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Global Recommendation Engine Market Size, Forecast: By Type: Collaborative Filtering, Content-Based Filtering, Hybrid Recommendation Systems, Others; By Deployment Type: Cloud Based, On-premises; By Technology: Context Aware, Geospatial Aware; By Application; By End Use; Regional Analysis; Competitive Landscape; 2024-2032
Global Recommendation Engine Market Outlook
The global recommendation engine market size reached nearly USD 3.76 billion in 2023. The market is projected to grow at a CAGR of 15.5% between 2024 and 2032 to reach a value of around USD 13.71 billion by 2032.
Key Trends in the Market
A recommendation engine refers to a technology which offers recommendations based on the behaviour patterns and preferences of consumers. This type of system uses statistical modelling and predictive analysis to provide a personalised experience to end users.
- The increasing popularity of OTT platforms, the growing demand for high-quality content across entertainment websites, and the increasing availability of linguistically diverse content are propelling the recommendation engine market growth.
- Technological advancements in the BFSI sector are heightening the usage of recommendation algorithms and personalised banking systems to enhance customer satisfaction. Moreover, the rising demand for personalised services in various end-use sectors, including healthcare, BFSI, and retail, among others, is driving the market.
- One of the key recommendation engine market trends is the increasing adoption of consumer devices such as smartphones, tablets, and laptops, among others. Furthermore, in the forecast period, the expansion of the e-commerce sector is expected to drive the deployment of recommendation engines to track consumer behaviour and improve their experiences.
Market Analysis
Based on type, the market is segmented into collaborative filtering, content-based filtering, and hybrid recommendation systems, among others. On the basis of deployment type, the market is classified into cloud based and on-premises. By technology, the recommendation engine market segmentation includes context aware and geospatial aware.
Based on application, the market is categorised into strategy and operations planning, product planning and proactive asset management, and personalised campaigns and customer discovery. By end use, the market is divided into IT and telecommunication, BFSI, retail, media and entertainment, and healthcare, among others. The major regional markets for recommendation engine include North America, Europe, the Asia Pacific, Latin America, and the Middle East and Africa.
The comprehensive EMR report provides an in-depth assessment of the market based on the Porter's five forces model along with giving a SWOT analysis. The report gives a detailed analysis of the key players in the global recommendation engine market, covering their competitive landscape and latest developments like mergers, acquisitions, investments and expansion plans.
- Netflix, Inc
- Amazon Web Services, Inc.
- Tinder
- Google LLC
- SAP SE
- Adobe Inc.
- Microsoft Corporation
- Salesforce Inc.
- Oracle Corporation
- Nosto Solutions Oy
- Dynamic Yield
- Others
Market Share by Application
Personalised campaigns and customer discovery account for a significant portion of the recommendation engine market share. Personalised campaigns and customer discovery curate specific content and videos for the target audience, improving the user experience. This also leads to a higher subscription rate across websites and content delivery platforms. Furthermore, increasing investments by various end users towards personalising their product and service recommendations in order to enhance their scalability and profitability are fuelling the segment’s growth.
Market Share by End Use
According to the recommendation engine market analysis, the retail sector is likely to represent a substantial market share in the forecast period. There is a heightening adoption of recommendation engines by retailers to better analyse their customers’ interests and preferences and cultivate customer loyalty.
Rapid digitalisation in the retail sector, along with a swift transition from traditional to technologically advanced retailing strategies, is expected to further garner the segment’s growth in the forecast period.
Competitive Landscape
Netflix, Inc is a company which offers high-quality streaming services. This company provides content of diverse genres, including anime, docu-dramas, and movies, among others. It was founded in 1997 and is headquartered in California, the United States.
Amazon Web Services, Inc. is a leading company which offers technological solutions, including cloud-based recommendation systems. The company also offers APIs to several end-use sectors as well as individuals. It was established in 2006 and is headquartered in Washington, the United States.
Tinder is an online dating company which also offers geosocial networking applications. The services provided by this company are based on personalised user recommendations. The company was founded in 2012 and is headquartered in California, the United States.
Other players considered in the recommendation engine market report include Google LLC, SAP SE, Adobe Inc., Microsoft Corporation, Salesforce Inc., Oracle Corporation, Nosto Solutions Oy, and Dynamic Yield, among others.
Key Highlights of the Report
| REPORT FEATURES | DETAILS |
| Base Year | 2023 |
| Historical Period | 2018-2023 |
| Forecast Period | 2024-2032 |
| Scope of the Report |
Historical and Forecast Trends, Industry Drivers and Constraints, Historical and Forecast Market Analysis by Segment:
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| Breakup by Type |
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| Breakup by Deployment Type |
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| Breakup by Technology |
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| Breakup by Application |
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| Breakup by End Use |
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| Breakup by Region |
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| Market Dynamics |
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| Competitive Landscape |
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| Companies Covered |
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*At Expert Market Research, we strive to always give you current and accurate information. The numbers depicted in the description are indicative and may differ from the actual numbers in the final EMR report.
1 Preface
2 Report Coverage – Key Segmentation and Scope
3 Report Description
3.1 Market Definition and Outlook
3.2 Properties and Applications
3.3 Market Analysis
3.4 Key Players
4 Key Assumptions
5 Executive Summary
5.1 Overview
5.2 Key Drivers
5.3 Key Developments
5.4 Competitive Structure
5.5 Key Industrial Trends
6 Market Snapshot
6.1 Global
6.2 Regional
7 Opportunities and Challenges in the Market
8 Global Recommendation Engine Market Analysis
8.1 Key Industry Highlights
8.2 Global Recommendation Engine Historical Market (2018-2023)
8.3 Global Recommendation Engine Market Forecast (2024-2032)
8.4 Global Recommendation Engine Market by Type
8.4.1 Collaborative Filtering
8.4.1.1 Historical Trend (2018-2023)
8.4.1.2 Forecast Trend (2024-2032)
8.4.2 Content-Based Filtering
8.4.2.1 Historical Trend (2018-2023)
8.4.2.2 Forecast Trend (2024-2032)
8.4.3 Hybrid Recommendation Systems
8.4.3.1 Historical Trend (2018-2023)
8.4.3.2 Forecast Trend (2024-2032)
8.4.4 Others
8.5 Global Recommendation Engine Market by Deployment Type
8.5.1 Cloud Based
8.5.1.1 Historical Trend (2018-2023)
8.5.1.2 Forecast Trend (2024-2032)
8.5.2 On-premises
8.5.2.1 Historical Trend (2018-2023)
8.5.2.2 Forecast Trend (2024-2032)
8.6 Global Recommendation Engine Market by Technology
8.6.1 Context Aware
8.6.1.1 Historical Trend (2018-2023)
8.6.1.2 Forecast Trend (2024-2032)
8.6.2 Geospatial Aware
8.6.2.1 Historical Trend (2018-2023)
8.6.2.2 Forecast Trend (2024-2032)
8.7 Global Recommendation Engine Market by Application
8.7.1 Strategy and Operations Planning
8.7.1.1 Historical Trend (2018-2023)
8.7.1.2 Forecast Trend (2024-2032)
8.7.2 Product Planning and Proactive Asset Management
8.7.2.1 Historical Trend (2018-2023)
8.7.2.2 Forecast Trend (2024-2032)
8.7.3 Personalised Campaigns and Customer Discovery
8.7.3.1 Historical Trend (2018-2023)
8.7.3.2 Forecast Trend (2024-2032)
8.8 Global Recommendation Engine Market by End Use
8.8.1 IT and Telecommunication
8.8.1.1 Historical Trend (2018-2023)
8.8.1.2 Forecast Trend (2024-2032)
8.8.2 BFSI
8.8.2.1 Historical Trend (2018-2023)
8.8.2.2 Forecast Trend (2024-2032)
8.8.3 Retail
8.8.3.1 Historical Trend (2018-2023)
8.8.3.2 Forecast Trend (2024-2032)
8.8.4 Media and Entertainment
8.8.4.1 Historical Trend (2018-2023)
8.8.4.2 Forecast Trend (2024-2032)
8.8.5 Healthcare
8.8.5.1 Historical Trend (2018-2023)
8.8.5.2 Forecast Trend (2024-2032)
8.8.6 Others
8.9 Global Recommendation Engine Market by Region
8.9.1 North America
8.9.1.1 Historical Trend (2018-2023)
8.9.1.2 Forecast Trend (2024-2032)
8.9.2 Europe
8.9.2.1 Historical Trend (2018-2023)
8.9.2.2 Forecast Trend (2024-2032)
8.9.3 Asia Pacific
8.9.3.1 Historical Trend (2018-2023)
8.9.3.2 Forecast Trend (2024-2032)
8.9.4 Latin America
8.9.4.1 Historical Trend (2018-2023)
8.9.4.2 Forecast Trend (2024-2032)
8.9.5 Middle East and Africa
8.9.5.1 Historical Trend (2018-2023)
8.9.5.2 Forecast Trend (2024-2032)
9 North America Recommendation Engine Market Analysis
9.1 United States of America
9.1.1 Historical Trend (2018-2023)
9.1.2 Forecast Trend (2024-2032)
9.2 Canada
9.2.1 Historical Trend (2018-2023)
9.2.2 Forecast Trend (2024-2032)
10 Europe Recommendation Engine Market Analysis
10.1 United Kingdom
10.1.1 Historical Trend (2018-2023)
10.1.2 Forecast Trend (2024-2032)
10.2 Germany
10.2.1 Historical Trend (2018-2023)
10.2.2 Forecast Trend (2024-2032)
10.3 France
10.3.1 Historical Trend (2018-2023)
10.3.2 Forecast Trend (2024-2032)
10.4 Italy
10.4.1 Historical Trend (2018-2023)
10.4.2 Forecast Trend (2024-2032)
10.5 Others
11 Asia Pacific Recommendation Engine Market Analysis
11.1 China
11.1.1 Historical Trend (2018-2023)
11.1.2 Forecast Trend (2024-2032)
11.2 Japan
11.2.1 Historical Trend (2018-2023)
11.2.2 Forecast Trend (2024-2032)
11.3 India
11.3.1 Historical Trend (2018-2023)
11.3.2 Forecast Trend (2024-2032)
11.4 ASEAN
11.4.1 Historical Trend (2018-2023)
11.4.2 Forecast Trend (2024-2032)
11.5 Australia
11.5.1 Historical Trend (2018-2023)
11.5.2 Forecast Trend (2024-2032)
11.6 Others
12 Latin America Recommendation Engine Market Analysis
12.1 Brazil
12.1.1 Historical Trend (2018-2023)
12.1.2 Forecast Trend (2024-2032)
12.2 Argentina
12.2.1 Historical Trend (2018-2023)
12.2.2 Forecast Trend (2024-2032)
12.3 Mexico
12.3.1 Historical Trend (2018-2023)
12.3.2 Forecast Trend (2024-2032)
12.4 Others
13 Middle East and Africa Recommendation Engine Market Analysis
13.1 Saudi Arabia
13.1.1 Historical Trend (2018-2023)
13.1.2 Forecast Trend (2024-2032)
13.2 United Arab Emirates
13.2.1 Historical Trend (2018-2023)
13.2.2 Forecast Trend (2024-2032)
13.3 Nigeria
13.3.1 Historical Trend (2018-2023)
13.3.2 Forecast Trend (2024-2032)
13.4 South Africa
13.4.1 Historical Trend (2018-2023)
13.4.2 Forecast Trend (2024-2032)
13.5 Others
14 Market Dynamics
14.1 SWOT Analysis
14.1.1 Strengths
14.1.2 Weaknesses
14.1.3 Opportunities
14.1.4 Threats
14.2 Porter’s Five Forces Analysis
14.2.1 Supplier’s Power
14.2.2 Buyer’s Power
14.2.3 Threat of New Entrants
14.2.4 Degree of Rivalry
14.2.5 Threat of Substitutes
14.3 Key Indicators for Demand
14.4 Key Indicators for Price
15 Competitive Landscape
15.1 Market Structure
15.2 Company Profiles
15.2.1 Netflix, Inc
15.2.1.1 Company Overview
15.2.1.2 Product Portfolio
15.2.1.3 Demographic Reach and Achievements
15.2.1.4 Certifications
15.2.2 Amazon Web Services, Inc.
15.2.2.1 Company Overview
15.2.2.2 Product Portfolio
15.2.2.3 Demographic Reach and Achievements
15.2.2.4 Certifications
15.2.3 Tinder
15.2.3.1 Company Overview
15.2.3.2 Product Portfolio
15.2.3.3 Demographic Reach and Achievements
15.2.3.4 Certifications
15.2.4 Google LLC
15.2.4.1 Company Overview
15.2.4.2 Product Portfolio
15.2.4.3 Demographic Reach and Achievements
15.2.4.4 Certifications
15.2.5 SAP SE
15.2.5.1 Company Overview
15.2.5.2 Product Portfolio
15.2.5.3 Demographic Reach and Achievements
15.2.5.4 Certifications
15.2.6 Adobe Inc.
15.2.6.1 Company Overview
15.2.6.2 Product Portfolio
15.2.6.3 Demographic Reach and Achievements
15.2.6.4 Certifications
15.2.7 Microsoft Corporation
15.2.7.1 Company Overview
15.2.7.2 Product Portfolio
15.2.7.3 Demographic Reach and Achievements
15.2.7.4 Certifications
15.2.8 Salesforce Inc.
15.2.8.1 Company Overview
15.2.8.2 Product Portfolio
15.2.8.3 Demographic Reach and Achievements
15.2.8.4 Certifications
15.2.9 Oracle Corporation
15.2.9.1 Company Overview
15.2.9.2 Product Portfolio
15.2.9.3 Demographic Reach and Achievements
15.2.9.4 Certifications
15.2.10 Nosto Solutions Oy
15.2.10.1 Company Overview
15.2.10.2 Product Portfolio
15.2.10.3 Demographic Reach and Achievements
15.2.10.4 Certifications
15.2.11 Dynamic Yield
15.2.11.1 Company Overview
15.2.11.2 Product Portfolio
15.2.11.3 Demographic Reach and Achievements
15.2.11.4 Certifications
15.2.12 Others
16 Key Trends and Developments in the Market
List of Key Figures and Tables
1. Global Recommendation Engine Market: Key Industry Highlights, 2018 and 2032
2. Global Recommendation Engine Historical Market: Breakup by Type (USD Billion), 2018-2023
3. Global Recommendation Engine Market Forecast: Breakup by Type (USD Billion), 2024-2032
4. Global Recommendation Engine Historical Market: Breakup by Deployment Mode (USD Billion), 2018-2023
5. Global Recommendation Engine Market Forecast: Breakup by Deployment Mode (USD Billion), 2024-2032
6. Global Recommendation Engine Historical Market: Breakup by Technology (USD Billion), 2018-2023
7. Global Recommendation Engine Market Forecast: Breakup by Technology (USD Billion), 2024-2032
8. Global Recommendation Engine Historical Market: Breakup by Application (USD Billion), 2018-2023
9. Global Recommendation Engine Market Forecast: Breakup by Application (USD Billion), 2024-2032
10. Global Recommendation Engine Historical Market: Breakup by End Use (USD Billion), 2018-2023
11. Global Recommendation Engine Market Forecast: Breakup by End Use (USD Billion), 2024-2032
12. Global Recommendation Engine Historical Market: Breakup by Region (USD Billion), 2018-2023
13. Global Recommendation Engine Market Forecast: Breakup by Region (USD Billion), 2024-2032
14. North America Recommendation Engine Historical Market: Breakup by Country (USD Billion), 2018-2023
15. North America Recommendation Engine Market Forecast: Breakup by Country (USD Billion), 2024-2032
16. Europe Recommendation Engine Historical Market: Breakup by Country (USD Billion), 2018-2023
17. Europe Recommendation Engine Market Forecast: Breakup by Country (USD Billion), 2024-2032
18. Asia Pacific Recommendation Engine Historical Market: Breakup by Country (USD Billion), 2018-2023
19. Asia Pacific Recommendation Engine Market Forecast: Breakup by Country (USD Billion), 2024-2032
20. Latin America Recommendation Engine Historical Market: Breakup by Country (USD Billion), 2018-2023
21. Latin America Recommendation Engine Market Forecast: Breakup by Country (USD Billion), 2024-2032
22. Middle East and Africa Recommendation Engine Historical Market: Breakup by Country (USD Billion), 2018-2023
23. Middle East and Africa Recommendation Engine Market Forecast: Breakup by Country (USD Billion), 2024-2032
24. Global Recommendation Engine Market Structure
Key Questions Answered in the Report
The market reached a value of nearly USD 3.76 billion in 2023.
The market is estimated to grow at a CAGR of 15.5% in between 2024 and 2032.
The market is estimated to witness a healthy growth in the forecast period of 2024-2032 to reach a value of around USD 13.71 billion by 2032.
The increasing popularity of OTT platforms, rising smartphone ownership, and growing expansion of the e-commerce sector are the major drivers of the market.
The key trends driving the recommendation engine market demand include the digitisation of the retail sector and technological advancements in the BFSI sector to provide more personalised banking services to clients.
Collaborative filtering, content-based filtering, and hybrid recommendation systems, among others, are the different types of recommendation engines.
Cloud based and on-premises are the major deployment types of recommendation engine.
Netflix, Inc, Amazon Web Services, Inc., Tinder, Google LLC, SAP SE, Adobe Inc., Microsoft Corporation, Salesforce Inc., Oracle Corporation, Nosto Solutions Oy, and Dynamic Yield, among others, are the key market players.
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