A recommendation engine is a software that examines accessible, structured data to make recommendations for information that a website user is interested in, such as a book, a video or a job posting, and others. The recommendation engines are widely used among e-commerce, content-based websites, and across social media platforms. A content-based recommender works with information that user provides, either unequivocally (rating) or certainly (clicking on the link). Based on the information, a user profile is created, which is then used to make recommendations to the user. As the user provides more data sources or engages in activities on the proposals, the recommendations keep getting precise. There are various verticals in which these applications can be utilized such as in e-commerce, IT & telecommunication, BFSI, education and training, and others. Amazon was the first website to utilize recommendation system to make the website user-friendly, that suggested books to the user as per the information collected based on the user activity.
Major players like Amazon Web Services, IBM, and others are already dominating the Content Recommendation Engine Market. These companies have introduced platforms that utilize the recommendation engines to provide information or product recommendation to the user. IBM has developed applications such as IBM MobileFirst for iOS ancillary sale, IBM MobileFirst for iOS dynamic buy which are user-friendly and provides recommendation as per the user’s history data.
Rapid digitalization in countries like India, China, UAE and others has lead to the growth of the market. Currently, each product is available on online platforms, which are expanding their reach in these countries. In India, e-commerce has rapidly grown with the increasing number of users. Online e-commerce platforms are utilizing content recommendation engines to provide relevant and similar information on products for the users as per their search.
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The prominent players in the content recommendation market are Amazon Web Services (US), Boomtrain (US), Certona (US), Curata (US), Cxense (Norway), Dynamic Yield (US), IBM (US), Kibo Commerce (US), Outbrain (US), Revcontent (US), Taboola (US), and Think Analytics (UK).
By component, the market is segmented into solution and service.
By organization size, the market is segmented into small & medium enterprises and large enterprises.
By filtering approach, the market has been segmented into collaborative filtering, content-based filtering, and hybrid filtering.
By vertical, the market is segmented into industrial, e-commerce, media, entertainment & gaming, retailer and consumer goods, IT & telecommunication, BFSI, education & training and healthcare & pharmaceuticals.
The global market for content recommendation is estimated to grow at a significant rate during the forecast period from 2018 to 2023. The geographical analysis of content recommendation engine market is studied for North America, Europe, Asia-Pacific, and the rest of the world.
North America is expected to dominate the content recommendation engine market due to the presence of major players and advances in technology. The main factor driving the content recommendation engine market in this region is the focus of the major players on enhancing the user experience on a website. Rising demand for analyzing a large amount of user data is fueling the European market. Asia-Pacific is estimated to be the fastest growing region in the content recommendation market during the forecast period as rapid digitalization is taking place in the region. Similarly, there has been an increase in shopping through e-commerce platforms in the region, that is contributing to the market growth.
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- Providers of content recommendation engine services
- Suppliers of IT hardware/software/services
- Software and system integrators
- IT infrastructure providers
- Marketing analytics executives
- System administrators
- App developers
- Third-party service providers
- Technology providers
Table of Contents
1 Executive Summary
2 Scope of the Report
2.1 Market Definition
2.2 Scope of the Study
2.2.1 Research Objectives
2.2.2 Assumptions & Limitations
2.3 Markets Structure
3 Market Research Methodology
3.1 Research Process
3.2 Secondary Research
3.3 Primary Research
3.4 Forecast Model
List of Tables
Table 1 Global Content Recommendation Engine Market, By Region, 2018–2023
Table 2 North America: Content Recommendation Engine Market, By Country, 2018–2023
Table 3 Europe: Content Recommendation Engine Market, By Country, 2018–2023
Table 4 Asia-Pacific: Content Recommendation Engine Market, By Country, 2018–2023
Table 5 Middle East & Africa: Content Recommendation Engine Market, By Country, 2018–2023
Table 6 Latin America: Content Recommendation Engine Market, By Country, 2018–2023
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