CHAPTER 1 INTRODUCTION
1.1. Market Definition
1.2. Executive Summary
1.3. The Scope of the Study
CHAPTER 2 RESEARCH METHODOLOGY
2.1. Secondary Research
2.2. Primary Research
2.3. Analytic Tools and Model
2.4. Economic Indicator
2.4.1 Base Year, Base Currency, Forecasting Period
2.5. Expert Validation
2.6. Study Timeline
CHAPTER 3 MARKET ANALYSIS
3.1. Industry Value Chain Analysis
3.2. Porter's Five Force Analysis
3.2.1. Bargaining Power of Buyers
3.2.2. Bargaining Power of Suppliers
3.2.3. Threats of Substitutes
3.2.4. Threats of New Entrants
3.2.5. Degree of Competition
3.3. PESTLE Analysis
3.3.1. Political
3.3.2. Economical
3.3.3. Social
3.3.4. Technological
3.3.5. Legal
3.3.6. Environmental
3.4. SWOT Analysis
3.4.1. Strengths
3.4.2. Weakness
3.4.3. Opportunities
3.4.4. Threats
3.5. Y-O-Y Analysis
CHAPTER 4 MARKET DYNAMICS
4.1. Market Drivers
4.1.1. Increasing rate of digitalisation
4.1.2. Growth in focus towards gaining the customer's experience
4.2. Market Restraints & Challenges
4.2.1. Association of concern to infrastructure compatibility
4.2.2. Concerns on over accessing of consumer’s data
4.2.3. Lack of expertise with technical skills
4.3. Market Opportunities
4.3.1. Rising the demand for personalisation
4.3.2. Increase use of AI in recommendation engine to offer personalised customer
Experience for deep learning
4.3.3. Rising demand to analyse large volumes of data
CHAPTER 5 GLOBAL RECOMMENDATION ENGINE MARKET – BY TYPE
5.1. Hybrid Recommendation
5.2. Collaborative Filtering
5.3. Content-Based Filtering
CHAPTER 6 GLOBAL RECOMMENDATION ENGINE MARKET – BY
TECHNOLOGY
6.1. Geospatial Aware
6.2. Context-Aware
6.2.1. Machine Learning and Deep Learning
6.2.2. Natural Language Processing
CHAPTER 7 GLOBAL RECOMMENDATION ENGINE MARKET – BY
DEPLOYMENT MODE
7.1. Cloud
7.2. On-Premises
CHAPTER 8 GLOBAL RECOMMENDATION ENGINE MARKET – BY
APPLICATION
8.1. Personalised Campaigns and Customer Discovery
8.2. Product Planning
8.3. Strategy and Operations Planning
8.4. Proactive Asset Management
8.5. Others
CHAPTER 9 GLOBAL RECOMMENDATION ENGINE MARKET – BY END USERS
9.1. Banking, Financial Service, Insurance (BFSI)
9.2. Media and Entertainment
9.3. Transportation
9.4. Healthcare
9.5. Retail
9.6. Others
CHAPTER 10 GLOBAL RECOMMENDATION ENGINE MARKET - BY
GEOGRAPHY
10.1. Introduction
10.2. North America
10.2.1. U.S.
10.2.2. Canada
10.2.3. Mexico
10.2.4. Costa Rica
10.3. South America
10.3.1. Brazil
10.3.2. Argentina
10.3.3. Chile
10.3.4. Columbia
10.3.5. Others
10.4. Europe
10.4.1. U.K.
10.4.2. Germany
10.4.3. France
10.4.4. Italy
10.4.5. Spain
10.4.6. Russia
10.4.7. Netherlands
10.4.8. Switzerland
10.4.9. Poland
10.4.10. Others
10.5. APAC
10.5.1. China
10.5.2. Japan
10.5.3. India
10.5.4. South Korea
10.5.5. Australia & New Zealand
10.5.6. Malaysia
10.5.7. Singapore
10.5.8. Others
10.6. Middle East & Africa
10.6.1. UAE
10.6.2. Saudi Arabia
10.6.3. Iran
10.6.4. Iraq
10.6.5. Qatar
10.6.6. South Africa
10.6.7. Algeria
10.6.8. Morocco
10.6.9. Nigeria
10.6.10. Egypt
10.6.11. Others
CHAPTER 11 GLOBAL RECOMMENDATION ENGINE MARKET - COMPANY
PROFILES
11.1. AWS, Inc.
11.2. IBM Corporation
11.3. Hewlett Packard Enterprise Company
11.4. Google LLC
11.5. Salesforce.com, Inc.
11.6. Microsoft Corporation
11.7. Sentient Technologies Holdings Limited.
11.8. Oracle Corporation
11.9. Intel Corporation
11.10. SAP SE
11.11. Fuzzy.AI
11.12. Infinite Analytics, Inc.
11.13. Kibo Commerce
11.14. Outbrain Inc.
CHAPTER 12 GLOBAL RECOMMENDATION ENGINE MARKET - COMPETITIVE
LANDSCAPE
12.1. Market Share Analysis
12.2. Strategies adopted by top companies
12.3. Mergers, Acquisitions, Collaborations & Agreements
CHAPTER 13 MARKET INSIGHTS
13.1. Industry Experts Insights
13.2. Analysts Opinions
13.3. Investment Opportunities
CHAPTER 14 APPENDIX
14.1. List of Tables
14.2. List of Figures
