Recommendation Engine Market by Type (Collaborative Filtering, Content-Based Filtering, and Hybrid Recommendation), Deployment Mode (Cloud and On-Premises), Technology, Application, End-User, and Region - Global Forecast to 2022

リコメンデーション・エンジンの世界市場予測(~2022年)

◆タイトル:Recommendation Engine Market by Type (Collaborative Filtering, Content-Based Filtering, and Hybrid Recommendation), Deployment Mode (Cloud and On-Premises), Technology, Application, End-User, and Region - Global Forecast to 2022
◆商品コード:TC-6096
◆調査・発行会社:MarketsandMarkets
◆発行日:2018年3月15日
◆ページ数:145
◆レポート形式:PDF / 英語
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【レポートの概要】

MarketsandMarketsが発行した当調査レポートでは、リコメンデーション・エンジンの世界市場について調査・分析し、エグゼクティブサマリー、市場インサイト、市場概観/市場動向、産業動向、種類別分析、導入方式別分析、技術別分析、地域別分析、リコメンデーション・エンジンの世界市場規模及び予測、市場動向、競争状況、関連企業分析などの情報をお届けいたします。

The recommendation engine market based on AI, is projected to grow at a CAGR of 40.7% during the forecast periodThe market for recommendation engine based on AI, is expected to grow from USD 801.1 million in 2017 to USD 4414.8 million by 2022, at a Compound Annual Growth Rate (CAGR) of 40.7% during the forecast period. The growth in focus toward enhancing the customer experience is a major factor driving the growth of the recommendation engine market. Moreover, enhancing customer experience is important to achieve customer engagement and retention, thereby achieving higher sales and Return on Investment (RoI). However, designing of targeted campings, as well as relevant product and content recommendations, could help organizations engage more customers. Hence, analysis of customer data here plays a vital role to understand the customer behavior and preferences. Furthermore, to analyze a large volume of data and automate the manual and tedious process of designing recommendations, enterprises need to design and lay out a plan of action. This could be accomplished by appropriate implementation of AI recommendation engine solutions into their operations.
Further, concerns related to infrastructure compatibility is expected to be a major restraint for the growth of recommendation engine market. As technological compatibility is linked to proper implementation of AI-based recommendation engines, improper implementation could lead to damages in the working mechanism of AI recommendation engine software and solutions.
The hybrid recommendation type is expected to grow at the fastest rate during the forecast period
Based on type, the recommendation engine market, include collaborative filtering, content-based filtering, and hybrid recommendation. The hybrid recommendation type helps various organizations combine 2 different data filtering types to achieve more accurate recommendations. Hence, this contributes to the adoption of hybrid recommendation type in the AI-powered recommendation systems.

The APAC region is expected to witness the highest growth rate during the forecast period
Asia Pacific (APAC) is expected to grow at the highest CAGR in the global recommendation engine market during the forecast period. Moreover, several factors, such as rapid expansion of local enterprises, increase in infrastructure developments, and growth in need to analyze customer data have driven the adoption of recommendation engines across different end-users. The North American region is expected to account for the largest market size during the forecast period. The major driving factors for the market are increase in need to understand the customer behavior and preferences and the need to achieve business insights from a large number of data to formulate various customer engagement strategies.
In the process to determine and verify the market size for several segments and subsegments gathered through secondary research, extensive primary interviews were conducted with key people.
• By Company Type – Tier 1 – 18%, Tier 2 – 47%, and Tier 3 –35%
• By Designation – C-level – 22%, Director-level – 42%, and Others – 36%
• By Region – North America – 24%, Europe– 48%, APAC – 16%, and MEA – 12%
The major vendors in the global recommendation engine market based on AI, are IBM (US), SAP (Germany), Salesforce (US), HPE (US), Oracle (US), Google (US), Microsoft (US), Intel (US), AWS (US), and Sentient Technologies (US).

 AI recommendation engine software and platform providers
 Venture capitalists and angel investors
 Information Technology (IT) management directors/managers
 Government organizations
 Research organizations
 Consultants/advisory firms
 IT governance directors/managers
 AI system integrators
 Managed Service Providers (MSPs)
 Value-added Resellers (VARs)

Research Coverage
The recommendation engine market powered by AI, has been segmented on the basis of types (collaborative filtering, content-based filtering, and hybrid recommendation), deployment modes, technologies, applications, end-users, and regions. The recommendation solutions help AI recommendation software and platform providers; venture capitalists/angel investors; IT management directors/managers; and BFSI, healthcare, retail, media and entertainment, and government organizations to improve business operations, enhance decision-making, and reduce costs. The deployment modes in the recommendation engine market are cloud and on-premises. Applications are segmented into personalized campaigns and customer discovery, product planning, strategy and operations planning, and proactive asset management. The technologies involved in the recommendation engine market are context aware and geospatial aware. The end-users segment includes BFSI, retail, healthcare, media and entertainment, transportation, and others (telecom, energy and utilities, manufacturing, and education). On the basis of regions, recommendation engine is segmented into North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America.
The report is expected to help the market leaders and new entrants in the recommendation engine market based on AI, in the following ways:
1. The report segments the market into various subsegments, hence it covers the market comprehensively. The report provides the closest approximations of the revenue numbers for the overall market and subsegments. The market numbers are further split into different application areas and regions.
2. The report helps to understand the overall growth of the market. It provides information on the key market drivers, restraints, challenges, and opportunities.
3. The report helps to better understand competitors and gain more insights to strengthen organizations position in the market. In addition, the study presents the positioning of the key players based on their product offerings and business strategies.

【レポートの目次】

TABLE OF CONTENTS

1 INTRODUCTION 14
1.1 OBJECTIVES OF THE STUDY 14
1.2 MARKET DEFINITION 15
1.3 YEARS CONSIDERED FOR THE STUDY 15
1.4 CURRENCY 16
1.5 STAKEHOLDERS 16
2 RESEARCH METHODOLOGY 17
2.1 RESEARCH DATA 17
2.1.1 SECONDARY DATA 18
2.1.2 PRIMARY DATA 18
2.1.2.1 Breakdown of primaries 18
2.1.2.2 Key industry insights 19
2.2 MARKET SIZE ESTIMATION 20
2.3 RESEARCH ASSUMPTIONS 22
2.3.1 AI RECOMMENDATION ENGINE MARKET: ASSUMPTIONS 22
2.4 LIMITATIONS 22
3 EXECUTIVE SUMMARY 23
4 PREMIUM INSIGHTS 28
4.1 ATTRACTIVE MARKET OPPORTUNITIES IN THE AI RECOMMENDATION ENGINE MARKET 28
4.2 AI RECOMMENDATION ENGINE MARKET, BY END-USER 28
4.3 AI RECOMMENDATION ENGINE MARKET, BY REGION 29
4.4 MARKET INVESTMENT SCENARIO 30
5 MARKET OVERVIEW AND INDUSTRY TRENDS 31
5.1 INTRODUCTION 31
5.2 AI RECOMMENDATION ENGINE AND DATA FILTERING MODELS 31
5.3 AI RECOMMENDATION ENGINE MARKET: USE CASES 32
5.3.1 USE CASE #1: AI-POWERED RECOMMENDATION SOLUTION TO INCREASE REVENUE IN THE ECOMMERCE SECTOR 32
5.3.2 USE CASE #2: AI-POWERED CUSTOMER RELATIONSHIP MANAGEMENT (CRM) SOLUTION TO DRIVE CUSTOMER ENGAGEMENT IN THE HOSPITALITY SECTOR 33
5.3.3 USE CASE: AI-POWERED RECOMMENDATION SOLUTION TO INCREASE CUSTOMER ENGAGEMENT IN THE ECOMMERCE SECTOR 33
5.3.4 USE CASE: AI-POWERED RECOMMENDATION SOLUTION TO GENERATE MORE ORDERS AND INCREASE REVENUE IN THE RETAIL SECTOR 34

5.4 MARKET DYNAMICS 35
5.4.1 DRIVERS 35
5.4.1.1 Increasing focus on enhancing the customer experience 35
5.4.1.2 Growing trend of digitalization 36
5.4.2 RESTRAINTS 36
5.4.2.1 Concerns over infrastructure compatibility 36
5.4.3 OPPORTUNITIES 36
5.4.3.1 Growing use of the deep learning technology in AI recommendation engine solutions 36
5.4.3.2 Increasing demand to analyze large volumes of data 37
5.4.4 CHALLENGES 37
5.4.4.1 Concerns over accessing customers’ personal data 37
5.4.4.2 Lack of skills and expertise 38
6 AI RECOMMENDATION ENGINE MARKET, BY TYPE 39
6.1 INTRODUCTION 40
6.2 COLLABORATIVE FILTERING 41
6.3 CONTENT-BASED FILTERING 42
6.4 HYBRID RECOMMENDATION 43
7 AI RECOMMENDATION ENGINE MARKET, BY TECHNOLOGY 44
7.1 INTRODUCTION 45
7.2 CONTEXT AWARE 46
7.2.1 MACHINE LEARNING AND DEEP LEARNING 47
7.2.2 NATURAL LANGUAGE PROCESSING 48
7.3 GEOSPATIAL AWARE 49
8 AI RECOMMENDATION ENGINE MARKET, BY APPLICATION 50
8.1 INTRODUCTION 51
8.2 PERSONALIZED CAMPAIGNS AND CUSTOMER DISCOVERY 52
8.3 PRODUCT PLANNING 53
8.4 STRATEGY AND OPERATIONS PLANNING 54
8.5 PROACTIVE ASSET MANAGEMENT 54
8.6 OTHERS 55
9 AI RECOMMENDATION ENGINE MARKET, BY DEPLOYMENT MODE 56
9.1 INTRODUCTION 57
9.2 CLOUD 58
9.3 ON-PREMISES 59

10 AI RECOMMENDATION ENGINE MARKET, BY END-USER 60
10.1 INTRODUCTION 61
10.2 RETAIL 62
10.3 MEDIA AND ENTERTAINMENT 63
10.4 TRANSPORTATION 64
10.5 BANKING, FINANCIAL SERVICES, AND INSURANCE 64
10.6 HEALTHCARE 65
10.7 OTHERS 66
11 AI RECOMMENDATION ENGINE MARKET, BY REGION 67
11.1 INTRODUCTION 68
11.2 NORTH AMERICA 69
11.2.1 UNITED STATES 71
11.2.2 CANADA 71
11.3 EUROPE 75
11.3.1 UNITED KINGDOM 76
11.3.2 GERMANY 76
11.3.3 SWITZERLAND 76
11.3.4 REST OF EUROPE 76
11.4 ASIA PACIFIC 81
11.4.1 CHINA 82
11.4.2 JAPAN 83
11.4.3 REST OF ASIA PACIFIC 83
11.5 MIDDLE EAST AND AFRICA 87
11.5.1 MIDDLE EAST 87
11.5.2 AFRICA 87
11.6 LATIN AMERICA 91
11.6.1 BRAZIL 91
11.6.2 REST OF LATIN AMERICA 91
12 COMPETITIVE LANDSCAPE 96
12.1 OVERVIEW 96
12.2 TOP PLAYERS OPERATING IN THE AI RECOMMENDATION ENGINE MARKET 97
12.3 COMPETITIVE SCENARIO 98
12.3.1 NEW PRODUCT LAUNCHES/PRODUCT ENHANCEMENTS 98
12.3.2 PARTNERSHIPS, AGREEMENTS, AND COLLABORATIONS 99
12.3.3 MERGERS AND ACQUISITIONS 101
12.3.4 BUSINESS EXPANSIONS 102

13 COMPANY PROFILES 103
13.1 INTRODUCTION 103
(Business Overview, Products/Solutions and Services Offered, Recent Developments, SWOT Analysis, MnM View)*
13.2 IBM 104
13.3 GOOGLE 108
13.4 AWS 111
13.5 MICROSOFT 114
13.6 SALESFORCE 117
13.7 SENTIENT TECHNOLOGIES 120
13.8 HPE 122
13.9 ORACLE 124
13.10 INTEL 127
13.11 SAP 130
*Details on Business Overview, Products/Solutions and Services Offered, Recent Developments, SWOT Analysis, MnM View might not be captured in case of unlisted companies.
13.12 KEY INNOVATORS 133
13.12.1 FUZZY.AI 133
13.12.2 INFINITE ANALYTICS 133
14 APPENDIX 135
14.1 DISCUSSION GUIDE 135
14.2 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 139
14.3 INTRODUCING RT: REAL-TIME MARKET INTELLIGENCE 141
14.4 AVAILABLE CUSTOMIZATIONS 142
14.5 RELATED REPORTS 142
14.6 AUTHOR DETAILS 143

LIST OF TABLES

TABLE 1 GLOBAL AI RECOMMENDATION ENGINE MARKET SIZE AND GROWTH RATE,
2015–2022 (USD MILLION, Y-O-Y %) 24
TABLE 2 AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE,
2015–2022 (USD MILLION) 40
TABLE 3 COLLABORATIVE FILTERING: AI RECOMMENDATION ENGINE MARKET SIZE,
BY REGION, 2015–2022 (USD MILLION) 41
TABLE 4 CONTENT-BASED FILTERING: AI RECOMMENDATION ENGINE MARKET SIZE,
BY REGION, 2015–2022 (USD MILLION) 42
TABLE 5 HYBRID RECOMMENDATION: AI RECOMMENDATION ENGINE MARKET SIZE,
BY REGION, 2015–2022 (USD MILLION) 43
TABLE 6 AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY,
2015–2022 (USD MILLION) 45
TABLE 7 CONTEXT AWARE: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE,
2015–2022 (USD MILLION) 46
TABLE 8 CONTEXT AWARE: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION) 47
TABLE 9 MACHINE LEARNING AND DEEP LEARNING MARKET SIZE, BY REGION,
2015–2022 (USD MILLION) 48
TABLE 10 NATURAL LANGUAGE PROCESSING MARKET SIZE, BY REGION,
2015–2022 (USD MILLION) 49
TABLE 11 GEOSPATIAL AWARE: AI RECOMMENDATION ENGINE MARKET SIZE,
BY REGION, 2015–2022 (USD MILLION) 49
TABLE 12 AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION,
2015–2022 (USD MILLION) 52
TABLE 13 PERSONALIZED CAMPAIGNS AND CUSTOMER DISCOVERY: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION) 53
TABLE 14 PRODUCT PLANNING: AI RECOMMENDATION ENGINE MARKET SIZE,
BY REGION, 2015–2022 (USD MILLION) 53
TABLE 15 STRATEGY AND OPERATIONS PLANNING: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION) 54
TABLE 16 PROACTIVE ASSET MANAGEMENT: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION) 55
TABLE 17 OTHERS: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION,
2015–2022 (USD MILLION) 55
TABLE 18 AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE,
2015–2022 (USD MILLION) 57
TABLE 19 CLOUD: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION,
2015–2022 (USD MILLION) 58
TABLE 20 ON-PREMISES: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION,
2015–2022 (USD MILLION) 59
TABLE 21 AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER,
2015–2022 (USD MILLION) 61
TABLE 22 RETAIL: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION,
2015–2022 (USD MILLION) 62
TABLE 23 MEDIA AND ENTERTAINMENT: AI RECOMMENDATION ENGINE MARKET SIZE,
BY REGION, 2015–2022 (USD MILLION) 63
TABLE 24 TRANSPORTATION: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION) 64
TABLE 25 BANKING, FINANCIAL SERVICES, AND INSURANCE: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION) 65
TABLE 26 HEALTHCARE: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION,
2015–2022 (USD MILLION) 65
TABLE 27 OTHERS: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION,
2015–2022 (USD MILLION) 66
TABLE 28 AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION,
2015–2022 (USD MILLION) 69
TABLE 29 DATA TRAFFIC IN NORTH AMERICA, 2016–2022 (PETABYTES/MONTH) 71
TABLE 30 MAJOR ERETAILERS IN NORTH AMERICA 71
TABLE 31 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY COUNTRY, 2015–2022 (USD MILLION) 73
TABLE 32 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY TYPE, 2015–2022 (USD MILLION) 73
TABLE 33 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY TECHNOLOGY, 2015–2022 (USD MILLION) 73
TABLE 34 NORTH AMERICA: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION) 74
TABLE 35 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY APPLICATION, 2015–2022 (USD MILLION) 74
TABLE 36 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY DEPLOYMENT MODE, 2015–2022 (USD MILLION) 75
TABLE 37 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY END-USER, 2015–2022 (USD MILLION) 75
TABLE 38 DATA TRAFFIC IN EUROPE, 2016–2022 (PETABYTES/MONTH) 76
TABLE 39 MAJOR ERETAILERS IN EUROPE 77
TABLE 40 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY,
2015–2022 (USD MILLION) 78
TABLE 41 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE,
2015–2022 (USD MILLION) 78
TABLE 42 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY,
2015–2022 (USD MILLION) 79
TABLE 43 EUROPE: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE,
BY TYPE, 2015–2022 (USD MILLION) 79
TABLE 44 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION,
2015–2022 (USD MILLION) 79
TABLE 45 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION) 80
TABLE 46 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER,
2015–2022 (USD MILLION) 80
TABLE 47 DATA TRAFFIC IN ASIA PACIFIC, 2016–2022 (PETABYTES/MONTH) 83
TABLE 48 MAJOR ERETAILERS IN ASIA PACIFIC 83
TABLE 49 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY,
2015–2022 (USD MILLION) 84
TABLE 50 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE,
2015–2022 (USD MILLION) 85
TABLE 51 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION) 85
TABLE 52 ASIA PACIFIC: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE,
BY TYPE, 2015–2022 (USD MILLION) 85
TABLE 53 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION) 86
TABLE 54 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION) 86
TABLE 55 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER,
2015–2022 (USD MILLION) 87
TABLE 56 DATA TRAFFIC IN MIDDLE EAST AND AFRICA, 2016–2022 (PETABYTES/MONTH) 87
TABLE 57 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY REGION, 2015–2022 (USD MILLION) 88
TABLE 58 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY TYPE, 2015–2022 (USD MILLION) 89
TABLE 59 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY TECHNOLOGY, 2015–2022 (USD MILLION) 89
TABLE 60 MIDDLE EAST AND AFRICA: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION) 89
TABLE 61 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY APPLICATION, 2015–2022 (USD MILLION) 90
TABLE 62 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY DEPLOYMENT MODE, 2015–2022 (USD MILLION) 90
TABLE 63 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY END-USER, 2015–2022 (USD MILLION) 91
TABLE 64 DATA TRAFFIC IN LATIN AMERICA, 2016–2022 (PETABYTES/MONTH) 91
TABLE 65 MAJOR ERETAILERS IN LATIN AMERICA 92
TABLE 66 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2015–2022 (USD MILLION) 92
TABLE 67 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE,
2015–2022 (USD MILLION) 93
TABLE 68 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION) 93
TABLE 69 LATIN AMERICA: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION) 93
TABLE 70 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION) 94
TABLE 71 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE,
BY DEPLOYMENT MODE, 2015–2022 (USD MILLION) 94
TABLE 72 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION) 95
TABLE 73 NEW PRODUCT LAUNCHES/PRODUCT ENHANCEMENTS, 2016–2017 98
TABLE 74 PARTNERSHIPS, AGREEMENTS, AND COLLABORATIONS, 2015–2017 99
TABLE 75 MERGERS AND ACQUISITIONS, 2016–2017 101
TABLE 76 BUSINESS EXPANSIONS, 2017 102


LIST OF FIGURES

FIGURE 1 AI RECOMMENDATION ENGINE MARKET: MARKET SEGMENTATION 15
FIGURE 2 GLOBAL AI RECOMMENDATION ENGINE MARKET: RESEARCH DESIGN 17
FIGURE 3 BREAKDOWN OF PRIMARY INTERVIEWS: BY COMPANY, DESIGNATION, AND REGION 18
FIGURE 4 DATA TRIANGULATION 19
FIGURE 5 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH 20
FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH 21
FIGURE 7 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY TYPE (2017 VS. 2022) 24
FIGURE 8 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY DEPLOYMENT MODE 25
FIGURE 9 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY TECHNOLOGY 25
FIGURE 10 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY APPLICATION
(2017 VS. 2022) 26
FIGURE 11 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY END-USER 26
FIGURE 12 AI RECOMMENDATION ENGINE MARKET: REGIONAL SNAPSHOT 27
FIGURE 13 INCREASING FOCUS ON ENHANCING THE CUSTOMER EXPERIENCE IS EXPECTED TO DRIVE THE GROWTH OF THE AI RECOMMENDATION ENGINE MARKET DURING THE FORECAST PERIOD 28
FIGURE 14 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT
THE HIGHEST CAGR DURING THE FORECAST PERIOD 28
FIGURE 15 ASIA PACIFIC IS EXPECTED TO HAVE THE FASTEST GROWTH RATE DURING
THE FORECAST PERIOD 29
FIGURE 16 ASIA PACIFIC IS EXPECTED TO BE THE BEST MARKET TO INVESTMENT IN, IN THE NEXT 5 YEARS 30
FIGURE 17 AI RECOMMENDATION ENGINE: DATA FILTERING MODELS 31
FIGURE 18 AI RECOMMENDATION ENGINE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES 35
FIGURE 19 HYBRID RECOMMENDATION SEGMENT IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD 40
FIGURE 20 GEOSPATIAL AWARE SEGMENT IS EXPECTED TO EXHIBIT A HIGHER CAGR DURING THE FORECAST PERIOD 45
FIGURE 21 NATURAL LANGUAGE PROCESSING SEGMENT IS EXPECTED TO EXHIBIT A HIGHER CAGR DURING THE FORECAST PERIOD 46
FIGURE 22 STRATEGY AND OPERATIONS PLANNING APPLICATION IS EXPECTED TO EXHIBIT THE HIGHEST CAGR DURING THE FORECAST PERIOD 51
FIGURE 23 CLOUD DEPLOYMENT MODE IS EXPECTED TO EXHIBIT A HIGHER CAGR DURING THE FORECAST PERIOD 57
FIGURE 24 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO EXHIBIT THE HIGHEST CAGR DURING THE FORECAST PERIOD 61
FIGURE 25 ASIA PACIFIC IS EXPECTED TO HAVE THE HIGHEST CAGR IN THE AI RECOMMENDATION ENGINE MARKET DURING THE FORECAST PERIOD 68
FIGURE 26 ASIA PACIFIC IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD 69
FIGURE 27 NORTH AMERICA: MARKET SNAPSHOT 70
FIGURE 28 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD 72
FIGURE 29 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD 77
FIGURE 30 ASIA PACIFIC: MARKET SNAPSHOT 82
FIGURE 31 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD 84
FIGURE 32 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD 88
FIGURE 33 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD 92
FIGURE 34 KEY DEVELOPMENTS BY THE LEADING PLAYERS IN THE AI RECOMMENDATION ENGINE MARKET 96
FIGURE 35 GEOGRAPHIC REVENUE MIX OF MARKET PLAYERS 103
FIGURE 36 IBM: COMPANY SNAPSHOT 104
FIGURE 37 IBM: SWOT ANALYSIS 106
FIGURE 38 GOOGLE: COMPANY SNAPSHOT 108
FIGURE 39 GOOGLE: SWOT ANALYSIS 110
FIGURE 40 AWS: COMPANY SNAPSHOT 111
FIGURE 41 AWS: SWOT ANALYSIS 112
FIGURE 42 MICROSOFT: COMPANY SNAPSHOT 114
FIGURE 43 MICROSOFT: SWOT ANALYSIS 116
FIGURE 44 SALESFORCE: COMPANY SNAPSHOT 117
FIGURE 45 SALESFORCE: SWOT ANALYSIS 119
FIGURE 46 HPE: COMPANY SNAPSHOT 122
FIGURE 47 ORACLE: COMPANY SNAPSHOT 124
FIGURE 48 INTEL: COMPANY SNAPSHOT 127
FIGURE 49 SAP: COMPANY SNAPSHOT 130


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