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推荐引擎市场 - 2028 年全球产业规模、份额、趋势、机会和预测。按类型、部署模型、企业规模、按应用、最终用户、地区和竞争细分

Recommendation Engine Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2028. Segmented By Type, By Deployment Model, By Enterprise Size, By Application, By End User, By Region and Competition
出版日期: | 出版商: TechSci Research | 英文 190 Pages | 商品交期: 2-3个工作天内

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简介目录

预计全球推荐引擎市场将在 2024-2028 年预测期内稳定成长。人们对增强客户体验的渴望日益强烈，这推动了对推荐引擎的需求。例如，IBM 公司于 2021 年 5 月扩展了适用于 OTT 和影片的 IBM Watson Advertising Accelerator。创建该工具的目的是帮助广告商超越上下文相关性。 Theamplifier 不依赖传统的广告 ID，而是使用人工智慧不断优化 OTT 广告文案，以实现更好的大规模行销活动效果。

推荐引擎是一个识别员工并向他们提供相关材料的系统。其他技术发展如何继续改变客户兴趣并利用可用资料的一个例子是行动应用程式。建议引擎被认为是 ICT 领域软体和应用产品的关键要素。推荐引擎的两个主要类别是基于内容的过滤和协作过滤。

推荐系​​统使用资讯分析技术来寻找符合使用者偏好的产品。由于多种原因，有许多建议引擎可用。其中包括图片推荐引擎、线上商店的产品推荐引擎、内容推荐引擎和产品建议引擎。人们日益渴望增强客户体验，这满足了对推荐引擎的需求。

市场概况
预测期 2024-2028
2022 年市场规模 47.1亿美元
2028 年市场规模 262.3亿美元
2023-2028 年复合年增长率 33.22%
成长最快的细分市场 云
最大的市场 北美洲

采用联合收割机技术推动市场成长

由于行业种类不断增加以及竞争随之加剧，许多公司正在尝试将包括电脑科学 (AI) 在内的技术与其应用程式、业务、分析和服务相结合。在世界各地，不少公司正在经历数位转型，重点是使用自动化技术来增加员工和客户的知识。由于向数位化的转变，零售商可以扩大客户群、改善客户关係、削减开支并提高员工士气。不断增加的客户体验改进方法和不断扩大的数位转型范围是推动全球推荐引擎市场的几个主要因素。例如，2021 年 3 月 SAP SE 收购了 Signavio。 Signavio 是企业业务流程智慧和流程管理领域的关键参与者。 Signavio 的解决方案已新增至 SAP 的业务流程智慧产品组合中，旨在与 SAP 的全面流程转型产品组合搭配使用。因此，预计市场在预测期内将会成长。

记录和观察客户行为的优势推动市场成长

由于顾客通常会根据实体店面货架上商品的位置做出购买决定，因此企业具有很强的观察和影响顾客行为的能力。随着互联网使用量的增加以及电子商务、行动购物和智慧技术等新销售管道的出现，零售业正在适应新技术和尖端技术。在自助结帐机和智慧销售点系统等最新技术的帮助下，市场正在快速成长。据 ZDNet 称，70% 的企业已经或正在实施数位转型计画。由于企业正在数位化转型，全球推荐引擎市场预计在预测期内将出现较高的复合年增长率。

由于行动和网路上客製化数位商务体验的需求不断增长，市场正在扩大

公司正在寻找可以利用的策略和工具。数以百万计的独特消费者可以透过使用私人资料从这些体验中受益。执行决定结果。如果实施得当，个人化的客户体验可以帮助企业在竞争中脱颖而出，赢得客户的忠诚度，并获得持久的竞争优势——所有这些在当前市场中都至关重要。

由于消费者的需求不断增长，许多组织的行销专业人员逐渐将注意力转向改善客户体验。例如，根据 Adob​​e 公司的数据，采用最强大的全通路客户参与策略的企业可以观察到年增 10%、平均订单价值成长 10%、成交率提高 25% 。此外，拥有强大的全通路客户互动策略和消费者服务改善计画的公司平均保留了 89% 的消费者，而策略较弱的公司只能保留 33%。鑑于营运管道数量不断增加，技术可确保品牌在所有管道上提供一致的服务资讯。在预估期间，市场预计将受益于对增强客户服务的不断增长的需求。

市场区隔

全球推荐引擎市场根据类型、部署模型、企业规模、应用程式、最终用户和地区进行划分。根据类型，市场分为协同过滤、基于内容的过滤和混合推荐。依部署模式，市场分为本地和云端；依企业规模，市场分为大型企业、中小企业。根据应用，市场分为个人化活动和客户交付、策略营运和规划、产品规划和主动资产管理。根据最终用户，市场分为零售和消费品、IT 和电信、医疗保健和生命科学、BFSI、媒体和娱乐等。依地区划分，市场分为北美、亚太、欧洲、南美、中东和非洲。

市场参与者

全球推荐引擎市场的主要市场参与者包括IBM公司、Hewlett Packard Enterprise Development LP、英特尔公司、亚马逊网路服务、Adobe、Salesforce, Inc、微软公司、甲骨文公司、Google有限责任公司和SAP SE。

报告范围：

在本报告中，除了以下详细介绍的产业趋势外，全球推荐引擎市场也分为以下几类。

推荐引擎市场，依类型：

  • 协同过滤
  • 基于内容的过滤
  • 混合推荐

推荐引擎市场（按部署模型）：

  • 本地部署
  • 云

推荐引擎市场，按应用：

  • 个人化活动和客户交付
  • 策略营运与规划
  • 产品规划
  • 主动资产管理

推荐引擎市场，依企业规模划分：

  • 大型企业
  • 中小企业

推荐引擎市场，依最终用户划分：

  • 零售与消费品
  • 资讯科技与电信
  • 医疗保健与生命科学
  • BFSI
  • 媒体
  • 娱乐
  • 其他的

推荐引擎市场（按地区）：

  • 北美洲
  • 美国
  • 加拿大
  • 墨西哥
  • 亚太
  • 中国
  • 印度
  • 日本
  • 韩国
  • 印尼
  • 欧洲
  • 德国
  • 英国
  • 法国
  • 俄罗斯
  • 西班牙
  • 南美洲
  • 巴西
  • 阿根廷
  • 中东和非洲
  • 沙乌地阿拉伯
  • 南非
  • 埃及
  • 阿联酋
  • 以色列

竞争格局

  • 公司概况：全球推荐引擎市场主要公司的详细分析。

可用的客製化：

  • 全球推荐引擎市场报告以及给定的市场资料，科技科学研究根据公司的具体需求提供客製化服务。该报告可以使用以下自订选项：

公司资讯

  • 其他市场参与者（最多五个）的详细分析和概况分析。

目录

第 1 章：产品概述

  • 市场定义
  • 市场范围
  • 涵盖的市场
  • 考虑学习的年份
  • 主要市场区隔

第 2 章：研究方法

  • 研究目的
  • 基线方法
  • 主要产业伙伴
  • 主要协会和二手资料来源
  • 预测方法
  • 数据三角测量与验证
  • 假设和限制

第 3 章：执行摘要

第 4 章：客户之声

第 5 章：全球推荐引擎市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按类型（协作过滤、基于内容的过滤、混合推荐）
    • 按部署模式（本地、云端）
    • 依企业规模（大型企业、中小企业）
    • 按应用（个人化活动和客户交付、策略营运和规划、产品规划和主动资产管理）
    • 按最终用户（零售和消费品、IT 和电信、医疗保健和生命科学、BFSI、媒体和娱乐、其他）
    • 按地区
  • 按公司划分 (2022)
  • 市场地图

第 6 章：北美推荐引擎市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按类型
    • 按部署模型
    • 按企业规模
    • 按应用
    • 按最终用户
  • 北美：国家分析
    • 美国
    • 加拿大
    • 墨西哥

第 7 章：亚太地区推荐引擎市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按类型
    • 按部署模型
    • 按企业规模
    • 按应用
    • 按最终用户
  • 亚太地区：国家分析
    • 中国
    • 印度
    • 日本
    • 韩国
    • 印尼

第 8 章：欧洲推荐引擎市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按类型
    • 按部署模型
    • 按企业规模
    • 按应用
    • 按最终用户
  • 欧洲：国家分析
    • 德国
    • 英国
    • 法国
    • 俄罗斯
    • 西班牙

第 9 章：南美洲推荐引擎市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按类型
    • 按部署模型
    • 按企业规模
    • 按应用
    • 按最终用户
  • 南美洲：国家分析
    • 巴西
    • 阿根廷

第 10 章：中东和非洲推荐引擎市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 按类型
    • 按部署模型
    • 按企业规模
    • 按应用
    • 按最终用户
  • 中东和非洲：国家分析
    • 沙乌地阿拉伯
    • 南非
    • 阿联酋
    • 以色列
    • 埃及

第 11 章：市场动态

  • 司机
  • 挑战

第 12 章：市场趋势与发展

第 13 章：公司简介

  • IBM公司
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 惠普企业开发有限公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 英特尔公司
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 亚马逊网路服务
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 土坯
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • Salesforce 公司
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 微软公司。
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 甲骨文公司，
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 谷歌有限责任公司
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • SAP系统公司
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services

第 14 章：策略建议

关于我们及免责声明

简介目录
Product Code: 15764

Global recommendation engine market is anticipated to grow at a steady pace in the forecast period, 2024-2028. The increased desire to enhance the customer experience is fueling the need for recommendation engines. For instance, IBM Corporation expanded its IBM Watson Advertising Accelerator for OTT and video in May 2021. This tool was created to assist advertisers in moving beyond contextual relevance. Instead of relying on conventional advertising IDs, The amplifier uses artificial intelligence to constantly optimize OTT ad copy for better campaign outcomes at scale.

A recommendation engine is a system that recognizes employees and offers them relevant material. One example of how other technical developments continue to alter customer interest and utilize the available data is mobile applications. The advice engine is recognized as a key element of software and application products in the ICT sector. The two primary categories of recommendation engines are content-based filtering and collaborative filtering.

The recommendation system uses information analysis techniques to seek products that complement the user's preferences. For a variety of reasons, many advice engines are available. These include the picture recommendation engine, the product recommendation engine for online stores, the content recommendation engine, and the product suggestion engine. The increasing desire to enhance customer experience is satisfying the need for engines of recommendation.

Market Overview
Forecast Period2024-2028
Market Size 2022USD 4.71 Billion
Market Size 2028USD 26.23 Billion
CAGR 2023-202833.22%
Fastest Growing SegmentCloud
Largest MarketNorth America

Adoption of combine technology Fueling the Market Growth

Due to the increasing variety of industries and the subsequent growth in competition, many companies are attempting to combine technology, including computer science (AI), with their applications, businesses, analytics, and services. Around the world, quite a few firms are going through a digital transformation with an emphasis on using automation technologies to increase employee and customer knowledge. Due to the shift to digital, retailers can grow their client base, improve their customer connections, cut expenses, and raise employee morale. Increasing customer experience improvement methods and the growing scope of digital transformation are a few of the main factors driving the global recommendation engine market. For instance, in March 2021 SAP SE purchased Signavio. Signavio was a key player in the enterprise business process intelligence and process management arena. The solutions from Signavio were added to SAP's portfolio of business process intelligence and were designed to work with SAP's comprehensive process transformation portfolio. Owing to this the market is expected to grow in the forecast period.

Advantage To Record and Observe Customer Behavior Propelling the Market Growth

Due to the fact that customers usually make their purchasing decisions based on the position of the item in the shelf in brick and mortar businesses have a significant amount of ability to observe and shape customer behavior. The retail sector is adjusting to new and cutting-edge technologies as internet usage is increasing and new sales channels like e-commerce, mobile shopping, and smart technologies are emerging. With the help of latest technologies, such as self-checkout kiosks and smart point-of-sale systems, the market is growing rapidly. According to ZDNet, 70% of businesses have or are implementing a digital transformation plan. Since companies are moving towards digital transformation, the global recommendation engine market is expected to register a high CAGR in the forecast period.

Retailers may use digital transformation to increase customer acquisition, improve customer engagement, save operational costs, and boost staff morale. Along with other advantages, recommendation engine have a favorable effect on revenue and profits. Over the course of the predicted period, this positive influence will generate sizable prospects for the adoption of recommendation engines.

Moreover, the industry for recommendation engines is always concerned about the issue of inaccurate labeling brought by shifting user preferences. However, engineers are always trying to increase the precision and utility of suggestions. This fact is restraining the market growth in the forecast period.

The Market is Expanding as a Result of Rising Demand for Customized Digital Commerce Experiences Across Mobile and the Web

Companies are looking for strategies and tools to take advantage of. Millions of unique consumers can benefit from these experiences by using private data. Execution determines the outcome. When properly implemented, personalized customer experience may help businesses stand out from the competition, win over customers' loyalty, and achieve a durable competitive advantage-all of which are crucial in the current market.

Due to the increasing demand from consumers, many marketing professionals across organizations have shifted their attention to improving customer experience over time. A 10% boost in year-over-year growth, a 10% rise in average order value, and a 25% increase in closure rates, for instance, according to Adobe company, can be observed by businesses with the strongest omnichannel customer engagement strategy. In addition, companies with strong omnichannel customer interaction strategies and consumer service improvement programs retain 89% of their consumers on average, as opposed to 33% for those with weaker strategies. Technologies make sure that the brands provide a consistent message about their services across all channels in light of the expanding number of channels in operation. During the projected period, the market is anticipated to benefit from the rising need for enhanced customer service.

Market Segmentation

The global recommendation engine market is divided based on type, deployment model, enterprise size, application, end user and region. Based on type, the market is divided into collaborative filtering, content-based filtering, and hybrid recommendation. Based on deployment model, the market is divided into on-premises and cloud, Based on enterprise size, the market is divided into large enterprises, small & medium enterprises. Based on application, the market is divided into Personalized Campaigns & Customer Delivery, Strategy Operations & Planning, Product Planning, and Proactive Asset Management. Based on end user, the market is segmented into retail & consumer goods, IT & telecom, healthcare & life science, BFSI, media & entertainment, and others. Based on region, the market is divided into North America, Asia-Pacific, Europe, South America, and Middle East & Africa.

Market Players

Major market players in the global recommendation engine market are IBM Corporation, Hewlett Packard Enterprise Development LP, Intel Corporation, Amazon Web Services, Adobe, Salesforce, Inc, Microsoft Corporation, Oracle Corporation, Google LLC, and SAP SE.

Report Scope:

In this report, the global recommendation engine market has been segmented into following categories, in addition to the industry trends which have also been detailed below.

Recommendation Engine Market, By Type:

  • Collaborative Filtering
  • Content-based Filtering
  • Hybrid recommendation

Recommendation Engine Market, By Deployment Model:

  • On-Premises
  • Cloud

Recommendation Engine Market, By Application:

  • Personalized Campaigns & Customer Delivery
  • Strategy Operations & Planning
  • Product Planning
  • Proactive Asset Management

Recommendation Engine Market, By Enterprise Size:

  • Large Enterprises
  • Small & Medium Enterprises

Recommendation Engine Market, By End User:

  • Retail & Consumer Goods
  • IT & Telecom
  • Healthcare & Life Science
  • BFSI
  • Media
  • Entertainment
  • Others

Recommendation Engine Market, By Region:

  • North America
  • United States
  • Canada
  • Mexico
  • Asia-Pacific
  • China
  • India
  • Japan
  • South Korea
  • Indonesia
  • Europe
  • Germany
  • United Kingdom
  • France
  • Russia
  • Spain
  • South America
  • Brazil
  • Argentina
  • Middle East & Africa
  • Saudi Arabia
  • South Africa
  • Egypt
  • UAE
  • Israel

Competitive Landscape

  • Company Profiles: Detailed analysis of the major companies present in the Global Recommendation Engine Market.

Available Customizations:

  • Global recommendation engine market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
  • 1.3. Markets Covered
  • 1.4. Years Considered for Study
  • 1.5. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Key Industry Partners
  • 2.4. Major Association and Secondary Sources
  • 2.5. Forecasting Methodology
  • 2.6. Data Triangulation & Validation
  • 2.7. Assumptions and Limitations

3. Executive Summary

4. Voice of Customers

5. Global Recommendation Engine Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Type (Collaborative Filtering, Content-based Filtering, Hybrid recommendation)
    • 5.2.2. By Deployment Model (On-Premises, Cloud)
    • 5.2.3. By Enterprise Size (Large Enterprises, Small and Medium Enterprises)
    • 5.2.4. By Application (Personalized Campaigns and Customer Delivery, Strategy Operations and Planning, Product Planning and Proactive Asset Management)
    • 5.2.5. By End User (Retail and Consumer Goods, IT and Telecom, Healthcare and Life Science, BFSI, Media and Entertainment, Others)
    • 5.2.6. By Region
  • 5.3. By Company (2022)
  • 5.4. Market Map

6. North America Recommendation Engine Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Type
    • 6.2.2. By Deployment Model
    • 6.2.3. By Enterprise Size
    • 6.2.4. By Application
    • 6.2.5. By End User
  • 6.3. North America: Country Analysis
    • 6.3.1. United States Recommendation Engine Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Type
        • 6.3.1.2.2. By Deployment Model
        • 6.3.1.2.3. By Enterprise Size
        • 6.3.1.2.4. By Application
        • 6.3.1.2.5. By End User
    • 6.3.2. Canada Recommendation Engine Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Type
        • 6.3.2.2.2. By Deployment Model
        • 6.3.2.2.3. By Enterprise Size
        • 6.3.2.2.4. By Application
        • 6.3.2.2.5. By End User
    • 6.3.3. Mexico Recommendation Engine Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Type
        • 6.3.3.2.2. By Deployment Model
        • 6.3.3.2.3. By Enterprise Size
        • 6.3.3.2.4. By Application
        • 6.3.3.2.5. By End User

7. Asia-Pacific Recommendation Engine Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Type
    • 7.2.2. By Deployment Model
    • 7.2.3. By Enterprise Size
    • 7.2.4. By Application
    • 7.2.5. By End User
  • 7.3. Asia-Pacific: Country Analysis
    • 7.3.1. China Recommendation Engine Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Type
        • 7.3.1.2.2. By Deployment Model
        • 7.3.1.2.3. By Enterprise Size
        • 7.3.1.2.4. By Application
        • 7.3.1.2.5. By End User
    • 7.3.2. India Recommendation Engine Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Type
        • 7.3.2.2.2. By Deployment Model
        • 7.3.2.2.3. By Enterprise Size
        • 7.3.2.2.4. By Application
        • 7.3.2.2.5. By End User
    • 7.3.3. Japan Recommendation Engine Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Type
        • 7.3.3.2.2. By Deployment Model
        • 7.3.3.2.3. By Enterprise Size
        • 7.3.3.2.4. By Application
        • 7.3.3.2.5. By End User
    • 7.3.4. South Korea Recommendation Engine Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Type
        • 7.3.4.2.2. By Deployment Model
        • 7.3.4.2.3. By Enterprise Size
        • 7.3.4.2.4. By Application
        • 7.3.4.2.5. By End User
    • 7.3.5. Indonesia Recommendation Engine Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Type
        • 7.3.5.2.2. By Deployment Model
        • 7.3.5.2.3. By Enterprise Size
        • 7.3.5.2.4. By Application
        • 7.3.5.2.5. By End User

8. Europe Recommendation Engine Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Type
    • 8.2.2. By Deployment Model
    • 8.2.3. By Enterprise Size
    • 8.2.4. By Application
    • 8.2.5. By End User
  • 8.3. Europe: Country Analysis
    • 8.3.1. Germany Recommendation Engine Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Type
        • 8.3.1.2.2. By Deployment Model
        • 8.3.1.2.3. By Enterprise Size
        • 8.3.1.2.4. By Application
        • 8.3.1.2.5. By End User
    • 8.3.2. United Kingdom Recommendation Engine Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Type
        • 8.3.2.2.2. By Deployment Model
        • 8.3.2.2.3. By Enterprise Size
        • 8.3.2.2.4. By Application
        • 8.3.2.2.5. By End User
    • 8.3.3. France Recommendation Engine Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Type
        • 8.3.3.2.2. By Deployment Model
        • 8.3.3.2.3. By Enterprise Size
        • 8.3.3.2.4. By Application
        • 8.3.3.2.5. By End User
    • 8.3.4. Russia Recommendation Engine Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Type
        • 8.3.4.2.2. By Deployment Model
        • 8.3.4.2.3. By Enterprise Size
        • 8.3.4.2.4. By Application
        • 8.3.4.2.5. By End User
    • 8.3.5. Spain Recommendation Engine Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Type
        • 8.3.5.2.2. By Deployment Model
        • 8.3.5.2.3. By Enterprise Size
        • 8.3.5.2.4. By Application
        • 8.3.5.2.5. By End User

9. South America Recommendation Engine Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Type
    • 9.2.2. By Deployment Model
    • 9.2.3. By Enterprise Size
    • 9.2.4. By Application
    • 9.2.5. By End User
  • 9.3. South America: Country Analysis
    • 9.3.1. Brazil Recommendation Engine Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Type
        • 9.3.1.2.2. By Deployment Model
        • 9.3.1.2.3. By Enterprise Size
        • 9.3.1.2.4. By Application
        • 9.3.1.2.5. By End User
    • 9.3.2. Argentina Recommendation Engine Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Type
        • 9.3.2.2.2. By Deployment Model
        • 9.3.2.2.3. By Enterprise Size
        • 9.3.2.2.4. By Application
        • 9.3.2.2.5. By End User

10. Middle East & Africa Recommendation Engine Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Type
    • 10.2.2. By Deployment Model
    • 10.2.3. By Enterprise Size
    • 10.2.4. By Application
    • 10.2.5. By End User
  • 10.3. Middle East & Africa: Country Analysis
    • 10.3.1. Saudi Arabia Recommendation Engine Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Type
        • 10.3.1.2.2. By Deployment Model
        • 10.3.1.2.3. By Enterprise Size
        • 10.3.1.2.4. By Application
        • 10.3.1.2.5. By End User
    • 10.3.2. South Africa Recommendation Engine Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Type
        • 10.3.2.2.2. By Deployment Model
        • 10.3.2.2.3. By Enterprise Size
        • 10.3.2.2.4. By Application
        • 10.3.2.2.5. By End User
    • 10.3.3. UAE Recommendation Engine Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Type
        • 10.3.3.2.2. By Deployment Model
        • 10.3.3.2.3. By Enterprise Size
        • 10.3.3.2.4. By Application
        • 10.3.3.2.5. By End User
    • 10.3.4. Israel Recommendation Engine Market Outlook
      • 10.3.4.1. Market Size & Forecast
        • 10.3.4.1.1. By Value
      • 10.3.4.2. Market Share & Forecast
        • 10.3.4.2.1. By Type
        • 10.3.4.2.2. By Deployment Model
        • 10.3.4.2.3. By Enterprise Size
        • 10.3.4.2.4. By Application
        • 10.3.4.2.5. By End User
    • 10.3.5. Egypt Recommendation Engine Market Outlook
      • 10.3.5.1. Market Size & Forecast
        • 10.3.5.1.1. By Value
      • 10.3.5.2. Market Share & Forecast
        • 10.3.5.2.1. By Type
        • 10.3.5.2.2. By Deployment Model
        • 10.3.5.2.3. By Enterprise Size
        • 10.3.5.2.4. By Application
        • 10.3.5.2.5. By End User

11. Market Dynamics

  • 11.1. Drivers
  • 11.2. Challenges

12. Market Trends & Developments

13. Company Profiles

  • 13.1. IBM Corporation
    • 13.1.1. Business Overview
    • 13.1.2. Key Revenue and Financials (If Available)
    • 13.1.3. Recent Developments
    • 13.1.4. Key Personnel
    • 13.1.5. Key Product/Services
  • 13.2. Hewlett Packard Enterprise Development LP
    • 13.2.1. Business Overview
    • 13.2.2. Key Revenue and Financials
    • 13.2.3. Recent Developments
    • 13.2.4. Key Personnel
    • 13.2.5. Key Product/Services
  • 13.3. Intel Corporation
    • 13.3.1. Business Overview
    • 13.3.2. Key Revenue and Financials (If Available)
    • 13.3.3. Recent Developments
    • 13.3.4. Key Personnel
    • 13.3.5. Key Product/Services
  • 13.4. Amazon Web Services
    • 13.4.1. Business Overview
    • 13.4.2. Key Revenue and Financials (If Available)
    • 13.4.3. Recent Developments
    • 13.4.4. Key Personnel
    • 13.4.5. Key Product/Services
  • 13.5. Adobe
    • 13.5.1. Business Overview
    • 13.5.2. Key Revenue and Financials (If Available)
    • 13.5.3. Recent Developments
    • 13.5.4. Key Personnel
    • 13.5.5. Key Product/Services
  • 13.6. Salesforce, Inc.
    • 13.6.1. Business Overview
    • 13.6.2. Key Revenue and Financials (If Available)
    • 13.6.3. Recent Developments
    • 13.6.4. Key Personnel
    • 13.6.5. Key Product/Services
  • 13.7. Microsoft Corporation.
    • 13.7.1. Business Overview
    • 13.7.2. Key Revenue and Financials
    • 13.7.3. Recent Developments
    • 13.7.4. Key Personnel
    • 13.7.5. Key Product/Services
  • 13.8. Oracle Corporation,
    • 13.8.1. Business Overview
    • 13.8.2. Key Revenue and Financials (If Available)
    • 13.8.3. Recent Developments
    • 13.8.4. Key Personnel
    • 13.8.5. Key Product/Services
  • 13.9. Google LLC
    • 13.9.1. Business Overview
    • 13.9.2. Key Revenue and Financials (If Available)
    • 13.9.3. Recent Developments
    • 13.9.4. Key Personnel
    • 13.9.5. Key Product/Services
  • 13.10. SAP SE
    • 13.10.1. Business Overview
    • 13.10.2. Key Revenue and Financials (If Available)
    • 13.10.3. Recent Developments
    • 13.10.4. Key Personnel
    • 13.10.5. Key Product/Services

14. Strategic Recommendations

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