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
The recommendation engine market based on AI, is projected to grow at a CAGR of 40.7% during the forecast period
The 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.
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.
The 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%
- 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)
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.
1 INTRODUCTION
1.1 OBJECTIVES OF THE STUDY
1.2 MARKET DEFINITION
1.3 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
2.3.1 AI RECOMMENDATION ENGINE MARKET: ASSUMPTIONS
2.4 LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE MARKET OPPORTUNITIES IN THE AI RECOMMENDATION ENGINE MARKET
4.2 AI RECOMMENDATION ENGINE MARKET, BY END-USER
4.3 AI RECOMMENDATION ENGINE MARKET, BY REGION
4.4 MARKET INVESTMENT SCENARIO
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 AI RECOMMENDATION ENGINE AND DATA FILTERING MODELS
5.3 AI RECOMMENDATION ENGINE MARKET: USE CASES
5.3.1 USE CASE #1: AI-POWERED RECOMMENDATION SOLUTION TO INCREASE REVENUE IN THE ECOMMERCE SECTOR
5.3.2 USE CASE #2: AI-POWERED CUSTOMER RELATIONSHIP MANAGEMENT (CRM) SOLUTION TO DRIVE CUSTOMER ENGAGEMENT IN THE HOSPITALITY SECTOR
5.3.3 USE CASE: AI-POWERED RECOMMENDATION SOLUTION TO INCREASE CUSTOMER ENGAGEMENT IN THE ECOMMERCE SECTOR
5.3.4 USE CASE: AI-POWERED RECOMMENDATION SOLUTION TO GENERATE MORE ORDERS AND INCREASE REVENUE IN THE RETAIL SECTOR
5.4 MARKET DYNAMICS
5.4.1 DRIVERS
5.4.1.1 Increasing focus on enhancing the customer experience
5.4.1.2 Growing trend of digitalization
5.4.2 RESTRAINTS
5.4.2.1 Concerns over infrastructure compatibility
5.4.3 OPPORTUNITIES
5.4.3.1 Growing use of the deep learning technology in AI recommendation engine solutions
5.4.3.2 Increasing demand to analyze large volumes of data
5.4.4 CHALLENGES
5.4.4.1 Concerns over accessing customers’ personal data
5.4.4.2 Lack of skills and expertise
6 AI RECOMMENDATION ENGINE MARKET, BY TYPE
6.1 INTRODUCTION
6.2 COLLABORATIVE FILTERING
6.3 CONTENT-BASED FILTERING
6.4 HYBRID RECOMMENDATION
7 AI RECOMMENDATION ENGINE MARKET, BY TECHNOLOGY
7.1 INTRODUCTION
7.2 CONTEXT AWARE
7.2.1 MACHINE LEARNING AND DEEP LEARNING
7.2.2 NATURAL LANGUAGE PROCESSING
7.3 GEOSPATIAL AWARE
8 AI RECOMMENDATION ENGINE MARKET, BY APPLICATION
8.1 INTRODUCTION
8.2 PERSONALIZED CAMPAIGNS AND CUSTOMER DISCOVERY
8.3 PRODUCT PLANNING
8.4 STRATEGY AND OPERATIONS PLANNING
8.5 PROACTIVE ASSET MANAGEMENT
8.6 OTHERS
9 AI RECOMMENDATION ENGINE MARKET, BY DEPLOYMENT MODE
9.1 INTRODUCTION
9.2 CLOUD
9.3 ON-PREMISES
10 AI RECOMMENDATION ENGINE MARKET, BY END-USER
10.1 INTRODUCTION
10.2 RETAIL
10.3 MEDIA AND ENTERTAINMENT
10.4 TRANSPORTATION
10.5 BANKING, FINANCIAL SERVICES, AND INSURANCE
10.6 HEALTHCARE
10.7 OTHERS
11 AI RECOMMENDATION ENGINE MARKET, BY REGION
11.1 INTRODUCTION
11.2 NORTH AMERICA
11.2.1 UNITED STATES
11.2.2 CANADA
11.3 EUROPE
11.3.1 UNITED KINGDOM
11.3.2 GERMANY
11.3.3 SWITZERLAND
11.3.4 REST OF EUROPE
11.4 ASIA PACIFIC
11.4.1 CHINA
11.4.2 JAPAN
11.4.3 REST OF ASIA PACIFIC
11.5 MIDDLE EAST AND AFRICA
11.5.1 MIDDLE EAST
11.5.2 AFRICA
11.6 LATIN AMERICA
11.6.1 BRAZIL
11.6.2 REST OF LATIN AMERICA
12 COMPETITIVE LANDSCAPE
12.1 OVERVIEW
12.2 TOP PLAYERS OPERATING IN THE AI RECOMMENDATION ENGINE MARKET
12.3 COMPETITIVE SCENARIO
12.3.1 NEW PRODUCT LAUNCHES/PRODUCT ENHANCEMENTS
12.3.2 PARTNERSHIPS, AGREEMENTS, AND COLLABORATIONS
12.3.3 MERGERS AND ACQUISITIONS
12.3.4 BUSINESS EXPANSIONS
13 COMPANY PROFILES
13.1 INTRODUCTION
(Business Overview, Products/Solutions and Services Offered, Recent Developments, SWOT Analysis, MnM View)*
13.2 IBM
13.3 GOOGLE
13.4 AWS
13.5 MICROSOFT
13.6 SALESFORCE
13.7 SENTIENT TECHNOLOGIES
13.8 HPE
13.9 ORACLE
13.10 INTEL
13.11 SAP
*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
13.12.1 FUZZY.AI
13.12.2 INFINITE ANALYTICS
14 APPENDIX
14.1 DISCUSSION GUIDE
14.2 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.3 INTRODUCING RT: REAL-TIME MARKET INTELLIGENCE
14.4 AVAILABLE CUSTOMIZATIONS
14.5 RELATED REPORTS
14.6 AUTHOR DETAILS
1.1 OBJECTIVES OF THE STUDY
1.2 MARKET DEFINITION
1.3 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
2.3.1 AI RECOMMENDATION ENGINE MARKET: ASSUMPTIONS
2.4 LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE MARKET OPPORTUNITIES IN THE AI RECOMMENDATION ENGINE MARKET
4.2 AI RECOMMENDATION ENGINE MARKET, BY END-USER
4.3 AI RECOMMENDATION ENGINE MARKET, BY REGION
4.4 MARKET INVESTMENT SCENARIO
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 AI RECOMMENDATION ENGINE AND DATA FILTERING MODELS
5.3 AI RECOMMENDATION ENGINE MARKET: USE CASES
5.3.1 USE CASE #1: AI-POWERED RECOMMENDATION SOLUTION TO INCREASE REVENUE IN THE ECOMMERCE SECTOR
5.3.2 USE CASE #2: AI-POWERED CUSTOMER RELATIONSHIP MANAGEMENT (CRM) SOLUTION TO DRIVE CUSTOMER ENGAGEMENT IN THE HOSPITALITY SECTOR
5.3.3 USE CASE: AI-POWERED RECOMMENDATION SOLUTION TO INCREASE CUSTOMER ENGAGEMENT IN THE ECOMMERCE SECTOR
5.3.4 USE CASE: AI-POWERED RECOMMENDATION SOLUTION TO GENERATE MORE ORDERS AND INCREASE REVENUE IN THE RETAIL SECTOR
5.4 MARKET DYNAMICS
5.4.1 DRIVERS
5.4.1.1 Increasing focus on enhancing the customer experience
5.4.1.2 Growing trend of digitalization
5.4.2 RESTRAINTS
5.4.2.1 Concerns over infrastructure compatibility
5.4.3 OPPORTUNITIES
5.4.3.1 Growing use of the deep learning technology in AI recommendation engine solutions
5.4.3.2 Increasing demand to analyze large volumes of data
5.4.4 CHALLENGES
5.4.4.1 Concerns over accessing customers’ personal data
5.4.4.2 Lack of skills and expertise
6 AI RECOMMENDATION ENGINE MARKET, BY TYPE
6.1 INTRODUCTION
6.2 COLLABORATIVE FILTERING
6.3 CONTENT-BASED FILTERING
6.4 HYBRID RECOMMENDATION
7 AI RECOMMENDATION ENGINE MARKET, BY TECHNOLOGY
7.1 INTRODUCTION
7.2 CONTEXT AWARE
7.2.1 MACHINE LEARNING AND DEEP LEARNING
7.2.2 NATURAL LANGUAGE PROCESSING
7.3 GEOSPATIAL AWARE
8 AI RECOMMENDATION ENGINE MARKET, BY APPLICATION
8.1 INTRODUCTION
8.2 PERSONALIZED CAMPAIGNS AND CUSTOMER DISCOVERY
8.3 PRODUCT PLANNING
8.4 STRATEGY AND OPERATIONS PLANNING
8.5 PROACTIVE ASSET MANAGEMENT
8.6 OTHERS
9 AI RECOMMENDATION ENGINE MARKET, BY DEPLOYMENT MODE
9.1 INTRODUCTION
9.2 CLOUD
9.3 ON-PREMISES
10 AI RECOMMENDATION ENGINE MARKET, BY END-USER
10.1 INTRODUCTION
10.2 RETAIL
10.3 MEDIA AND ENTERTAINMENT
10.4 TRANSPORTATION
10.5 BANKING, FINANCIAL SERVICES, AND INSURANCE
10.6 HEALTHCARE
10.7 OTHERS
11 AI RECOMMENDATION ENGINE MARKET, BY REGION
11.1 INTRODUCTION
11.2 NORTH AMERICA
11.2.1 UNITED STATES
11.2.2 CANADA
11.3 EUROPE
11.3.1 UNITED KINGDOM
11.3.2 GERMANY
11.3.3 SWITZERLAND
11.3.4 REST OF EUROPE
11.4 ASIA PACIFIC
11.4.1 CHINA
11.4.2 JAPAN
11.4.3 REST OF ASIA PACIFIC
11.5 MIDDLE EAST AND AFRICA
11.5.1 MIDDLE EAST
11.5.2 AFRICA
11.6 LATIN AMERICA
11.6.1 BRAZIL
11.6.2 REST OF LATIN AMERICA
12 COMPETITIVE LANDSCAPE
12.1 OVERVIEW
12.2 TOP PLAYERS OPERATING IN THE AI RECOMMENDATION ENGINE MARKET
12.3 COMPETITIVE SCENARIO
12.3.1 NEW PRODUCT LAUNCHES/PRODUCT ENHANCEMENTS
12.3.2 PARTNERSHIPS, AGREEMENTS, AND COLLABORATIONS
12.3.3 MERGERS AND ACQUISITIONS
12.3.4 BUSINESS EXPANSIONS
13 COMPANY PROFILES
13.1 INTRODUCTION
(Business Overview, Products/Solutions and Services Offered, Recent Developments, SWOT Analysis, MnM View)*
13.2 IBM
13.3 GOOGLE
13.4 AWS
13.5 MICROSOFT
13.6 SALESFORCE
13.7 SENTIENT TECHNOLOGIES
13.8 HPE
13.9 ORACLE
13.10 INTEL
13.11 SAP
*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
13.12.1 FUZZY.AI
13.12.2 INFINITE ANALYTICS
14 APPENDIX
14.1 DISCUSSION GUIDE
14.2 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.3 INTRODUCING RT: REAL-TIME MARKET INTELLIGENCE
14.4 AVAILABLE CUSTOMIZATIONS
14.5 RELATED REPORTS
14.6 AUTHOR DETAILS
LIST OF TABLES
Table 1 GLOBAL AI RECOMMENDATION ENGINE MARKET SIZE AND GROWTH RATE, 2015–2022 (USD MILLION, Y-O-Y %)
Table 2 AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 3 COLLABORATIVE FILTERING: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 4 CONTENT-BASED FILTERING: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 5 HYBRID RECOMMENDATION: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 6 AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 7 CONTEXT AWARE: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 8 CONTEXT AWARE: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 9 MACHINE LEARNING AND DEEP LEARNING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 10 NATURAL LANGUAGE PROCESSING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 11 GEOSPATIAL AWARE: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 12 AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 13 PERSONALIZED CAMPAIGNS AND CUSTOMER DISCOVERY: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 14 PRODUCT PLANNING: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 15 STRATEGY AND OPERATIONS PLANNING: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 16 PROACTIVE ASSET MANAGEMENT: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 17 OTHERS: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 18 AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 19 CLOUD: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 20 ON-PREMISES: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 21 AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 22 RETAIL: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 23 MEDIA AND ENTERTAINMENT: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 24 TRANSPORTATION: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 25 BANKING, FINANCIAL SERVICES, AND INSURANCE: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 26 HEALTHCARE: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 27 OTHERS: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 28 AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 29 DATA TRAFFIC IN NORTH AMERICA, 2016–2022 (PETABYTES/MONTH)
Table 30 MAJOR ERETAILERS IN NORTH AMERICA
Table 31 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2015–2022 (USD MILLION)
Table 32 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 33 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 34 NORTH AMERICA: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 35 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 36 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 37 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 38 DATA TRAFFIC IN EUROPE, 2016–2022 (PETABYTES/MONTH)
Table 39 MAJOR ERETAILERS IN EUROPE
Table 40 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2015–2022 (USD MILLION)
Table 41 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 42 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 43 EUROPE: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 44 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 45 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 46 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 47 DATA TRAFFIC IN ASIA PACIFIC, 2016–2022 (PETABYTES/MONTH)
Table 48 MAJOR ERETAILERS IN ASIA PACIFIC
Table 49 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2015–2022 (USD MILLION)
Table 50 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 51 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 52 ASIA PACIFIC: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 53 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 54 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 55 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 56 DATA TRAFFIC IN MIDDLE EAST AND AFRICA, 2016–2022 (PETABYTES/MONTH)
Table 57 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 58 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 59 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 60 MIDDLE EAST AND AFRICA: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 61 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 62 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 63 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 64 DATA TRAFFIC IN LATIN AMERICA, 2016–2022 (PETABYTES/MONTH)
Table 65 MAJOR ERETAILERS IN LATIN AMERICA
Table 66 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2015–2022 (USD MILLION)
Table 67 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 68 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 69 LATIN AMERICA: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 70 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 71 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 72 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 73 NEW PRODUCT LAUNCHES/PRODUCT ENHANCEMENTS, 2016–2017
Table 74 PARTNERSHIPS, AGREEMENTS, AND COLLABORATIONS, 2015–2017
Table 75 MERGERS AND ACQUISITIONS, 2016–2017
Table 76 BUSINESS EXPANSIONS, 2017
Table 1 GLOBAL AI RECOMMENDATION ENGINE MARKET SIZE AND GROWTH RATE, 2015–2022 (USD MILLION, Y-O-Y %)
Table 2 AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 3 COLLABORATIVE FILTERING: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 4 CONTENT-BASED FILTERING: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 5 HYBRID RECOMMENDATION: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 6 AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 7 CONTEXT AWARE: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 8 CONTEXT AWARE: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 9 MACHINE LEARNING AND DEEP LEARNING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 10 NATURAL LANGUAGE PROCESSING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 11 GEOSPATIAL AWARE: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 12 AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 13 PERSONALIZED CAMPAIGNS AND CUSTOMER DISCOVERY: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 14 PRODUCT PLANNING: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 15 STRATEGY AND OPERATIONS PLANNING: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 16 PROACTIVE ASSET MANAGEMENT: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 17 OTHERS: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 18 AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 19 CLOUD: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 20 ON-PREMISES: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 21 AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 22 RETAIL: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 23 MEDIA AND ENTERTAINMENT: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 24 TRANSPORTATION: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 25 BANKING, FINANCIAL SERVICES, AND INSURANCE: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 26 HEALTHCARE: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 27 OTHERS: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 28 AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 29 DATA TRAFFIC IN NORTH AMERICA, 2016–2022 (PETABYTES/MONTH)
Table 30 MAJOR ERETAILERS IN NORTH AMERICA
Table 31 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2015–2022 (USD MILLION)
Table 32 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 33 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 34 NORTH AMERICA: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 35 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 36 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 37 NORTH AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 38 DATA TRAFFIC IN EUROPE, 2016–2022 (PETABYTES/MONTH)
Table 39 MAJOR ERETAILERS IN EUROPE
Table 40 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2015–2022 (USD MILLION)
Table 41 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 42 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 43 EUROPE: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 44 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 45 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 46 EUROPE: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 47 DATA TRAFFIC IN ASIA PACIFIC, 2016–2022 (PETABYTES/MONTH)
Table 48 MAJOR ERETAILERS IN ASIA PACIFIC
Table 49 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2015–2022 (USD MILLION)
Table 50 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 51 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 52 ASIA PACIFIC: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 53 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 54 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 55 ASIA PACIFIC: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 56 DATA TRAFFIC IN MIDDLE EAST AND AFRICA, 2016–2022 (PETABYTES/MONTH)
Table 57 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 58 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 59 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 60 MIDDLE EAST AND AFRICA: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 61 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 62 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 63 MIDDLE EAST AND AFRICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 64 DATA TRAFFIC IN LATIN AMERICA, 2016–2022 (PETABYTES/MONTH)
Table 65 MAJOR ERETAILERS IN LATIN AMERICA
Table 66 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY COUNTRY, 2015–2022 (USD MILLION)
Table 67 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 68 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 69 LATIN AMERICA: CONTEXT AWARE AI RECOMMENDATION ENGINE MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 70 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 71 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 72 LATIN AMERICA: AI RECOMMENDATION ENGINE MARKET SIZE, BY END-USER, 2015–2022 (USD MILLION)
Table 73 NEW PRODUCT LAUNCHES/PRODUCT ENHANCEMENTS, 2016–2017
Table 74 PARTNERSHIPS, AGREEMENTS, AND COLLABORATIONS, 2015–2017
Table 75 MERGERS AND ACQUISITIONS, 2016–2017
Table 76 BUSINESS EXPANSIONS, 2017
LIST OF FIGURES
Figure 1 AI RECOMMENDATION ENGINE MARKET: MARKET SEGMENTATION
Figure 2 GLOBAL AI RECOMMENDATION ENGINE MARKET: RESEARCH DESIGN
Figure 3 BREAKDOWN OF PRIMARY INTERVIEWS: BY COMPANY, DESIGNATION, AND REGION
Figure 4 DATA TRIANGULATION
Figure 5 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
Figure 6 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
Figure 7 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY TYPE (2017 VS. 2022)
Figure 8 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY DEPLOYMENT MODE
Figure 9 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY TECHNOLOGY
Figure 10 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY APPLICATION (2017 VS. 2022)
Figure 11 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY END-USER
Figure 12 AI RECOMMENDATION ENGINE MARKET: REGIONAL SNAPSHOT
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
Figure 14 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 15 ASIA PACIFIC IS EXPECTED TO HAVE THE FASTEST GROWTH RATE DURING THE FORECAST PERIOD
Figure 16 ASIA PACIFIC IS EXPECTED TO BE THE BEST MARKET TO INVESTMENT IN, IN THE NEXT 5 YEARS
Figure 17 AI RECOMMENDATION ENGINE: DATA FILTERING MODELS
Figure 18 AI RECOMMENDATION ENGINE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
Figure 19 HYBRID RECOMMENDATION SEGMENT IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 20 GEOSPATIAL AWARE SEGMENT IS EXPECTED TO EXHIBIT A HIGHER CAGR DURING THE FORECAST PERIOD
Figure 21 NATURAL LANGUAGE PROCESSING SEGMENT IS EXPECTED TO EXHIBIT A HIGHER CAGR DURING THE FORECAST PERIOD
Figure 22 STRATEGY AND OPERATIONS PLANNING APPLICATION IS EXPECTED TO EXHIBIT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 23 CLOUD DEPLOYMENT MODE IS EXPECTED TO EXHIBIT A HIGHER CAGR DURING THE FORECAST PERIOD
Figure 24 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO EXHIBIT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 25 ASIA PACIFIC IS EXPECTED TO HAVE THE HIGHEST CAGR IN THE AI RECOMMENDATION ENGINE MARKET DURING THE FORECAST PERIOD
Figure 26 ASIA PACIFIC IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 27 NORTH AMERICA: MARKET SNAPSHOT
Figure 28 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 29 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 30 ASIA PACIFIC: MARKET SNAPSHOT
Figure 31 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 32 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 33 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 34 KEY DEVELOPMENTS BY THE LEADING PLAYERS IN THE AI RECOMMENDATION ENGINE MARKET
Figure 35 GEOGRAPHIC REVENUE MIX OF MARKET PLAYERS
Figure 36 IBM: COMPANY SNAPSHOT
Figure 37 IBM: SWOT ANALYSIS
Figure 38 GOOGLE: COMPANY SNAPSHOT
Figure 39 GOOGLE: SWOT ANALYSIS
Figure 40 AWS: COMPANY SNAPSHOT
Figure 41 AWS: SWOT ANALYSIS
Figure 42 MICROSOFT: COMPANY SNAPSHOT
Figure 43 MICROSOFT: SWOT ANALYSIS
Figure 44 SALESFORCE: COMPANY SNAPSHOT
Figure 45 SALESFORCE: SWOT ANALYSIS
Figure 46 HPE: COMPANY SNAPSHOT
Figure 47 ORACLE: COMPANY SNAPSHOT
Figure 48 INTEL: COMPANY SNAPSHOT
Figure 49 SAP: COMPANY SNAPSHOT
Figure 1 AI RECOMMENDATION ENGINE MARKET: MARKET SEGMENTATION
Figure 2 GLOBAL AI RECOMMENDATION ENGINE MARKET: RESEARCH DESIGN
Figure 3 BREAKDOWN OF PRIMARY INTERVIEWS: BY COMPANY, DESIGNATION, AND REGION
Figure 4 DATA TRIANGULATION
Figure 5 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
Figure 6 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
Figure 7 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY TYPE (2017 VS. 2022)
Figure 8 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY DEPLOYMENT MODE
Figure 9 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY TECHNOLOGY
Figure 10 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY APPLICATION (2017 VS. 2022)
Figure 11 AI RECOMMENDATION ENGINE MARKET SNAPSHOT, BY END-USER
Figure 12 AI RECOMMENDATION ENGINE MARKET: REGIONAL SNAPSHOT
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
Figure 14 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 15 ASIA PACIFIC IS EXPECTED TO HAVE THE FASTEST GROWTH RATE DURING THE FORECAST PERIOD
Figure 16 ASIA PACIFIC IS EXPECTED TO BE THE BEST MARKET TO INVESTMENT IN, IN THE NEXT 5 YEARS
Figure 17 AI RECOMMENDATION ENGINE: DATA FILTERING MODELS
Figure 18 AI RECOMMENDATION ENGINE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
Figure 19 HYBRID RECOMMENDATION SEGMENT IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 20 GEOSPATIAL AWARE SEGMENT IS EXPECTED TO EXHIBIT A HIGHER CAGR DURING THE FORECAST PERIOD
Figure 21 NATURAL LANGUAGE PROCESSING SEGMENT IS EXPECTED TO EXHIBIT A HIGHER CAGR DURING THE FORECAST PERIOD
Figure 22 STRATEGY AND OPERATIONS PLANNING APPLICATION IS EXPECTED TO EXHIBIT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 23 CLOUD DEPLOYMENT MODE IS EXPECTED TO EXHIBIT A HIGHER CAGR DURING THE FORECAST PERIOD
Figure 24 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO EXHIBIT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 25 ASIA PACIFIC IS EXPECTED TO HAVE THE HIGHEST CAGR IN THE AI RECOMMENDATION ENGINE MARKET DURING THE FORECAST PERIOD
Figure 26 ASIA PACIFIC IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 27 NORTH AMERICA: MARKET SNAPSHOT
Figure 28 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 29 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 30 ASIA PACIFIC: MARKET SNAPSHOT
Figure 31 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 32 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 33 MEDIA AND ENTERTAINMENT END-USER IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 34 KEY DEVELOPMENTS BY THE LEADING PLAYERS IN THE AI RECOMMENDATION ENGINE MARKET
Figure 35 GEOGRAPHIC REVENUE MIX OF MARKET PLAYERS
Figure 36 IBM: COMPANY SNAPSHOT
Figure 37 IBM: SWOT ANALYSIS
Figure 38 GOOGLE: COMPANY SNAPSHOT
Figure 39 GOOGLE: SWOT ANALYSIS
Figure 40 AWS: COMPANY SNAPSHOT
Figure 41 AWS: SWOT ANALYSIS
Figure 42 MICROSOFT: COMPANY SNAPSHOT
Figure 43 MICROSOFT: SWOT ANALYSIS
Figure 44 SALESFORCE: COMPANY SNAPSHOT
Figure 45 SALESFORCE: SWOT ANALYSIS
Figure 46 HPE: COMPANY SNAPSHOT
Figure 47 ORACLE: COMPANY SNAPSHOT
Figure 48 INTEL: COMPANY SNAPSHOT
Figure 49 SAP: COMPANY SNAPSHOT
