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市場調查報告書

內容推薦引擎的全球市場的預測 (∼2022年):解決方案、服務

Content Recommendation Engine Market by Component (Solution, Service), Filtering Approach, Organization Size, Vertical (E-commerce, Media, Entertainment & Gaming, Retail & Consumer Goods, Hospitality), and Region - Global Forecast to 2022

出版商 MarketsandMarkets 商品編碼 618276
出版日期 內容資訊 英文 127 Pages
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內容推薦引擎的全球市場的預測 (∼2022年):解決方案、服務 Content Recommendation Engine Market by Component (Solution, Service), Filtering Approach, Organization Size, Vertical (E-commerce, Media, Entertainment & Gaming, Retail & Consumer Goods, Hospitality), and Region - Global Forecast to 2022
出版日期: 2018年03月21日 內容資訊: 英文 127 Pages
簡介

全球內容推薦引擎的市場在預測期間預計以33.7%的年複合成長率發展,從2017年的11億6000萬美元,成長到2022年的49億5000萬美元的規模。對強化客戶經驗的焦點擴大,迅速的數位化,龐大的顧客資料分析的必要性等要素預計促進該市場的成長。

本報告提供全球內容推薦引擎的市場調查,市場定義和概要,市場成長的各種影響因素及市場機會分析,產業趨勢,案例研究,各過濾方法、組織規模、產業、地區的趨勢與市場規模的變化與預測,競爭環境,主要企業簡介等彙整。

第1章 簡介

第2章 調查手法

第3章 摘要整理

第4章 重要考察

第5章 市場概要、產業趨勢

  • 簡介
  • 市場動態
    • 促進因素
    • 阻礙因素
    • 市場機會
    • 課題
  • 產業趨勢
    • 簡介
    • 個性化階段
    • 過濾方法
    • 案例研究
      • InnoGames、Outbrain
      • Huggies、Outbrain

第6章 市場分析、預測:各零件

  • 簡介
  • 解決方案
  • 服務

第7章 市場分析、預測:各過濾方法

  • 簡介
  • 協調過濾
  • 內容為基礎的過濾
  • 混合過濾

第8章 市場分析、預測:各組織規模

  • 簡介
  • 中小規模企業
  • 大規模企業

第9章 市場分析、預測:各產業

  • 簡介
  • 電子商務
  • 媒體、娛樂、遊戲
  • 零售、消費品
  • 飯店
  • IT & 通訊
  • 銀行、金融服務、保險
  • 教育、訓練
  • 醫療保健、醫藥品
  • 其他

第10章 地區分析

  • 簡介
  • 北美
  • 歐洲
  • 亞太地區
  • 中東、非洲
  • 南美

第11章 競爭環境

  • 概要
  • 競爭情形、趨勢
    • 新產品的投入、產品的強化
    • 協定、聯盟、合作
    • 收購
    • 擴張
  • 企業排行榜

第12章 企業簡介

  • IBM
  • AMAZON WEB SERVICES
  • REVCONTENT
  • TABOOLA
  • OUTBRAIN
  • CXENSE
  • DYNAMIC YIELD
  • CURATA
  • BOOMTRAIN
  • THINKANALYTICS
  • KIBO COMMERCE
  • CERTONA
  • 主要創新者
    • RECOMBEE
    • UBERFLIP
    • NEWZMATE

第13章 附錄

目錄
Product Code: TC 6113

"Increasing focus on enhancing customer experience and rapid digitalization are factors that are expected to drive the content recommendation engine market."

The content recommendation engine market is projected to grow from USD 1.16 billion in 2017 to USD 4.95 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 33.7% during the forecast period. Factors, such as increased focus on enhancing customer experience, rapid digitalization, and need for analyzing large volumes of customer data are expected to drive the content recommendation engine market. Protecting sensitive information of customers is a key factor restraining the growth of the market.

Based on component, the solution segment is estimated to lead the content recommendation engine market in 2017.

Based on component, the solution segment is estimated to lead the content recommendation engine market in 2017, as enterprises use it as a targeted marketing tool in their email campaigns and on their websites to enhance the ROI through effective customer engagement. Enterprises are increasingly deploying content recommendation system due to various benefits, such as enhanced customer satisfaction through the personalized online shopping experience.

Based on vertical, the E-commerce segment is estimated to lead the content recommendation engine market in 2017.

Based on vertical, the E-commerce segment is estimated to lead the content recommendation engine market in 2017. Increasing Internet penetration, the rise in the number of smartphones users, and the explosion of digital data have enabled organizations in E-commerce vertical to adopt content recommendation engine and enhance user experience.

The Asia Pacific content recommendation engine market is expected to grow at the highest CAGR during the forecast period.

The content recommendation engine market has been studied for North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America. The content recommendation engine is being adopted in the Asia Pacific region. Factors such as the rise of Over the Top (OTT) players and rapid digitization are expected to lead to the increasing deployment of content recommendation engine platforms in the Asia Pacific region. The demand for content recommendation engine solution in small and medium enterprises in the Asia Pacific region is high.

In-depth interviews were conducted with Chief Executive Officers (CEOs), marketing directors, innovation and technology directors, and executives from various key organizations operating in the content recommendation engine marketplace.

  • By Company Type: Tier 1: 14%, Tier 2: 43%, and Tier 3: 43%
  • By Designation: C-Level: 37%, Director Level: 13%, and Others: 50%
  • By Region: North America: 37%, Europe: 25%, Asia Pacific: 25%, and Rest of the World: 13%

Key vendors profiled in the report are:

  • 1. Amazon Web Services (US)
  • 2. Boomtrain (US)
  • 3. Certona (US)
  • 4. Curata (US)
  • 5. Cxense (Norway)
  • 6. Dynamic Yield (US)
  • 7. IBM (US)
  • 8. Kibo Commerce (US)
  • 9. Outbrain (US)
  • 10. Revcontent (US)
  • 11. Taboola (US)
  • 12. ThinkAnalytics (UK)

Research Coverage:

The content recommendation engine market has been segmented on the basis of component, filtering approach, organization size, vertical, and region. Based on component, the content recommendation engine market has been segmented into solution and service. Based on filtering approach, the market has been segmented into collaborative filtering, content-based filtering, and hybrid filtering. The content recommendation engine market has been segmented based on organization size into large enterprises and small and medium enterprises. Based on vertical, the market has been segmented into E-commerce, media, entertainment & gaming, retail & consumer goods, hospitality, IT & telecommunication, BFSI, education & training, healthcare & pharmaceutical, and others (which includes manufacturing, automotive, and supply chain management). The content recommendation engine market has been studied for North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America.

Key Benefits of Buying the Report:

The report will help market leaders and new entrants in the content recommendation engine market in the following ways:

  • The report will help market leaders/new entrants in this market by providing them the closest approximations of revenues of the content recommendation engine market and its subsegments. This report will also help stakeholders better understand the competitor landscape, gain more insights to position their businesses better, and implement suitable go-to-market strategies. The report will help stakeholders understand the pulse of the market and provide them with information on key market drivers, restraints, challenges, and opportunities.

TABLE OF CONTENTS

1. INTRODUCTION

  • 1.1. OBJECTIVES OF THE STUDY
  • 1.2. MARKET DEFINITION
  • 1.3. MARKET SCOPE
    • 1.3.1. YEARS CONSIDERED FOR THE STUDY
  • 1.4. CURRENCY
  • 1.5. STAKEHOLDERS

2. RESEARCH METHODOLOGY

  • 2.1. RESEARCH DATA
    • 2.1.1. SECONDARY DATA
    • 2.1.2. PRIMARY DATA
      • 2.1.2.1. Breakdown of primaries
      • 2.1.2.2. Key industry insights
  • 2.2. MARKET SIZE ESTIMATION
  • 2.3. RESEARCH ASSUMPTIONS
  • 2.4. LIMITATIONS

3. EXECUTIVE SUMMARY

4. PREMIUM INSIGHTS

  • 4.1. ATTRACTIVE OPPORTUNITIES IN THE CONTENT RECOMMENDATION ENGINE MARKET
  • 4.2. NORTH AMERICA: CONTENT RECOMMENDATION ENGINE MARKET, BY COMPONENT
  • 4.3. EUROPE: CONTENT RECOMMENDATION ENGINE MARKET, BY ORGANIZATION SIZE
  • 4.4. ASIA PACIFIC: CONTENT RECOMMENDATION ENGINE MARKET, BY VERTICAL
  • 4.5. CONTENT RECOMMENDATION ENGINE MARKET, BY TOP THREE VERTICALS & TOP THREE REGIONS

5. MARKET DYNAMICS AND INDUSTRY TRENDS

  • 5.1. MARKET DYNAMIC
    • 5.1.1. INTRODUCTION
    • 5.1.2. DRIVERS
      • 5.1.2.1. Increasing focus on enhancing customer experience
      • 5.1.2.2. Rapid digitalization
      • 5.1.2.3. Increasing need for analyzing large volumes of customer data
    • 5.1.3. RESTRAINTS
      • 5.1.3.1. Protecting sensitive information of customers
    • 5.1.4. OPPORTUNITIES
      • 5.1.4.1. Growing use of AI in recommendation engine to offer personalized customer experience
      • 5.1.4.2. Increasing demand for personalization
    • 5.1.5. CHALLENGES
      • 5.1.5.1. Issues related to technology and infrastructural compatibilities
      • 5.1.5.2. Lack of technical expertise
  • 5.2. INDUSTRY TRENDS
    • 5.2.1. INTRODUCTION
    • 5.2.2. PHASES OF PERSONALIZATION
    • 5.2.3. FILTERING APPROACHES IN RECOMMENDATION ENGINE
    • 5.2.4. CASE STUDIES
      • 5.2.4.1. Case study 1: InnoGames uses Outbrain targeting tools to reach audience and ensure App download growth
      • 5.2.4.2. Case study 2: Huggies effectively engages target audience with the help of Outbrain Content Discovery Platform

6. CONTENT RECOMMENDATION ENGINE MARKET, BY COMPONENT

  • 6.1. INTRODUCTION
  • 6.2. SOLUTION
  • 6.3. SERVICE

7. CONTENT RECOMMENDATION ENGINE MARKET, BY FILTERING APPROACH

  • 7.1. INTRODUCTION
  • 7.2. COLLABORATIVE FILTERING
  • 7.3. CONTENT-BASED FILTERING
  • 7.4. HYBRID FILTERING

8. CONTENT RECOMMENDATION ENGINE MARKET, BY ORGANIZATION SIZE

  • 8.1. INTRODUCTION
  • 8.2. SMALL AND MEDIUM ENTERPRISES
  • 8.3. LARGE ENTERPRISES

9. CONTENT RECOMMENDATION ENGINE MARKET, BY VERTICAL

  • 9.1. INTRODUCTION
  • 9.2. E-COMMERCE
  • 9.3. MEDIA, ENTERTAINMENT & GAMING
  • 9.4. RETAIL & CONSUMER GOODS
  • 9.5. HOSPITALITY
  • 9.6. IT & TELECOMMUNICATION
  • 9.7. BFSI
  • 9.8. EDUCATION & TRAINING
  • 9.9. HEALTHCARE & PHARMACEUTICAL
  • 9.10. OTHERS

10. REGIONAL ANALYSIS

  • 10.1. INTRODUCTION
  • 10.2. NORTH AMERICA
  • 10.3. EUROPE
  • 10.4. ASIA PACIFIC
  • 10.5. MIDDLE EAST & AFRICA
  • 10.6. LATIN AMERICA

11. COMPETITIVE LANDSCAPE

  • 11.1. OVERVIEW
  • 11.2. COMPETITIVE SITUATION AND TRENDS
    • 11.2.1. NEW PRODUCT LAUNCHES & PRODUCT ENHANCEMENTS
    • 11.2.2. AGREEMENTS, COLLABORATIONS & PARTNERSHIPS
    • 11.2.3. ACQUISITIONS
    • 11.2.4. EXPANSIONS
  • 11.3. MARKET RANKING OF KEY PLAYERS

12. COMPANY PROFILES (Business overview, Products/Solutions/Services Offered, Recent Developments, SWOT analysis, MNM view)*

  • 12.1. IBM
  • 12.2. AMAZON WEB SERVICES
  • 12.3. REVCONTENT
  • 12.4. TABOOLA
  • 12.5. OUTBRAIN
  • 12.6. CXENSE
  • 12.7. DYNAMIC YIELD
  • 12.8. CURATA
  • 12.9. BOOMTRAIN
  • 12.10. THINKANALYTICS
  • 12.11. KIBO COMMERCE
  • 12.12. CERTONA
  • 12.13. KEY INNOVATORS
    • 12.13.1. RECOMBEE
    • 12.13.2. UBERFLIP
    • 12.13.3. NEWZMATE

*Details on Business overview, Products/Solutions/Services Offered, Recent Developments, SWOT analysis, MNM view might not be captured in case of unlisted companies.

13. APPENDIX

  • 13.1. INDUSTRY EXCERPTS
  • 13.2. DISCUSSION GUIDE
  • 13.3. KNOWLEDGE STORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
  • 13.4. INTRODUCING RT: REAL-TIME MARKET INTELLIGENCE
  • 13.5. AVAILABLE CUSTOMIZATIONS
  • 13.6. RELATED REPORTS
  • 13.7. AUTHOR DETAILS

LIST OF TABLES

  • TABLE 1: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 2: SOLUTION: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 3: SERVICE: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 4: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY ORGANIZATION SIZE, 2015-2022 (USD MILLION)
  • TABLE 5: SMALL AND MEDIUM ENTERPRISES: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 6: LARGE ENTERPRISES: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 7: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY VERTICAL, 2015-2022 (USD MILLION)
  • TABLE 8: E-COMMERCE: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 9: MEDIA, ENTERTAINMENT & GAMING: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 10: RETAIL & CONSUMER GOODS: CONTENT RECOMMENDATION ENGINE MARKET, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 11: HOSPITALITY: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 12: IT & TELECOMMUNICATION: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 13: BFSI: CONTENT RECOMMENDATION ENGINE MARKET, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 14: EDUCATION & TRAINING: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 15: HEALTHCARE & PHARMACEUTICAL: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 16: OTHERS: CONTENT RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 17: CONTENT RECOMMENDATION ENGINE MARKET, BY REGION, 2015-2022 (USD MILLION)
  • TABLE 18: NORTH AMERICA: CONTENT RECOMMENDATION ENGINE MARKET, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 19: NORTH AMERICA: CONTENT RECOMMENDATION ENGINE MARKET, BY ORGANIZATION SIZE, 2015-2022 (USD MILLION)
  • TABLE 20: NORTH AMERICA: CONTENT RECOMMENDATION ENGINE MARKET, BY VERTICAL, 2015-2022 (USD MILLION)
  • TABLE 21: NORTH AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR E-COMMERCE, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 22: NORTH AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR MEDIA, ENTERTAINMENT & GAMING, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 23: NORTH AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR RETAIL & CONSUMER GOODS, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 24: NORTH AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR HOSPITALITY, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 25: NORTH AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR IT & TELECOMMUNICATION, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 26: NORTH AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR BFSI, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 27: EUROPE: CONTENT RECOMMENDATION ENGINE MARKET, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 28: EUROPE: CONTENT RECOMMENDATION ENGINE MARKET, BY ORGANIZATION SIZE, 2015-2022 (USD MILLION)
  • TABLE 29: EUROPE: CONTENT RECOMMENDATION ENGINE MARKET, BY VERTICAL, 2015-2022 (USD MILLION)
  • TABLE 30: EUROPE: CONTENT RECOMMENDATION ENGINE MARKET FOR E-COMMERCE, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 31: EUROPE: CONTENT RECOMMENDATION ENGINE MARKET FOR MEDIA, ENTERTAINMENT & GAMING, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 32: EUROPE: CONTENT RECOMMENDATION ENGINE MARKET FOR RETAIL & CONSUMER GOODS, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 33: EUROPE: CONTENT RECOMMENDATION ENGINE MARKET FOR HOSPITALITY, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 34: EUROPE: CONTENT RECOMMENDATION ENGINE MARKET FOR IT & TELECOMMUNICATION, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 35: EUROPE: CONTENT RECOMMENDATION ENGINE MARKET FOR BFSI, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 36: ASIA PACIFIC: CONTENT RECOMMENDATION ENGINE MARKET, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 37: ASIA PACIFIC: CONTENT RECOMMENDATION ENGINE MARKET, BY ORGANIZATION SIZE, 2015-2022 (USD MILLION)
  • TABLE 38: ASIA PACIFIC: CONTENT RECOMMENDATION ENGINE MARKET, BY VERTICAL, 2015-2022 (USD MILLION)
  • TABLE 39: ASIA PACIFIC: CONTENT RECOMMENDATION ENGINE MARKET FOR E-COMMERCE, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 40: ASIA PACIFIC: CONTENT RECOMMENDATION ENGINE MARKET FOR MEDIA, ENTERTAINMENT & GAMING, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 41: ASIA PACIFIC: CONTENT RECOMMENDATION ENGINE MARKET FOR RETAIL & CONSUMER GOODS, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 42: ASIA PACIFIC: CONTENT RECOMMENDATION ENGINE MARKET FOR HOSPITALITY, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 43: ASIA PACIFIC: CONTENT RECOMMENDATION ENGINE MARKET FOR IT & TELECOMMUNICATION, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 44: ASIA PACIFIC: CONTENT RECOMMENDATION ENGINE MARKET FOR BFSI, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 45: MIDDLE EAST & AFRICA: CONTENT RECOMMENDATION ENGINE MARKET, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 46: MIDDLE EAST & AFRICA: CONTENT RECOMMENDATION ENGINE MARKET, BY ORGANIZATION SIZE, 2015-2022 (USD MILLION)
  • TABLE 47: MIDDLE EAST & AFRICA: CONTENT RECOMMENDATION ENGINE MARKET, BY VERTICAL, 2015-2022 (USD MILLION)
  • TABLE 48: MIDDLE EAST & AFRICA: CONTENT RECOMMENDATION ENGINE MARKET FOR E-COMMERCE, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 49: MIDDLE EAST & AFRICA: CONTENT RECOMMENDATION ENGINE MARKET FOR MEDIA, ENTERTAINMENT & GAMING, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 50: MIDDLE EAST & AFRICA: CONTENT RECOMMENDATION ENGINE MARKET FOR RETAIL & CONSUMER GOODS, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 51: MIDDLE EAST & AFRICA: CONTENT RECOMMENDATION ENGINE MARKET FOR HOSPITALITY, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 52: MIDDLE EAST & AFRICA: CONTENT RECOMMENDATION ENGINE MARKET FOR IT & TELECOMMUNICATION, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 53: MIDDLE EAST & AFRICA: CONTENT RECOMMENDATION ENGINE MARKET FOR BFSI, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 54: LATIN AMERICA: CONTENT RECOMMENDATION ENGINE MARKET, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 55: LATIN AMERICA: CONTENT RECOMMENDATION ENGINE MARKET, BY ORGANIZATION SIZE, 2015-2022 (USD MILLION)
  • TABLE 56: LATIN AMERICA: CONTENT RECOMMENDATION ENGINE MARKET, BY VERTICAL, 2015-2022 (USD MILLION)
  • TABLE 57: LATIN AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR E-COMMERCE, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 58: LATIN AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR MEDIA, ENTERTAINMENT & GAMING, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 59: LATIN AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR RETAIL & CONSUMER GOODS, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 60: LATIN AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR HOSPITALITY, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 61: LATIN AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR IT & TELECOMMUNICATION, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 62: LATIN AMERICA: CONTENT RECOMMENDATION ENGINE MARKET FOR BFSI, BY COMPONENT, 2015-2022 (USD MILLION)
  • TABLE 63: MARKET EVALUATION FRAMEWORK
  • TABLE 64: NEW PRODUCT LAUNCHES & PRODUCT ENHANCEMENTS, APRIL 2017- JANUARY 2018
  • TABLE 65: AGREEMENTS, COLLABORATIONS & PARTNERSHIPS, JULY 2017- JANUARY 2018
  • TABLE 66: ACQUISITIONS, JUNE 2015- FEBRUARY 2017
  • TABLE 67: EXPANSIONS, MAY 2015-JUNE 2017
  • TABLE 68: MARKET RANKING OF KEY PLAYERS IN THE CONTENT RECOMMENDATION ENGINE MARKET, 2017

LIST OF FIGURES

  • FIGURE 1: MARKET SEGMENTATION
  • FIGURE 2: CONTENT RECOMMENDATION ENGINE MARKET: RESEARCH DESIGN
  • FIGURE 3: DATA TRIANGULATION
  • FIGURE 4: MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
  • FIGURE 5: MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
  • FIGURE 6: ASSUMPTIONS
  • FIGURE 7: CONTENT RECOMMENDATION ENGINE MARKET, BY COMPONENT, 2017 & 2022 (USD MILLION)
  • FIGURE 8: CONTENT RECOMMENDATION ENGINE MARKET, BY ORGANIZATION SIZE, 2017 & 2022 (USD MILLION)
  • FIGURE 9: CONTENT RECOMMENDATION ENGINE MARKET, BY VERTICAL, 2017
  • FIGURE 10: CONTENT RECOMMENDATION ENGINE MARKET, BY REGION, 2017 & 2022 (USD MILLION)
  • FIGURE 11: ASIA PACIFIC CONTENT RECOMMENDATION ENGINE MARKET IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
  • FIGURE 12: FOCUS ON ENHANCING CUSTOMER EXPERIENCE IS DRIVING THE CONTENT RECOMMENDATION ENGINE MARKET
  • FIGURE 13: SERVICE COMPONENT SEGMENT IS PROJECTED TO GROW AT A HIGHER CAGR DURING THE FORECAST PERIOD
  • FIGURE 14: SMALL AND MEDIUM ENTERPRISES SEGMENT IS EXPECTED TO GROW AT A HIGHER CAGR FROM 2017 TO 2022
  • FIGURE 15: RETAIL & CONSUMER GOODS SEGMENT IS PROJECTED TO GROW AT THE HIGHEST CAGR DURING FORECAST PERIOD
  • FIGURE 16: NORTH AMERICA IS ESTIMATED TO LEAD THE CONTENT RECOMMENDATION ENGINE MARKET IN 2017
  • FIGURE 17: CONTENT RECOMMENDATION ENGINE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
  • FIGURE 18: RECOMMENDATION ENGINE: PHASES OF PERSONALIZATION
  • FIGURE 19: RECOMMENDATION ENGINE: FILTERING APPROACHES
  • FIGURE 20: THE SERVICE SEGMENT IS ESTIMATED TO GROW AT A HIGHER CAGR AS COMPARED TO THE SOLUTION SEGMENT DURING THE FORECAST PERIOD
  • FIGURE 21: BASED ON ORGANIZATION SIZE, THE LARGE ENTERPRISES SEGMENT IS ESTIMATED TO ACCOUNT FOR A LARGER SHARE OF THE CONTENT RECOMMENDATION ENGINE MARKET IN 2017
  • FIGURE 22: THE RETAIL & CONSUMER GOODS VERTICAL SEGMENT IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
  • FIGURE 23: NORTH AMERICA IS ESTIMATED TO BE THE LARGEST MARKET FOR CONTENT RECOMMENDATION ENGINE MARKET IN 2017
  • FIGURE 24: THE CONTENT RECOMMENDATION ENGINE MARKET IN ASIA PACIFIC AND LATIN AMERICA IS PROJECTED TO REGISTER HIGH CAGRS DURING THE FORECAST PERIOD
  • FIGURE 25: NORTH AMERICA: CONTENT RECOMMENDATION ENGINE MARKET SNAPSHOT
  • FIGURE 26: ASIA PACIFIC: CONTENT RECOMMENDATION ENGINE MARKET SNAPSHOT
  • FIGURE 27: COMPANIES ADOPTED AGREEMENTS, COLLABORATIONS & PARTNERSHIPS AS KEY GROWTH STRATEGIES BETWEEN MAY 2015 AND JANUARY 2018 80  
  • FIGURE 28: IBM: COMPANY SNAPSHOT
  • FIGURE 29: IBM: SWOT ANALYSIS
  • FIGURE 30: AMAZON WEB SERVICES: COMPANY SNAPSHOT
  • FIGURE 31: AMAZON WEB SERVICES: SWOT ANALYSIS
  • FIGURE 32: REVCONTENT: SWOT ANALYSIS
  • FIGURE 33: TABOOLA: SWOT ANALYSIS
  • FIGURE 34: OUTBRAIN: SWOT ANALYSIS
  • FIGURE 35: CXENSE: COMPANY SNAPSHOT
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