Global Recommendation Engines Market (2022 to 2027) – Industry Trends, Share, Size, Growth, Opportunity and Forecast


Dublin, May 03, 2022 (GLOBE NEWSWIRE) — The “Recommendation Engines Market: Global Industry Trends, Share, Size, Growth, Opportunities and Forecast 2022-2027” report has been added to from offer.

The global recommendation engine market reached a value of US$2.7 billion in 2021. Going forward, the market is expected to reach US$16.3 billion by 2027, growing at a CAGR of 35.61 % in 2022-2027. Keeping in mind the uncertainties of COVID-19, the analyst continuously monitors and assesses the direct and indirect influence of the pandemic on the various end-use industries. This information is included in the report as a major market contributor.

Recommendation engine refers to a data filtering tool that allows marketers to offer relevant product recommendations to customers in real time. It relies on advanced data analysis techniques and algorithms, such as machine learning (ML) and artificial intelligence (AI), which can suggest relevant product catalogs to an individual. Additionally, it may display products on websites, apps, and emails, based on customer preferences, browser history, attributes, and situational context. At present, it is widely used in business-to-consumer (B2C) e-commerce areas such as entertainment, mobile apps, and education, which require a personalization strategy.

Recommendation Engine Market Trends

The coronavirus disease (COVID-19) pandemic and complete shutdowns imposed by government agencies in many countries have encouraged businesses to shift to online retail platforms. This represents one of the major factors driving the demand for recommendation engines to increase sales and maintain a positive customer relationship.

Apart from this, the thriving e-commerce industry due to growing internet penetration, growing addiction to smartphones, and emerging trend of social media is helping the market grow. It can also be attributed to changing consumer consumption habits and the growing need for convenience, immediacy and simplicity when shopping.

Additionally, the increasing adoption of the omnichannel approach to sales focused on providing a seamless customer experience is driving the market. Additionally, due to the rapid expansion of businesses globally, there is an increase in demand for recommendation engines to handle large volumes of data and actively engage users. They are also gaining traction in small and medium-sized enterprises (SMBs) around the world to enable them to increase overall sales by selling new products to existing customers and maximizing average order value.

Key market segmentation

This report provides an analysis of the main trends in each sub-segment of the global recommendation engines market, as well as forecasts at the global, regional and country levels from 2022 to 2027. The report has categorized the market based on type, technology, deployment mode, application and end user.

Breakdown by type:

  • Collaborative filtering
  • Content-Based Filtering
  • Hybrid recommender systems
  • Others

Breakdown by technology:

  • Aware of the context
  • geospatial aware

Breakdown by deployment mode:

Breakdown by application:

  • Operations strategy and planning
  • Product planning and proactive asset management
  • Personalized campaigns and customer discovery

Breakdown by end user:

  • IT and Telecommunications
  • BFSI
  • Retail
  • Media and Entertainment
  • Health care
  • Others

Breakdown by region:

  • North America
  • United States
  • Canada
  • Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Indonesia
  • Others
  • Europe
  • Germany
  • France
  • UK
  • Italy
  • Spain
  • Russia
  • Others
  • Latin America
  • Brazil
  • Mexico
  • Others
  • Middle East and Africa

Competitive landscape

The competitive landscape of the industry has also been examined along with the profiles of major players i.e. Adobe Inc., Inc., Dynamic Yield (McDonald’s), Google LLC (Alphabet Inc.), Hewlett Packard Enterprise Development LP, Intel Corporation, International Business Machines Corporation, Kibo Software Inc., Microsoft Corporation, Oracle Corporation, Recolize GmbH, Inc. and SAP SE.

Key questions answered by this report

  • How has the global recommendation engine market behaved so far and how will it behave in the coming years?
  • What has been the impact of COVID-19 on the global recommendation engine market?
  • What are the main regional markets?
  • What is the market breakdown by type?
  • What is the shattering of the technology-based market?
  • What is the market breakdown by deployment mode?
  • What is the market breakdown by application?
  • What is the market breakdown by end user?
  • What are the different stages of the industry value chain?
  • What are the key drivers and challenges in the industry?
  • What is the structure of the global recommendation engine market and who are the key players?
  • How competitive is the industry?

Main topics covered:

1 Preface

2 Scope and methodology

3 Executive summary

4 Presentation
4.1 Overview
4.2 Key Industry Trends

5 Global Recommendation Engine Market
5.1 Market Overview
5.2 Market Performance
5.3 Impact of COVID-19
5.4 Market Forecast

6 Market Breakdown by Type
6.1 Collaborative filtering
6.1.1 Market trends
6.1.2 Market Forecast
6.2 Content-Based Filtering
6.2.1 Market trends
6.2.2 Market Forecast
6.3 Hybrid recommender systems
6.3.1 Market trends
6.3.2 Market Forecast
6.4 Others
6.4.1 Market trends
6.4.2 Market Forecast

7 Market Breakdown by Technology
7.1 Aware of context
7.1.1 Market trends
7.1.2 Market Forecast
7.2 Geospatial Aware
7.2.1 Market trends
7.2.2 Market Forecast

8 Market Breakdown by Deployment Mode
8.1 On-site
8.1.1 Market trends
8.1.2 Market Forecast
8.2 Cloud-based
8.2.1 Market trends
8.2.2 Market Forecast

9 Market Breakdown by Application
9.1 Strategy and planning of operations
9.1.1 Market trends
9.1.2 Market Forecast
9.2 Product Planning and Proactive Asset Management
9.2.1 Market trends
9.2.2 Market Forecast
9.3 Personalized campaigns and customer discovery
9.3.1 Market trends
9.3.2 Market Forecast

10 Market Breakdown by End User
10.1 Computers and Telecommunications
10.1.1 Market Trends
10.1.2 Market Forecast
10.2 BFSI
10.2.1 Market Trends
10.2.2 Market Forecast
10.3 Retail
10.3.1 Market Trends
10.3.2 Market Forecast
10.4 Media and entertainment
10.4.1 Market Trends
10.4.2 Market Forecast
10.5 Healthcare
10.5.1 Market Trends
10.5.2 Market Forecast
10.6 Others
10.6.1 Market Trends
10.6.2 Market Forecast

11 Market Breakdown by Region

12 SWOT Analysis

13 Value chain analysis

14 Analysis of the five forces of carriers

15 Price Analysis

16 Competitive landscape
16.1 Market structure
16.2 Key Players
16.3 Profiles of Key Players
16.3.1 Adobe Inc. Company Overview Product portfolio Finance SWOT Analysis
16.3.2 Inc. Presentation of the company Product portfolio Finance SWOT Analysis
16.3.3 Dynamic Yield (McDonald’s) Company overview Product Portfolio
16.3.4 Google LLC (Alphabet Inc.) Company Overview Product portfolio SWOT Analysis
16.3.5 Hewlett Packard Enterprise Development LP Company Overview Product portfolio Finance SWOT Analysis
16.3.6 Intel Corporation Company Overview Product Portfolio Finance SWOT Analysis
16.3.7 International Society of Commercial Machinery Company Overview Product Portfolio Finance SWOT Analysis
16.3.8 Kibo Software Inc. Company Overview Product Portfolio
16.3.9 Microsoft Corporation Company Overview Product portfolio Finance SWOT Analysis
16.3.10 Oracle Corporation Company Overview Product Portfolio Finance SWOT Analysis
16.3.11 Recolize GmbH Company Overview Product Portfolio
16.3.12 Inc. Company Overview Product Portfolio Finance SWOT Analysis
16.3.13 SAP OS Company Overview Product Portfolio Finance SWOT Analysis

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