. Mushi Labs
13.1.16. Nexocod
13.1.17. Oracle Corporation
13.1.18. Recombee
13.1.19. Salesforce, Inc.
13.1.20. SAP SE
13.1.21. Segmentify
13.1.22. Sentient.io
13.1.23. Taboola, Inc.
13.1.24. The International Business Machines Corporation
13.2. Key Product Portfolio
14. Appendix
14.1. Discussion Guide
14.2. License & Pricing
List of Figures
FIGURE 1. CONTENT RECOMMENDATION ENGINE MARKET RESEARCH PROCESS
FIGURE 2. CONTENT RECOMMENDATION ENGINE MARKET SIZE, 2023 VS 2030
FIGURE 3. CONTENT RECOMMENDATION ENGINE MARKET SIZE, 2018-2030 (USD MILLION)
FIGURE 4. CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2023 VS 2030 (%)
FIGURE 5. CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2023 VS 2024 VS 2030 (USD MILLION)
FIGURE 6. CONTENT RECOMMENDATION ENGINE MARKET DYNAMICS
FIGURE 7. CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2023 VS 2030 (%)
FIGURE 8. CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2023 VS 2024 VS 2030 (USD MILLION)
FIGURE 9. CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY PLATFORM, 2023 VS 2030 (%)
FIGURE 10. CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY PLATFORM, 2023 VS 2024 VS 2030 (USD MILLION)
FIGURE 11. CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2023 VS 2030 (%)
FIGURE 12. CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2023 VS 2024 VS 2030 (USD MILLION)
FIGURE 13. AMERICAS CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
FIGURE 14. AMERICAS CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
FIGURE 15. UNITED STATES CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY STATE, 2023 VS 2030 (%)
FIGURE 16. UNITED STATES CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY STATE, 2023 VS 2024 VS 2030 (USD MILLION)
FIGURE 17. ASIA-PACIFIC CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
FIGURE 18. ASIA-PACIFIC CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
FIGURE 19. EUROPE, MIDDLE EAST & AFRICA CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
FIGURE 20. EUROPE, MIDDLE EAST & AFRICA CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
FIGURE 21. CONTENT RECOMMENDATION ENGINE MARKET, FPNV POSITIONING MATRIX, 2023
FIGURE 22. CONTENT RECOMMENDATION ENGINE MARKET SHARE, BY KEY PLAYER, 2023

Companies Mentioned

  • ActiveCampaign, LLC
  • Algolia
  • Amazon Web Services, Inc.
  • Braze, Inc.
  • Dashword
  • Dynamic Yield Ltd
  • Google LLC
  • Gravity R&D
  • Hewlett Packard Enterprise Development LP
  • HubSpot, Inc.
  • InData Labs
  • Intel Corporation
  • MarketMuse, Inc
  • Microsoft Corporation
  • Mushi Labs
  • Nexocod
  • Oracle Corporation
  • Recombee
  • Salesforce, Inc.
  • SAP SE
  • Segmentify
  • Sentient.io
  • Taboola, Inc.
  • The International Business Machines Corporation

Methodology

Loading
LOADING...

Table Information