Global AI-based Recommendation Engine Market 2024 by Company, Regions, Type and Application, Forecast to 2030

AI-based recommendation system is a sophisticated tool that analyzes data to suggest relevant items to users. These systems are the driving force behind the "You might also like" sections across various digital platforms, whether it be in online shopping, streaming services, or social media. From a technical standpoint, these systems leverage machine learning algorithms to sift through large datasets. They identify patterns, preferences, and behaviors of users to predict what might interest them next. These algorithms can range from simple rule-based engines to complex neural networks that learn and evolve with each user interaction. They analyze past behavior, consider similar user profiles, and sometimes even incorporate external data to make their suggestions as relevant as possible.
According to our (Global Info Research) latest study, the global AI-based Recommendation Engine market size was valued at US$ 1965 million in 2023 and is forecast to a readjusted size of USD 3226 million by 2030 with a CAGR of 7.4% during review period.
The global AI-based recommendation system market refers to the use of artificial intelligence (AI) technologies to provide personalized recommendations to individuals based on their preferences, behaviors, and historical data. AI-based recommendation systems utilize algorithms and machine learning techniques to analyze large datasets and offer suggestions for products, services, content, or actions.
The market for AI-based recommendation systems is driven by several factors:
Growing demand for personalized experiences: With the increasing volume of digital content, products, and services available, consumers are seeking personalized experiences that cater to their specific needs and preferences. AI-based recommendation systems help businesses deliver tailored recommendations, enhancing customer engagement, satisfaction, and loyalty.
Rising e-commerce and online streaming activities: The proliferation of e-commerce platforms and online streaming services has generated vast amounts of data regarding consumer preferences and behavior. AI-based recommendation systems analyze this data to provide relevant product recommendations, improve cross-selling and upselling, and enhance the overall customer shopping or content consumption experience.
Advancements in AI and machine learning technologies: The advancements in AI and machine learning algorithms have significantly improved the capabilities of recommendation systems. Deep learning techniques, natural language processing, and collaborative filtering algorithms enable more accurate and effective personalized recommendations, driving the adoption of AI-based recommendation systems across various industries.
Focus on enhancing customer engagement and retention: Businesses are increasingly recognizing the importance of customer engagement and retention for long-term success. AI-based recommendation systems help in creating personalized customer experiences, increasing customer satisfaction, and encouraging repeat purchases or usage, thereby improving customer retention rates and revenue generation.
Integration of recommendation systems in various industries: AI-based recommendation systems are employed in diverse industries, including e-commerce, media and entertainment, healthcare, banking and finance, and travel and hospitality. These systems help in suggesting relevant products, content, treatments, financial services, or travel options, catering to the specific preferences and needs of individuals in each industry.
In conclusion, the global AI-based recommendation system market is witnessing significant growth due to the increased demand for personalized experiences, the rise in e-commerce and online streaming activities, advancements in AI and machine learning technologies, and the focus on customer engagement and retention. By leveraging AI algorithms and techniques, recommendation systems improve customer experiences, drive customer loyalty, and boost business revenue. With the continuous expansion of digital content and services, the AI-based recommendation system market is expected to grow further in the coming years.The global AI-based recommendation system market refers to the use of artificial intelligence (AI) technologies to provide personalized recommendations to individuals based on their preferences, behaviors, and historical data. AI-based recommendation systems utilize algorithms and machine learning techniques to analyze large datasets and offer suggestions for products, services, content, or actions.
The market for AI-based recommendation systems is driven by several factors:
Growing demand for personalized experiences: With the increasing volume of digital content, products, and services available, consumers are seeking personalized experiences that cater to their specific needs and preferences. AI-based recommendation systems help businesses deliver tailored recommendations, enhancing customer engagement, satisfaction, and loyalty.
Rising e-commerce and online streaming activities: The proliferation of e-commerce platforms and online streaming services has generated vast amounts of data regarding consumer preferences and behavior. AI-based recommendation systems analyze this data to provide relevant product recommendations, improve cross-selling and upselling, and enhance the overall customer shopping or content consumption experience.
Advancements in AI and machine learning technologies: The advancements in AI and machine learning algorithms have significantly improved the capabilities of recommendation systems. Deep learning techniques, natural language processing, and collaborative filtering algorithms enable more accurate and effective personalized recommendations, driving the adoption of AI-based recommendation systems across various industries.
Focus on enhancing customer engagement and retention: Businesses are increasingly recognizing the importance of customer engagement and retention for long-term success. AI-based recommendation systems help in creating personalized customer experiences, increasing customer satisfaction, and encouraging repeat purchases or usage, thereby improving customer retention rates and revenue generation.
Integration of recommendation systems in various industries: AI-based recommendation systems are employed in diverse industries, including e-commerce, media and entertainment, healthcare, banking and finance, and travel and hospitality. These systems help in suggesting relevant products, content, treatments, financial services, or travel options, catering to the specific preferences and needs of individuals in each industry.
In conclusion, the global AI-based recommendation system market is witnessing significant growth due to the increased demand for personalized experiences, the rise in e-commerce and online streaming activities, advancements in AI and machine learning technologies, and the focus on customer engagement and retention. By leveraging AI algorithms and techniques, recommendation systems improve customer experiences, drive customer loyalty, and boost business revenue. With the continuous expansion of digital content and services, the AI-based recommendation system market is expected to grow further in the coming years.
This report is a detailed and comprehensive analysis for global AI-based Recommendation Engine market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2024, are provided.
Key Features:
Global AI-based Recommendation Engine market size and forecasts, in consumption value ($ Million), 2019-2030
Global AI-based Recommendation Engine market size and forecasts by region and country, in consumption value ($ Million), 2019-2030
Global AI-based Recommendation Engine market size and forecasts, by Type and by Application, in consumption value ($ Million), 2019-2030
Global AI-based Recommendation Engine market shares of main players, in revenue ($ Million), 2019-2024
The Primary Objectives in This Report Are:
To determine the size of the total market opportunity of global and key countries
To assess the growth potential for AI-based Recommendation Engine
To forecast future growth in each product and end-use market
To assess competitive factors affecting the marketplace
This report profiles key players in the global AI-based Recommendation Engine market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include Microsoft, Google, Andi Search, Metaphor AI, Brave, Phind, Perplexity AI, NeevaAI, Qubit, Dynamic Yield, etc.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.
Market segmentation
AI-based Recommendation Engine market is split by Type and by Application. For the period 2019-2030, the growth among segments provides accurate calculations and forecasts for Consumption Value by Type and by Application. This analysis can help you expand your business by targeting qualified niche markets.
Market segmentation
AI-based Recommendation Engine market is split by Type and by Application. For the period 2018-2029, the growth among segments provides accurate calculations and forecasts for Consumption Value by Type and by Application. This analysis can help you expand your business by targeting qualified niche markets.
Market segment by Type
Collaborative Filtering
Content Based Filtering
Hybrid Recommendation
Market segment by Application
E-commerce Platform
Finance
Social Media
Others
Market segment by players, this report covers
Microsoft
Google
Andi Search
Metaphor AI
Brave
Phind
Perplexity AI
NeevaAI
Qubit
Dynamic Yield
Market segment by regions, regional analysis covers
North America (United States, Canada and Mexico)
Europe (Germany, France, UK, Russia, Italy and Rest of Europe)
Asia-Pacific (China, Japan, South Korea, India, Southeast Asia and Rest of Asia-Pacific)
South America (Brazil, Rest of South America)
Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)
The content of the study subjects, includes a total of 13 chapters:
Chapter 1, to describe AI-based Recommendation Engine product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top players of AI-based Recommendation Engine, with revenue, gross margin, and global market share of AI-based Recommendation Engine from 2019 to 2024.
Chapter 3, the AI-based Recommendation Engine competitive situation, revenue, and global market share of top players are analyzed emphatically by landscape contrast.
Chapter 4 and 5, to segment the market size by Type and by Application, with consumption value and growth rate by Type, by Application, from 2019 to 2030.
Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2019 to 2024.and AI-based Recommendation Engine market forecast, by regions, by Type and by Application, with consumption value, from 2024 to 2030.
Chapter 11, market dynamics, drivers, restraints, trends, Porters Five Forces analysis.
Chapter 12, the key raw materials and key suppliers, and industry chain of AI-based Recommendation Engine.
Chapter 13, to describe AI-based Recommendation Engine research findings and conclusion.