HOME Top Market Reports Recommendation Engine Market by Type (Collaborative Filtering, Content-Based Filtering, and Hybrid Recommendation), Deployment Mode (Cloud and On-Premises), Technology, Application, End-User, and Region - Global Forecast to 2022

Recommendation Engine Market by Type (Collaborative Filtering, Content-Based Filtering, and Hybrid Recommendation), Deployment Mode (Cloud and On-Premises), Technology, Application, End-User, and Region - Global Forecast to 2022

By: marketsandmarkets.com
Publishing Date: March 2018
Report Code: TC 6096

 

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The recommendation engine market based on AI, is projected to grow at a significant rate over the next 5 years, due to the need to retain old customers and attract new customers, achieve higher sales and Return on Investment (RoI), and analyze large volumes of customer data to create recommendations. Increase in focus to enhance the customer experience and growth in the digitalization trend are some of the major driving factors for the market. The market size based on AI, is expected to grow from USD 588.9 Million in 2016 to USD 4414.8 Million by 2022, at a Compound Annual Growth Rate (CAGR) of 40.7% during the forecast period. The base year considered for this study is 2016, and the forecast period considered is from 2017 to 2022.

The objective of the study is to define, describe, and forecast the  recommendation engine market powered by AI, on the basis of types (collaborative filtering, content-based filtering, and hybrid recommendations) deployment modes (cloud and on-premises), technologies (context aware and geospatial aware), applications (personalized campaigns and customer discovery, product planning, strategy and operations planning, and proactive asset management), end-users (Banking, Financial Services, and Insurance (BFSI); media and entertainement; healthcare; retail; and transportation), and regions. Moreover, the report aims at providing detailed information about the major factors influencing the growth of market, such as drivers, restraints, opportunities, and challenges.

The research methodology used to estimate and forecast the global recommendation engine market size initiated with the capturing of data on the key vendor revenues through secondary research, annual reports, Institute of Electrical and Electronic Engineers (IEEE), Factiva, Bloomberg, and press releases. Moreover, the vendor offerings were taken into consideration to determine the market segmentation. The bottom-up procedure was employed to arrive at the overall market size from the revenues of the key market players. Post-arrival at the overall market size, the total market was split into several segments and subsegments, which were then verified through primary research by conducting extensive interviews with the key indivsiduals, such as Chief Executive Officers (CEOs), Vice Presidents (VPs), directors, and executives. The data triangulation and market breakdown procedures were employed to complete the overall market engineering process and arrive at the exact statistics for all segments and subsegments. The breakdown of the profiles of the primary participants is depicted in the figure given below:

Recommendation Engine Market

To know about the assumptions considered for the study, download the pdf brochure

The major vendors in the global recommendation engine market based on AI, are IBM (US), SAP (Germany), Salesforce (US), HPE (US), Oracle (US), Google (US), Microsoft (US), Intel (US), AWS (US), and Sentient Technologies (US).

Target Audience

  • AI recommendation engine software and platform providers
  • Venture capitalists and angel investors
  • Information Technology (IT) management directors/managers
  • Government organizations
  • Research organizations
  • Consultants/advisory firms
  • IT governance directors/managers
  • AI system integrators
  • Managed Service Providers (MSPs)
  • Value-added Resellers (VARs)

Scope of the Report

The research report segments the recommendation market based on AI, into the following segments:

By Type:

  • Collaborative filtering
  • Content-based filtering
  • Hybrid recommendation

By Deployment Mode:

  • Cloud
  • On-premises

By Technology:

  • Context aware
  • Geospatial aware

By Application:

  • Personalized campaigns and customer discovery
  • Product planning
  • Strategy and operations planning
  • Proactive asset management

By End-user:

  • Manufacturing
  • Healthcare
  • BFSI
  • Media and entertainment
  • Transportation
  • Others (telecom, energy and utilities, manufacturing, and education)

By Region:

  • North America
  • Europe
  • Asia Pacific (APAC)
  • Middle East and Africa (MEA)
  • Latin America

Available Customizations

With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report:

Product Analysis

  • Product matrix which gives a detailed comparison of product portfolio of each company

Geographic Analysis

  • Further breakdown of the APAC recommendation engine market based on AI
  • Further breakdown of the North American market based on AI
  • Further breakdown of the MEA market based on AI
  • Further breakdown of the European market based on AI
  • Further breakdown of the Latin America market based on AI

Company Information

  • Detailed analysis and profiling of additional market players (Up to 5)

Table of Contents

1 Introduction (Page No. - 14)
    1.1 Objectives of the Study
    1.2 Market Definition
    1.3 Years Considered for the Study
    1.4 Currency
    1.5 Stakeholders

2 Research Methodology (Page No. - 17)
    2.1 Research Data
           2.1.1 Secondary Data
           2.1.2 Primary Data
                    2.1.2.1 Breakdown of Primaries
                    2.1.2.2 Key Industry Insights
    2.2 Market Size Estimation
    2.3 Research Assumptions
           2.3.1 AI Recommendation Engine Market: Assumptions
    2.4 Limitations

3 Executive Summary (Page No. - 23)

4 Premium Insights (Page No. - 28)
    4.1 Attractive Market Opportunities in the AI Recommendation Engine Market
    4.2 AI Market By End-User
    4.3 AI Market By Region
    4.4 Market Investment Scenario

5 Market Overview and Industry Trends (Page No. - 31)
    5.1 Introduction
    5.2 AI Recommendation Engine and Data Filtering Models
    5.3 AI Recommendation Engine Market: Use Cases
           5.3.1 Use Case #1: AI-Powered Recommendation Solution to Increase Revenue in the Ecommerce Sector
           5.3.2 Use Case #2: AI-Powered Customer Relationship Management (CRM) Solution to Drive Customer Engagement in the Hospitality Sector
           5.3.3 Use Case: AI-Powered Recommendation Solution to Increase Customer Engagement in the Ecommerce Sector
           5.3.4 Use Case: AI-Powered Recommendation Solution to Generate More Orders and Increase Revenue in the Retail Sector
    5.4 Market Dynamics
           5.4.1 Drivers
                    5.4.1.1 Increasing Focus on Enhancing the Customer Experience
                    5.4.1.2 Growing Trend of Digitalization
           5.4.2 Restraints
                    5.4.2.1 Concerns Over Infrastructure Compatibility
           5.4.3 Opportunities
                    5.4.3.1 Growing Use of the Deep Learning Technology in AI Recommendation Engine Solutions
                    5.4.3.2 Increasing Demand to Analyze Large Volumes of Data
           5.4.4 Challenges
                    5.4.4.1 Concerns Over Accessing Customers’ Personal Data
                    5.4.4.2 Lack of Skills and Expertise

6 AI Recommendation Engine Market, By Type (Page No. - 39)
    6.1 Introduction
    6.2 Collaborative Filtering
    6.3 Content-Based Filtering
    6.4 Hybrid Recommendation

7 Market, By Technology (Page No. - 44)
    7.1 Introduction
    7.2 Context Aware
           7.2.1 Machine Learning and Deep Learning
           7.2.2 Natural Language Processing
    7.3 Geospatial Aware

8 AI Recommendation Engine Market, By Application (Page No. - 50)
    8.1 Introduction
    8.2 Personalized Campaigns and Customer Discovery
    8.3 Product Planning
    8.4 Strategy and Operations Planning
    8.5 Proactive Asset Management
    8.6 Others

9 AI Recommendation Engine Market, By Deployment Mode (Page No. - 56)
    9.1 Introduction
    9.2 Cloud
    9.3 On-Premises

10 AI Recommendation Engine Market, By End-User (Page No. - 60)
     10.1 Introduction
     10.2 Retail
     10.3 Media and Entertainment
     10.4 Transportation
     10.5 Banking, Financial Services, and Insurance
     10.6 Healthcare
     10.7 Others

11 AI Recommendation Engine Market, By Region (Page No. - 67)
     11.1 Introduction
     11.2 North America
             11.2.1 United States
             11.2.2 Canada
     11.3 Europe
             11.3.1 United Kingdom
             11.3.2 Germany
             11.3.3 Switzerland
             11.3.4 Rest of Europe
     11.4 Asia Pacific
             11.4.1 China
             11.4.2 Japan
             11.4.3 Rest of Asia Pacific
     11.5 Middle East and Africa
             11.5.1 Middle East
             11.5.2 Africa
     11.6 Latin America
             11.6.1 Brazil
             11.6.2 Rest of Latin America

12 Competitive Landscape (Page No. - 96)
     12.1 Overview
     12.2 Top Players Operating in the AI Recommendation Engine Market
     12.3 Competitive Scenario
             12.3.1 New Product Launches/Product Enhancements
             12.3.2 Partnerships, Agreements, and Collaborations
             12.3.3 Mergers and Acquisitions
             12.3.4 Business Expansions

13 Company Profiles (Page No. - 103)
     13.1 Introduction
(Business Overview, Products/Solutions and Services Offered, Recent Developments, SWOT Analysis, MnM View)*
     13.2 IBM
     13.3 Google
     13.4 AWS
     13.5 Microsoft
     13.6 Salesforce
     13.7 Sentient Technologies
     13.8 HPE
     13.9 Oracle
     13.10 Intel
     13.11 SAP
*Details on Business Overview, Products/Solutions and Services Offered, Recent Developments, SWOT Analysis, MnM View Might Not Be Captured in Case of Unlisted Companies.
     13.12 Key Innovators
             13.12.1 Fuzzy.AI
             13.12.2 Infinite Analytics

14 Appendix (Page No. - 135)
     14.1 Discussion Guide
     14.2 Knowledge Store: Marketsandmarkets’ Subscription Portal
     14.3 Introducing RT: Real-Time Market Intelligence
     14.4 Available Customizations
     14.5 Related Reports
     14.6 Author Details


List of Tables (76 Tables)

Table 1 Global AI Recommendation Engine Market Size and Growth Rate, 2015–2022 (USD Million, Y-O-Y %)
Table 2 AI Recommendation Engine Market Size, By Type, 2015–2022 (USD Million)
Table 3 Collaborative Filtering: Market Size, By Region, 2015–2022 (USD Million)
Table 4 Content-Based Filtering: Market Size, By Region, 2015–2022 (USD Million)
Table 5 Hybrid Recommendation: Market Size, By Region, 2015–2022 (USD Million)
Table 6 AI Recommendation Engine Market Size, By Technology, 2015–2022 (USD Million)
Table 7 Context Aware: Market Size, By Type, 2015–2022 (USD Million)
Table 8 Context Aware: Market Size, By Region, 2015–2022 (USD Million)
Table 9 Machine Learning and Deep Learning Market Size, By Region, 2015–2022 (USD Million)
Table 10 Natural Language Processing Market Size, By Region, 2015–2022 (USD Million)
Table 11 Geospatial Aware: Market Size, By Region, 2015–2022 (USD Million)
Table 12 AI Recommendation Engine Market Size, By Application, 2015–2022 (USD Million)
Table 13 Personalized Campaigns and Customer Discovery: Market Size, By Region, 2015–2022 (USD Million)
Table 14 Product Planning: Market Size, By Region, 2015–2022 (USD Million)
Table 15 Strategy and Operations Planning: Market Size, By Region, 2015–2022 (USD Million)
Table 16 Proactive Asset Management: Market Size, By Region, 2015–2022 (USD Million)
Table 17 Others: Market Size, By Region, 2015–2022 (USD Million)
Table 18 AI Recommendation Engine Market Size, By Deployment Mode, 2015–2022 (USD Million)
Table 19 Cloud: Market Size, By Region, 2015–2022 (USD Million)
Table 20 On-Premises: Market Size, By Region, 2015–2022 (USD Million)
Table 21 AI Recommendation Engine Market Size, By End-User, 2015–2022 (USD Million)
Table 22 Retail: Market Size, By Region, 2015–2022 (USD Million)
Table 23 Media and Entertainment: Market Size, By Region, 2015–2022 (USD Million)
Table 24 Transportation: Market Size, By Region, 2015–2022 (USD Million)
Table 25 Banking, Financial Services, and Insurance: Market Size, By Region, 2015–2022 (USD Million)
Table 26 Healthcare: Market Size, By Region, 2015–2022 (USD Million)
Table 27 Others: Market Size, By Region, 2015–2022 (USD Million)
Table 28 AI Recommendation Engine Market Size, By Region, 2015–2022 (USD Million)
Table 29 Data Traffic in North America, 2016–2022 (Petabytes/Month)
Table 30 Major Eretailers in North America
Table 31 North America: Market Size, By Country, 2015–2022 (USD Million)
Table 32 North America: Market Size, By Type, 2015–2022 (USD Million)
Table 33 North America: Market Size, By Technology, 2015–2022 (USD Million)
Table 34 North America: Context Aware Market Size, By Type, 2015–2022 (USD Million)
Table 35 North America: Market Size, By Application, 2015–2022 (USD Million)
Table 36 North America: Market Size, By Deployment Mode, 2015–2022 (USD Million)
Table 37 North America: Market Size, By End-User, 2015–2022 (USD Million)
Table 38 Data Traffic in Europe, 2016–2022 (Petabytes/Month)
Table 39 Major Eretailers in Europe
Table 40 Europe: Market Size, By Country, 2015–2022 (USD Million)
Table 41 Europe: Market Size, By Type, 2015–2022 (USD Million)
Table 42 Europe: Market Size, By Technology, 2015–2022 (USD Million)
Table 43 Europe: Context Aware AI Recommendation Engine Market Size, By Type, 2015–2022 (USD Million)
Table 44 Europe: Market Size, By Application, 2015–2022 (USD Million)
Table 45 Europe: Market Size, By Deployment Mode, 2015–2022 (USD Million)
Table 46 Europe: Market Size, By End-User, 2015–2022 (USD Million)
Table 47 Data Traffic in Asia Pacific, 2016–2022 (Petabytes/Month)
Table 48 Major Eretailers in Asia Pacific
Table 49 Asia Pacific: Market Size, By Country, 2015–2022 (USD Million)
Table 50 Asia Pacific: Market Size, By Type, 2015–2022 (USD Million)
Table 51 Asia Pacific: Market Size, By Technology, 2015–2022 (USD Million)
Table 52 Asia Pacific: Context Aware AI Recommendation Engine Market Size, By Type, 2015–2022 (USD Million)
Table 53 Asia Pacific: Market Size, By Application, 2015–2022 (USD Million)
Table 54 Asia Pacific: Market Size, By Deployment Mode, 2015–2022 (USD Million)
Table 55 Asia Pacific: Market Size, By End-User, 2015–2022 (USD Million)
Table 56 Data Traffic in Middle East and Africa, 2016–2022 (Petabytes/Month)
Table 57 Middle East and Africa: Market Size, By Region, 2015–2022 (USD Million)
Table 58 Middle East and Africa: Market Size, By Type, 2015–2022 (USD Million)
Table 59 Middle East and Africa: Market Size, By Technology, 2015–2022 (USD Million)
Table 60 Middle East and Africa: Context Aware AI Recommendation Engine Market Size, By Type, 2015–2022 (USD Million)
Table 61 Middle East and Africa: Market Size, By Application, 2015–2022 (USD Million)
Table 62 Middle East and Africa: Market Size, By Deployment Mode, 2015–2022 (USD Million)
Table 63 Middle East and Africa: Market Size, By End-User, 2015–2022 (USD Million)
Table 64 Data Traffic in Latin America, 2016–2022 (Petabytes/Month)
Table 65 Major Eretailers in Latin America
Table 66 Latin America: Market Size, By Country, 2015–2022 (USD Million)
Table 67 Latin America: Market Size, By Type, 2015–2022 (USD Million)
Table 68 Latin America: Market Size, By Technology, 2015–2022 (USD Million)
Table 69 Latin America: Context Aware AI Recommendation Engine Market Size, By Type, 2015–2022 (USD Million)
Table 70 Latin America: Market Size, By Application, 2015–2022 (USD Million)
Table 71 Latin America: Market Size, By Deployment Mode, 2015–2022 (USD Million)
Table 72 Latin America: Market Size, By End-User, 2015–2022 (USD Million)
Table 73 New Product Launches/Product Enhancements, 2016–2017
Table 74 Partnerships, Agreements, and Collaborations, 2015–2017
Table 75 Mergers and Acquisitions, 2016–2017
Table 76 Business Expansions, 2017


List of Figures (49 Figures)

Figure 1 AI Recommendation Engine Market Segmentation
Figure 2 Global Market: Research Design
Figure 3 Breakdown of Primary Interviews: By Company, Designation, and Region
Figure 4 Data Triangulation
Figure 5 Market Size Estimation Methodology: Bottom-Up Approach
Figure 6 Market Size Estimation Methodology: Top-Down Approach
Figure 7 Market Snapshot By Type (2017 vs 2022)
Figure 8 Market Snapshot By Deployment Mode
Figure 9 Market Snapshot By Technology
Figure 10 Market Snapshot By Application (2017 vs 2022)
Figure 11 Market Snapshot By End-User
Figure 12 Market Regional Snapshot
Figure 13 Increasing Focus on Enhancing the Customer Experience is Expected to Drive the Growth of the AI Recommendation Engine Market During the Forecast Period
Figure 14 Media and Entertainment End-User is Expected to Grow at the Highest CAGR During the Forecast Period
Figure 15 Asia Pacific is Expected to Have the Fastest Growth Rate During the Forecast Period
Figure 16 Asia Pacific is Expected to Be the Best Market to Investment In, in the Next 5 Years
Figure 17 AI Recommendation Engine: Data Filtering Models
Figure 18 AI Recommendation Engine Market: Drivers, Restraints, Opportunities, and Challenges
Figure 19 Hybrid Recommendation Segment is Expected to Grow at the Highest CAGR During the Forecast Period
Figure 20 Geospatial Aware Segment is Expected to Exhibit A Higher CAGR During the Forecast Period
Figure 21 Natural Language Processing Segment is Expected to Exhibit A Higher CAGR During the Forecast Period
Figure 22 Strategy and Operations Planning Application is Expected to Exhibit the Highest CAGR During the Forecast Period
Figure 23 Cloud Deployment Mode is Expected to Exhibit A Higher CAGR During the Forecast Period
Figure 24 Media and Entertainment End-User is Expected to Exhibit the Highest CAGR During the Forecast Period
Figure 25 Asia Pacific is Expected to Have the Highest CAGR in the AI Recommendation Engine Market During the Forecast Period
Figure 26 Asia Pacific is Expected to Grow at the Highest CAGR During the Forecast Period
Figure 27 North America: Market Snapshot
Figure 28 Media and Entertainment End-User is Expected to Grow at the Highest CAGR During the Forecast Period
Figure 29 Media and Entertainment End-User is Expected to Grow at the Highest CAGR During the Forecast Period
Figure 30 Asia Pacific: Market Snapshot
Figure 31 Media and Entertainment End-User is Expected to Grow at the Highest CAGR During the Forecast Period
Figure 32 Media and Entertainment End-User is Expected to Grow at the Highest CAGR During the Forecast Period
Figure 33 Media and Entertainment End-User is Expected to Grow at the Highest CAGR During the Forecast Period
Figure 34 Key Developments By the Leading Players in the AI Recommendation Engine Market
Figure 35 Geographic Revenue Mix of Market Players
Figure 36 IBM: Company Snapshot
Figure 37 IBM: SWOT Analysis
Figure 38 Google: Company Snapshot
Figure 39 Google: SWOT Analysis
Figure 40 AWS: Company Snapshot
Figure 41 AWS: SWOT Analysis
Figure 42 Microsoft: Company Snapshot
Figure 43 Microsoft: SWOT Analysis
Figure 44 Salesforce: Company Snapshot
Figure 45 Salesforce: SWOT Analysis
Figure 46 HPE: Company Snapshot
Figure 47 Oracle: Company Snapshot
Figure 48 Intel: Company Snapshot
Figure 49 SAP: Company Snapshot

The global recommendation engine market based on AI, is expected to grow from USD 801.1 Million in 2017 to USD 4414.8 Million by 2022, at a Compound Annual Growth Rate (CAGR) of 40.7% during the forecast period. The major driving factors for the market are growth in focus toward enhancing the customer experience and upsurge in rate of digitalization.

The collaborative filtering segment is expected to account for the largest market size during the forecast period. The collaborative filtering technique uses a large volume of information, such as users’ behavior, preferences, and activities from the past records to segment users based on similarity of likings. Several industry verticals, such as retail, media and entertainment, transportation, Banking, Financial Services, and Insurance (BFSI), healthcare, energy and utilities, manufacturing, and education have deployed recommendation engines powered by AI for various applications, including personalized campaigns and customer discovery, product planning, strategy and operations planning, and proactive asset management.

The cloud deployment mode segment is expected to account for the larger market size and is expected to grow at a higher CAGR during the forecast period. The cloud-based solutions offer wide and agile solutions to the end-users in the recommendation engine market.

The context aware segment is expected to account for the larger market size during the forecast period. On contrary, the geospatial aware segment is expected to be the faster growing technology type during the forecast period due to the need to understand users’ behavior and preferences based on the past location records.

The personalized campaigns and customer discovery application is expected to account for the largest market size during the forecast period, while the strategy and operations planning application is expected to have the highest CAGR during the forecast period. Various end-users have included recommendation engines based on AI for product planning, proactive asset management, intelligent data collection, and cross-layer network-wide correlation of monitoring data.

The retail end-user is expected to be the highest contributor during the forecast period, in terms of revenue, while the media and entertainment end-user is projected to grow at the highest CAGR during the forecast period. Both end-users have used recommendation engines powered by AI to achieve benefits, such as customer retention and increased revenue and Return on Investment (RoI), by deploying AI-powered recommendation engines. In addition, growth in government support toward enhancing digitalization across various countries coupled with increase in the eCommerce market has driven the demand for recommendation engines. The other end-users, such as transportation, BFSI, healthcare, energy and utilities, manufacturing, and education have contributed significantly to the growth of the recommendation engine market based on AI, due to an increased focus of the companies to enhance customer experience on the basis of their purchasing and searching pattern.

Recommendation Engine Market

The global recommendation engine market based on AI, has been segmented on the basis of major geographic regions into North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America. North America is expected to be the largest revenue-generating region, as there is a high focus on innovations in the US and Canada. These countries have the most competitive and rapidly changing market across the globe. APAC is expected to be the fastest-growing region in the market. Moreover, APAC is expected to be the highly potential market due to rise in the eCommerce market and enormous growth of data across all end-users.

Furthermore, concerns over accessing customers’ personal data and dearth of skills and expertise could affect the growth of the recommendation engine market. These factors account for the risk of failure to operate in the competitive world, which necessitates the need for recommendation engines powered by AI.

The major vendors offering recommendation engine based on AI, across the globe include IBM (US), SAP (Germany), Salesforce (US), HPE (US), Oracle (US), Google (US), Microsoft (US), Intel (US), AWS (US), and Sentient Technologies (US).

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