• 出版社/出版日：Mordor Intelligence / 2021年2月20日
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The Recommendation Engine Market was valued at USD 1.2 billion in 2020, at a CAGR of over 34.3% during the forecast period (2021 – 2026). With the growing amount of information over the internet along with a significant rise in the number of users, it is becoming essential for companies to search, map and provide them with the relevant chunk of information according to their preferences and tastes.
– The rising need for customer retention as well as increased revenue and Return on Investment (RoI) by deploying AI-powered recommendation engines, is expected to drive the demand of the Recommendation Engine Market.
– The advancement of digitalization across emerging economies coupled with an increase in the eCommerce market has driven the demand for recommendation engines.
– Integration of Machine Learning model across the AI-based cloud platforms is driving the automation across multiple end-user industries terminal.
Key Market Trends
Retail Sector is Gaining Traction Across Emerging Economies
– The retail sector is integrating recommendation engines system powered by AI to achieve business benefits, such as customer retention and increased revenue and Return on Investment (RoI), thereby deploying AI-powered recommendation engines. The recommendation engine uses data filtering tools that make use of algorithms and data to recommend the most relevant items to a particular user.
– Also, increasing government support toward enhancing digitalization across various developing countries coupled with the growing eCommerce market has driven the demand for recommendation engines across the emerging economies.
– With the increasing amount of information over the internet along with a significant rise in the number of users, the product recommendation engine improves the use of machine learning, thereby creating a much better process for customer satisfaction and retention across the retail segment.
North America to Register the Highest Growth Rate During the Forecast Period
– North America is expected to be a significant revenue-generating region, thereby highly focusing on the growth of innovations across the US and Canada regions. These countries have the most competitive and rapidly changing market across the globe.
– Moreover, North America is expected to be a high potential marketplace due to the rise in the eCommerce market and the enormous growth of automated data across the various end-user segments.
– The growing need to understand customer behavior and preferences, through business insights to formulate various customer engagement strategies across the emerging economies of the North America region is expected to drive the Recommendation Engine market demand during the forecast period.
The recommendation engine market is moderately competitive and consists of a few major players. In terms of market share, some of the players are currently dominate the market. However, with the advancement in the analytics across AI-based platforms, new players are increasing their market presence thereby expanding their business footprint across the emerging economies.
– November 2019 – Cloudera had launched its new Cloudera Connect to help partners innovate faster, grow the machine learning and analytics markets, and build profitable businesses. Partners gain the resources and expertise to accelerate customer adoption and value with end-to-end solutions based on the Cloudera Data Platform (CDP).
– October 2019 – Kibo had entered into an agreement to acquire leading testing and optimization provider, Monetate. The acquisition will expand Kibo’s personalization capabilities and will not only complement its best-in-class Certona solution but will also extend the reach of Kibo’s end-to-end cloud commerce platform. The combined solution set will deliver an unparalleled, best-in-class unified cloud commerce platform that will provide consumers with a robust, personalized omnichannel commerce experience.
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– The market estimate (ME) sheet in Excel format
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1.1 Study Assumptions & Market Definition
1.2 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
4.1 Market Overview
4.2 Market Drivers
4.2.1 Increasing Demand for Customization of Digital Commerce Experience Across Mobile and Web
4.2.2 Growing Adoption by Retailers for Controlling Merchandising and Inventory Rules
4.3 Market Restraints
4.3.1 Complexity Regarding Incorrect Labeling Due to Changing User Preferences
4.4 Industry Attractiveness – Porter’s Five Force Analysis
4.4.1 Bargaining Power of Suppliers
4.4.2 Bargaining Power of Buyers/Consumers
4.4.3 Threat of New Entrants
4.4.4 Threat of Substitute Products
4.4.5 Intensity of Competitive Rivalry
4.5 Technology Snapshot
4.5.1 Geospatial Aware
4.5.2 Context Aware (Machine Learning and Deep Learning, Natural Language Processing)
4.6 Assessment of Impact of Covid-19 on the Industry
4.7 Emerging Use-cases (Key use-cases pertaining to the utilization of Recommendation Engine across multiple end-users)
5 MARKET SEGMENTATION
5.1 By Deployment Mode
5.2 By Types
5.2.1 Collaborative Filtering
5.2.2 Content-Based Filtering
5.2.3 Hybrid Recommendation Systems
5.2.4 Other Types
5.3 By End-user Industry
5.3.2 Media & Entertainment
5.3.6 Other End-user Industries
5.4.1 North America
5.4.4 Latin America
5.4.5 Middle-East & Africa
6 COMPETITIVE LANDSCAPE
6.1 Company Profiles
6.1.1 IBM Corporation
6.1.2 Google LLC (Alphabet Inc.)
6.1.3 Amazon Web Services Inc.
6.1.4 Microsoft Corporation
6.1.5 Salesforce.com inc.
6.1.6 Sentinent Technologies
6.1.7 Oracle Corporation
6.1.8 Intel Corporation
6.1.9 SAP SE
6.1.10 Hewlett Packard Enterprise Company
6.1.11 Qubit Digital Ltd.
6.1.12 Evergage, Inc.
6.1.13 Monetate Inc.
6.1.14 Adobe Inc.
6.1.15 Dynamic Yield Inc.
6.1.16 Certona Corporation
6.1.17 Emarsys UK Ltd.
6.1.18 RichRelevance, Inc.
6.1.19 Netflix Inc.
7 INVESTMENT ANALYSIS
8 MARKET OPPORTUNITIES AND FUTURE TRENDS