Market Overview:
Market Research Future (MRFR), in its recently published research report, asserts that the global content recommendation engine market 2020 is expected to grow exponentially over the review period, recording a substantial market valuation of USD 6 billion and a healthy 30% CAGR in the forecast period.
Major Players Operating in Global Content Recommendation Engine Market as Identified as:
Boomtrain (US), Amazon Web Services (US), Certona (US), Curata (US), Cxense (Norway), Dynamic Yield (US), IBM (US), Outbrain (US), Revcontent (US), Kibo Commerce (US), Taboola (US), and ThinkAnalytics (UK).
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Drivers and Restraints:
The rise in digitisation has increased the use of online shopping. With the rise in digitalization, there has been an increase in online shopping in the Asia-Pacific region through various e-commerce platforms, which has encouraged websites to use the content recommendation engines to provide enhanced user experience and expand their client base. Increasing focus on improving customer experience is a key factor expected to drive the demand for engine content recommendation during the forecast era. These recommendation engines allow user-friendly searching and present consumers with the products or information as per the previous search. Additionally, mobile phone ownership is rapidly leading to e-commerce growth and driving e-commerce websites implementing recommendation engines. Furthermore, developments in the SME segment are expected to increase companies ‘adoption of the Content Recommendation Engine, as these solutions enable companies to achieve benefits, monitor the number of users viewing the product, search for services, and others. In addition, a increasing need to analyze large amounts of consumer data is expected to propel the demand for content recommendations engines. In addition, cell phone ownership is rapidly contributing to e-commerce growth and accelerating the adoption of e-commerce websites to recommendation engines. Furthermore, developments in the SME segment are expected to increase companies ‘adoption of the Content Recommendation Engine, as these solutions enable businesses to achieve benefits, monitor the number of users viewing the product, search for services, and others. In addition, a increasing need to analyze large amounts of consumer data is expected to propel the demand for content recommendation engines.
Segmental Analysis:
Based on the component, the market is segmented into solution and service. The services segment of the content recommendation engine market is poised to grow significantly.
Based on the organization size, the market is bifurcated into small & medium enterprises (SMEs) and large enterprises. The large enterprise size segment is slated to hold the leading share of the market during the review period.
Based on the filtering approach, the market has been segmented into content-based filtering, collaborative filtering, and hybrid filtering. The collaborative filtering segment is projected to hold the dominant share of the market.
Based on the vertical, the market is segmented into industrial, entertainment & gaming, e-commerce, media, retailer and consumer goods, IT & telecommunication, BFSI, education & training, and healthcare & pharmaceuticals.
Regional Analysis:
The geographical overview of the global market has been analyzed in four major regions, including the Asia Pacific, North America, Europe, and the rest of the world.
Because of the involvement of major players and technical advancements North America is expected to dominate the content recommendation engine market. The key factor driving this region’s content recommendation engine market is the focus of the major players on improving the user experience on a website. The European market is being fueled by growing demand for processing a vast volume of user data. Throughout the forecast era, Asia-Pacific is expected to be the fastest growing region on the content recommendation engine market, as rapid digitalization occurs in the region. In the same way, shopping in the area has increased across e-commerce platforms, that is contributing to the market growth.
Table of Content:
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
4 Market Landscape
4.1 Porter’s Five Forces Analysis
4.1.1 Threat Of New Entrants
4.1.2 Bargaining Power Of Buyers
4.1.3 Threat Of Substitutes
4.1.4 Segment Rivalry
4.2 Value Chain/Supply Chain Of Global Content Recommendation Engine Market
Continued………
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