Global Recommendation Engine Market Size, Share, Trends, Growth, Report And Forecast 2023-2028

Recommendation Engine Market Outlook

According to the report by Expert Market Research (EMR), the global recommendation engine market size reached a value of USD 3.26 billion in 2022. Aided by the growing need for personalisation in customer services and the widening applications of recommendation engines in diverse industries, the market is projected to further grow at a CAGR of 15.45% between 2023 and 2028 to reach a value of USD 15.45 billion by 2028.

A recommendation engine is a data filtering tool that uses algorithms and data to recommend the most relevant items to a particular user. They are commonly used in various services and applications, such as e-commerce, streaming platforms, social media, and news websites, to provide a more personalised and engaging user experience. These engines are vital for customer retention, user engagement, and sales growth.

The increasing focus on personalising customer experiences is a key factor driving the global recommendation engine market growth. With the escalating volume of data and growing consumer demand for personalised content and products, there has been a significant shift towards recommendation engines. These engines use machine learning and AI technologies to analyse user behaviour and preferences, enabling businesses to provide customised services and products, thereby driving demand.

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In addition, the surge in online shopping and digital streaming platforms has also contributed to the growing recommendation engine market demand. E-commerce platforms like Amazon and streaming services like Netflix and Spotify utilise sophisticated recommendation engines to suggest products, movies, or songs based on user behaviour, preferences, and previous purchases or interactions.

The broadening applications of recommendation engines in various sectors significantly contribute to the recommendation engine market expansion. In the retail industry, recommendation engines are crucial for suggesting products that consumers might be interested in, based on their browsing history and past purchases. The entertainment and media industry utilises recommendation engines to offer personalised content suggestions, enhancing user engagement and satisfaction. Furthermore, recommendation engines are increasingly being used in the news and social media sector to suggest news articles, posts, or pages based on users’ interests and reading habits.

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Moreover, with the rapid development of AI and machine learning technologies, recommendation engines have become more accurate and efficient. AI-based recommendation engines can analyse complex and large datasets in real-time, providing more precise and personalised recommendations, thereby enhancing user experience and boosting the recommendation engine market development.

Recommendation Engine Market Segmentation

The market can be divided based on type, deployment type, technology, application, end use, and region.

Market Breakup by Type

• Collaborative Filtering
• Content-Based Filtering
• Hybrid Recommendation Systems
• Others

Market Breakup by Deployment Type

• Cloud Based
• On-premises

Market Breakup by Technology

• Context Aware
• Geospatial Aware

Market Breakup by Application

• Strategy and Operations Planning
• Product Planning and Proactive Asset Management
• Personalised Campaigns and Customer Discovery

Market Breakup by End Use

• IT and Telecommunication
• BFSI
• Retail
• Media and Entertainment
• Healthcare
• Others

Market Breakup by Region

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East and Africa

Competitive Landscape

The EMR report looks into the market shares, plant turnarounds, capacities, investments, and acquisitions and mergers, among other major developments, of the global recommendation engine companies. Some of the major key players explored in the report by Expert Market Research are as follows:

• 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

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