Recommendation Engine Market - Industry Analysis, Market Size, Share, Trends,Application Analysis, Growth and Forecast 2020 - 2025

 Overview

The recommendation system uses data analysis techniques to identify items which match the user's taste and preferences. There are different types of recommendation engines for different applications. These include the product recommendation engine, the content recommendation engine, the e-commerce product recommendation and the movie recommendation engine. Recommendation Engine Market size is projected to reach $12.03 billion by 2025 from $ 1.14 billion in 2018, with a CAGR of 32.39% during 2020-2025.Growing emphasis on improving consumer experience and increasing trend of digitalization are some of the main driving factors for the demand of recommendation engines. In addition to these factors, the increase in demand for analyzing large volumes of data and increasing use of deep learning technology in recommendation engine is anticipated to provide the market with growth opportunities.

Recommendation Engine Industry Outlook
The report: “Recommendation Engine Market Report – Forecast (2020-2025)”, by IndustryARC, covers an in-depth analysis of the following segments of the Recommendation Engine Market. 

Key Takeaways
  • Asia Pacific Region holds the largest Recommendation engine market share owing to increasing demand from small and medium sized enterprises. 
  • The Hybrid Recommendation Engine Segment is expected to grow at a higher CAGR during the forecast period.
  • The Retail End User Segment account for the maximum market share in the Global Recommendation Engine Market.

By Type - Segment Analysis
During the forecast period the hybrid recommendation type is projected to grow at a CAGR of 36.65%. The recommendation engine market depending on type includes collaborative filtering, content-based filtering and hybrid recommendation. The hybrid recommendation type helps different organizations to incorporate two different types of data filtering to make more reliable recommendations. This leads to the adoption of hybrid recommendation type in the AI-powered recommendation systems. Netflix use hybrid recommendation engine. The website makes suggestions by analyzing the viewing and browsing behaviors of similar users (i.e. collaborative filtering) as well as by providing films that share features with highly rated films (content-based filtering) by an user. Netflix subscribers in United States have increased by more than 11% during 2017-2019. This has led to increased demand for recommendation engine market in this region.

By End User - Segment Analysis
During 2020-2025, the retail sector is projected to be grow at a CAGR of 34.65% during 2020-2025. The segment uses AI-powered recommendation engines to gain advantages, such as customer retention and increased revenue through the implementation of recommendation engines based by AI. For instance, The National Retail Federation predicts that U.S. retail sales will rise between 3.8% and 4.4% in 2019 to more than $3.8 trillion amid risks from the continuing trade war, volatile stock market, and government shutdown effects. As the retail sector uses the recommendation engines extensively, this increase will help the recommendation engine market to grow.

Geography - Segment Analysis
Asia Pacific held the largest market share of the 32.25% in 2018.  Several factors, such as rapid expansion of local businesses, increased technology advancements, and growth in need to analyze customer data, have driven the adoption of recommendation engines across various end-users. Determinants such as the increase of Over the Top (OTT) players and rapid digitization are expected to drive to increasing deployment of content recommendation engine platforms in the Asia Pacific region. In the Asia Pacific region, demand for content recommendation engine solutions is significant in small and medium-sized enterprises. In India, e-commerce has grown rapidly with an increasing number of users. Online e-commerce platforms use the recommendation engines to provide users with highly relevant and related product details as per their search. For instance, According to India Brand Equity Foundation (IBEF), India's e-commerce revenue is projected to rise from $39 billion in 2017 to $120 billion in 2020, increasing at the world's highest annual rate of 51%.This will in turn increase the demand for the recommendation engines in the APAC region.

Drivers – Recommendation Engine Market
  • Increase in Digitalization Rate 
With the rise in digitalization, online shopping has increased across rising e-commerce platforms. Such recommendation engines allow user-friendly browsing and show the products or information to the customer as per the previous search. In addition, mobile phone ownership is rapidly contributing to e-commerce growth and prompting e-commerce websites to embrace recommendation engines. According to Global System for Mobile Communications Association (GSMA), the number of mobile subscribers are expected to reach 5.8 billion in 2025, from 5.1 billion in 2018. This anticipated rise would boost the growth of the e-commerce industry, which is driving the market for the Recommendation Engine.
  • Focus on improving customer satisfaction
Development in emphasis to improve customer experience is a major factor driving the development of the demand for the recommendation engine. In addition, it is important to enhance customer experience to achieve customer engagement and retention, higher sales and return on investment (RoI). Smart product recommendations also allow natural, logical opportunities for upselling and cross-selling. Customers show interest through their actions and experience, and the product recommendation system automatically matches the behavior with the right recommendations. Small transactions will become larger, and customers who may not have been on the track to make a purchase are suddenly interested in doing so. For instance, 31% of ecommerce site revenue is generated from customized recommendations and almost 35% of Amazon’s revenue is generated by its recommendation engine.

Challenges – Recommendation Engine Market
  • Protection of sensitive customer data 
The more personal data generated by a recommendation engine, the more reliable feedback can be received by consumers. User data obtained by the recommender may include user identity information, demographic profile, behavioral data and history of purchase, ranking history, and more. These information may be highly sensitive to privacy. Providing these information in the clear to the recommender could present unacceptable risks in terms of privacy. The recommender might sell the data to a third party without the permission of the client or even stolen by motivated attackers. In 2018, 64% of the businesses were targets of web-based attacks. Also, it is estimated that around $6 trillion will be spent on cybersecurity worldwide by 2021.Therefore, it is important to protect user data in recommendation systems. But the lack of efficient systems for the data protection will cause a hindrance to the rapidly growing Recommendation Engine Market.

Market Landscape
Technology launches, expansion and R&D activities are key strategies adopted by players in the Recommendation Engine market. In 2018, the market of Recommendation Engine has been fragmented by the top five players accounting for 35% of the share. The major vendors in the global recommendation engine market based on AI are IBM, SAP, Salesforce, HPE, Oracle, Google, Microsoft, Intel, AWS, and Sentient Technologies.

Expansion/Technology Launches
  • Amazon revealed in June 2019 the general availability of Amazon Personalize, an AWS platform which enables the creation of websites, mobile apps, content management and email marketing systems that recommend products, offer personalized search results and customize funnels on the fly.
  • In September 2019, Alibaba-owned UCWeb has announced that they are planning to expand into e-commerce by creating a recommendation engine that will create product listings alongside existing company offerings such as videos.

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