Content Recommendation Engine Global Market Report 2018-2022 - Forecast to Reach $4.95 Billion Growing at a CAGR of 33.7%
DUBLIN, March 28, 2018 /PRNewswire/ --
The "Content Recommendation Engine Market by Component (Solution, Service), Filtering Approach, Organization Size, Vertical (E-commerce, Media, Entertainment & Gaming, Retail & Consumer Goods, Hospitality), and Region - Global Forecast to 2022" report has been added to ResearchAndMarkets.com's offering.
The content recommendation engine market is projected to grow from USD 1.16 Billion in 2017 to USD 4.95 Billion by 2022, at a CAGR of 33.7% from 2017 to 2022. Increasing focus on enhancing customer experience, rapid digitalization, and need for analyzing large volumes of customer data are factors driving the market across the globe.
The content recommendation engine market has been segmented based on component, filtering approach, organization size, vertical, and region. Based on component, the market has been segmented into solution and service. Based on filtering approach, the market has been segmented into collaborative filtering, content-based filtering, and hybrid filtering. Based on organization size, the market has been segmented into small and medium enterprises and large enterprises. Based on vertical, the market has been segmented into E-commerce, media, entertainment & gaming, retail & consumer goods, hospitality, IT & telecommunication, BFSI, education & training, healthcare & pharmaceuticals, and others. The others segment includes manufacturing, automotive, and supply chain management. The content recommendation engine market has been studied for North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America.
Among components, the service segment of the content recommendation engine market is projected to grow at the highest CAGR during the forecast period. The high growth of the service segment can be attributed to the growing need to deploy development services, consulting services, implementation services, training services, support services, and others. The solution segment is expected to lead the market during the forecast period.
Based on organization size, the small and medium enterprises segment of the content recommendation engine market is projected to grow at the highest CAGR during the forecast period. The growth of this segment of the market can be attributed to the increased adoption of content recommendation engine by small and medium enterprises, as these solutions help enterprises obtain benefits, such as keeping track of a number of users viewing and searching their products and services. The large enterprises segment is projected to lead the market during the forecast period.
Based on vertical, the E-commerce segment is projected to lead the content recommendation engine market during the forecast period. The growth of the E-commerce segment of the market can be attributed to the rise in the use of mobile applications and websites to order products and services online. This retail & consumer goods segment of the market is projected to grow at the highest CAGR during the forecast period.
The Asia Pacific content recommendation engine market is projected to grow at the highest CAGR from 2017 to 2022. The growth of the Asia Pacific market can be attributed to the rise in Over the Top (OTT) players and rapid digitization, which is expected to lead to the increasing deployment of content recommendation engine in the region.
The content recommendation engine market in China, Japan, India, Australia, Indonesia, and Korea is projected to witness significant growth during the forecast period, owing to the increase in the number of mobile users and investments made by various companies for deploying content recommendation engine.
The factor restraining the growth of the content recommendation engine market is the need to protect the sensitive information of the customers in content recommendation engine, and the use of this technology could lead hackers to breach systems and access the source code.
Key players in the content recommendation engine market include IBM (US), Amazon Web Services (US), Cxense (Norway), Taboola (US), Outbrain (US), Revcontent (US), Kibo Commerce (US), Dynamic Yield (US), Curata (US), Boomtrain (US), ThinkAnalytics(UK), Certona (US), Recombee (Czech Republic), Uberflip (Canada), and Newzmate (US). These companies are adopting different growth strategies such as expansions, mergers & acquisitions, partnerships, and new product launches to increase their market shares in the market.
Market Dynamic
Drivers
- Increasing Focus on Enhancing Customer Experience
- Rapid Digitalization
- Increasing Need for Analyzing Large Volumes of Customer Data
Restraints
- Protecting Sensitive Information of Customers
Opportunities
- Growing Use of AI in Recommendation Engine to Offer Personalized Customer Experience
- Increasing Demand for Personalization
Challenges
- Issues Related to Technology and Infrastructural Compatibilities
- Lack of Technical Expertise
Key Topics Covered
1 Introduction
1.1 Objectives of the Study
1.2 Market Definition
1.3 Market Scope
1.3.1 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
2.4 Limitations
3 Executive Summary
4 Premium Insights
4.1 Attractive Opportunities in the Content Recommendation Engine Market
4.2 North America: Market By Component
4.3 Europe: Market By Organization Size
4.4 Asia Pacific: Market By Vertical
4.5 Market By Top Three Verticals & Top Three Regions
5 Market Dynamics and Industry Trends
5.1 Market Dynamic
5.1.1 Introduction
5.1.2 Drivers
5.1.2.1 Increasing Focus on Enhancing Customer Experience
5.1.2.2 Rapid Digitalization
5.1.2.3 Increasing Need for Analyzing Large Volumes of Customer Data
5.1.3 Restraints
5.1.3.1 Protecting Sensitive Information of Customers
5.1.4 Opportunities
5.1.4.1 Growing Use of AI in Recommendation Engine to Offer Personalized Customer Experience
5.1.4.2 Increasing Demand for Personalization
5.1.5 Challenges
5.1.5.1 Issues Related to Technology and Infrastructural Compatibilities
5.1.5.2 Lack of Technical Expertise
5.2 Industry Trends
5.2.1 Introduction
5.2.2 Phases of Personalization
5.2.3 Filtering Approaches in Recommendation Engine
5.2.4 Case Studies
5.2.4.1 Case Study 1: Innogames Uses Outbrain Targeting Tools to Reach Audience and Ensure App Download Growth
5.2.4.2 Case Study 2: Huggies Effectively Engages Target Audience With the Help of Outbrain Content Discovery Platform
6 Content Recommendation Engine Market, By Component
6.1 Introduction
6.2 Solution
6.3 Service
7 Market By Filtering Approach
7.1 Introduction
7.2 Collaborative Filtering
7.3 Content-Based Filtering
7.4 Hybrid Filtering
8 Market By Organization Size
8.1 Introduction
8.2 Small and Medium Enterprises
8.3 Large Enterprises
9 Content Recommendation Engine Market, By Vertical
9.1 Introduction
9.2 E-Commerce
9.3 Media, Entertainment & Gaming
9.4 Retail & Consumer Goods
9.5 Hospitality
9.6 IT & Telecommunication
9.7 BFSI
9.8 Education & Training
9.9 Healthcare & Pharmaceutical
9.10 Others
10 Regional Analysis
10.1 Introduction
10.2 North America
10.3 Europe
10.4 Asia Pacific
10.5 Middle East & Africa
10.6 Latin America
11 Competitive Landscape
11.1 Overview
11.2 Competitive Situation and Trends
11.2.1 New Product Launches & Product Enhancements
11.2.2 Agreements, Collaborations & Partnerships
11.2.3 Acquisitions
11.2.4 Expansions
11.3 Market Ranking of Key Players
12 Company Profiles
12.1 IBM
12.2 Amazon Web Services
12.3 Revcontent
12.4 Taboola
12.5 Outbrain
12.6 Cxense
12.7 Dynamic Yield
12.8 Curata
12.9 Boomtrain
12.10 Thinkanalytics
12.11 Kibo Commerce
12.12 Certona
12.13 Key Innovators
12.13.1 Recombee
12.13.2 Uberflip
12.13.3 Newzmate
For more information about this report visit https://www.researchandmarkets.com/research/xm647v/content?w=5
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