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推薦引擎市場 - 2028 年全球產業規模、佔有率、趨勢、機會和預測。按類型、部署模型、企業規模、按應用、最終用戶、地區和競爭細分

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個工作天內

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簡介目錄

預計全球推薦引擎市場將在 2024-2028 年預測期內穩定成長。人們對增強客戶體驗的渴望日益強烈，這推動了對推薦引擎的需求。例如，IBM 公司於 2021 年 5 月擴展了適用於 OTT 和影片的 IBM Watson Advertising Accelerator。創建該工具的目的是幫助廣告商超越上下文相關性。 Theamplifier 不依賴傳統的廣告 ID，而是使用人工智慧不斷最佳化 OTT 廣告文案，以實現更好的大規模行銷活動效果。

推薦引擎是一個識別員工並向他們提供相關材料的系統。其他技術發展如何繼續改變客戶興趣並利用可用資料的一個例子是行動應用程式。建議引擎被認為是 ICT 領域軟體和應用產品的關鍵要素。推薦引擎的兩個主要類別是基於內容的過濾和協作過濾。

推薦系​​統使用資訊分析技術來尋找符合使用者偏好的產品。由於多種原因，有許多建議引擎可用。其中包括圖片推薦引擎、線上商店的產品推薦引擎、內容推薦引擎和產品建議引擎。人們日益渴望增強客戶體驗，這滿足了對推薦引擎的需求。

市場概況
預測期 2024-2028
2022 年市場規模 47.1億美元
2028 年市場規模 262.3億美元
2023-2028 年年複合成長率 33.22%
成長最快的細分市場 雲
最大的市場 北美洲

採用聯合收割機技術推動市場成長

由於行業種類不斷增加以及競爭隨之加劇，許多公司正在嘗試將包括電腦科學 (AI) 在內的技術與其應用程式、業務、分析和服務相結合。在世界各地，不少公司正在經歷數位轉型，重點是使用自動化技術來增加員工和客戶的知識。由於向數位化的轉變，零售商可以擴大客戶群、改善客戶關係、削減開支並提高員工士氣。不斷增加的客戶體驗改進方法和不斷擴大的數位轉型範圍是推動全球推薦引擎市場的幾個主要因素。例如，2021 年 3 月 SAP SE 收購了 Signavio。 Signavio 是企業業務流程智慧和流程管理領域的關鍵參與者。 Signavio 的解決方案已新增至 SAP 的業務流程智慧產品組合中，旨在與 SAP 的全面流程轉型產品組合搭配使用。因此，預計市場在預測期內將會成長。

記錄和觀察客戶行為的優勢推動市場成長

由於顧客通常會根據實體店面貨架上商品的位置做出購買決定，因此企業具有很強的觀察和影響顧客行為的能力。隨著網際網路使用量的增加以及電子商務、行動購物和智慧技術等新銷售管道的出現，零售業正在適應新技術和尖端技術。在自助結帳機和智慧銷售點系統等最新技術的幫助下，市場正在快速成長。據 ZDNet 稱，70% 的企業已經或正在實施數位轉型計畫。由於企業正在數位化轉型，全球推薦引擎市場預計在預測期內將出現較高的年複合成長率。

由於行動和網路上客製化數位商務體驗的需求不斷成長，市場正在擴大

公司正在尋找可以利用的策略和工具。數以百萬計的獨特消費者可以透過使用私人資料從這些體驗中受益。執行決定結果。如果實施得當，個人化的客戶體驗可以幫助企業在競爭中脫穎而出，贏得客戶的忠誠度，並獲得持久的競爭優勢——所有這些在當前市場中都至關重要。

由於消費者的需求不斷成長，許多組織的行銷專業人員逐漸將注意力轉向改善客戶體驗。例如，根據 Adob​​e 公司的數據，採用最強大的全通路客戶參與策略的企業可以觀察到年增 10%、平均訂單價值成長 10%、成交率提高 25% 。此外，擁有強大的全通路客戶互動策略和消費者服務改善計畫的公司平均保留了 89% 的消費者，而策略較弱的公司只能保留 33%。鑑於營運管道數量不斷增加，技術可確保品牌在所有管道上提供一致的服務資訊。在預估期間，市場預計將受益於對增強客戶服務的不斷成長的需求。

市場區隔

全球推薦引擎市場根據類型、部署模型、企業規模、應用程式、最終用戶和地區進行分類。根據類型，市場分為協同過濾、基於內容的過濾和混合推薦。依部署模式，市場分為本地和雲端；依企業規模，市場分為大型企業、中小企業。根據應用，市場分為個人化活動和客戶交付、策略營運和規劃、產品規劃和主動資產管理。根據最終用戶，市場分為零售和消費品、IT 和電信、醫療保健和生命科學、BFSI、媒體和娛樂等。依地區分類，市場分為北美、亞太、歐洲、南美、中東和非洲。

市場參與者

全球推薦引擎市場的主要市場參與者包括IBM公司、Hewlett Packard Enterprise Development LP、英特爾公司、亞馬遜網路服務、Adobe、Salesforce, Inc、微軟公司、甲骨文公司、Google有限責任公司和SAP SE。

報告範圍：

在本報告中，除了以下詳細介紹的產業趨勢外，全球推薦引擎市場也分為以下幾類。

推薦引擎市場，依類型：

  • 協同過濾
  • 基於內容的過濾
  • 混合推薦

推薦引擎市場（按部署模型）：

  • 本地部署
  • 雲

推薦引擎市場，按應用：

  • 個人化活動和客戶交付
  • 策略營運與規劃
  • 產品規劃
  • 主動資產管理

推薦引擎市場，依企業規模分類：

  • 大型企業
  • 中小企業

推薦引擎市場，依最終用戶分類：

  • 零售與消費品
  • 資訊科技與電信
  • 醫療保健與生命科學
  • BFSI
  • 媒體
  • 娛樂
  • 其他

推薦引擎市場（按地區）：

  • 北美洲
  • 美國
  • 加拿大
  • 墨西哥
  • 亞太
  • 中國
  • 印度
  • 日本
  • 韓國
  • 印尼
  • 歐洲
  • 德國
  • 英國
  • 法國
  • 俄羅斯
  • 西班牙
  • 南美洲
  • 巴西
  • 阿根廷
  • 中東和非洲
  • 沙烏地阿拉伯
  • 南非
  • 埃及
  • 阿拉伯聯合大公國
  • 以色列

競爭格局

  • 公司概況：全球推薦引擎市場主要公司的詳細分析。

可用的客製化：

  • 全球推薦引擎市場報告以及給定的市場資料，科技科學研究根據公司的具體需求提供客製化服務。該報告可以使用以下自訂選項：

公司資訊

  • 其他市場參與者（最多五個）的詳細分析和概況分析。

目錄

第 1 章：產品概述

  • 市場定義
  • 市場範圍
  • 涵蓋的市場
  • 研究年份
  • 主要市場區隔

第 2 章：研究方法

  • 研究目的
  • 基線方法
  • 主要產業夥伴
  • 主要協會和二手資料來源
  • 預測方法
  • 數據三角測量與驗證
  • 假設和限制

第 3 章：執行摘要

第 4 章：客戶之聲

第 5 章：全球推薦引擎市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按類型（協作過濾、基於內容的過濾、混合推薦）
    • 按部署模式（本地、雲端）
    • 依企業規模（大型企業、中小企業）
    • 按應用（個人化活動和客戶交付、策略營運和規劃、產品規劃和主動資產管理）
    • 按最終用戶（零售和消費品、IT 和電信、醫療保健和生命科學、BFSI、媒體和娛樂、其他）
    • 按地區
  • 按公司分類 (2022)
  • 市場地圖

第 6 章：北美推薦引擎市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按類型
    • 按部署模型
    • 按企業規模
    • 按應用
    • 按最終用戶
  • 北美：國家分析
    • 美國
    • 加拿大
    • 墨西哥

第 7 章：亞太地區推薦引擎市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按類型
    • 按部署模型
    • 按企業規模
    • 按應用
    • 按最終用戶
  • 亞太地區：國家分析
    • 中國
    • 印度
    • 日本
    • 韓國
    • 印尼

第 8 章：歐洲推薦引擎市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按類型
    • 按部署模型
    • 按企業規模
    • 按應用
    • 按最終用戶
  • 歐洲：國家分析
    • 德國
    • 英國
    • 法國
    • 俄羅斯
    • 西班牙

第 9 章：南美洲推薦引擎市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按類型
    • 按部署模型
    • 按企業規模
    • 按應用
    • 按最終用戶
  • 南美洲：國家分析
    • 巴西
    • 阿根廷

第 10 章：中東和非洲推薦引擎市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按類型
    • 按部署模型
    • 按企業規模
    • 按應用
    • 按最終用戶
  • 中東和非洲：國家分析
    • 沙烏地阿拉伯
    • 南非
    • 阿拉伯聯合大公國
    • 以色列
    • 埃及

第 11 章：市場動態

  • 促進要素
  • 挑戰

第 12 章：市場趨勢與發展

第 13 章：公司簡介

  • IBM公司
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 惠普企業開發有限公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 英特爾公司
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • 亞馬遜網路服務
    • 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
  • 微軟公司。
    • 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
  • 谷歌有限責任公司
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services
  • SAP系統公司
    • Business Overview
    • Key Revenue and Financials (If Available)
    • Recent Developments
    • Key Personnel
    • Key Product/Services

第 14 章：策略建議

關於我們及免責聲明

簡介目錄
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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